Guest editorial

Citation

Wong, W.-K. (2021), "Guest editorial", Studies in Economics and Finance, Vol. 38 No. 3, pp. 525-528. https://doi.org/10.1108/SEF-06-2021-498

Editorial statement and research ideas for behavioral economics and behavioral finance

Both Behavioral Economics and Behavioral Finance are the milestones of modern development in both Economics and Finance because many empirical phenomena, anomalies and paradoxes cannot be explained by traditional theories in Economics and Finance. For example, the Efficient Market Hypothesis concludes that no investor can perform better than the market because the hypothesis claims that all the information has been reflected in stock prices. Nonetheless, it is well known that some investors not only perform better than other investors but also outperform the market. Moreover, some anomalies and paradoxes, for example, the diversification puzzle, the Friedman-Savage paradox, overreaction, under-reaction and excess volatility, could persist while some other anomalies could die off, for example, the January effect, and some could re-appear again.

To explain empirical phenomena and explain some paradoxes and anomalies that cannot be explained by traditional theories, academics can use the ideas from both Behavioral Economics and Behavioral Finance. To do so, academics can first develop theoretical theories and thereafter establish the corresponding econometric estimations or testing models in Behavioral Economics and Behavioral Finance. They could examine both powers and sizes and determine whether the econometric estimation or testing is efficient. Thereafter, scholars could use the models to study some interesting problems in Behavioral Economics and Behavioral Finance.

There are many directions in developing theories for Behavioral Economics and Behavioral Finance. For example, one can develop theories for different utility functions, like utilities of risk aversion, risk-seeking, regret aversion and disappointment aversion, S-shaped and reverse S-shaped utility functions and multivariate utility functions. One could develop theories for different risk measures, including mean-variance rules, Sharpe ratio, Omega ratio, Value-at-Risk (VaR), conditional-VaR, Kappa ratio, Farinelli–Tibiletti ratio and economic performance measure. There are many other theories for Behavioral Economics and Behavioral Finance, for example, stochastic dominance (SD), almost SD and indifference curves for different types of utility functions, arbitrage opportunity and market efficiency, portfolio optimization, capital asset pricing model, diversification, models with Background Risk (Guo et al., 2018), Bayesian models for investors with conservatism and representativeness heuristics (Lam et al., 2010) and investors’ behavior model with segregating or integrating multiple outcomes (Egozcue et al., 2014), cost of capital (Wong and Chan, 2004), economic and financial indicators, technical analysis, trading rules, trading strategies, contagion, cointegration, causality, nonlinearity, jump, wavelet and copulas. There are also many applications by using the theories in Behavioral Economics and Behavioral Finance. Readers may refer to Wong (2020) for more information.

To explore new theories and applications and to contribute to the literature in this direction, the special issue on Behavioral Economics and Behavioral Finance edited by Haim Levy, Oliver Linton, Thierry Post and Wing-Keung Wong is devoted to advancements in the theory development on Behavioral Economics and Behavioral Finance in 2017. In this special issue, we invite scholars to submit their original research or review articles that fit in the scope and spirit of the call for papers.

The special issue of Behavioral Economics and Behavioral Finance has published 8 papers including Dialga and Vallée (2018), Selmi et al. (2019), Xu et al. (2019), Liu and Tan (2019), Oehler et al. (2019), Sharma and Chakraborty (2019), Xin et al. (2019) and Lu et al. (2020). Among them, applying the concepts in Behavioral Economics and Behavioral Finance, Dialga and Vallée (2018) and Lu et al. (2020) examine different trading rules or indices, Xu et al. (2019) examine the effects of volatilities, Selmi et al. (2019) examine the stock-bond nexus, Sharma and Chakraborty (2019) examine Indian investors’ preference in lottery-like stocks and Liu and Tan (2019) forecast stock prices.

In particular, applying both benefits of the doubt (BOD) methods and principal components analysis (PCA) and using country-specific weight system to construct the Index of Economic Freedom, Dialga and Vallée (2018) find that different from using the equal-weights approach, the conclusions drawn by using both PCA and BOD approaches are similar. Applying copula quantile-on-quantile regression to examine the stock-bond nexus for South Africa, India, the USA and the UK, Selmi et al. (2019) find significant heterogeneity in the correction of the bond-stock returns. Applying the model of short interest rate and stochastic processes to examine the long-term volatility behaviors between long-term options’ implied volatilities and realized volatilities, Xu et al. (2019) find that interest rate risk explains the monotonic increase in long-term option implied volatility that can be explained by risk capital costs and other factors. Using an experimental method, Liu and Tan (2019) find the hard–easy effect to make optimistic forecasts for the hard-to-predict stocks. They conclude that when making judgments on hard tasks, overconfident investors get forecasts that are over-optimistic and less accurate. Oehler et al. (2019) use personality traits and individuals with comparable personalities to examine whether investor personality affects experimental asset market outcomes in terms of price levels, price bubbles and limit orders. Using both portfolio analysis and the Fama–MacBeth regressions to investigate extreme positive returns of stocks in the Indian stock market, Sharma and Chakraborty (2019) do not find any negative relationship between expected returns and extreme positive returns. They find that high MAX deciles perform better than low MAX deciles and conclude that Indian investors do not prefer to invest in lottery-like stocks. Moreover, using bivariate thin-plate spline model to analyze intraday trading and explore the relationship between intraday filter trading profitability and intraday realized volatilities for different technical trading rules, Xin et al. (2019) find that range-based realized volatility outperforms the return-based realized volatility in getting higher predictive power on filtering the technical trading rules for the high-frequency futures data from the Hang Seng index. Also, applying both Sharpe ratio (SR) and economic performance measure (EPM) to compare the performance of lump-sum (LS) and dollar-cost averaging (DCA) rules under both accumulative and disaccumulative approaches by simulating uptrend asset prices, Lu et al. (2020) conclude that DCA outperforms LS in the not-so-uptrend markets and DCA outperforms LS in more uptrend markets.