Introduction
Behavioural finance research focuses on the psychology of financial decision-making, i.e., how behaviour influences finance (Byrne & Utkus 2013). Majorities recognise how emotions could influence investment decisions. Investors have often understood the relevance of fear, greed, and peer group pressure in stock market investments. Additionally, biases are now a critical component of behavioural finance. For instance, investors may opt for some conventional rules when making complex investment decisions. That is, behavioural finance explores issues in investment decision-making by relying on insights from psychological studies and applying them to financial investment and decision-making. In this literature review, various aspects of modern portfolio theory, asset valuation, and EMH are explored. Specifically, the study will cover the problems of the capital asset pricing model (CAPM) in pricing stock returns or portfolio returns, whether the size effect is against the EMH, the main differences between behavioural finance and standard finance, and whether overconfidence causes investors to overestimate their knowledge and underestimate risks in making investment decisions.
Behavioural Finance Theories
Modern Portfolio Theory (MPT) is an investment theory that strives to optimise portfolio expected return for a specific amount of portfolio risk or equally reduce the risk for a particular level of anticipated return through careful selection of proportions of different assets (Omisore, Yusuf & Christopher 2012). That is, investors are risk-averse and tend to choose specific portfolios that optimise anticipated returns based on a certain level of market risk, demonstrating that investment risk is a part of any higher reward. Risk is seen as a lower return based on the expectation, and it reflects a deviation from the mean return. For diversified individual stocks, the inherent risk tends to be lower relative to a single individual stock. The MPT shows that investment goes beyond picking stocks, and it must ensure the best combination of stocks through diversification to minimise risks (construct an efficient frontier of asset combinations to optimise returns for a particular risk level). The MPT is based on a mathematical model, but it is widely challenged (Omisore, Yusuf & Christopher 2012). Nonetheless, its primary assumption is asset diversification for hedging against inherent market risks and firms’ unique risks.
The MPT views an asset as an investment portfolio with its own merits (risks and returns). Thus, changes in prices are essential. In this regard, assets with higher expected returns are also higher risky assets.
In the traditional approach, in this case, agents are rational, and the price of an asset should equal its fundamental value. The fundamental value reflects the discounted value of the expected future cash flows in which the expectation is based on the analysis of all available information, and the discount rate reflects a form of generally acknowledged preference specifications. According to the Efficient Markets Hypothesis (EMH), asset prices are right because agents who understand laws of investments and have rational preferences are responsible for setting such prices. EMH presents the claim that the market has sufficient information. ‘No free lunch’ is expected. That is, no investment approach can consistently deliver higher returns relative to the market average based on a risk-adjusted basis considering the available information when the investment is realised (Omisore, Yusuf & Christopher 2012). According to behavioural finance, one may reasonably consider some elements of asset pricing deviations from the fundamental value, and such deviations occur because of noisy traders or traders who are not entirely rational.
The Capital Asset Pricing Model (CAPM)
Certain critics have observed some issues associated with the capital asset pricing model (CAPM) in pricing stock returns or portfolio returns. The CAPM is based on the assumption that investors are risk-averse, and when they select various portfolios, they are only concerned about the mean and variance of a single time investment return. Consequently, most investors tend to prefer ‘mean variance-efficient’ portfolios based on the assumptions that such portfolios would reduce the difference of portfolio return taking into account the expected return and that the portfolio would optimise expected return when the variance is considered. The model has been referred to as the mean-variance model (Fama & French, 2004). The primary strength of the CAPM is that it provides robust and instinctively attractive predictions concerning the assessment of risk and the link between the anticipated return and risk. Regrettably, the CAPM has extremely weak empirical evidence – so weak to undo its robustness and applications (Fama & French 2004). The empirical issues associated with the CAPM could capture the theoretical shortcomings of the model, reflecting an approach of simplified assumptions.
Additionally, such challenges lead to difficulties in implementing and testing the validity of the model to assess portfolio or risk returns accurately. To put it plainly, the CAPM condition involves anticipated returns, applications of market betas, portfolio betas noted in the variance (Fama & French 2004). This implies that the CAPM model and its efficiency of the market portfolio mainly draw from multiple impractical assumptions, including full support and either unlimited risk-free borrowing and loaning or unhindered short offering of risky assets. However, every single intriguing model includes impossible disentanglements, which is the reason they should be tested against available information (Fama & French 2004).
The Size Effect
The size effect (January effect) is considered among the anomalies of the EMH. These anomalies reflect empirical findings that appear as erratic with the supported models of asset-pricing behaviour. The size effect going against EMH is shown through small firms that have significant returns relative to other larger firms based on a risk-adjusted basis (Latif et al. 2011). That is, the small-capitalisation companies tend to earn significantly more average returns than forecasted by the CAPM. Empirical evidence associates January effect to year-end, tax-loss selling of shares unequally, and liquidity associated with January (Latif et al. 2011). Higher volumes and low-interest rates are noted in January and relate with higher returns. The evidence further demonstrates that returns in January tend to be 3.5% higher than in other months. Investors tend to sell in December and purchase back in January to support the tax-loss hypothesis (Latif et al. 2011). That is, firms strive for tax-loss savings at the end of every tax year (December), ensure portfolio rebalancing, inventory control, and control stock market specialists’ influences on the January effect.
According to Bodie, Kane, and Marcus (2001), the side effect is mainly associated with small firms because they tend to have stocks with the greatest price variability during the year. In this case, such firms have weakened significantly to warrant tax-loss selling. From a theoretical viewpoint, the January effect has critical flaws. First, if the positive outcomes noted in the size effect is due to a manifestation of the buying pressure, then there should be a corresponding poor December effect when the tax-loss incentives initiate selling pressure (Bodie, Kane & Marcus 2001). Second, the anticipated January effect does not relate to the EMH. That is investors who understand the size effect should rush to acquire such portfolios in December with expected greater margins in January. As such, the purchasing pressure would shift from January to December.
Rational investors would automatically not allow probable abnormal January returns to hold. Nonetheless, evidence still suggests that small firms did indeed outperformed their larger counterparts every January between 1963 and 1979, as indicated by Keim’s findings (Bodie, Kane & Marcus 2001). While theoretical objections exist, some empirical evidence suggests that tax-loss selling is the primary driver of the January effect (Bodie, Kane & Marcus 2001; Latif et al. 2011). Fama (1998) showed that EMH survives findings on long-term return anomalies because such anomalies tend to be chance results, outward overreaction or underreaction to market information, and pre-event and post-event returns abnormal returns relate to chance and may occur on equal measures. More importantly, based on the EMH prediction that such anomalies could be due to methodology, it is noted that a significant number of long-term returns tends to disappear if reasonable changes are applied in a method (Fama 1998). This implies that findings on anomalies do not offer sufficient reasons to reject the EMH (Fama 1998).
Behavioural Finance vs. Standard Finance
Fundamental differences exist between behavioural finance and standard finance. Supporters of behavioural finance contend that poorly informed and unsophisticated speculators may lead financial markets to be inefficient. The discourse between neoclassical and behavioural finance is colossal, and some of the time clarifies contrasts in policy suggestions on such issues as financial regulations, monetary policy, corporate governance, or the privatisation of social security (De Bondt et al. 2008). The standard finance emanated on “the arbitrage principles of Miller and Modigliani, the portfolio construction principles of Markowitz, and the CAPM of Lintner and Sharpe” (Statman 1995, p. 21). On the contrary, the standard finance does not do well from a theoretical perspective because most investors tend not to apply arbitrage principles, ignore its opportunities, fail to apply the principles in selecting portfolios. They do not drive stock returns to the rates equal to the CAPM (Statman 1995). Standard finance views investors as rational. They do not experience setbacks of frames, cognitive mistakes, do not understand the pain of regret, and lack the lapses related to self-control.
Conversely, investors in behavioural finance may not always be rational. Moreover, normal persons may experience confusion due to frames, influenced by cognitive errors, regret, and find it hard to control self. Statman (1995) asserts that behavioural finance is based on a robust model relative to standard finance because it accounts for human behaviour in investment. Further, it provides effective approaches to handle major puzzles associated with investments, such as why investors prefer cash dividends, why investors do not want to realise losses, identification of anticipated returns, and the nature and design of securities and financial regulations. Investment presents many more challenges. Some appear insignificant, such as why the dollar-cost averaging persists, yet it is inconsistent with standard finance. Some challenges are critical, for instance, why investors may not apply principles of portfolio construction. Behavioural finance may offer solutions to these challenges. Financial investors who comprehend elements of behavioural finance would understand their motives and behaviours and adjust them and investment decision-making. Additionally, institutional investors would also comprehend the motives and beliefs of their customers and will, in turn, inform them appropriately.
Overconfidence
Based on theoretical models, overconfident investors tend to trade excessively (Barber & Odean 2001). Thus, it is imperative to explore whether overconfidence causes investors to overestimate their knowledge and underestimate risks in making investment decisions. Reflecting on the above statement, psychologists have demonstrated that overconfidence is responsible for knowledge overestimation, risk underestimation, and inflated ego to manage events (Nofsinger 2001). Notably, portfolio selection is a difficult task, and it is exactly this type of involvement at which individuals tend to display the utmost overconfidence (Nofsinger 2001). Additionally, Shefrin (2000) observes that overconfidence is responsible for bad bets due to informational disadvantage and frequent trades in speculative markets, which are not prudent. Thus, overconfidence is seen as an inherent biased human trait, and men tend to be more overconfidence than women are (Barber & Odean 2001). Overconfidence in uncertain situations is driven by limited information and familiar patterns based on the assumption that future events are most likely to be the same. Still, limited thought is given to assess factors behind the familiar patterns. It is the anomaly of human decision-making in investing.
Conclusion
Academics and practitioners present investment theories to guide investment decisions. While these theories are flawed to the extent that they may lack clear supporting empirical evidence, they have been effective in guiding investors. Behavioural finance has emerged to explain the shortcomings of standard finance. In this case, the psychology of investing explains aspects of investing not previously understood using standard finance. This observation provides an opportunity for behavioural finance to thrive and become mainstream in investing.
Reference List
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