Trading vs Poker

I've heard many people say that if you are good at poker, you will be a good trader. I never quite related to that fact well enough in my college days. Not any more!

Let me first make it clear that I am talking about trading being similar to poker, and not investing. Investing, obviously based on fundamentals, is very similar to bridge. But that's a topic for some other day, which will come very soon.


So yeah, back to poker. The analogies I am making is for the secondary market trader functioning in fairly liquid markets. Let me begin with the similarities which exist in the setup of poker and trading.

Poker is a zero sum game, and trading is nearly zero sum, with its trade execution costs (transaction costs, bid-ask spreads, etc)

The cards held by the player represent the fundamentals of the company's stock. Hardly useful for trading, especially during a constrained time horizon. Your trading strategy will win depending on a lot of factors, and fundamentals play a very small role. Rockets are busted as frequently as they end up winning.

Each step while opening the community cards represent the unveiling of key macroeconomic market parameters, e.g. change in interest rates. They are macroeconomic because the affect everyone's holding. They will favour some companies/positions and work against some other. The trader exits the position if the data unveiled hampers the position. The trader might also have to exit the position if the cost of continuing the position (e.g. margin calls) is out of his budget, even though the odds are in favour.

The bets by a player represent the information the company is trying to communicate to market players, in the form of press releases and Financial Statements publications. Frequently enough, they hide/misrepresent information, or they hold up their biggest piece of information till the fourth quarter (which represents the biggest bets being made on the river).

Reading people's faces represents the Behavioral Finance element. The trader will win the pot if he reads the mind of the junta correctly. Understanding the sentiments of the junta is the (I can't stress this enough!) most important art to be a good player/trader.

Apart from these, various participation strategies exist which are similar in trading and poker

There's also the reputation factor. Consider a trader who is widely perceived as a god by the market. If he decides to take a certain non-anonymous position in the market, that position will be regarded a sure shot success strategy. Therefore, it will be hard for him to find a dealer who takes the counter-position of this trade.Similarly, a player with a reputation of being tight, will always find a lot of folds when he decides to participate in the pot.



There's a poker strategy which many people use, I call it a test bet. Its similar to a c-bet, but the motivation is different. Immediately after the flop (and sometimes the turn), I'll put in a small bet to judge what others are upto. If this bet results in some action, I am in a much better position to judge whether to raise/fold/call. This is similar to the trading strategy where traders place small limit orders above and below the current market price, to gauge what the market is upto. In case these small lots get executed, they might pull in (similar to a raise) or pull out (similar to a fold) the big lots.

I am sure there are many more strategies which are similar in poker and trading. But I haven't played poker in a while, and I haven't done trading at all. Any more analogies are welcome!

The Realty: contd...

This is in continuation to my previous blog, "The Realty".

In my previous article, I barely touched upon the "other" factors. I included all of them in a single subjective variable, which, as many pointed out too, is wrong. Here, I will try to include those other factors in an as objective way as possible. This way, I intend to end up with a multi-variable regression with 5 to 10 independent variables.

This analysis is certainly fraught with dangers, the biggest one being that the independent variables can themselves be correlated among themselves. I do not have the tools and knowledge (more importantly the enthusiasm) to correct for that.

Besides, I am using the data of only 10-odd cities which is clearly a very small sample size.

Including these other variables independently should also probably reduce the R-square value of population density. But lets keep the conclusions for later.

Lets list those other factors, their construction and the data source that I'll use for those factors:

1) Infrastructure and Transportation: This is a tough one. Here, I intend to measure the existing level of infrastructure in the city, and in particular, the transportation facilities, on a per capita basis. Thus, it should include factors like road length (road area might be better), rail length, number of buses, number of train bogeys, niche transport facilities (metros, availability of air transport) etc. So the proxy that can be chosen for this variable is

Total Road Length/Population]

2) Education opportunities per capita: This can be a bit tricky. I can't simply use the Total No. of Educational institutions per capita, because it grossly undermines the value of higher education opportunities and also doesn't take into account the fact that institutions can have variable intake capacities. The best way will be to use a number relating to the total intake in various colleges, needless to say, on a per capita basis of existing population. The value of this variable is

[Total Intake in all the city's colleges/Population]

3) Income per capita and job opportunities: This one is straight forward. I'll just use the per capita income of the city. This variable is also a great indicator for the existing job opportunities for two reasons, one, because a high per capita income means there are opportunities waiting to be ceased in the city and second, a high per capita income in turn creates a lot of trickle-down job opportunities (by the way, I am a firm believer in trickle-down economics. There's too much regulation and taxes in this world at the moment). Reiterating, this variable is Per Capita Income

4) Climate and Environment: There are two dimensions to this factor. First is, how the mean temperature in the city compare to the country average. The second is how much deviation is there in the temperature (month-on-month standard deviation) over the whole year. I am tempted to include precipitation here, but the effect of the amount of rainfall is uncertain, as there's no preferred range of precipitation, at least its not a narrow one. Things like proximity to beaches or hill stations will add to this factor, but this fact is best incorporated by adjusting the variable value in a subjective manner. Thus, this variable is (before adjustments) (|xyz| means absolute value of xyz):

|Mean City temp. - Mean India temp.| x Std. Dev. in the city's temp

5) Safety of Living: For this, I have to pick a type of incident which best represents the unsafety of the place and for which the stats are also highly published. This variable can best be proxied by the number of rape cases in the city, per capita.

6) Taxes, Regulatory and Legal issues: This variable should measure how convenient is the legal framework of the city to settle in. This is, by definition, a subjective variable. Still, I think it can be proxied by the Road Tax percentage in the city. Thus, a proxy for this variable is
Average Road tax rate %

6) Herding behavior, Metro Premium: A premium can be assumed for cities that are already developed, esp. if it's a metro. Thus this is a binary variable indicating if it's a metro.

As expected, data collection of this level is a daunting task. I tried collecting this data, but I am having many difficulties in completing this task. This exercise has to be left incomplete.

Regret ratio

Consider two situations:
A trader starts with a million in his hands. He enters the market at a good time and, in the next three years, reaches a position valued at 50 million. After which, a recession hits, and his position is reduced to 5 million in another 2 years' time. He now exits his position. In 5 years, he made 4 million, a cool 400% return.
Now consider another (counter-cyclical) trader, who starts with the same 1 million. After 3 years, his position reaches a valuation of just 10 thousand. He recovers in the next 2 years, and ends up with a position valued at the same 5 million.

I recently finished reading 'Fooled by Randomness', and Taleb describes a situation very similar to the above in that book. I have two adjectives for the guy, arrogant and genius, in that order.

But the question he raises is whether these two 400% returns are equal? Clearly not! Even if these paths are equally volatile, these returns are not the same. Conventional risk-adjusted return measures do not work here. Even ratios which aim to distinguish between upside and downside deviation (like sortino) are not much of a use. The problem here is more behavioural, and the way they perceive their returns.

The first trader will always think of his experience with regret and sorrow. He will keep thinking how great it could have been, had he exited at the peak. Because he perceives it as a missed opportunity, he will underrate his return. The opposite is true for the other trader. He will be a lot more satisfied with his return, and he will perceive this return to be more than the actual value.
It is this difference in perception that I want to capture with a number. I suggest the following:

Regret ratio = (Maximum Valuation - Ending Valuation) / (Ending Valuation - Minimum Valuation).

This ratio should work very well in all those cases where the return is positive during the complete 5-year period. I do not intend to measure regrets when the total return during that period was anyway negative.Hence, it is a non-negative number.

The numerator here is essentially a measure of the feeling of regret with the experience. The denominator measures the opposite feeling of satisfaction or good luck with the experience. Their ratio should denote the net effect of regret from the experience. The higher this number, the larger is the feeling of regret. The smaller this number, the more the person gets a feeling of satisfaction.

Algorithmic Recession

I hereby make a bet that the ongoing revolution of algorithmic trading is going cause a global recession once in the next 100 years
My reasoning is fairly simple. Due to the stochastic nature of market movements, we will continue to see minor upturns and downturns (essentially caused by behavioral reasons, rather than any fundamental one). The algorithms in place that time will somehow pick on one of these upturns, and inflate it. All the algorithms in place will try to benefit from this anomaly, participate in this bubble and inflate it. Until some human intervenes, and exits the bubble. This will be followed by many humans and algorithms doing the same, causing the bubble to crash.
This story seems to have the two necessary and sufficient ingredients of a boom-bust cycle. It contains a financial or technical innovation (algo trading) being used excessively, which is necessary to fuel a bubble. The restoration of sanity, due to human intervention, triggers the crash-correction.

Baldyman

This one is dedicated to all those lucky people who have more head than hair! I have composed a few one-line appreciations for you all

Jaan jaane se darr nahi lagta sahib, baal jaane se lagta hai

Baal vivah is what you need, baal a bit more importantly then vivah to satisfy your lust

Baal vivah is what you need, marry some hair, its a "saat janmo ka bandhan"

When you kneel down to propose, she has to put on her shades

You wish you could produce more baal than bachhe

The only transplant available is not a viable option

Use fevicol instead of hair-oil, mazboot jod hai, tootega nahi

Batra's is your favourite medication

You wonder why isn't the lack of hair at some place below

There's a new term on the block, "hair-fetish"

There's a mirror traveling with you, but one which you yourself cannot use

The place is not fertile, and hence a turnoff

Experience is the comb which god has given you

You wonder why people complain about grey hair

What's the difference between a king a bald man: a king has a heir apparent, a bald man has no hair apparent

People can see whats on your mind, coz there's nothing to cover that

A shower can get you brain-washed

There's no story which is hair-raising for you

You are proud of your "aerodynamic shape"

You wonder what a hairline fracture is

But you can still get Steffi Graf

IIM F

The HRD Ministry today approved the setting up of a new IIM, IIM-F, short for IIM - Fake. This will be first IIM which will not be in India, and the ministry has chosen a central European city of Fucking, as the location of the new institute.

The admission process to this institute will also be different. The first hurdle, which the aspirants have to pass is clear the FAT, abbreviation for Faltu Admission Test. The format of the test is yet to be finalized, but sources said that the test will aim to judge mainly one quality of the aspirants, FATe, thus justifying the name given to the test. Inside sources said that, the test might consist tossing coins for 2 hours and 15 minutes, with top 1% of the aspirants who turn up the most number of heads will be given a chance to move to the next stage of selection process, the interviews.

The interviewers are expected to check whether the candidate can think "into the box" or not. Inside sources also said the interviewers do not want alumni of the top engineering institutes, like TII-B, TII-D, etc to get into this IIM, because they don't think in this way.

The course structure is yet to be finalized but an outline has been made. There will be periodic assignments given in each of the courses. Credits will be given only if the student copies blatantly from a source. Negative credits will be given if the assignment is found to be original. When a company called Xerox came to know about this course structure, it immediately agreed to come to the institute for placements, promising that it will take atleast 100% of the students. Thus, It's poised to become the first institute with 100% placement guarantee even before its inception.

The ministry has decided to reserve 50% of the seats in this prestigious institute for the residents of Fucking. Although this move might improve the economy of the city, its believed that rampant corruption among the administrators will make it very easy to prove that the candidate belongs to the city.

The Realty

Living in Mumbai for four years and seeing the real estate prices there made me think, why the hell are they so high! Conventional wisdom says, Realty prices are determined by three factors: first, location, second, location, and third, location. While this is definitely true for a city, I wanted to know what made Mumbai as a whole so expensive, compared to say, Bangalore.

Hence, I did a small study of my own to find the various factors on which it depends, and how strongly these factors explain the actual Realty prices observed in the city.

The first and most important factor has to be demand for land. Supply is fixed, and hence only demand determines the prices. But how do you measure demand? The population of the city is definitely a measure of demand, but we do have a better measure. Consider one sq. km of land, fixed supply. The demand for that piece will depend on the number of people living on that one sq. km. Hence, the density of population should to be a better estimate of demand. Lets first do a regression of Real Estate prices versus Density of population.

City Delhi	Rate 15000	Density 12000
Mumbai	25000	22000
Kolkata	17000	24000
Chennai	13000	22000
Bangalore	12000	8000
Hyderabad	11000	18000
Ahmedabad	12000	22000
Pune	12000	7000
Surat	8000	15000
Jaipur	7000	19000
Lucknow	4000	3000
Patna	3000	1800

This table shows the data I used to do this single variable regression. Rate represents the average rental value per month of a 2BHK, approx 1200 sq. ft. apartment. The value is an average of the first 20 results I got on 99acres.com, when I searched for properties in that city. Density represents the Density of population measured in persons per sq. km in the city, as taken from census data and wikipedia. I get the following from the regression:

R-squared = 0.3893

hmm...Density does explain about 40% of the variation in realty prices, but this doesn't sound convincing. Intuitively, there must be other factors which also play a part. Lets just list some of those "other" factors.

If the city's infrastructure, especially public transportation facilities are very good, people can always live in the outskirts and travel comfortably to their place of interest. Good infrastructure facilities should relax the pressure on realty prices. Factors like good climate, job opportunities will put an upward pressure on prices. There will be other factors also, like safety of living etc. There is no objective way to quantify these factors. So, I will just put another independent variable called "other" and do a multiple regression analysis. This variable can have values from 1 to 5. 5 representing the maximum pressure on prices. This is a highly subjective variable and disagreements may exist.

Thus, Mumbai with its pathetic transportation and great job opportunities should be rated pretty high on this scale. Delhi, with good transportation and great job opportunities should be in above average range. Patna, a fairly unsafe place with few job opportunities should score a minimum in this factor.

Thus, I get the following table:

City	Rate	Density	Other
Delhi	15000	12000	4
Mumbai	25000	22000	5
Kolkata	17000	24000	3
Chennai	13000	22000	3
Bangalore	12000	8000	4
Hyderabad	11000	18000	3
Ahmedabad	12000	22000	2
Pune	12000	7000	4
Surat	8000	15000	2
Jaipur	7000	19000	2
Lucknow	4000	3000	1
Patna	3000	1800	1

'Rate' is the dependent variable, which is being explained by two independent variables: 'Density' and 'other'. I get the following stats from regressing this data:

R-squared = 0.8780
R-squared adjusted = 0.8509

This definitely looks better. These two factors combined have done a good job by explaining more than 85% of the variation in realty prices.

If, on the other hand, I regress the rates with only one factor 'other', R-squared is 0.711, which means that other factors single-handedly explain about 70% of the variation in prices. The result is simple to interpret, that is, non-quantifiable 'other' factors play a more important role in explaining the variation than density of population. Nevertheless, Density is important because it is the only variable which is deterministic in this analysis.

In conclusion, there is no complete objective way to estimate realty prices. Subjective factors play a major part and they have to be taken into account to undertake such a study. I feel this framework is a decent starting point to identify cities with undervalued properties. I am also pretty confident that this framework can be applied in an international context, provided we account for purchasing power parity variations, but I leave this international perspective to an institutional study.

Disclaimer: This is a completely independent research without plagiarism (except that some data was taken from websites, but I've acknowledged them). Any resemblance to a prior research is coincidental. I claim no copyright on this study. For the full analysis, I can be contacted on my gmail address.