This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got.
I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are)
Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010.
One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit.
Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells.
So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.

How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.

This is begging the question.

After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.

This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain).
Furthermore, Dewey (2019) critiques the 5% interest rate.

She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain

Moving on:

The exact mechanism of drain, or transfers from India to Britain was quite simple.

Convenient.

Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.

- Chandra et al. (1989)

The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.

The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):

the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).

Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.

The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.

So what's going on here? Well Roy (2019) explains it better:

Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.

So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):

It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.

While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided.
It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:

she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)

​

Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.

Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.

The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.

While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.

When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.

So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:

Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles. India bought something and paid for it. State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.

Indeed, if paying for what you buy is drain, then several billions of us are drained every day.

The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.

It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):

The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.

​

Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.

Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):

Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.

​

The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.

It doesn't appear that others appreciate their insight Roy (2019):

K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.

​

Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.

Dewey (1978) points out reliability issues with Indian agriculutural statistics, however this calorie decline persists to this day. Some of it is attributed to less food being consumed at home Smith (2015), a lower infectious disease burden Duh & Spears (2016) and diversified diets Vankatesh et al. (2016).

If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.

This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual¹). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, much like Japan. The list continues eternally.
Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no.
From Gupta (1980):

This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period, the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.

A view echoed in Raychaudhuri (1983):

Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.

So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):

All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.

The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):

Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable

So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground.
^{1. Several authors have affirmed that Indian identity is a colonial artefact. For example see Rajan 1969:}

^{Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.}

^{or see Bryant 2000:}

^{But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist. [...] Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.}

Bibliography

Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press
Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian
Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost
Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian
Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice
Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times
Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan
Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times
Tuovila, Alicia (2019). Expenditure method. Investopedia
Dewey, Clive (2019). Changing the guard: The dissolution of the nationalist–Marxist orthodoxy in the agrarian and agricultural history of India. The Indian Economic & Social History Review
Chandra, Bipan et al. (1989). India's Struggle for Independence, 1857-1947. Penguin Books
Frankema, Ewout & Booth, Anne (2019). Fiscal Capacity and the Colonial State in Asia and Africa, c. 1850-1960. Cambridge University Press
Dalal, Sucheta (2019). IL&FS Controversy: Centre is Paying Up on Sovereign Guarantees to ADB, KfW for Group's Loan. TheWire
Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press
Sunderland, David (2013). Financing the Raj: The City of London and Colonial India, 1858-1940. Boydell Press
Dewey, Clive (1978). Patwari and Chaukidar: Subordinate officials and the reliability of India’s agricultural statistics. Athlone Press
Smith, Lisa (2015). The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy
Duh, Josephine & Spears, Dean (2016). Health and Hunger: Disease, Energy Needs, and the Indian Calorie Consumption Puzzle. The Economic Journal
Vankatesh, P. et al. (2016). Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis. Agricultural Economics Research Review
Gupta, Shaibal (1980). Potential of Industrial Revolution in Pre-British India. Economic and Political Weekly
Raychaudhuri, Tapan (1983). I - The mid-eighteenth-century background. Cambridge University Press
Yasuba, Yasukichi (1986). Standard of Living in Japan Before Industrialization: From what Level did Japan Begin? A Comment. The Journal of Economic History
Tomblinson, B.R. (1985). Writing History Sideways: Lessons for Indian Economic Historians from Meiji Japan. Cambridge University Press
Rajan, M.S. (1969). The Impact of British Rule in India. Journal of Contemporary History
Bryant, G.J. (2000). Indigenous Mercenaries in the Service of European Imperialists: The Case of the Sepoys in the Early British Indian Army, 1750-1800. War in History

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https://preview.redd.it/gp18bjnlabr41.jpg?width=768&format=pjpg&auto=webp&s=6054e7f52e8d52da403016139ae43e0e799abf15
Download PDF of this article here: https://docdro.id/6eLgUPo
In light of the recent fall in oil prices due to the Saudi-Russian dispute and dampening demand for oil due to the lockdowns implemented globally, O&G stocks have taken a severe beating, falling approximately 50% from their highs at the beginning of the year. Not spared from this onslaught is Hibiscus Petroleum Berhad (Hibiscus), a listed oil and gas (O&G) exploration and production (E&P) company.
Why invest in O&G stocks in this particularly uncertain period? For one, valuations of these stocks have fallen to multi-year lows, bringing the potential ROI on these stocks to attractive levels. Oil prices are cyclical, and are bound to return to the mean given a sufficiently long time horizon. The trick is to find those companies who can survive through this downturn and emerge into “normal” profitability once oil prices rebound.
In this article, I will explore the upsides and downsides of investing in Hibiscus. I will do my best to cater this report to newcomers to the O&G industry – rather than address exclusively experts and veterans of the O&G sector. As an equity analyst, I aim to provide a view on the company primarily, and will generally refrain from providing macro views on oil or opinions about secular trends of the sector. I hope you enjoy reading it!
Stock code: 5199.KL
Stock name: Hibiscus Petroleum Berhad
Financial information and financial reports: https://www.malaysiastock.biz/Corporate-Infomation.aspx?securityCode=5199
Company website: https://www.hibiscuspetroleum.com/
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Company Snapshot

Hibiscus Petroleum Berhad (5199.KL) is an oil and gas (O&G) upstream exploration and production (E&P) company located in Malaysia. As an E&P company, their business can be basically described as:
· looking for oil,
· drawing it out of the ground, and
· selling it on global oil markets.
This means Hibiscus’s profits are particularly exposed to fluctuating oil prices. With oil prices falling to sub-$30 from about $60 at the beginning of the year, Hibiscus’s stock price has also fallen by about 50% YTD – from around RM 1.00 to RM 0.45 (as of 5 April 2020).
https://preview.redd.it/3dqc4jraabr41.png?width=641&format=png&auto=webp&s=7ba0e8614c4e9d781edfc670016a874b90560684
https://preview.redd.it/lvdkrf0cabr41.png?width=356&format=png&auto=webp&s=46f250a713887b06986932fa475dc59c7c28582e
While the company is domiciled in Malaysia, its two main oil producing fields are located in both Malaysia and the UK. The Malaysian oil field is commonly referred to as the North Sabah field, while the UK oil field is commonly referred to as the Anasuria oil field. Hibiscus has licenses to other oil fields in different parts of the world, notably the Marigold/Sunflower oil fields in the UK and the VIC cluster in Australia, but its revenues and profits mainly stem from the former two oil producing fields.
Given that it’s a small player and has only two primary producing oil fields, it’s not surprising that Hibiscus sells its oil to a concentrated pool of customers, with 2 of them representing 80% of its revenues (i.e. Petronas and BP). Fortunately, both these customers are oil supermajors, and are unlikely to default on their obligations despite low oil prices.
At RM 0.45 per share, the market capitalization is RM 714.7m and it has a trailing PE ratio of about 5x. It doesn’t carry any debt, and it hasn’t paid a dividend in its listing history. The MD, Mr. Kenneth Gerard Pereira, owns about 10% of the company’s outstanding shares.
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Reserves (Total recoverable oil) & Production (bbl/day)

To begin analyzing the company, it’s necessary to understand a little of the industry jargon. We’ll start with Reserves and Production.
In general, there are three types of categories for a company’s recoverable oil volumes – Reserves, Contingent Resources and Prospective Resources. Reserves are those oil fields which are “commercial”, which is defined as below:
^{As defined by the SPE PRMS, Reserves are “… quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions.” Therefore, Reserves must be discovered (by drilling}, recoverable (with current technology), remaining in the subsurface (at the effective date of the evaluation) and “commercial” based on the development project proposed.)
^{Note that Reserves are associated with development projects. To be considered as “commercial”, there must be a firm intention to proceed with the project in a reasonable time frame (typically 5 years}, and such intention must be based upon all of the following criteria:)
^{- A reasonable assessment of the future economics of the development project meeting defined investment and operating criteria; - A reasonable expectation that there will be a market for all or at least the expected sales quantities of production required to justify development; - Evidence that the necessary production and transportation facilities are available or can be made available; and - Evidence that legal, contractual, environmental and other social and economic concerns will allow for the actual implementation of the recovery project being evaluated.}
Contingent Resources and Prospective Resources are further defined as below:
^{- Contingent Resources: potentially recoverable volumes associated with a development plan that targets discovered volumes but is not (yet} commercial (as defined above); and) ^{- Prospective Resources: potentially recoverable volumes associated with a development plan that targets as yet undiscovered volumes.}
In the industry lingo, we generally refer to Reserves as ‘P’ and Contingent Resources as ‘C’. These ‘P’ and ‘C’ resources can be further categorized into 1P/2P/3P resources and 1C/2C/3C resources, each referring to a low/medium/high estimate of the company’s potential recoverable oil volumes:
^{- Low/1C/1P estimate: there should be reasonable certainty that volumes actually recovered will equal or exceed the estimate; - Best/2C/2P estimate: there should be an equal likelihood of the actual volumes of petroleum being larger or smaller than the estimate; and - High/3C/3P estimate: there is a low probability that the estimate will be exceeded.}
Hence in the E&P industry, it is easy to see why most investors and analysts refer to the 2P estimate as the best estimate for a company’s actual recoverable oil volumes. This is because 2P reserves (‘2P’ referring to ‘Proved and Probable’) are a middle estimate of the recoverable oil volumes legally recognized as “commercial”.
However, there’s nothing stopping you from including 2C resources (riskier) or utilizing 1P resources (conservative) as your estimate for total recoverable oil volumes, depending on your risk appetite. In this instance, the company has provided a snapshot of its 2P and 2C resources in its analyst presentation:
https://preview.redd.it/o8qejdyc8br41.png?width=710&format=png&auto=webp&s=b3ab9be8f83badf0206adc982feda3a558d43e78
Basically, what the company is saying here is that by 2021, it will have classified as 2P reserves at least 23.7 million bbl from its Anasuria field and 20.5 million bbl from its North Sabah field – for total 2P reserves of 44.2 million bbl (we are ignoring the Australian VIC cluster as it is only estimated to reach first oil by 2022).
Furthermore, the company is stating that they have discovered (but not yet legally classified as “commercial”) a further 71 million bbl of oil from both the Anasuria and North Sabah fields, as well as the Marigold/Sunflower fields. If we include these 2C resources, the total potential recoverable oil volumes could exceed 100 million bbl.
In this report, we shall explore all valuation scenarios giving consideration to both 2P and 2C resources.
https://preview.redd.it/gk54qplf8br41.png?width=489&format=png&auto=webp&s=c905b7a6328432218b5b9dfd53cc9ef1390bd604
The company further targets a 2021 production rate of 20,000 bbl (LTM: 8,000 bbl), which includes 5,000 bbl from its Anasuria field (LTM: 2,500 bbl) and 7,000 bbl from its North Sabah field (LTM: 5,300 bbl).
This is a substantial increase in forecasted production from both existing and prospective oil fields. If it materializes, annual production rate could be as high as 7,300 mmbbl, and 2021 revenues (given FY20 USD/bbl of $60) could exceed RM 1.5 billion (FY20: RM 988 million).
However, this targeted forecast is quite a stretch from current production levels. Nevertheless, we shall consider all provided information in estimating a valuation for Hibiscus.
To understand Hibiscus’s oil production capacity and forecast its revenues and profits, we need to have a better appreciation of the performance of its two main cash-generating assets – the North Sabah field and the Anasuria field.
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North Sabah oil field
https://preview.redd.it/62nssexj8br41.png?width=1003&format=png&auto=webp&s=cd78f86d51165fb9a93015e49496f7f98dad64dd
Hibiscus owns a 50% interest in the North Sabah field together with its partner Petronas, and has production rights over the field up to year 2040. The asset contains 4 oil fields, namely the St Joseph field, South Furious field, SF 30 field and Barton field.
For the sake of brevity, we shall not delve deep into the operational aspects of the fields or the contractual nature of its production sharing contract (PSC). We’ll just focus on the factors which relate to its financial performance. These are:
· Average uptime
· Total oil sold
· Average realized oil price
· Average OPEX per bbl
With regards to average uptime, we can see that the company maintains relative high facility availability, exceeding 90% uptime in all quarters of the LTM with exception of Jul-Sep 2019. The dip in average uptime was due to production enhancement projects and maintenance activities undertaken to improve the production capacity of the St Joseph and SF30 oil fields.
Hence, we can conclude that management has a good handle on operational performance. It also implies that there is little room for further improvement in production resulting from increased uptime.
As North Sabah is under a production sharing contract (PSC), there is a distinction between gross oil production and net oil production. The former relates to total oil drawn out of the ground, whereas the latter refers to Hibiscus’s share of oil production after taxes, royalties and expenses are accounted for. In this case, we want to pay attention to net oil production, not gross.
We can arrive at Hibiscus’s total oil sold for the last twelve months (LTM) by adding up the total oil sold for each of the last 4 quarters. Summing up the figures yields total oil sold for the LTM of approximately 2,075,305 bbl.
Then, we can arrive at an average realized oil price over the LTM by averaging the average realized oil price for the last 4 quarters, giving us an average realized oil price over the LTM of USD 68.57/bbl. We can do the same for average OPEX per bbl, giving us an average OPEX per bbl over the LTM of USD 13.23/bbl.
Thus, we can sum up the above financial performance of the North Sabah field with the following figures:
· Total oil sold: 2,075,305 bbl
· Average realized oil price: USD 68.57/bbl
· Average OPEX per bbl: USD 13.23/bbl
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Anasuria oil field
https://preview.redd.it/586u4kfo8br41.png?width=1038&format=png&auto=webp&s=7580fc7f7df7e948754d025745a5cf47d4393c0f
Doing the same exercise as above for the Anasuria field, we arrive at the following financial performance for the Anasuria field:
· Total oil sold: 1,073,304 bbl
· Average realized oil price: USD 63.57/bbl
· Average OPEX per bbl: USD 23.22/bbl
As gas production is relatively immaterial, and to be conservative, we shall only consider the crude oil production from the Anasuria field in forecasting revenues.
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Valuation (Method 1)

Putting the figures from both oil fields together, we get the following data:
https://preview.redd.it/7y6064dq8br41.png?width=700&format=png&auto=webp&s=2a4120563a011cf61fc6090e1cd5932602599dc2
Given that we have determined LTM EBITDA of RM 632m, the next step would be to subtract ITDA (interest, tax, depreciation & amortization) from it to obtain estimated LTM Net Profit. Using FY2020’s ITDA of approximately RM 318m as a guideline, we arrive at an estimated LTM Net Profit of RM 314m (FY20: 230m). Given the current market capitalization of RM 714.7m, this implies a trailing LTM PE of 2.3x.
Performing a sensitivity analysis given different oil prices, we arrive at the following net profit table for the company under different oil price scenarios, assuming oil production rate and ITDA remain constant:
https://preview.redd.it/xixge5sr8br41.png?width=433&format=png&auto=webp&s=288a00f6e5088d01936f0217ae7798d2cfcf11f2
From the above exercise, it becomes apparent that Hibiscus has a breakeven oil price of about USD 41.8863/bbl, and has a lot of operating leverage given the exponential rate of increase in its Net Profit with each consequent increase in oil prices.
Considering that the oil production rate (EBITDA) is likely to increase faster than ITDA’s proportion to revenues (fixed costs), at an implied PE of 4.33x, it seems likely that an investment in Hibiscus will be profitable over the next 10 years (with the assumption that oil prices will revert to the mean in the long-term).
​

Valuation (Method 2)

Of course, there are a lot of assumptions behind the above method of valuation. Hence, it would be prudent to perform multiple methods of valuation and compare the figures to one another.
As opposed to the profit/loss assessment in Valuation (Method 1), another way of performing a valuation would be to estimate its balance sheet value, i.e. total revenues from 2P Reserves, and assign a reasonable margin to it.
https://preview.redd.it/o2eiss6u8br41.png?width=710&format=png&auto=webp&s=03960cce698d9cedb076f3d5f571b3c59d908fa8
From the above, we understand that Hibiscus’s 2P reserves from the North Sabah and Anasuria fields alone are approximately 44.2 mmbbl (we ignore contribution from Australia’s VIC cluster as it hasn’t been developed yet).
Doing a similar sensitivity analysis of different oil prices as above, we arrive at the following estimated total revenues and accumulated net profit:
https://preview.redd.it/h8hubrmw8br41.png?width=450&format=png&auto=webp&s=6d23f0f9c3dafda89e758b815072ba335467f33e
Let’s assume that the above average of RM 9.68 billion in total realizable revenues from current 2P reserves holds true. If we assign a conservative Net Profit margin of 15% (FY20: 23%; past 5 years average: 16%), we arrive at estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion. Given the current market capitalization of RM 714 million, we might be able to say that the equity is worth about twice the current share price.
However, it is understandable that some readers might feel that the figures used in the above estimate (e.g. net profit margin of 15%) were randomly plucked from the sky. So how do we reconcile them with figures from the financial statements? Fortunately, there appears to be a way to do just that.
Intangible Assets
I refer you to a figure in the financial statements which provides a shortcut to the valuation of 2P Reserves. This is the carrying value of Intangible Assets on the Balance Sheet.
As of 2QFY21, that amount was RM 1,468,860,000 (i.e. RM 1.468 billion).
https://preview.redd.it/hse8ttb09br41.png?width=881&format=png&auto=webp&s=82e48b5961c905fe9273cb6346368de60202ebec
Quite coincidentally, one might observe that this figure is dangerously close to the estimated accumulated Net Profit from 2P Reserves of RM 1.452 billion we calculated earlier. But why would this amount matter at all?
To answer that, I refer you to the notes of the Annual Report FY20 (AR20). On page 148 of the AR20, we find the following two paragraphs:
^{E&E assets comprise of rights and concession and conventional studies. Following the acquisition of a concession right to explore a licensed area, the costs incurred such as geological and geophysical surveys, drilling, commercial appraisal costs and other directly attributable costs of exploration and appraisal including technical and administrative costs, are capitalised as conventional studies, presented as intangible assets.
E&E assets are assessed for impairment when facts and circumstances suggest that the carrying amount of an E&E asset may exceed its recoverable amount. The Group will allocate E&E assets to cash generating unit (“CGU”}s or groups of CGUs for the purpose of assessing such assets for impairment. Each CGU or group of units to which an E&E asset is allocated will not be larger than an operating segment as disclosed in Note 39 to the financial statements.)
Hence, we can determine that firstly, the intangible asset value represents capitalized costs of acquisition of the oil fields, including technical exploration costs and costs of acquiring the relevant licenses. Secondly, an impairment review will be carried out when “the carrying amount of an E&E asset may exceed its recoverable amount”, with E&E assets being allocated to “cash generating units” (CGU) for the purposes of assessment.
On page 169 of the AR20, we find the following:
^{Carrying amounts of the Group’s intangible assets, oil and gas assets and FPSO are reviewed for possible impairment annually including any indicators of impairment. For the purpose of assessing impairment, assets are grouped at the lowest level CGUs for which there is a separately identifiable cash flow available. These CGUs are based on operating areas, represented by the 2011 North Sabah EOR PSC (“North Sabah”}, the Anasuria Cluster, the Marigold and Sunflower fields, the VIC/P57 exploration permit (“VIC/P57”) and the VIC/L31 production license (“VIC/L31”).)
So apparently, the CGUs that have been assigned refer to the respective oil producing fields, two of which include the North Sabah field and the Anasuria field. In order to perform the impairment review, estimates of future cash flow will be made by management to assess the “recoverable amount” (as described above), subject to assumptions and an appropriate discount rate.
Hence, what we can gather up to now is that management will estimate future recoverable cash flows from a CGU (i.e. the North Sabah and Anasuria oil fields), compare that to their carrying value, and perform an impairment if their future recoverable cash flows are less than their carrying value. In other words, if estimated accumulated profits from the North Sabah and Anasuria oil fields are less than their carrying value, an impairment is required.
So where do we find the carrying values for the North Sabah and Anasuria oil fields? Further down on page 184 in the AR20, we see the following:
^{Included in rights and concession are the carrying amounts of producing field licenses in the Anasuria Cluster amounting to RM668,211,518 (2018: RM687,664,530}, producing field licenses in North Sabah amounting to RM471,031,008 (2018: RM414,333,116))
Hence, we can determine that the carrying values for the North Sabah and Anasuria oil fields are RM 471m and RM 668m respectively. But where do we find the future recoverable cash flows of the fields as estimated by management, and what are the assumptions used in that calculation?
Fortunately, we find just that on page 185:
^{17 INTANGIBLE ASSETS (CONTINUED})
^(a Anasuria Cluster)
^{The Directors have concluded that there is no impairment indicator for Anasuria Cluster during the current financial year. In the previous financial year, due to uncertainties in crude oil prices, the Group has assessed the recoverable amount of the intangible assets, oil and gas assets and FPSO relating to the Anasuria Cluster. The recoverable amount is determined using the FVLCTS model based on discounted cash flows (“DCF”} derived from the expected cash in/outflow pattern over the production lives.)
^{The key assumptions used to determine the recoverable amount for the Anasuria Cluster were as follows:
(i} Discount rate of 10%;)
⁽ⁱⁱ Future cost inflation factor of 2% per annum;)
⁽ⁱⁱⁱ Oil price forecast based on the oil price forward curve from independent parties; and,)
^(iv Oil production profile based on the assessment by independent oil and gas reserve experts.)
^{Based on the assessments performed, the Directors concluded that the recoverable amount calculated based on the valuation model is higher than the carrying amount.
(b} North Sabah)
^{The acquisition of the North Sabah assets was completed in the previous financial year. Details of the acquisition are as disclosed in Note 15 to the financial statements.
The Directors have concluded that there is no impairment indicator for North Sabah during the current financial year.}
Here, we can see that the recoverable amount of the Anasuria field was estimated based on a DCF of expected future cash flows over the production life of the asset. The key assumptions used by management all seem appropriate, including a discount rate of 10% and oil price and oil production estimates based on independent assessment. From there, management concludes that the recoverable amount of the Anasuria field is higher than its carrying amount (i.e. no impairment required). Likewise, for the North Sabah field.
How do we interpret this? Basically, what management is saying is that given a 10% discount rate and independent oil price and oil production estimates, the accumulated profits (i.e. recoverable amount) from both the North Sabah and the Anasuria fields exceed their carrying amounts of RM 471m and RM 668m respectively.
In other words, according to management’s own estimates, the carrying value of the Intangible Assets of RM 1.468 billion approximates the accumulated Net Profit recoverable from 2P reserves.
To conclude Valuation (Method 2), we arrive at the following:
​

	Our estimates	Management estimates
Accumulated Net Profit from 2P Reserves	RM 1.452 billion	RM 1.468 billion

​

Financials

By now, we have established the basic economics of Hibiscus’s business, including its revenues (i.e. oil production and oil price scenarios), costs (OPEX, ITDA), profitability (breakeven, future earnings potential) and balance sheet value (2P reserves, valuation). Moving on, we want to gain a deeper understanding of the 3 statements to anticipate any blind spots and risks. We’ll refer to the financial statements of both the FY20 annual report and the 2Q21 quarterly report in this analysis.
For the sake of brevity, I’ll only point out those line items which need extra attention, and skip over the rest. Feel free to go through the financial statements on your own to gain a better familiarity of the business.
https://preview.redd.it/h689bss79br41.png?width=810&format=png&auto=webp&s=ed47fce6a5c3815dd3d4f819e31f1ce39ccf4a0b
Income Statement
First, we’ll start with the Income Statement on page 135 of the AR20. Revenues are straightforward, as we’ve discussed above. Cost of Sales and Administrative Expenses fall under the jurisdiction of OPEX, which we’ve also seen earlier. Other Expenses are mostly made up of Depreciation & Amortization of RM 115m.
Finance Costs are where things start to get tricky. Why does a company which carries no debt have such huge amounts of finance costs? The reason can be found in Note 8, where it is revealed that the bulk of finance costs relate to the unwinding of discount of provision for decommissioning costs of RM 25m (Note 32).
https://preview.redd.it/4omjptbe9br41.png?width=1019&format=png&auto=webp&s=eaabfc824134063100afa62edfd36a34a680fb60
This actually refers to the expected future costs of restoring the Anasuria and North Sabah fields to their original condition once the oil reserves have been depleted. Accounting standards require the company to provide for these decommissioning costs as they are estimable and probable. The way the decommissioning costs are accounted for is the same as an amortized loan, where the initial carrying value is recognized as a liability and the discount rate applied is reversed each year as an expense on the Income Statement. However, these expenses are largely non-cash in nature and do not necessitate a cash outflow every year (FY20: RM 69m).
Unwinding of discount on non-current other payables of RM 12m relate to contractual payments to the North Sabah sellers. We will discuss it later.
Taxation is another tricky subject, and is even more significant than Finance Costs at RM 161m. In gist, Hibiscus is subject to the 38% PITA (Petroleum Income Tax Act) under Malaysian jurisdiction, and the 30% Petroleum tax + 10% Supplementary tax under UK jurisdiction. Of the RM 161m, RM 41m of it relates to deferred tax which originates from the difference between tax treatment and accounting treatment on capitalized assets (accelerated depreciation vs straight-line depreciation). Nonetheless, what you should take away from this is that the tax expense is a tangible expense and material to breakeven analysis.
Fortunately, tax is a variable expense, and should not materially impact the cash flow of Hibiscus in today’s low oil price environment.
Note: Cash outflows for Tax Paid in FY20 was RM 97m, substantially below the RM 161m tax expense.
https://preview.redd.it/1xrnwzm89br41.png?width=732&format=png&auto=webp&s=c078bc3e18d9c79d9a6fbe1187803612753f69d8
Balance Sheet
The balance sheet of Hibiscus is unexciting; I’ll just bring your attention to those line items which need additional scrutiny. I’ll use the figures in the latest 2Q21 quarterly report (2Q21) and refer to the notes in AR20 for clarity.
We’ve already discussed Intangible Assets in the section above, so I won’t dwell on it again.
Moving on, the company has Equipment of RM 582m, largely relating to O&G assets (e.g. the Anasuria FPSO vessel and CAPEX incurred on production enhancement projects). Restricted cash and bank balances represent contractual obligations for decommissioning costs of the Anasuria Cluster, and are inaccessible for use in operations.
Inventories are relatively low, despite Hibiscus being an E&P company, so forex fluctuations on carrying value of inventories are relatively immaterial. Trade receivables largely relate to entitlements from Petronas and BP (both oil supermajors), and are hence quite safe from impairment. Other receivables, deposits and prepayments are significant as they relate to security deposits placed with sellers of the oil fields acquired; these should be ignored for cash flow purposes.
^{Note: Total cash and bank balances do not include approximately RM 105 m proceeds from the North Sabah December 2019 offtake (which was received in January 2020})
Cash and bank balances of RM 90m do not include RM 105m of proceeds from offtake received in 3Q21 (Jan 2020). Hence, the actual cash and bank balances as of 2Q21 approximate RM 200m.
Liabilities are a little more interesting. First, I’ll draw your attention to the significant Deferred tax liabilities of RM 457m. These largely relate to the amortization of CAPEX (i.e. Equipment and capitalized E&E expenses), which is given an accelerated depreciation treatment for tax purposes.
The way this works is that the government gives Hibiscus a favorable tax treatment on capital expenditures incurred via an accelerated depreciation schedule, so that the taxable income is less than usual. However, this leads to the taxable depreciation being utilized quicker than accounting depreciation, hence the tax payable merely deferred to a later period – when the tax depreciation runs out but accounting depreciation remains. Given the capital intensive nature of the business, it is understandable why Deferred tax liabilities are so large.
We’ve discussed Provision for decommissioning costs under the Finance Costs section earlier. They are also quite significant at RM 266m.
Notably, the Other Payables and Accruals are a hefty RM 431m. What do they relate to? Basically, they are contractual obligations to the sellers of the oil fields which are only payable upon oil prices reaching certain thresholds. Hence, while they are current in nature, they will only become payable when oil prices recover to previous highs, and are hence not an immediate cash outflow concern given today’s low oil prices.
Cash Flow Statement
There is nothing in the cash flow statement which warrants concern.
Notably, the company generated OCF of approximately RM 500m in FY20 and RM 116m in 2Q21. It further incurred RM 330m and RM 234m of CAPEX in FY20 and 2Q21 respectively, largely owing to production enhancement projects to increase the production rate of the Anasuria and North Sabah fields, which according to management estimates are accretive to ROI.
Tax paid was RM 97m in FY20 and RM 61m in 2Q21 (tax expense: RM 161m and RM 62m respectively).
​

Risks

There are a few obvious and not-so-obvious risks that one should be aware of before investing in Hibiscus. We shall not consider operational risks (e.g. uptime, OPEX) as they are outside the jurisdiction of the equity analyst. Instead, we shall focus on the financial and strategic risks largely outside the control of management. The main ones are:
· Oil prices remaining subdued for long periods of time
· Fluctuation of exchange rates
· Customer concentration risk
· 2P Reserves being less than estimated
· Significant current and non-current liabilities
· Potential issuance of equity
Oil prices remaining subdued
Of topmost concern in the minds of most analysts is whether Hibiscus has the wherewithal to sustain itself through this period of low oil prices (sub-$30). A quick and dirty estimate of annual cash outflow (i.e. burn rate) assuming a $20 oil world and historical production rates is between RM 50m-70m per year, which considering the RM 200m cash balance implies about 3-4 years of sustainability before the company runs out of cash and has to rely on external assistance for financing.
Table 1: Hibiscus EBITDA at different oil price and exchange rates
https://preview.redd.it/gxnekd6h9br41.png?width=670&format=png&auto=webp&s=edbfb9621a43480d11e3b49de79f61a6337b3d51
The above table shows different EBITDA scenarios (RM ‘m) given different oil prices (left column) and USD:MYR exchange rates (top row). Currently, oil prices are $27 and USD:MYR is 1:4.36.
Given conservative assumptions of average OPEX/bbl of $20 (current: $15), we can safely say that the company will be loss-making as long as oil remains at $20 or below (red). However, we can see that once oil prices hit $25, the company can tank the lower-end estimate of the annual burn rate of RM 50m (orange), while at RM $27 it can sufficiently muddle through the higher-end estimate of the annual burn rate of RM 70m (green).
Hence, we can assume that as long as the average oil price over the next 3-4 years remains above $25, Hibiscus should come out of this fine without the need for any external financing.
Customer Concentration Risk
With regards to customer concentration risk, there is not much the analyst or investor can do except to accept the risk. Fortunately, 80% of revenues can be attributed to two oil supermajors (Petronas and BP), hence the risk of default on contractual obligations and trade receivables seems to be quite diminished.
2P Reserves being less than estimated
2P Reserves being less than estimated is another risk that one should keep in mind. Fortunately, the current market cap is merely RM 714m – at half of estimated recoverable amounts of RM 1.468 billion – so there’s a decent margin of safety. In addition, there are other mitigating factors which shall be discussed in the next section (‘Opportunities’).
Significant non-current and current liabilities
The significant non-current and current liabilities have been addressed in the previous section. It has been determined that they pose no threat to immediate cash flow due to them being long-term in nature (e.g. decommissioning costs, deferred tax, etc). Hence, for the purpose of assessing going concern, their amounts should not be a cause for concern.
Potential issuance of equity
Finally, we come to the possibility of external financing being required in this low oil price environment. While the company should last 3-4 years on existing cash reserves, there is always the risk of other black swan events materializing (e.g. coronavirus) or simply oil prices remaining muted for longer than 4 years.
Furthermore, management has hinted that they wish to acquire new oil assets at presently depressed prices to increase daily production rate to a targeted 20,000 bbl by end-2021. They have room to acquire debt, but they may also wish to issue equity for this purpose. Hence, the possibility of dilution to existing shareholders cannot be entirely ruled out.
However, given management’s historical track record of prioritizing ROI and optimal capital allocation, and in consideration of the fact that the MD owns 10% of outstanding shares, there is some assurance that any potential acquisitions will be accretive to EPS and therefore valuations.
​

Opportunities

As with the existence of risk, the presence of material opportunities also looms over the company. Some of them are discussed below:
· Increased Daily Oil Production Rate
· Inclusion of 2C Resources
· Future oil prices exceeding $50 and effects from coronavirus dissipating
Increased Daily Oil Production Rate
The first and most obvious opportunity is the potential for increased production rate. We’ve seen in the last quarter (2Q21) that the North Sabah field increased its daily production rate by approximately 20% as a result of production enhancement projects (infill drilling), lowering OPEX/bbl as a result. To vastly oversimplify, infill drilling is the process of maximizing well density by drilling in the spaces between existing wells to improve oil production.
The same improvements are being undertaken at the Anasuria field via infill drilling, subsea debottlenecking, water injection and sidetracking of existing wells. Without boring you with industry jargon, this basically means future production rate is likely to improve going forward.
By how much can the oil production rate be improved by? Management estimates in their analyst presentation that enhancements in the Anasuria field will be able to yield 5,000 bbl/day by 2021 (current: 2,500 bbl/day).
Similarly, improvements in the North Sabah field is expected to yield 7,000 bbl/day by 2021 (current: 5,300 bbl/day).
This implies a total 2021 expected daily production rate from the two fields alone of 12,000 bbl/day (current: 8,000 bbl/day). That’s a 50% increase in yields which we haven’t factored into our valuation yet.
Furthermore, we haven’t considered any production from existing 2C resources (e.g. Marigold/Sunflower) or any potential acquisitions which may occur in the future. By management estimates, this can potentially increase production by another 8,000 bbl/day, bringing total production to 20,000 bbl/day.
While this seems like a stretch of the imagination, it pays to keep them in mind when forecasting future revenues and valuations.
Just to play around with the numbers, I’ve come up with a sensitivity analysis of possible annual EBITDA at different oil prices and daily oil production rates:
Table 2: Hibiscus EBITDA at different oil price and daily oil production rates
https://preview.redd.it/jnpfhr5n9br41.png?width=814&format=png&auto=webp&s=bbe4b512bc17f576d87529651140cc74cde3d159
The left column represents different oil prices while the top row represents different daily oil production rates.
The green column represents EBITDA at current daily production rate of 8,000 bbl/day; the orange column represents EBITDA at targeted daily production rate of 12,000 bbl/day; while the purple column represents EBITDA at maximum daily production rate of 20,000 bbl/day.
Even conservatively assuming increased estimated annual ITDA of RM 500m (FY20: RM 318m), and long-term average oil prices of $50 (FY20: $60), the estimated Net Profit and P/E ratio is potentially lucrative at daily oil production rates of 12,000 bbl/day and above.
2C Resources
Since we’re on the topic of improved daily oil production rate, it bears to pay in mind the relatively enormous potential from Hibiscus’s 2C Resources. North Sabah’s 2C Resources alone exceed 30 mmbbl; while those from the yet undiagnosed Marigold/Sunflower fields also reach 30 mmbbl. Altogether, 2C Resources exceed 70 mmbbl, which dwarfs the 44 mmbbl of 2P Reserves we have considered up to this point in our valuation estimates.
To refresh your memory, 2C Resources represents oil volumes which have been discovered but are not yet classified as “commercial”. This means that there is reasonable certainty of the oil being recoverable, as opposed to simply being in the very early stages of exploration. So, to be conservative, we will imagine that only 50% of 2C Resources are eligible for reclassification to 2P reserves, i.e. 35 mmbbl of oil.
https://preview.redd.it/mto11iz7abr41.png?width=375&format=png&auto=webp&s=e9028ab0816b3d3e25067447f2c70acd3ebfc41a
This additional 35 mmbbl of oil represents an 80% increase to existing 2P reserves. Assuming the daily oil production rate increases similarly by 80%, we will arrive at 14,400 bbl/day of oil production. According to Table 2 above, this would yield an EBITDA of roughly RM 630m assuming $50 oil.
Comparing that estimated EBITDA to FY20’s actual EBITDA:

	FY20	FY21 (incl. 2C)	Difference
Daily oil production (bbl/day)	8,626	14,400	+66%
Average oil price (USD/bbl)	$68.57	$50	-27%
Average OPEX/bbl (USD)	$16.64	$20	+20%
EBITDA (RM ‘m)	632	630	-

Hence, even conservatively assuming lower oil prices and higher OPEX/bbl (which should decrease in the presence of higher oil volumes) than last year, we get approximately the same EBITDA as FY20.
For the sake of completeness, let’s assume that Hibiscus issues twice the no. of existing shares over the next 10 years, effectively diluting shareholders by 50%. Even without accounting for the possibility of the acquisition of new oil fields, at the current market capitalization of RM 714m, the prospective P/E would be about 10x. Not too shabby.
Future oil prices exceeding $50 and effects from coronavirus dissipating
Hibiscus shares have recently been hit by a one-two punch from oil prices cratering from $60 to $30, as a result of both the Saudi-Russian dispute and depressed demand for oil due to coronavirus. This has massively increased supply and at the same time hugely depressed demand for oil (due to the globally coordinated lockdowns being implemented).
Given a long enough timeframe, I fully expect OPEC+ to come to an agreement and the economic effects from the coronavirus to dissipate, allowing oil prices to rebound. As we equity investors are aware, oil prices are cyclical and are bound to recover over the next 10 years.
When it does, valuations of O&G stocks (including Hibiscus’s) are likely to improve as investors overshoot expectations and begin to forecast higher oil prices into perpetuity, as they always tend to do in good times. When that time arrives, Hibiscus’s valuations are likely to become overoptimistic as all O&G stocks tend to do during oil upcycles, resulting in valuations far exceeding reasonable estimates of future earnings. If you can hold the shares up until then, it’s likely you will make much more on your investment than what we’ve been estimating.

Conclusion

Wrapping up what we’ve discussed so far, we can conclude that Hibiscus’s market capitalization of RM 714m far undershoots reasonable estimates of fair value even under conservative assumptions of recoverable oil volumes and long-term average oil prices. As a value investor, I hesitate to assign a target share price, but it’s safe to say that this stock is worth at least RM 1.00 (current: RM 0.45). Risk is relatively contained and the upside far exceeds the downside. While I have no opinion on the short-term trajectory of oil prices, I can safely recommend this stock as a long-term Buy based on fundamental research.

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Typing this as I go along. I may at any point rescind my statement that the money players are collectively putting into the game is going straight to the investors, rather than for our benefit.
So I'm going to take a closer look at this.
Key points I've personally noted going through the report:

• Revenue (or Turnover in the UK) was 74.4M GBP. Whoever wrote that article I linked earlier needs to learn to read financial statements.
• Of the 74.4M GBP revenue, 46.9M came from subscriptions, 27M came from MTX, 375.6K from advertising, and 142.8K from other sources.
• COGS 24.5M, leaving 49.9M Gross Profit.
• Expenses totalled 21.7M GBP, so that their operating profit was 28.1
• Add 345K in net interest received, subtract 360K on profit on ordinary activities, and their profit for 2016 is 28.8M, or 28.9M if you include forex effects.
• Looks like the UK does balance sheets a little differently than we do it here in NA. They balance it as A-L=E, while I was taught it with A=L+E
• Anyway, Net Assets (A-L) is 30.5M. What caught my eye is a huge ass 34.5M for A/R within 1 year.
• In 2016, they made a loan of 26.1M to parent company. Question is which one? Before or after the acquisition on the 20th of June 2016?
• The final page of the report shows this balance is with Shanghai Hongtou Network Technology
• In 2016, 30.4M GBP was paid as dividends to the owners - the overlords, so to speak.

I'm not sure if I'm interpreting this correctly, but does this mean the marginal revenue from implementing microtransactions is not for the benefit of the game, but for the benefit of the shareholders/investors/parent company/Chinese overlords?
This is a worrying thought that Jagex is being used as collateral for whatever troubles the Chinese overlords might be facing. They've even downsized the staff count if you compare it with 2014's report, which checks out with u/TrendyKip's insider insight on the situation.
The fact that they even had to borrow 26.1M GBP worries me because of this article.
EDIT
This reply possibly debunks what I initially saw as alarming observations.

What is XTRD?

XTRD is a technology company that are introducing a new infrastructure that would allow banks, hedge funds, and large institutional traders to easily access cryptocurrency markets.
XTRD is launching three separate products in sequential stages to solve the ongoing problems caused by having so many disparate markets. Firstly a unified FIX API followed by XTRD Dark Pools and finally the XTRD Single Point of Access or SPA.
Our goal is to build trading infrastructure in the cyptospace and become one of the first full service shops in the cryptocurrency markets for large traders and funds.

• A single FIX API for trading across all connected exchanges
• A robust GUI for manual cross execution on all crypto markets
• A large liquidity pool, based on orders books from all connected exchanges
• Best prices and best top of book execution net of fees
• Low transaction fees
• 99.99999% reliability and uptime
• Fast execution
• Parent/child orders on multiple exchanges to minimize individual market impact
• Advanced order types common in the equity and FX trading space
• Establish XTRD as a premier market-making entity to mitigate spreads and increase liquidity in the cryptocurrency space
• Derivative trading - XTRD plans to connect to LedgerX (US based, approved by the CFTC) for cryptocurrency options and swaps to offer unified hedging and derivate trading strategies
• Robust, US based technical support
• Reliable and familiar deployment methods for institutions:
  • IPSec as a connectivity option across the Internet
  • Cross connection options
  • Collocation space or VPS (Virtual Private Servers) that clients can rent from XTRD
  • UAT/Sandbox environment for testing

What are the industry issues?

COMPLEX WEB OF EXCHANGES. A combination of differing KYC policies, means of funding, interfaces and APIs results in a fragmented patchwork of liquidity for cryptocurrencies. Trading in an automated fashion with full awareness of best pricing and current liquidity necessitates the opening and use of accounts on multiple exchanges, coding to multiple API’s, following varying funding and withdrawal procedures. Once those hurdles are cleared, market participants must convert fiat currency to BTC or ETH and then forward the ETH on to an exchange that may not accept fiat, necessitating yet another transaction to convert back to fiat. Major concerns for market participants range from unmitigated slippage and counterparty risk to hacking prevention and liquidity.
HIGH FEES. Execution costs are even more of a factor. Typical exchange commissions are in the 0.1% – 0.25% range per transaction (10 to 25 basis points), but the effective fees are much higher when taking into bid and ask spreads maintained by the exchanges. As most exchanges are unregulated, there is generally no central authority or regulator to examine internal exchange orders that separate proprietary activity from customer activity and ensure fair pricing.
THIN LIQUIDITY. A large institutional order, representing a sizable percentage of daily volume can move the market for a product, and related products in an exchange by a factor of 5-10%. That means a single order to buy $1,000,000 worth of bitcoin can cost an extra $50,000-$100,000 per transaction given a lack of liquidity if not managed correctly and executed on only one exchange. By way of comparison, similar trades on FX exchanges barely move markets a fraction of a percent; those price changes cost traders money, and deter investment.

What are the XTRD solutions?

FIX API
An API is an “Application Programming Interface”, a set of rules that computer programs use to communicate. FIX stands for “Financial Information eXchange”, the API standard used by most financial organizations as the intermediary protocol to communicate amongst disparate systems such as market data, execution, trade reporting, and order entry for the past 25 years.XTRD is fixing the problem of having 100 different APIs for 100 exchanges by creating a single FIX based API for market data and execution – the same FIX API that all current financial institutions utilize.XTRD will leverage our data center presences in DC3 Chicago and NY4 New Jersey to host FIX trading clients and reduce their trading latencies to single milliseconds, a time acceleration of 100x when it comes to execution vs internet. More infrastructure and private worldwide internet lines will be added in 2018 and beyond to enable secure, low latency execution for all XTRD clients, FIX and PRO.
XTRD PRO
XTRD PRO is a professional trading platform that will fix the basic problems with trading across crypto exchanges – the need to open multiple web pages, having to click around multiple windows, only being able to use basic order types, and not seeing all your positions, trades, and market data in one place.XTRD PRO will be standalone, downloadable, robust end-to-end encrypted software that will consolidate all market data from exchanges visually into one order book, provide a consolidated position and order view across all your exchange accounts, and enable client side orders not available on exchanges – keyboard macro shortcuts, VWAP/TWAP, shaving the bid and offer, hit through 1% of the inside, reserve orders that bid 100 but show 1, SMART order routing to best exchange and intelligent order splicing across exchanges based on execution costs net of fees, OCO and OTO, many others.
XTRD SPA
XTRD SPA is the solution to bridge cross-exchange liquidity issues. XTRD is creating Joint Venture partnerships with trusted cryptocurrency exchanges to provide clients on those exchanges execution across other exchanges where they do not have accounts by leveraging XTRD’s liquidity pools.An order placed by a client at CEX.IO, XTRD’s first JV partner, can be executed by XTRD at a different exchange where there may be a better price or higher liquidity for a digital asset. Subsequently, XTRD will deliver the position to CEX.IO and then CEX.IO will deliver the execution to the client, with XTRD acting as just another market participant at the CEX.IO exchange.XTRD does not take custody of funds, we are a technology partner with exchanges. All local exchange rules, procedures, and AML/KYC policies apply.
XTRD DARK
Institutions and large market participants who have large orders of 100 BTC or more generally must execute across multiple markets, increasing their counterparty risk, paying enormous commissions and spreads, and generally having to deal with the vagaries of the crypto space. Alternatives are OTC brokers that charge multiple percents or private peer-to-peer swaps which are difficult to effectuate unless one is deeply in the space.XTRD is launching XTRD DARK – a dark liquidity pool to trade crypto vs fiat that matches buyers and sellers of large orders, discreetly and anonymously, at a much lower cost. Liquidity is not displayed so large orders do not move thin markets as they would publicly. The liquidity will come from direct XTRD DARK participants as well as aggregation of retail order flow into block orders, XTRD’s own liquidity pools, connections with decentralized exchanges to effectuate liquidity swaps, and OTC broker order flow.XTRD is partnering with a fiat banking providebroker dealer to onboard all XTRD DARK participants for the fiat currency custody side with full KYC/AML procedures.

XTRD Tokenomics

• TOKEN USAGE. XTRD will generate the XTRD utility tokens via smart contact during the Token Generation Event (TGE) in Q1 of 2018. The XTRD utility token will be utilized on the XTRD network by market participants to pay for execution, VPS, market data, software licensing, and other fees. Market participants will be able to purchase XTRD tokens directly from XTRD’s liquidity pool. The TGE will be preceded by a private institutional sale and a SAFT (Simple Agreement for Future Token) Platform based presale. The XTRD token will be fully ERC20 compliant to ensure that it functions properly on the Ethereum blockchain, similarly to other ERC20 tokens.
• XTRD TOKEN LIQUIDITY AGGREGATION. The XTRD token will be used to create liquidity in the overall cryptocurrency market. Along with the utility of the XTRD token to pay for fees on the XTRD platform of products, most of the XTRD token revenue will be used to increase the inventory of other cryptocurrencies that are in demand by our customers. This will create points of liquidity for our customers to access across the worldwide crypto exchange ecosystem. These liquidity points will be created using XTRD tokens that are paid back into the system for fees. 70% of funds raised in the token sale will be used for liquidity aggregation.
• XTRD STAKING. Discounts of 25% on XTRD services (execution, colocation, market data, software licensing) will be available for token holders in general and discounts of 40% on XTRD services will be available for token holders who maintain an average monthly stake of at least 50,000 XTRD tokens. Fiat will be accepted at no discount to par. Execution fees will generally be set as a percentage of the gross trade amount, based on a combination of factors such as liquidity, the pair being traded, market conditions at the time of the trade, and so on. All charges will be marked to market and remain constant, no matter the value of the XTRD token (a $10 charge will be 2 XTRD if $5 each or 0.5 XTRD if $20 each).
• TOKEN LEGAL CONSIDERATIONS. XTRD tokens are ERC20 compliant utility tokens functioning on the Ethereum blockchain. The value of XTRD is derived purely from serving as a medium of payment for services by market participants in the XTRD trading ecosystem. XTRD tokens confer no voting rights, profit participation, equity, ownership of intellectual property, revenue sharing, rights to dividends, transfer of ownership upon company sale, control of company assets, or any decision-making ability regarding XTRD or its’ operations. XTRD tokens are not designed for speculation. In summary, XTRD tokens are not securities. XTRD Digital Assets, Inc has obtained a qualified legal opinion concurring that XTRD tokens are not to be considered “securities” under applicable U.S. securities laws given their failure to meet all prongs of the Howey Test.

Who is XTRD intended for?

XTRD is mainly aimed at major institutions, hedge funds, algorithmic traders who are currently unable to enter the crypto markets.
These firms include companies such as Divisa Capital run by XTRD Advisor Mushegh Tovmasyan.

XTRD Weekly Updates

• XTRD Update: May 20th, 2018
• XTRD Update: May 28th, 2018
• XTRD Update: June 4th, 2018
• XTRD Update: June 11th, 2018
• XTRD Update: June 18th, 2018
• XTRD Update: June 25th, 2018
• XTRD Update: July 2nd, 2018
• XTRD Update: July 9th, 2018
• XTRD Update: July 16th, 2018

Upcoming Events

• Futurist Conference - August 14-16th Toronto
• Blockchain Shift - October 11-12th Miami
• The Trading Show New York - September 26th

AMA's

Further AMA's will be coming soon!

• OhHeyMatty AMA
• CryptoBobby AMA
• TheCryptoLark AMA

XTRD In The Media

• Bloomberg Markets Bitcoin Growing Pains. Bloomberg
• XTRD Brings Wall St Trading Infrastructure To Crypto Markets - Fintech News
• Bad Crypto Meets Alex Kravets - Podcast Interview
• Alex Kravets On Modernizing Crypto Exchanges - Podcast Interview
• How Exchanges Work On Wall Street And How They Will Work In Crypto Markets. - Crypto 101 Podcast
• Ethereum Outperforms Bitcoin - Article Feature
• Crypto Markets Are Broken - CoinTelegraph Alex Guest Writer
• Wall Street Comes To Crypto - CryptoCoinNews Article / Interview
• Cryptocurrency Businesses Real Value - Business News Daily
• Cryptocurrency Market Psychology - CBS News
• Bitcoin Slides Again - Expert Option - Reuters

Resources

• Telegram
• Twitter
• Facebook
• Medium
• Reddit
• Whitepaper
• Website

More information will be added to this thread as the project develops.
We are currently looking for key community members to assist in building out this thread.
If you are interested please email [[email protected]](mailto:[email protected])

To summarize my experience with the "product." It is 99.9% stalling and wasting your time with shit like this https://www.youtube.com/watch?v=za3tUUoj2iQ and hyping their own products. Dudes acting like "whaaaaaaaa" and showing phone screens with profits. Facebook is flooded with these people showing off how their boy "[insert name here]" is selling money and yada yada. Most of the content they post and host is just recruitment hype videos to bring more people in. Seriously... Like all day every day it felt like. Most the videos feature some hipsters and text screenshots of them being like: "OMG I gots some mad pips brah, 100 million pipz lulz #winning [intense sarcasm]."
Do yourself a favor and unfriend anyone who tries to sell you this shit. Because they are not your friend, and they view you as a $35 a month paycheck if you sign up. Most the common stuff you see in groups is people touting the wins, but don't fool yourself... Everyone is keeping their losses quiet because no one wants to make a fool out of themselves. I mean who makes videos of them watching someone else in video chat? Someone who doesn't know what the hell their doing or how to work technology... Which they happen to have some miracle program that will "change" your life. Oh, and the haters, HA you should just ignore them because they all don't like grape koolaide.
TL:DR Oh, look ma some YOLO saying swag fags are saying they can teach me to make mad bread. But it is just a job selling a job selling jobs to other people who in turn will be selling said jobs to others who will do the same.
4/24/16 EDIT This is the kind of shit that constantly keeps popping up in my feeds and spamming my cell phone in texts.
I just got started with Global Visionariez and iMarketsLive, What is next?! First off, Welcome to our family! My name is[removed name of person] Founder of GV, an organization that is changing lives and lifestyles around the globe. It's an honor to have you apart of the revolutionary team where our visions align towards a common cause. We have some of the best leadership hand to hand with the greatest opportunity in the game right now, which makes it a complete power house! The goal is the be able take the average person and have them take the road less travelled towards becoming an entrepreneur and attaining true freedom! The skills you will learn working along side with GV and IML are long lasting skills that you can pass down for generations to come and will help you develop and craft yourself into becoming the best version of yourself. We live by the Triple T's; TRADE. TRAVEL. TRANSFORM. Lifestyle by design. As we believe these are some of the main 3 keys towards freedom and happiness! 📈.✈.🚧.
Let's get started, you just bought your new car, now let's adjust the seats it to how you like it and what you want. The first thing you want to do is be able to get activated on your services and plugged into the sessions.
🔌 STAY PLUGGED IN AROUND THE CAMPFIRE 🔸 Subscribe To GV Updates [removed link]
📡Want to EARN before you LEARN and get connected to the Automatic Mirror Trader? [link removed] (1) FIRST create your broker account (Trading Funds Account) Choose the broker of your choice, we suggest Tradersway or FXCM 🔸 Tradersway ($8/month on FxSignalsLive - Better Leverage) INCLUDING PROMOTION (Less Deposit Fees, 18+, Deposit Bonus) [link removed] 🔸 FXCM (FREE on FxSignalsLive): [link removed] (2) How To Set Up Your Mirror-Trader? [more yolo swag links removed...]
📈📉 Want to start trading yourself? It's CRUCIAL to LEARN before you try to EARN trading yourself! Practice, practice, practice before you go LIVE. RULE #1, Don't EVER EVER EVER try to PREDICT the markets yourself, Don't have a gambling mindset! Be smart, Be strategic. Start with a DEMO account until you feel comfortable enough to trade with REAL money (30-90+ DAYS). If you want to start trading your real money deposit and connect with an MIRROR-TRADER [link removed] and trade with Chris Terry during LONDON / NY Sessions! Keep in mind, TRADING is 80% Mental (Psychology) and 20% Fundamental (Skillset) 📚(1) EDUCATE YOURSELF (STUDY more than you TRADE): 🔸 IML EDUCATION: Log into imarketslive.com -> TRADING -> TRADING LIBRARY 🔸 BEGINNERS KNOWLEDGE A-Z: [link removed] ; ⚠COMPLETE Pre-School before trading live 🔸 GVWSA COURSE: [link removed] ($50 one time) 🐾GV WALLSTREET (GVWSA): Train with Quillan Black through his legendary discounted course for IML members ONLY for $50 one time, completely up to you if you would like access, feel free to ask anyone around GV if it was worth it. If you plan to go to the next level trading and marking up charts you want to plug in ASAP! (2) SET UP MT4 (Trading Platform) + IML HARMONIC SCANNER 🔸 Download "MetaTrader 4" through App Store 🔸 Download MT4 / Scanner (Windows) [link removed] 🔸 Download MT4 /Scanner (MAC) [link removed] ⚠ Make sure you go through the Harmonic Scanner Course in IML Backoffice before you start using it. The Harmonic Scanner is primarily a confirmation tool to combine with your own analysis do NOT take trades off of it based of its entry calls, it is not 100% right neither would any software ever be. Use this in the right way and you will rock your world! (3) TRADE and LEARN with C. Terry 🔸 LONDON SESSION (Tues, Wed, Thurs) ⏰ 2am EST - 3am EST [more links removed] 🔸 NEW YORK SESSION (Mon - Fri) FOREX + FUTURES ⏰ 8am EST - 12pm EST [link removed] FULL RISK DISCLOSURE: Trading contains substantial risk and is not for every investor. An investor could potentially lose all or more than the initial investment. Risk capital is money that can be lost without jeopardizing ones financial security or life style. Only risk capital should be used for trading and only those with sufficient risk capital should consider trading. Past performance is not necessarily indicative of future results.
🗣 Paid sharing a retail service that is teaching you a financially independent skillset and helping you generate wealth in the worst economy? The FACT that you can potentially earn RESIDUAL income by sharing (marketing) a service that you would share for free anyways! The average MILLIONAIRE has 7 streams of income and never puts all of their eggs in one basket, especially throughout your journey of becoming a trader, the comfort of knowing you have a weekly residual income while you are trading in the markets truly powerful! YOUR FIRST GOAL ASAP........PERFECT STORM BONUS IN YOUR FIRST 14 DAYS. How to SHARE: What is Forex?: [link removed] What is IML (Overview): [link removed] Compensation Plan PDF: [link removed] Compensation Plan Video: [link removed] **TAG THE NEWEST MEMBER TO HAVE THEM START OFF THE RIGHT FOOT👇👇👇
edit 2 Removed links from post, I apologize I didn't notice the rule for this subreddit and fixed it before an admin got onto me. =) Edit/update 3* 5-21-16 Been a while now, still end up getting spam texts and other crap in my inbox on social media and email.

 

Getting Started with Lykke

 

What is Lykke?

Lykke is building a global marketplace for the free exchange of financial assets. By leveraging the power of emerging technology, our platform eliminates market inefficiencies, promotes equal access from anywhere in the world, and supports the trade of any object of value. The Lykke Exchange is fast and secure. Users receive direct ownership of assets with immediate settlement from any mobile device. You can try Lykke Wallet, available for both iOS / Android. Here’s a quick video to get you started.

What is Blockchain Technology?

The Lykke marketplace uses the distributed ledger, which is blockchain technology pioneered by Bitcoin. This technology incorporates a protocol for decentralized data storage in the chain of blocks, where the consistency of the data is guaranteed by the cryptography and consensus of multiple nodes.

What are LKK coins?

A Lykke coin (LKK) is a cryptographic token that represents ownership of Lykke, a Swiss registered corporation. There is no mining and currently it's not listed on other exchanges (although that may change). 100 LKK represent 1 share of Lykke Corp. Read more at our Information Memorandum  

Useful Links

 

• Lykke White Paper
  
• Lykke - The Future of Markets
  
• Lykke Wallet - Trade FX and Digital Assets, available for iOS / Android
  
• Lykke Explorer - Search Transactions, Assets, Addresses or Blocks
  
• Lykke Streams - Collaboration Platform
  
• Lykke B2B - Lykke Accelerator
  
• Lykke Github - Our Software is Open Source
  
• Lykke News - Company News and Official Statements
  
• Lykke Blog - Recent developments, analysis and other articles  
  

Most Frequently Asked Questions

 

How does LKK1Y works?

Lykke 1-year forward coin (LKK1Y) is essentially a derivative on LKK. Buyer of 1 LKK1Y can purchase the right to receive 1 LKK in 365 days after they ask for the delivery. In other words, there is no fixed maturity date: if you buy a Lykke forward contract, you can execute it at any date and after 1 year passes the Lykke coins will be delivered. You can read more at What is Lykke Forward? blog post.

How does Lykke earn money?

Commissions are zero, so Lykke earns revenue from value-add services such as liquidity provision, issuance services, white-labeling, and B2B consulting.

If I own Lykke coins, am I considered to be a shareholder of Lykke Corp?

Yes. If you own Lykke coins, you are entitled to become shareholders of Lykke Corp, provided that you submit to our minimum KYC requirements. Shareholders have additional rights, such as voting and receiving dividends.

Will LKK coins pay dividends?

Yes. They will when the company decides to pay profits to investors, rather than re-invest to fuel growth. As with most startups, dividends may be some time away.

Are there plans to list LKK in other exchanges?

Yes, the roadmap currently states it’s a medium term goal.

How does Lykke differ from other crypto ventures?

Lykke is not a cryptocurrency or distributed ledger technology venture; we are building a marketplace that integrates seamlessly with the existing financial system. Our trading venue uses a matching engine to cross buy and sell orders. The accounting, delivery, and settlement of traded assets use distributed ledger technology. Our initial focus is the foreign exchange market with a daily transaction volume of 4 trillion USD, the biggest financial market in the world.

I am a US citizen and can't download the App. When will Lykke be available in my country?

Hopefully soon, this is only temporary as Lykke requires licenses to be fully compliant in many countries. It’s currently not available in Australia, Japan, Canada and the US (which greatly differs from other countries as states have different laws). At first only some states would be available (or only certain assets could be tradeable) but eventually with more blockchain technology acceptance and adoption more should follow.

Is Lykke Exchange only available on mobile?

Currently yes, however a web trading platform is being developed and part of the medium term roadmap.
 

Published Articles

 

• Richard Olsen of OANDA fame on his new startup Lykke LeapRate Interview
  
• OANDA’s Richard Olsen Launches Lykke Wallet for iOS Devices
  
• Swiss Startup Lykke Seeks To Disrupt The Retail Trading Market With Blockchain Tech
  
• Blockchain Will Absorb the World of Finance: Richard Olsen
  
• Bitcoin Legend Nick Szabo Joins Lykke Advisory Board
  
• Lykke cofounder Sergey Ivliev is Named Best Risk Manager in Russia and CIS
  
• Lykke at IATA World Financial Symposium
  
• Lykke Exchange is now Licensed to Provide Financial Services in Asian and African Countries
  
• Lykke Joins the Hyperledger Project
  
• Richard Olsen is the Forex Person of the Year 2016
  
• WISeKey and Lykke to Integrate Cybersecurity into Blockchain Applications
  
• Trading Cryptocurrencies and Digital Assets Securely
  
• Why does Lykke Coin price go up?
  
• Lykke: Perfecting the Alpha Engine, Creating Global Opportunity!
  

 

Period: 108.98 days

	Submissions	Comments
Total	999	10425
Rate (per day)	9.17	95.73
Unique Redditors	361	695
Combined Score	4162	17424

Top Submitters' Top Submissions

1. 296 points, 24 submissions: tuckerbalch
   
   1. Project 2 Megathread (optimize_something) (33 points, 475 comments)
   2. project 3 megathread (assess_learners) (27 points, 1130 comments)
   3. For online students: Participation check #2 (23 points, 47 comments)
   4. ML / Data Scientist internship and full time job opportunities (20 points, 36 comments)
   5. Advance information on Project 3 (19 points, 22 comments)
   6. participation check #3 (19 points, 29 comments)
   7. manual_strategy project megathread (17 points, 825 comments)
   8. project 4 megathread (defeat_learners) (15 points, 209 comments)
   9. project 5 megathread (marketsim) (15 points, 484 comments)
   10. QLearning Robot project megathread (12 points, 691 comments)
2. 278 points, 17 submissions: davebyrd
   
   1. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes (37 points, 10 comments)
   2. Project 1 Megathread (assess_portfolio) (34 points, 466 comments)
   3. marketsim grades are up (25 points, 28 comments)
   4. Midterm stats (24 points, 32 comments)
   5. Welcome to CS 7646 MLT! (23 points, 132 comments)
   6. How to interact with TAs, discuss grades, performance, request exceptions... (18 points, 31 comments)
   7. assess_portfolio grades have been released (18 points, 34 comments)
   8. Midterm grades posted to T-Square (15 points, 30 comments)
   9. Removed posts (15 points, 2 comments)
   10. assess_portfolio IMPORTANT README: about sample frequency (13 points, 26 comments)
3. 118 points, 17 submissions: yokh_cs7646
   
   1. Exam 2 Information (39 points, 40 comments)
   2. Reformat Assignment Pages? (14 points, 2 comments)
   3. What did the real-life Michael Burry have to say? (13 points, 2 comments)
   4. PSA: Read the Rubric carefully and ahead-of-time (8 points, 15 comments)
   5. How do I know that I'm correct and not just lucky? (7 points, 31 comments)
   6. ML Papers and News (7 points, 5 comments)
   7. What are "question pools"? (6 points, 4 comments)
   8. Explanation of "Regression" (5 points, 5 comments)
   9. GT Github taking FOREVER to push to..? (4 points, 14 comments)
   10. Dead links on the course wiki (3 points, 2 comments)
4. 67 points, 13 submissions: harshsikka123
   
   1. To all those struggling, some words of courage! (20 points, 18 comments)
   2. Just got locked out of my apartment, am submitting from a stairwell (19 points, 12 comments)
   3. Thoroughly enjoying the lectures, some of the best I've seen! (13 points, 13 comments)
   4. Just for reference, how long did Assignment 1 take you all to implement? (3 points, 31 comments)
   5. Grade_Learners Taking about 7 seconds on Buffet vs 5 on Local, is this acceptable if all tests are passing? (2 points, 2 comments)
   6. Is anyone running into the Runtime Error, Invalid DISPLAY variable when trying to save the figures as pdfs to the Buffet servers? (2 points, 9 comments)
   7. Still not seeing an ML4T onboarding test on ProctorTrack (2 points, 10 comments)
   8. Any news on when Optimize_Something grades will be released? (1 point, 1 comment)
   9. Baglearner RMSE and leaf size? (1 point, 2 comments)
   10. My results are oh so slightly off, any thoughts? (1 point, 11 comments)
5. 63 points, 10 submissions: htrajan
   
   1. Sample test case: missing data (22 points, 36 comments)
   2. Optimize_something test cases (13 points, 22 comments)
   3. Met Burt Malkiel today (6 points, 1 comment)
   4. Heads up: Dataframe.std != np.std (5 points, 5 comments)
   5. optimize_something: graph (5 points, 29 comments)
   6. Schedule still reflecting shortened summer timeframe? (4 points, 3 comments)
   7. Quick clarification about InsaneLearner (3 points, 8 comments)
   8. Test cases using rfr? (3 points, 5 comments)
   9. Input format of rfr (2 points, 1 comment)
   10. [Shameless recruiting post] Wealthfront is hiring! (0 points, 9 comments)
6. 62 points, 7 submissions: swamijay
   
   1. defeat_learner test case (34 points, 38 comments)
   2. Project 3 test cases (15 points, 27 comments)
   3. Defeat_Learner - related questions (6 points, 9 comments)
   4. Options risk/reward (2 points, 0 comments)
   5. manual strategy - you must remain in the position for 21 trading days. (2 points, 9 comments)
   6. standardizing values (2 points, 0 comments)
   7. technical indicators - period for moving averages, or anything that looks past n days (1 point, 3 comments)
7. 61 points, 9 submissions: gatech-raleighite
   
   1. Protip: Better reddit search (22 points, 9 comments)
   2. Helpful numpy array cheat sheet (16 points, 10 comments)
   3. In your experience Professor, Mr. Byrd, which strategy is "best" for trading ? (12 points, 10 comments)
   4. Industrial strength or mature versions of the assignments ? (4 points, 2 comments)
   5. What is the correct (faster) way of doing this bit of pandas code (updating multiple slice values) (2 points, 10 comments)
   6. What is the correct (pythonesque?) way to select 60% of rows ? (2 points, 11 comments)
   7. How to get adjusted close price for funds not publicly traded (TSP) ? (1 point, 2 comments)
   8. Is there a way to only test one or 2 of the learners using grade_learners.py ? (1 point, 10 comments)
   9. OMS CS Digital Career Seminar Series - Scott Leitstein recording available online? (1 point, 4 comments)
8. 60 points, 2 submissions: reyallan
   
   1. [Project Questions] Unit Tests for assess_portfolio assignment (58 points, 52 comments)
   2. Financial data, technical indicators and live trading (2 points, 8 comments)
9. 59 points, 12 submissions: dyllll
   
   1. Please upvote helpful posts and other advice. (26 points, 1 comment)
   2. Books to further study in trading with machine learning? (14 points, 9 comments)
   3. Is Q-Learning the best reinforcement learning method for stock trading? (4 points, 4 comments)
   4. Any way to download the lessons? (3 points, 4 comments)
   5. Can a TA please contact me? (2 points, 7 comments)
   6. Is the vectorization code from the youtube video available to us? (2 points, 2 comments)
   7. Position of webcam (2 points, 15 comments)
   8. Question about assignment one (2 points, 5 comments)
   9. Are udacity quizzes recorded? (1 point, 2 comments)
   10. Does normalization of indicators matter in a Q-Learner? (1 point, 7 comments)
10. 56 points, 2 submissions: jan-laszlo
    
    1. Proper git workflow (43 points, 19 comments)
    2. Adding you SSH key for password-less access to remote hosts (13 points, 7 comments)
11. 53 points, 1 submission: agifft3_omscs
    
    1. [Project Questions] Unit Tests for optimize_something assignment (53 points, 94 comments)
12. 50 points, 16 submissions: BNielson
    
    1. Regression Trees (7 points, 9 comments)
    2. Two Interpretations of RFR are leading to two different possible Sharpe Ratios -- Need Instructor clarification ASAP (5 points, 3 comments)
    3. PYTHONPATH=../:. python grade_analysis.py (4 points, 7 comments)
    4. Running on Windows and PyCharm (4 points, 4 comments)
    5. Studying for the midterm: python questions (4 points, 0 comments)
    6. Assess Learners Grader (3 points, 2 comments)
    7. Manual Strategy Grade (3 points, 2 comments)
    8. Rewards in Q Learning (3 points, 3 comments)
    9. SSH/Putty on Windows (3 points, 4 comments)
    10. Slight contradiction on ProctorTrack Exam (3 points, 4 comments)
13. 49 points, 7 submissions: j0shj0nes
    
    1. QLearning Robot - Finalized and Released Soon? (18 points, 4 comments)
    2. Flash Boys, HFT, frontrunning... (10 points, 3 comments)
    3. Deprecations / errata (7 points, 5 comments)
    4. Udacity lectures via GT account, versus personal account (6 points, 2 comments)
    5. Python: console-driven development (5 points, 5 comments)
    6. Buffet pandas / numpy versions (2 points, 2 comments)
    7. Quant research on earnings calls (1 point, 0 comments)
14. 45 points, 11 submissions: Zapurza
    
    1. Suggestion for Strategy learner mega thread. (14 points, 1 comment)
    2. Which lectures to watch for upcoming project q learning robot? (7 points, 5 comments)
    3. In schedule file, there is no link against 'voting ensemble strategy'? Scheduled for Nov 13-20 week (6 points, 3 comments)
    4. How to add questions to the question bank? I can see there is 2% credit for that. (4 points, 5 comments)
    5. Scratch paper use (3 points, 6 comments)
    6. The big short movie link on you tube says the video is not available in your country. (3 points, 9 comments)
    7. Distance between training data date and future forecast date (2 points, 2 comments)
    8. News affecting stock market and machine learning algorithms (2 points, 4 comments)
    9. pandas import in pydev (2 points, 0 comments)
    10. Assess learner server error (1 point, 2 comments)
15. 43 points, 23 submissions: chvbs2000
    
    1. Is the Strategy Learner finalized? (10 points, 3 comments)
    2. Test extra 15 test cases for marketsim (3 points, 12 comments)
    3. Confusion between the term computing "back-in time" and "going forward" (2 points, 1 comment)
    4. How to define "each transaction"? (2 points, 4 comments)
    5. How to filling the assignment into Jupyter Notebook? (2 points, 4 comments)
    6. IOError: File ../data/SPY.csv does not exist (2 points, 4 comments)
    7. Issue in Access to machines at Georgia Tech via MacOS terminal (2 points, 5 comments)
    8. Reading data from Jupyter Notebook (2 points, 3 comments)
    9. benchmark vs manual strategy vs best possible strategy (2 points, 2 comments)
    10. global name 'pd' is not defined (2 points, 4 comments)
16. 43 points, 15 submissions: shuang379
    
    1. How to test my code on buffet machine? (10 points, 15 comments)
    2. Can we get the ppt for "Decision Trees"? (8 points, 2 comments)
    3. python question pool question (5 points, 6 comments)
    4. set up problems (3 points, 4 comments)
    5. Do I need another camera for scanning? (2 points, 9 comments)
    6. Is chapter 9 covered by the midterm? (2 points, 2 comments)
    7. Why grade_analysis.py could run even if I rm analysis.py? (2 points, 5 comments)
    8. python question pool No.48 (2 points, 6 comments)
    9. where could we find old versions of the rest projects? (2 points, 2 comments)
    10. where to put ml4t-libraries to install those libraries? (2 points, 1 comment)
17. 42 points, 14 submissions: larrva
    
    1. is there a mistake in How-to-learn-a-decision-tree.pdf (7 points, 7 comments)
    2. maximum recursion depth problem (6 points, 10 comments)
    3. [Urgent]Unable to use proctortrack in China (4 points, 21 comments)
    4. manual_strategynumber of indicators to use (3 points, 10 comments)
    5. Assignment 2: Got 63 points. (3 points, 3 comments)
    6. Software installation workshop (3 points, 7 comments)
    7. question regarding functools32 version (3 points, 3 comments)
    8. workshop on Aug 31 (3 points, 8 comments)
    9. Mount remote server to local machine (2 points, 2 comments)
    10. any suggestion on objective function (2 points, 3 comments)
18. 41 points, 8 submissions: Ran__Ran
    
    1. Any resource will be available for final exam? (19 points, 6 comments)
    2. Need clarification on size of X, Y in defeat_learners (7 points, 10 comments)
    3. Get the same date format as in example chart (4 points, 3 comments)
    4. Cannot log in GitHub Desktop using GT account? (3 points, 3 comments)
    5. Do we have notes or ppt for Time Series Data? (3 points, 5 comments)
    6. Can we know the commission & market impact for short example? (2 points, 7 comments)
    7. Course schedule export issue (2 points, 15 comments)
    8. Buying/seeking beta v.s. buying/seeking alpha (1 point, 6 comments)
19. 38 points, 4 submissions: ProudRamblinWreck
    
    1. Exam 2 Study topics (21 points, 5 comments)
    2. Reddit participation as part of grade? (13 points, 32 comments)
    3. Will birds chirping in the background flag me on Proctortrack? (3 points, 5 comments)
    4. Midterm Study Guide question pools (1 point, 2 comments)
20. 37 points, 6 submissions: gatechben
    
    1. Submission page for strategy learner? (14 points, 10 comments)
    2. PSA: The grading script for strategy_learner changed on the 26th (10 points, 9 comments)
    3. Where is util.py supposed to be located? (8 points, 8 comments)
    4. PSA:. The default dates in the assignment 1 template are not the same as the examples on the assignment page. (2 points, 1 comment)
    5. Schedule: Discussion of upcoming trading projects? (2 points, 3 comments)
    6. [defeat_learners] More than one column for X? (1 point, 1 comment)
21. 37 points, 3 submissions: jgeiger
    
    1. Please send/announce when changes are made to the project code (23 points, 7 comments)
    2. The Big Short on Netflix for OMSCS students (week of 10/16) (11 points, 6 comments)
    3. Typo(?) for Assess_portfolio wiki page (3 points, 2 comments)
22. 35 points, 10 submissions: ltian35
    
    1. selecting row using .ix (8 points, 9 comments)
    2. Will the following 2 topics be included in the final exam(online student)? (7 points, 4 comments)
    3. udacity quiz (7 points, 4 comments)
    4. pdf of lecture (3 points, 4 comments)
    5. print friendly version of the course schedule (3 points, 9 comments)
    6. about learner regression vs classificaiton (2 points, 2 comments)
    7. is there a simple way to verify the correctness of our decision tree (2 points, 4 comments)
    8. about Building an ML-based forex strategy (1 point, 2 comments)
    9. about technical analysis (1 point, 6 comments)
    10. final exam online time period (1 point, 2 comments)
23. 33 points, 2 submissions: bhrolenok
    
    1. Assess learners template and grading script is now available in the public repository (24 points, 0 comments)
    2. Tutorial for software setup on Windows (9 points, 35 comments)
24. 31 points, 4 submissions: johannes_92
    
    1. Deadline extension? (26 points, 40 comments)
    2. Pandas date indexing issues (2 points, 5 comments)
    3. Why do we subtract 1 from SMA calculation? (2 points, 3 comments)
    4. Unexpected number of calls to query, sum=20 (should be 20), max=20 (should be 1), min=20 (should be 1) -bash: syntax error near unexpected token `(' (1 point, 3 comments)
25. 30 points, 5 submissions: log_base_pi
    
    1. The Massive Hedge Fund Betting on AI [Article] (9 points, 1 comment)
    2. Useful Python tips and tricks (8 points, 10 comments)
    3. Video of overview of remaining projects with Tucker Balch (7 points, 1 comment)
    4. Will any material from the lecture by Goldman Sachs be covered on the exam? (5 points, 1 comment)
    5. What will the 2nd half of the course be like? (1 point, 8 comments)
26. 30 points, 4 submissions: acschwabe
    
    1. Assignment and Exam Calendar (ICS File) (17 points, 6 comments)
    2. Please OMG give us any options for extra credit (8 points, 12 comments)
    3. Strategy learner question (3 points, 1 comment)
    4. Proctortrack: Do we need to schedule our test time? (2 points, 10 comments)
27. 29 points, 9 submissions: _ant0n_
    
    1. Next assignment? (9 points, 6 comments)
    2. Proctortrack Onboarding test? (6 points, 11 comments)
    3. Manual strategy: Allowable positions (3 points, 7 comments)
    4. Anyone watched Black Scholes documentary? (2 points, 16 comments)
    5. Buffet machines hardware (2 points, 6 comments)
    6. Defeat learners: clarification (2 points, 4 comments)
    7. Is 'optimize_something' on the way to class GitHub repo? (2 points, 6 comments)
    8. assess_portfolio(... gen_plot=True) (2 points, 8 comments)
    9. remote job != remote + international? (1 point, 15 comments)
28. 26 points, 10 submissions: umersaalis
    
    1. comments.txt (7 points, 6 comments)
    2. Assignment 2: report.pdf (6 points, 30 comments)
    3. Assignment 2: report.pdf sharing & plagiarism (3 points, 12 comments)
    4. Max Recursion Limit (3 points, 10 comments)
    5. Parametric vs Non-Parametric Model (3 points, 13 comments)
    6. Bag Learner Training (1 point, 2 comments)
    7. Decision Tree Issue: (1 point, 2 comments)
    8. Error in Running DTLearner and RTLearner (1 point, 12 comments)
    9. My Results for the four learners. Please check if you guys are getting values somewhat near to these. Exact match may not be there due to randomization. (1 point, 4 comments)
    10. Can we add the assignments and solutions to our public github profile? (0 points, 7 comments)
29. 26 points, 6 submissions: abiele
    
    1. Recommended Reading? (13 points, 1 comment)
    2. Number of Indicators Used by Actual Trading Systems (7 points, 6 comments)
    3. Software Install Instructions From TA's Video Not Working (2 points, 2 comments)
    4. Suggest that TA/Instructor Contact Info Should be Added to the Syllabus (2 points, 2 comments)
    5. ML4T Software Setup (1 point, 3 comments)
    6. Where can I find the grading folder? (1 point, 4 comments)
30. 26 points, 6 submissions: tomatonight
    
    1. Do we have all the information needed to finish the last project Strategy learner? (15 points, 3 comments)
    2. Does anyone interested in cryptocurrency trading/investing/others? (3 points, 6 comments)
    3. length of portfolio daily return (3 points, 2 comments)
    4. Did Michael Burry, Jamie&Charlie enter the short position too early? (2 points, 4 comments)
    5. where to check participation score (2 points, 1 comment)
    6. Where to collect the midterm exam? (forgot to take it last week) (1 point, 3 comments)
31. 26 points, 3 submissions: hilo260
    
    1. Is there a template for optimize_something on GitHub? (14 points, 3 comments)
    2. Marketism project? (8 points, 6 comments)
    3. "Do not change the API" (4 points, 7 comments)
32. 26 points, 3 submissions: niufen
    
    1. Windows Server Setup Guide (23 points, 16 comments)
    2. Strategy Learner Adding UserID as Comment (2 points, 2 comments)
    3. Connect to server via Python Error (1 point, 6 comments)
33. 26 points, 3 submissions: whoyoung99
    
    1. How much time you spend on Assess Learner? (13 points, 47 comments)
    2. Git clone repository without fork (8 points, 2 comments)
    3. Just for fun (5 points, 1 comment)
34. 25 points, 8 submissions: SharjeelHanif
    
    1. When can we discuss defeat learners methods? (10 points, 1 comment)
    2. Are the buffet servers really down? (3 points, 2 comments)
    3. Are the midterm results in proctortrack gone? (3 points, 3 comments)
    4. Will these finance topics be covered on the final? (3 points, 9 comments)
    5. Anyone get set up with Proctortrack? (2 points, 10 comments)
    6. Incentives Quiz Discussion (2-01, Lesson 11.8) (2 points, 3 comments)
    7. Anyone from Houston, TX (1 point, 1 comment)
    8. How can I trace my error back to a line of code? (assess learners) (1 point, 3 comments)
35. 25 points, 5 submissions: jlamberts3
    
    1. Conda vs VirtualEnv (7 points, 8 comments)
    2. Cool Portfolio Backtesting Tool (6 points, 6 comments)
    3. Warren Buffett wins $1M bet made a decade ago that the S&P 500 stock index would outperform hedge funds (6 points, 12 comments)
    4. Windows Ubuntu Subsystem Putty Alternative (4 points, 0 comments)
    5. Algorithmic Trading Of Digital Assets (2 points, 0 comments)
36. 25 points, 4 submissions: suman_paul
    
    1. Grade statistics (9 points, 3 comments)
    2. Machine Learning book by Mitchell (6 points, 11 comments)
    3. Thank You (6 points, 6 comments)
    4. Assignment1 ready to be cloned? (4 points, 4 comments)
37. 25 points, 3 submissions: Spareo
    
    1. Submit Assignments Function (OS X/Linux) (15 points, 6 comments)
    2. Quantsoftware Site down? (8 points, 38 comments)
    3. ML4T_2017Spring folder on Buffet server?? (2 points, 5 comments)
38. 24 points, 14 submissions: nelsongcg
    
    1. Is it realistic for us to try to build our own trading bot and profit? (6 points, 21 comments)
    2. Is the risk free rate zero for any country? (3 points, 7 comments)
    3. Models and black swans - discussion (3 points, 0 comments)
    4. Normal distribution assumption for options pricing (2 points, 3 comments)
    5. Technical analysis for cryptocurrency market? (2 points, 4 comments)
    6. A counter argument to models by Nassim Taleb (1 point, 0 comments)
    7. Are we demandas to use the sample for part 1? (1 point, 1 comment)
    8. Benchmark for "trusting" your trading algorithm (1 point, 5 comments)
    9. Don't these two statements on the project description contradict each other? (1 point, 2 comments)
    10. Forgot my TA (1 point, 6 comments)
39. 24 points, 11 submissions: nurobezede
    
    1. Best way to obtain survivor bias free stock data (8 points, 1 comment)
    2. Please confirm Midterm is from October 13-16 online with proctortrack. (5 points, 2 comments)
    3. Are these DTlearner Corr values good? (2 points, 6 comments)
    4. Testing gen_data.py (2 points, 3 comments)
    5. BagLearner of Baglearners says 'Object is not callable' (1 point, 8 comments)
    6. DTlearner training RMSE none zero but almost there (1 point, 2 comments)
    7. How to submit analysis using git and confirm it? (1 point, 2 comments)
    8. Passing kwargs to learners in a BagLearner (1 point, 5 comments)
    9. Sampling for bagging tree (1 point, 8 comments)
    10. code failing the 18th test with grade_learners.py (1 point, 6 comments)
40. 24 points, 4 submissions: AeroZach
    
    1. questions about how to build a machine learning system that's going to work well in a real market (12 points, 6 comments)
    2. Survivor Bias Free Data (7 points, 5 comments)
    3. Genetic Algorithms for Feature selection (3 points, 5 comments)
    4. How far back can you train? (2 points, 2 comments)
41. 23 points, 9 submissions: vsrinath6
    
    1. Participation check #3 - Haven't seen it yet (5 points, 5 comments)
    2. What are the tasks for this week? (5 points, 12 comments)
    3. No projects until after the mid-term? (4 points, 5 comments)
    4. Format / Syllabus for the exams (2 points, 3 comments)
    5. Has there been a Participation check #4? (2 points, 8 comments)
    6. Project 3 not visible on T-Square (2 points, 3 comments)
    7. Assess learners - do we need to check is method implemented for BagLearner? (1 point, 4 comments)
    8. Correct number of days reported in the dataframe (should be the number of trading days between the start date and end date, inclusive). (1 point, 0 comments)
    9. RuntimeError: Invalid DISPLAY variable (1 point, 2 comments)
42. 23 points, 8 submissions: nick_algorithm
    
    1. Help with getting Average Daily Return Right (6 points, 7 comments)
    2. Hint for args argument in scipy minimize (5 points, 2 comments)
    3. How do you make money off of highly volatile (high SDDR) stocks? (4 points, 5 comments)
    4. Can We Use Code Obtained from Class To Make Money without Fear of Being Sued (3 points, 6 comments)
    5. Is the Std for Bollinger Bands calculated over the same timespan of the Moving Average? (2 points, 2 comments)
    6. Can't run grade_learners.py but I'm not doing anything different from the last assignment (?) (1 point, 5 comments)
    7. How to determine value at terminal node of tree? (1 point, 1 comment)
    8. Is there a way to get Reddit announcements piped to email (or have a subsequent T-Square announcement published simultaneously) (1 point, 2 comments)
43. 23 points, 1 submission: gong6
    
    1. Is manual strategy ready? (23 points, 6 comments)
44. 21 points, 6 submissions: amchang87
    
    1. Reason for public reddit? (6 points, 4 comments)
    2. Manual Strategy - 21 day holding Period (4 points, 12 comments)
    3. Sharpe Ratio (4 points, 6 comments)
    4. Manual Strategy - No Position? (3 points, 3 comments)
    5. ML / Manual Trader Performance (2 points, 0 comments)
    6. T-Square Submission Missing? (2 points, 3 comments)
45. 21 points, 6 submissions: fall2017_ml4t_cs_god
    
    1. PSA: When typing in code, please use 'formatting help' to see how to make the code read cleaner. (8 points, 2 comments)
    2. Why do Bollinger Bands use 2 standard deviations? (5 points, 20 comments)
    3. How do I log into the [email protected]? (3 points, 1 comment)
    4. Is midterm 2 cumulative? (2 points, 3 comments)
    5. Where can we learn about options? (2 points, 2 comments)
    6. How do you calculate the analysis statistics for bps and manual strategy? (1 point, 1 comment)
46. 21 points, 5 submissions: Jmitchell83
    
    1. Manual Strategy Grades (12 points, 9 comments)
    2. two-factor (3 points, 6 comments)
    3. Free to use volume? (2 points, 1 comment)
    4. Is MC1-Project-1 different than assess_portfolio? (2 points, 2 comments)
    5. Online Participation Checks (2 points, 4 comments)
47. 21 points, 5 submissions: Sergei_B
    
    1. Do we need to worry about missing data for Asset Portfolio? (14 points, 13 comments)
    2. How do you get data from yahoo in panda? the sample old code is below: (2 points, 3 comments)
    3. How to fix import pandas as pd ImportError: No module named pandas? (2 points, 4 comments)
    4. Python Practice exam Question 48 (2 points, 2 comments)
    5. Mac: "virtualenv : command not found" (1 point, 2 comments)
48. 21 points, 3 submissions: mharrow3
    
    1. First time reddit user .. (17 points, 37 comments)
    2. Course errors/types (2 points, 2 comments)
    3. Install course software on macOS using Vagrant .. (2 points, 0 comments)
49. 20 points, 9 submissions: iceguyvn
    
    1. Manual strategy implementation for future projects (4 points, 15 comments)
    2. Help with correlation calculation (3 points, 15 comments)
    3. Help! maximum recursion depth exceeded (3 points, 10 comments)
    4. Help: how to index by date? (2 points, 4 comments)
    5. How to attach a 1D array to a 2D array? (2 points, 2 comments)
    6. How to set a single cell in a 2D DataFrame? (2 points, 4 comments)
    7. Next assignment after marketsim? (2 points, 4 comments)
    8. Pythonic way to detect the first row? (1 point, 6 comments)
    9. Questions regarding seed (1 point, 1 comment)
50. 20 points, 3 submissions: JetsonDavis
    
    1. Push back assignment 3? (10 points, 14 comments)
    2. Final project (9 points, 3 comments)
    3. Numpy versions (1 point, 2 comments)
51. 20 points, 2 submissions: pharmerino
    
    1. assess_portfolio test cases (16 points, 88 comments)
    2. ML4T Assignments (4 points, 6 comments)

Top Commenters

1. tuckerbalch (2296 points, 1185 comments)
2. davebyrd (1033 points, 466 comments)
3. yokh_cs7646 (320 points, 177 comments)
4. rgraziano3 (266 points, 147 comments)
5. j0shj0nes (264 points, 148 comments)
6. i__want__piazza (236 points, 127 comments)
7. swamijay (227 points, 116 comments)
8. _ant0n_ (205 points, 149 comments)
9. ml4tstudent (204 points, 117 comments)
10. gatechben (179 points, 107 comments)
11. BNielson (176 points, 108 comments)
12. jameschanx (176 points, 94 comments)
13. Artmageddon (167 points, 83 comments)
14. htrajan (162 points, 81 comments)
15. boyko11 (154 points, 99 comments)
16. alyssa_p_hacker (146 points, 80 comments)
17. log_base_pi (141 points, 80 comments)
18. Ran__Ran (139 points, 99 comments)
19. johnsmarion (136 points, 86 comments)
20. jgorman30_gatech (135 points, 102 comments)
21. dyllll (125 points, 91 comments)
22. MikeLachmayr (123 points, 95 comments)
23. awhoof (113 points, 72 comments)
24. SharjeelHanif (106 points, 59 comments)
25. larrva (101 points, 69 comments)
26. augustinius (100 points, 52 comments)
27. oimesbcs (99 points, 67 comments)
28. vansh21k (98 points, 62 comments)
29. W1redgh0st (97 points, 70 comments)
30. ybai67 (96 points, 41 comments)
31. JuanCarlosKuriPinto (95 points, 54 comments)
32. acschwabe (93 points, 58 comments)
33. pharmerino (92 points, 47 comments)
34. jgeiger (91 points, 28 comments)
35. Zapurza (88 points, 70 comments)
36. jyoms (87 points, 55 comments)
37. omscs_zenan (87 points, 44 comments)
38. nurobezede (85 points, 64 comments)
39. BelaZhu (83 points, 50 comments)
40. jason_gt (82 points, 36 comments)
41. shuang379 (81 points, 64 comments)
42. ggatech (81 points, 51 comments)
43. nitinkodial_gatech (78 points, 59 comments)
44. harshsikka123 (77 points, 55 comments)
45. bkeenan7 (76 points, 49 comments)
46. moxyll (76 points, 32 comments)
47. nelsongcg (75 points, 53 comments)
48. nickzelei (75 points, 41 comments)
49. hunter2omscs (74 points, 29 comments)
50. pointblank41 (73 points, 36 comments)
51. zheweisun (66 points, 48 comments)
52. bs_123 (66 points, 36 comments)
53. storytimeuva (66 points, 36 comments)
54. sva6 (66 points, 31 comments)
55. bhrolenok (66 points, 27 comments)
56. lingkaizuo (63 points, 46 comments)
57. Marvel_this (62 points, 36 comments)
58. agifft3_omscs (62 points, 35 comments)
59. ssung40 (61 points, 47 comments)
60. amchang87 (61 points, 32 comments)
61. joshuak_gatech (61 points, 30 comments)
62. fall2017_ml4t_cs_god (60 points, 50 comments)
63. ccrouch8 (60 points, 45 comments)
64. nick_algorithm (60 points, 29 comments)
65. JetsonDavis (59 points, 35 comments)
66. yjacket103 (58 points, 36 comments)
67. hilo260 (58 points, 29 comments)
68. coolwhip1234 (58 points, 15 comments)
69. chvbs2000 (57 points, 49 comments)
70. suman_paul (57 points, 29 comments)
71. masterm (57 points, 23 comments)
72. RolfKwakkelaar (55 points, 32 comments)
73. rpb3 (55 points, 23 comments)
74. venkatesh8 (54 points, 30 comments)
75. omscs_avik (53 points, 37 comments)
76. bman8810 (52 points, 31 comments)
77. snladak (51 points, 31 comments)
78. dfihn3 (50 points, 43 comments)
79. mlcrypto (50 points, 32 comments)
80. omscs-student (49 points, 26 comments)
81. NellVega (48 points, 32 comments)
82. booglespace (48 points, 23 comments)
83. ccortner3 (48 points, 23 comments)
84. caa5042 (47 points, 34 comments)
85. gcalma3 (47 points, 25 comments)
86. krushnatmore (44 points, 32 comments)
87. sn_48 (43 points, 22 comments)
88. thenewprofessional (43 points, 16 comments)
89. urider (42 points, 33 comments)
90. gatech-raleighite (42 points, 30 comments)
91. chrisong2017 (41 points, 26 comments)
92. ProudRamblinWreck (41 points, 24 comments)
93. kramey8 (41 points, 24 comments)
94. coderafk (40 points, 28 comments)
95. niufen (40 points, 23 comments)
96. tholladay3 (40 points, 23 comments)
97. SaberCrunch (40 points, 22 comments)
98. gnr11 (40 points, 21 comments)
99. nadav3 (40 points, 18 comments)
100. gt7431a (40 points, 16 comments)

Top Submissions

1. [Project Questions] Unit Tests for assess_portfolio assignment by reyallan (58 points, 52 comments)
2. [Project Questions] Unit Tests for optimize_something assignment by agifft3_omscs (53 points, 94 comments)
3. Proper git workflow by jan-laszlo (43 points, 19 comments)
4. Exam 2 Information by yokh_cs7646 (39 points, 40 comments)
5. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes by davebyrd (37 points, 10 comments)
6. Project 1 Megathread (assess_portfolio) by davebyrd (34 points, 466 comments)
7. defeat_learner test case by swamijay (34 points, 38 comments)
8. Project 2 Megathread (optimize_something) by tuckerbalch (33 points, 475 comments)
9. project 3 megathread (assess_learners) by tuckerbalch (27 points, 1130 comments)
10. Deadline extension? by johannes_92 (26 points, 40 comments)

Top Comments

1. 34 points: jgeiger's comment in QLearning Robot project megathread
2. 31 points: coolwhip1234's comment in QLearning Robot project megathread
3. 30 points: tuckerbalch's comment in Why Professor is usually late for class?
4. 23 points: davebyrd's comment in Deadline extension?
5. 20 points: jason_gt's comment in What would be a good quiz question regarding The Big Short?
6. 19 points: yokh_cs7646's comment in For online students: Participation check #2
7. 17 points: i__want__piazza's comment in project 3 megathread (assess_learners)
8. 17 points: nathakhanh2's comment in Project 2 Megathread (optimize_something)
9. 17 points: pharmerino's comment in Midterm study Megathread
10. 17 points: tuckerbalch's comment in Midterm grades posted to T-Square

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