We are not perfect. Human beings make mistakes. We are not rational decision-makers. It’s as simple as that.
I guess this is not surprising to anyone reading this. But you might be surprised to hear that a large part of the foundational theories that govern economic science rests upon the assumption that the participants in the market are rational. By “market”, I don’t just mean stock market, rather any form of place where buyers and sellers are willingly coming to trade goods and services. Kahneman and Tversky (1979) introduced their magnum opus in prospect theory. It helped demystify a lot of conventional wisdom about how the participants, in groups and individually, were not rational entities. They argue that human beings are full of cognitive biases which deter a rational decision-making process.
There have been more than 100 such biases identified ever since the 80s. Some are quite persistent and pervasive, others are not so much. In this article, my goal is to give you just a brief intro to the major biases that are affecting your investment decision-making process. But before that, let’s ponder about why the discretionary decision-making process is so much prone to errors. I think it can be narrowed down to three major reasons:
- Same facts but different decisions
I think it is self-explanatory by the title itself. There have been numerous studies done on it. Even the so-called “experts” are fallible to it. Just think about the time you went to different doctors with the same medical history and x-rays files and got entirely different results. Now, I don’t know whether it speaks to the specific doctor’s expertise or there might be other factors playing a role there. But one thing I can say is that this little experiment goes beyond just medicine. Researchers have tested this phenomenon of models vs experts in many disciplines. Grove et al. (2000) conduct a meta-analysis of studies and report that a systematic or algorithmic decision-making process beats human experts 46 percent of the time where experts beat models just 6 percent of the time.
- Story-based decision making, not evidence-based
I am going to elaborate on this theme by giving an example. Since I know many investors in Nepal are big fans of technical analysis, I am going to make a statement here that will probably make many of you reading this very angry. You might even close this article but as a research student, I feel it is my duty to at least tell you what has been tested and found. So, here is the statement: “There has been no evidence that TA works”. If you are still with me, let me give you the reason why I think so.
Well, the reason is nothing fancy but it’s just not shown in the evidence. There have been thousands of studies done in TA and no statistically significant results (after trading costs like commission, slippage, fees) have been demonstrated which can be used in forming actual trading strategies and implementing them. If you don’t believe me, just google it and read the results of articles published in high-profile journals. The closest I have come to see a study that shows the relevance of TA was done by Lo et al. (2000) and even in this paper, the researchers use sophisticated algorithmic models to implement the trading strategy, not a human being. The reason is very intuitive. Let’s take an example of the most famous TA pattern, the Head and Shoulders. The way you define H&S and the way I define H&S might very well be different. Edwards and Magee, who wrote perhaps the first major literature in TA about 50 years ago, the definition of H&S might again be different from a trader in New York trading for a proprietary trading firm. Do you see my point? It is highly subjective and the more subjective it is, the more human element goes into it. Hence, it leads to more behavioral biases and errors in decision-making.
Now, you might point out to me that you know many people who are using TA and making excellent returns. Yes, you are right they are using TA and making money. But that is just an effect of randomness. Here, consistency is the key. It says nothing about the person’s skill or the effectiveness of TA. It might just be a form of survivorship bias. Only after a long period, say 15 or 20 years, if we see that the person’s TA trading strategy has outperformed the benchmark or a cheap buy and hold strategy and his results are statistically significant then, we can conclude that there is merit to using TA.
Don’t get me wrong, I too look at charts every once in a while. I think it might add value to generating good entry and exit signals. But creating different line angles and patterns and overlaying the charts with various indicators, that’s not for me. Until now, I haven’t even touched on the market efficiency dynamics and the reasoning of why TA doesn’t work. Again, I would like to emphasize this point that if suddenly the world changes its behavior and researchers start to find evidence that TA can be used to generate excess returns that outperform the benchmark index, I would be the first person to switch. But yeah, just show me the evidence.
We all have this problem. Either due to our inherent capabilities or because of the perceived expertise of the subject matter, we tend to overshoot on average. The biases acting behind overconfidence can be summarized as hindsight bias and self-attribution bias. Hindsight bias simply refers to believing that the past events were obvious and predictable ex-ante than they actually were. For example, think about the time you bought a stock and for few months it did not move and after a year or so, it just sky-rockets. You might be quick to point out that you knew it all along that this would happen because you had done “extreme research”. Self-attribution bias has to do with placing skill and knowledge when the outcomes are favorable and blaming on luck when the outcomes are unfavorable. I guess we all have done this one. I know I definitely have.
We haven’t even talked about the main biases until now. This article is already getting too long. I suppose I ranted about TA a bit too much. Sorry for that. I will have to put the rest of the content in the next article that will serve as part 2 of this one. But meanwhile, you can think about the things mentioned above and try to remember the instances where your decision-making process was not quite rational.
Bivek Neupane is a MSc. Finance and Economics student. He is also a CFA candidate. He is specializing in quantitative finance and research. His other interests include Factor modeling, Portfolio optimization, Behavioral finance, Alternative asset management, etc. If you have any questions, do not hesitate to contact him. You can connect with him via LinkedIn, his blog, or e-mail.
Kahneman and Tversky, 1979. An Analysis of Decision under Risk, Econometrica. <https://www.uzh.ch/cmsssl/suz/dam/jcr:00000000-64a0-5b1c-0000-00003b7ec704/10.05-kahneman-tversky-79.pdf>
Grove et al., 2000. Clinical versus mechanical prediction: A meta-analysis.
Lo et al. 2000. Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. <https://www.cis.upenn.edu/~mkearns/teaching/cis700/lo.pdf>
 Kahneman was awarded Nobel Prize in Economic Science in 2002.
 You might be thinking “This guy knows nothing about TA and is just talking nonsense.” Well, to your surprise, actually I do know a thing or two about TA. I have read over 5 books on TA and used to actually trade using TA until I started asking questions.
 This is in contrast to the time-tested and evidence-based trading strategies like a mean reversion, statistical arbitrage, factor investing like quantitative value and momentum investing, etc.