Overconfidence and representativeness bias in stock investment decisions

The last two to three decades are a stark reminder of the volatility of the financial markets.

Over the decades, the erosion and resurgence of financial fundamentals occurred in a stochastic manner. With the emergence of transformative new technologies and digital disruptions in the financial industry, markets remain extremely volatile and vulnerable to exogenous and endogenous shocks. Despite the ongoing volatility in the domestic and global market, investments at the retail level are gaining significant momentum.

This could be due to the astronomical change in the financial industry, digital awareness, new investments and trading platforms, and the integration of global financial markets. The above reasons can be attributed to retail investments becoming more lucrative, toxic and volatile. However, investors are more fragile and vulnerable as investor sentiment, heuristics, biases and herd behavior can hugely influence investors' future investment decisions.

Fundamentally, heuristics and biases are mental shortcuts that facilitate probability judgments and instant problem-solving when making investment decisions. Overemphasis on these judgments leads to irrational and inaccurate investment decisions.

Today, let's understand the nuances of how overconfidence and representativeness bias significantly influence future investment decisions. What remedies are there to avoid the same biases when investing in capital markets?

What is Overconfidence Bias and Representativeness Bias?

Overconfidence is a radical human judgment error because people are susceptible to psychological biases. Psychological research shows that overconfident investors trade irrationally and excessively in markets that have lower expected future returns. Belief in overconfidence can distort the investment decisions of individuals and businesses in a suboptimal manner. This bias is considered a dangerous and widespread bias in the field of behavioral finance and capital markets investing. There are three different forms: overprecision, overplacement and overestimation.

In financial markets, investors with overconfidence underestimate risk, ignore the asymmetric information about the stocks, and often trade based on non-fundamental factors with exorbitant costs, resulting in low profits and poor-performing stocks and leading to divergence in asset prices. This tendency is remarkably widespread in financial markets as a basic sentiment intuition. An early correction of this trend is desirable in order to avoid significant losses on the financial markets in the future.

Representativeness bias falls into the category of heuristics and is an easily accessible mental shortcut process that facilitates problem solving in a given similar situation. The representativeness heuristic is a behavioral bias that is the intuition that, under uncertainty, investors tend to believe that a notable performance of a particular event in the recent past is “representative” of the overall performance that is event will also occur in the future.

Such heuristics destroy investors' rational investment decisions. First and foremost, investors violate the Bayesian investment principle and attach great importance to the recency effect and the impairment of past information. Extensive psychological research has emphasized the role of representational heuristics and emphasized that this bias influences human judgment and leads to systematic errors in investment decisions and portfolio rebalancing.

Novel award winners and psychologists Kahneman and Tversky (1972) intuitively documented, and subsequently adopted by proponents of behavioral finance, that heuristic tendencies such as representativeness lead to overreactions and explain stock market anomalies. In the stock markets, investors exposed to these heuristics interpret past performance, such as winners (losers), as winners (losers) continuously win (lose) in the current and future scenario.

Investors are susceptible to these biases and begin their judgments taking into account the company's successive earnings and profits in the future based on past performance. Similarly, investors do not pick losing stocks on the assumption that the stocks will generate short-term losses. In this context, investors neglect the globally relevant and existing rational random model behavior on the stock markets.

How to avoid excessive trust and representativeness bias in stock investment decisions?
The functionalities of the markets are based on basic theoretical principles such as fundamental analysis, future forecasting, present value analysis, trend analysis, charts, patterns, etc. In order to avoid overconfidence and representativeness bias, which are innate behavioral biases prevalent in financial markets, one has done this Real-time analysis of the markets based on market fundamentals, news, technical analysis and a critical assessment of the economy, industry and company in which the stock is traded. In addition, to eliminate the unwarranted and illogical belief created by this bias, investors must rationally use their skills to predict the markets based on theoretical principles.

In order to avoid the representativeness bias, investors should essentially not make investment decisions based on the company's growth trajectory in recent years and assume that the company will grow continuously in the near future. Furthermore, former winning firms may be current losing firms and vice versa.

In summary, the investment process is a science and requires sound theoretical analysis. First and foremost, investment strategies are determined and portfolios are rebalanced. Therefore, investors should use wishful thinking, due diligence and market fundamentals to eliminate the components of overconfidence and representativeness bias.

Jyoti Kumari Rao is an Assistant Professor at the Department of Finance, IBS Hyderabad, Hyderabad. Her research area includes behavioral finance, asset price dynamics, corporate finance and capital markets, with a focus on the interactions between asset prices, corporate finance, investor sentiment and volatility in capital markets.