Understanding random market behaviour

 By Somali K Chakrabarti

For an investor, the pain of selling a stock at a loss far exceeds the pleasure of selling the stock at an equal amount of gain.

Strange but true!

Such behavioral aspects of investing and many more are brought out in the study of behavioral finance, that was introduced in the late 1980s, owing to anomalies in stock price prediction by the two main existing theories of academic finance, i.e. Modern Portfolio Theory’ and ‘Efficient Market Hypothesis’.

According to the ‘Efficient Market Theory’, put forth by Eugene Fama, financial markets are believed to be efficient and investors are understood to make rational decisions. Further, market participants are supposed to be sophisticated, informed and known to act only on available information. Since market participants are believed to have equal access to information, it is implied that stock prices always reflect the best information about fundamental values of the stocks. According to the efficient market theory and Capital Asset Pricing Model (CAPM) the price of a stock is the Present Value (PV) of all the entire future earnings of the company i.e. the future dividend paid by the company. 

The ‘Modern Portfolio Theory’ pioneered by Harry Markowitz suggested that an investor can maximise returns by holding a diversified portfolio of assets with different levels of risk.

 However stock prices were found to exhibit more volatility than efficient market hypothesis could explain.  

The market price of a stock often grossly varied from the price arrived at by efficient market theory.  This unexplained volatility and the difference between the market price and the rational price of the stock became a subject of research. It was also found that as compared to the individual stocks, the aggregate stock price (index) showed a higher variance from the values predicted by the efficient market theory.

The research brought out interesting aspects of behavioural finance. In the paper ‘From efficient market theory to behavioral finance’, Robert J Schiller explains the reason for the apparent randomness in the movement of stock prices using models such as feedback model, smart money versus ordinary investors and prospect theory.


Feedback model

As expectations of rise in a stock price increase, more and more people start buying the stock. As a result a momentum sets in, which takes prices farther away from the levels predicted by the academic finance models. Similarly when there is an expectation of a fall in price, more and more people join in to sell the stock, driving prices lower than supported by the financial models. This is one of the strong reasons for bubbles and bursts. Feedback may be one of the essential sources of the randomness seen in stock prices.


Obstacles to smart money

Recognizing the difference between smart money and the ordinary investors, the efficient markets theory asserts that when the irrational optimist buys a stock, the smart investor sells and vice versa. However in reality every investor has the shades of an ordinary investor and a smart investor.  Many times when most owners of a stock are too optimistic about a stock, they may not be willing to lend shares to short sellers; so  there may not be enough shares available for short selling.  The lack of short selling can push the stock price further upwards, amplifying  the effect of irrational optimism and can cause financial anomalies in bubbles and bursts.  


Prospect Theory

Prospect theory is used to explain deviations from the traditional paradigm of rational behaviour. According to this theory since people value gains and losses differently, an investor feels more difficulty in selling shares at a loss than the exuberance the person experiences when selling the stock at an equal amount of gain.



Understanding how other market participants may act can help investors in making good investment decisions. Traders often experience a varied range of emotions from exuberance to despair during different stages of a trade with abrupt changes in the stock price movement, with negative emotions weighing much heavily than the positive emotions. Viewing the random and unexpected market movement in light of behavioral finance theory can prepare investors better to ride out the volatility by inculcating trading discipline, while minimizing the emotional impacts. It can prevent investors from buying overpriced shares or from dumping oversold but still valuable stocks at a huge loss seeing other investors rushing for the exits and thus avert committing gross wrong trades that are to be regretted later.

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4 thoughts on “Understanding random market behaviour

  1. Good article Somali! The one thing you can perhaps add to this article is the difference in mindsets between traders and investors. I find that you have mixed up the two in your article, although they are distinctly separate from each other. Because traders have a short-term view (days to hours to minutes) and investors have a longer term view (years to months) it would be interesting to know what Behavioural Finance theory has to say on the difference in trading mindsets between these two communities. Also, be aware that most trading houses nowadays use algorithmic trading in order to completely eliminate effect of ‘human emotion’ from the trading equation altogether.


    1. Thanks for your comment Abhijit.
      As you have suggested about differentiating between investors and traders, I would like to mention some points highlighted in a behavioral finance paper (by Maximilian Koestner, Steffen Meyer, and Andreas Hackethal). It states that initially traders tend to overtrade and there is a significant reduction of trading activity as investors gained more experience. Also traders may have a tendency to pick up few volatile stocks that are more likely to lead to a notably successful investment so that they can tell their peers about their successful trades.
      Another point that you have brought out is the use of algorithmic trading systems.
      I came across a very interesting research paper that suggests that though the automated systems seek to circumvent the behavioral aspects of market participants, but market returns are still driven by the behavioral aspects, not of traders, but of the trading system trading system research and development project management. Humans monitoring automated trading systems may adjust trading decision parameters in real-time or make on/off decisions. The paper is ‘Automated Finance: The Assumptions and Behavioral Aspects of Algorithmic Trading’ by Andrew Kumiega, Illinois Institute of Technology. You may like to read it.


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