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Bias | Portfolio Yoga

How long will you Stay Invested

In November of this year, my personal portfolio made an all time high. The previous time I had seen an all time high was way back in early January of 2018. A couple of months, it would have been three years since I last made an all time high for the portfolio (not inclusive of dividends though). 

Equity returns are lumpy in nature. Unfortunately most of us don’t have the patience to hold through the worst of times in preparation for the oncoming best of times. Fear has a way of playing its tricks on our mind at the worst possible times.

Markets loathe giving free money to one and all. Before the 2008 financial crisis led crash, Indian Markets used to crash once in two years on an average. When I say crash, I mean a drop of at least 30% from the previous peak.

In 1996, Nifty 50 rose from 800 to 1200 by the middle of the year but by the end, it was down 800. In 1997, it once again rose to 1300 just to fall back to 950 by early January 1998. The bounce 1200 odd before jettisoning all the gains and falling back to 800 by late 1998. From there started the Dot Com bubble rally that took Nifty 50 to 1800 levels before the crash took it over time back to 920 in late 2001. 

Then we had the crash of 2004, 2006 and of course, the crash of 2008. Post 2008, Markets drifted lower for a considerable length of time only once – 2011 and even then did not go below the 30% mark. In fact, after 2008, March 2020 was the first time we saw a drawdown greater than 30%. 

The financial crisis and the way the Federal Reserve responded has changed the behavior of the market. Just when it seemed the normalcy of balance sheets of the Central Bank will be restored, we were hit with Covid which has resulted in an unprecedented flow of liquidity. 

While the recent rise in Indian Equities thanks to the generous inflow of funds from FII’s, do note that India is the only country into which liquidity is pouring. For most other emerging markets, its the other way round. To me, this is indicative that much of the flow may not be speculative in nature and at least a large part could be because of a change in perception with regards to the future growth of the economy.

Markets are expensive, goes the headline. Most measure the valuation of the market by using Nifty 50 Trailing Price to Earning as the proxy. But why Nifty 50 and why not Sensex or the Nifty 500 or the Price Earnings of the market as a whole. Is it because we all have fallen prey to availability bias?

In 2020, Sensex went up by 15.8%, Nifty 50 by 14.90%. Sensex Price to Earnings on the other hand went up by 28.80% while Nifty 50 PE went up by 35.90%. At the end of the year, Nifty PE stood at 38.45 vs Sensex PE of 33.50. Would you say Sensex is cheaper than Nifty?

No one knows the future but one can be pretty confident that the earnings of companies for the financial year 2021-2022 will be way better than 2020-2021. How much better would make it easy to get a fix on how expensive the market really is. Oh, by the way Nifty PE is Standalone earnings while the true picture will be shown by using Consolidated earnings. But since NSE doesn’t provide it, we don’t bother with it.

Then there is Authority Bias. We stop questioning things just because someone with authority says so and if he says so, how could it be wrong. So, when claims are made using a single example of who SIP is better than Lumpsium, we don’t stop to question the stupidity of comparing an Apple with a Pineapple. 

Questions are always asked of a bull market – be it at the beginning, the middle or the end. There will always be some indicator or parameter that can be used to defend a bull case or make a bear case. In Statistics, one school of thought says that if you have at least 30 independent samples, it can be used to make some decent predictions. We end up making predictions with a sample size of two or three and then wonder why we went wrong.

Equity is Risk when looked at a short term time frame. There is no getting away from it. If you invest money today, the risk to capital exists at the very least for one year if not more. But as time passes, the risk moves on from the capital invested to the gains and as one moves even further, it’s just part of the gains that will be at risk.

The longer you stay in the markets, lower the risks of ruin (unless of course you are leveraged in which case, the risk of ruin may never go). But to stay longer, you should invest only so much that allows you the comfort of good sleep regardless of market conditions. A secondary requirement of course is to invest in something you deeply understand or trust. The reason I could stay with the strategy during its long drawdown had more to do with my trust rather than any superior skill sets. Building that takes time but once built, it serves you for the lifetime.

Selection & Survivor Bias

One of the stories that was often told to newbie’s in the stock market was how investing just a small sum of 14,500 in 1993 public issue of Infosys would have been worth a few million rupees as on date. There is no falsehood in the statement either since Infosys had for long been a darling stock of the market and has provided unprecedented returns to any investor who invested when it IPO’ed and held on to the stock till date.

But what is not immediately seen is how this return is due to pure Selection and Survivor Bias. Selection Bias is defined in Wikipedia as

“Selection bias is the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed. It is sometimes referred to as the selection effect.”

When we select Infosys, we are selecting one among maybe hundreds of IPO’s that was seen in the same year (1993). Compounding the error, we fall prey to another common bias which goes by the name “Survivor bias”. Once again, the definition of the bias from Wikipedia

“Survivorship bias, or survival bias, is the logical error of concentrating on the people or things that “survived” some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions in several different ways.”

The reason we selected Infosys was that it was a survivor among the hundreds if not thousands of companies that traded in those days. If you remember a company (which was a hot stock in those days) by name, Orkay Silk Mills, you will know what I am leading you towards.

When analyzing financial databases, its very important that both these factors are taken into consideration as otherwise your results will be very biased and in all probability under estimates the risk and over estimates the reward.

When Mutual Funds are analyzed, you need to keep a eye open to these biases since over time, its a list of those funds / fund houses that could survive. Bad funds generally get merged with a better fund, small AMC’s get taken over and their funds merged with other schemes.

In other words, if you were to get a list of say the best funds of 2000, its very much probable that a lot many funds don’t even exist today. Bad funds don’t survive for long since it showcases failure of the fund manager / AMC and faster it gets deleted, the better it is.

Using ValueResearchOnline, I found 169 funds which have been merged with other schemes and are no longer quoted. Even this list (post 2003) misses fund houses such as IL&FS and hence not complete. Add to that, I could also find another 124 funds that were “Redeemed”.

The question is, what would your return be if you were invested in any of those funds that got merged over time and how does that compare to what the fund that now in existence (into which it was merged) performed.

The art of investing has to begin by starting to ask the right questions. Do remember, there is no Free Money out there. A large cap fund cannot outperform its benchmark by a yard unless it did something different. Now, whether this out performance came in due to additional risk (investing in a large mid cap stock for instance) or cutting of risk (going into cash) comes with its own Pro’s and Con’s. Understanding that aspect enables you to understand performance better and stick with it for a much longer period that you otherwise would have been comfortable with.

If you are serious about learning the biases and fallacies that affect our judgement, do read Thinking, Fast and Slow by Daniel Kahneman, am sure it will provide you with a perspective that is easily missed by majority of investors out there.

Hidden Bias in Chart Reading

I am not a great fan of Chart Reading. Its like evaluating a painting, I may like one style and you may like another. Even technicians admit that what they see may not be what the other guy sees and since the probability of being right or wrong is always 50%, its no wonder that some one has to right and that in itself is shown as the reason why chart reading is a great asset to trading.

Biases are unfortunately ever present when it comes to skills where you cannot prove it to be wrong or right. Hence while everyone has to agree that 10 + 10 = 20, a single chart maybe read in any number of ways depending on what the chart reader assumes it to be showing / implicating at that moment of time.

For example, look at this chart picked from here.

Image

The author uses the chart to explain why his bearish bias is correct by showing how the Risk Appetite Index has broken a trend line and indicating a reversal of the current trend. But is that the correct way to read the chart?

The Index has twice dipped strongly. The first time was in June 2012 and the same has been marked. But that was the low for the market (and in hindsight, for the foreseeable future). Second was even more of a deadly drop in March 2013, but Index seemed to care a boot as it accelerated upwards through the year.

No strategy has a 100% win ratio (other than Bernie Madoff ofcourse 🙂 ), but unless its thoroughly tested and its faults known, one is better off not using / knowing such a strategy rather than use something that just feeds our bias regardless of it working or not.