Editorial statement and research ideas for behavioral financial economics in the emerging market

Citation

Wong, W.-K. (2021), "Editorial statement and research ideas for behavioral financial economics in the emerging market", International Journal of Emerging Markets, Vol. 16 No. 5, pp. 946-951. https://doi.org/10.1108/IJOEM-07-2021-991

Behavioral financial economics is a major milestone in the development of modern theory in financial economics, especially for the emerging market because many studies have been showing that there are some anomalies and paradoxes and many financial phenomena, like Internet bubble and financial crises (Shiller, 2000), in financial economics, especially for the emerging market that traditional theories in financial economics cannot explain. For example, using the traditional theory of market efficiency, it is impossible for investors to outperform the market because the market efficiency theory (Markowitz, 1991) claims that stocks are always trading at their fair values, and all the information relevant to stock prices has been reflected in stock prices and is quickly priced in the market, concluding that price movement follows a random walk model. However, in reality, some investors do outperform other investors and perform better than the market, and most, if not all, stock prices do not follow any random walk model. Nonetheless, many empirical findings have shown that some anomalies and paradoxes, for example, the diversification puzzle, excess volatility, herd behavior, under-reaction and overreaction, could persist, and other anomalies, for example, calendar anomalies, may disappear, and some may reappear later on.

To find reasons or obtain solutions for some anomalies and paradoxes that traditional theories in financial economics cannot explain, scholars incorporate ideas and concepts from both psychology and sociology with financial economics (Selden, 1912). To do so, scholars could first establish some theoretical models in behavioral financial economics, develop the corresponding statistics and conduct simulations to show that their statistics are efficient and powerful. Thereafter, academics and practitioners could then use the models and statistics to examine whether the anomalies and paradoxes could still hold empirically and analyze important issues in traditional financial economics. They could also use the models and statistics to analyze some interesting and important empirical problems in financial economics.

There are many areas in establishing new theories for behavioral financial economics. For example, Bentham (1780) develops the principle of utility to achieve the greatest amount of happiness and the minimum amount of pain. Academics could follow this direction to develop new theories on different utilities, for example, risk aversion (Bernoulli, 1738), risk-loving (Wong, 2007), skewness-loving (Åstebro et al., 2015), prospect theory (Kahneman and Tversky, 1979), reverse S-shaped utility functions (Wong and Chan, 2008), disappointment aversion (Guo et al., 2021), regret aversion (Guo et al., 2015), loss aversion (Tversky and Kahneman, 1991), etc. One could work on bounded rationality (Simon, 1982) for individuals to make decisions when facing restrictions on rationality, cognitive limitations and time and apply nudge theory (Cai, 2020) for investors to get their desired results to be better off and for regulators to make better ethical decisions. One could develop theories in stochastic dominance (Guo and Wong, 2016), almost stochastic dominance (Guo et al., 2014) and different risk measures, for example, Farinelli–Tibiletti ratio (Guo et al., 2019) and Kappa ratio (Niu et al., 2017), to compare different assets and portfolios for different types of individuals. Other theories in behavioral financial economics include arbitrage opportunity (Guo et al., 2017), portfolio optimization (Bai et al., 2009), background risk (Guo et al., 2018), diversification (Egozcue and Wong, 2010), segregating or integrating models (Egozcue et al., 2014), cost of capital (Wong and Chan, 2004), technical analysis and trading strategies (Chan et al., 2014), money management and asset valuation (Brown and Cliff, 2005), financial crisis (Hassan et al., 2021), contagion (Wan and Wong, 2001), cointegration and causality (Bai et al., 2011), wavelet (Raza et al., 2016), artificial intelligence (Russell and Norvig, 2002), copulas (Egozcue et al., 2013) and many others.

One main area in behavioral financial economics is to study anomalies, paradoxes and puzzles, including equity premium puzzle (Mehra and Prescott, 1985), risk-free rate puzzle (Weil, 1989), intertemporal choice (Loewenstein and Thaler, 1989), small-firm effect (Roll, 1981), neglected firm effect (Arbel and Strebel, 1982), herd behavior (Batmunkh et al., 2020), calendar anomalies (Lean et al., 2007), Gambler's fallacy (Clotfelter and Cookth, 1993), the diversification puzzle, excess volatility, under-reaction and overreaction. Scholars could develop theories to explain anomalies. Some explain anomalies by using the concept of mispricing (Lakonishok et al., 1994) from a benchmark model of asset prices, for example, the capital asset pricing model (CAPM), Fama–French three-factor model, Fama–French–Carhart four-factor model, Stambaugh and Yuan's four-factor model, Fama–French five-factor model, Merton's intertemporal CAPM theory, consumption CAPM theory, etc. while some, for example, Fama and French (1993), explain anomalies by using the idea of unmeasured risk. One could use different heuristics, for example, conservative and representative heuristics (Lam et al., 2010) and satisficing heuristics (Papi, 2012), to explain anomalies. Scholars could also use other ideas of behavioral financial economics, for example, herd behavior (Batmunkh et al., 2020) and framing effects (Levin et al., 1998), and other cognitive effects to explain the decisions made by different investors.

In order to develop new theories and contribute to the literature in behavioral financial economics with applications, the special issue on Behavioral Financial Economics in Emerging Markets edited by Ephraim Clark, João Paulo Torre Vieito, Aviral Kumar Tiwari and Wing-Keung Wong is devoted to advancements in the development on behavioral financial economics in emerging markets in 2020. In this special issue, we invite academics and practitioners to submit their research or review manuscripts that fit in the spirit and scope of our special issue. The special issue of Behavioral Financial Economics in Emerging Markets has published five papers including Hong et al. (2021), Lo et al. (2021), Shen et al. (2021), Wong (2021) and Yeap et al. (2021).

Among them, Hong et al. (2021) extend Figlewski's (1984) margin buying model by developing a new margin setting method and apply the model to compute margin levels for China. They find that under different changing market conditions, the margins will go down when the corresponding asset prices go up and vice versa. They also find that both costs of margin buying from investors' perspectives and the operational costs from brokers' perspectives adopted by stock exchanges are significantly higher than those incurred by the margin system.

Using a dynamic Copula-CoVaR approach to examine the daily risk spillover between London and Shanghai futures markets, Shen et al. (2021) find that both squared Euclidean distance test and kernel estimation method determine their optimal Copula functions and marginal distribution, and the impact of the risk spillover from the Shanghai Metal Exchange on the corresponding London Exchange is less significant than those in the opposite direction. They also find that the degree of risk spillover for both zinc and copper is more significant when exerted from the London Metal Exchange to the corresponding Shanghai Exchange when depressed in the London market. In addition, they find evidence that the dynamic correlation between London and Shanghai future markets depends on the global economy.

By using the stakeholder perspective, including firm age, a corporate credit risk index, corporate financial performance and an evaluation of a firm's corporate governance, Lo et al. (2021) first develop a new sustainability index. Thereafter, they employ both regression and fuzzy set qualitative comparative analysis (FsQCA) to obtain several models to optimize firms' sustainability. The findings by using both regression and FsQCA conclude that all the factors used in their paper have significant impacts on the sustainability of firms. Their FsQCA solutions imply that the internal resources and capabilities of a firm and the fit of combinations of institutional factors can be used to explain the firms' sustainability.

Examining the market risk, the dependence structure and the joint distribution of the portfolio formed by currency exchange rates and the extreme events through the extreme value theory, Yeap et al. (2021) find that the marginal returns of CNY, USD, SGD, THB and JPY follow the Bayesian GARCH(1,1) model with Student's t errors and show that Student's t and Gumbel are the best elliptical and Archimedean copulas, respectively, for CNY, USD, SGD, THB and JPY. Last, Wong (2021) discusses some research ideas and provides an editorial statement in behavioral financial economics.