Hierarchical Time-Varying Estimation of Asset Pricing Models

This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered.

On Comparative Analysis for the Black-Scholes Model in the Generalized Fractional Derivatives Sense via Jafari Transform

The Black-Scholes model is well known for determining the behavior of capital asset pricing models in the finance sector. The present article deals with the Black-Scholes model via the Caputo fractional derivative and Atangana-Baleanu fractional derivative operator in the Caputo sense, respectively. The Jafari transform is merged with the Adomian decomposition method and new iterative transform method. It is worth mentioning that the Jafari transform is the unification of several existing transforms. Besides that, the convergence and uniqueness results are carried out for the aforesaid model. In mathematical terms, the variety of equations and their solutions have been discovered and identified with various novel features of the projected model. To provide additional context for these ideas, numerous sorts of illustrations and tabulations are presented. The precision and efficacy of the proposed technique suggest its applicability for a variety of nonlinear evolutionary problems.

Do select macroeconomic factors drive momentum returns?

In this paper, we examine the presence of short-term and long-term momentum returns in Indian stock market. The study also tries to shed light on the power of asset pricing models and select macroeconomic variables in explaining momentum returns. The results confirm the presence of short-term and long-term momentum returns in Indian stock market. It is also found that Carhart four-factor model’s performance is relatively superior to other factor models such as one factor capital asset pricing model and Fama–French three-factor model in terms of capturing momentum returns. Finally, macroeconomic variables which are considered for analysis do not have any power to explain momentum returns.

THE SEARCH FOR TIME-SERIES PREDICTABILITY-BASED ANOMALIES

This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.

Weather, investor psychology and stock returns: evidence from Fiji's stock market

PurposeThis paper investigates whether weather affects stock market returns in Fiji's stock market.Design/methodology/approachThe author employed an exponential general autoregressive conditional heteroskedastic (EGARCH) modeling framework to examine the effect of weather changes on stock market returns over the sample period 9/02/2000–31/12/2020.FindingsThe results show that weather (temperature, rain, humidity and sunshine duration) have robust but heterogenous effects on stock market returns in Fiji.Research limitations/implicationsIt is useful for scholars to modify asset pricing models to include weather-related variables (temperature, rain, humidity and sunshine duration) to better understand Fiji's stock market dynamics (even though they are often viewed as economically neutral variables).Practical implicationsInvestors and traders should consider their mood while making stock market decisions to lessen mood-induced errors.Originality/valueThis is the first attempt to examine the effect of weather (temperature, rain, humidity and sunshine duration) on stock market returns in Fiji's stock market.

Short- and Long-Horizon Behavioral Anomalies and Institutional Investors’ Nudge Behavior: Based on the Data of Listed Chinese Tobacco Companies

We use the data of listed tobacco companies in China to study the existence of short- and long-horizon behavioral anomalies and the impact of institutional investors’ behavior on them. We found that the existing asset pricing models cannot explain the short- and long-horizon behavioral anomalies based on tobacco enterprise data. Conversely, the short- and long-horizon behavioral anomalies can explain the exciting asset pricing factors. Compared with existing asset pricing models, behavioral anomalies have a stronger ability to explain anomalies. Behavioral anomalies could pass the cross-sectionally test and strengthened over time. The above results indicate that behavioral anomalies exist in China tobacco enterprisest significantly and are time-varying. We found that the limits to arbitrage and cognitive bias lead to the existence of behavioral anomalies through mechanism tests. Institutional investors did not play the role of price discovery. Instead, their nudge behavior strengthens the short- and long-horizon behavioral anomalies. Therefore, tobacco regulatory agencies should guide listed tobacco companies to broaden information channels to reduce information asymmetry in the market through relevant policies, strengthen the supervision of institutional investors’ bubble riding behavior, and promote the healthy development of the tobacco market.

Buy Now and Pay (Dearly) Later: Unraveling Consumer Financial Spinning

In this challenging and innovative article, we propose a framework for the consumer behavior named “consumer financial spinning”. It occurs when borrowers-consumers of products with high financial stakes accumulate unsustainable debt and disconnect from their initial financial hierarchy of needs, wealth-related goals, and preferences over their household portfolio of assets. Three behaviors characterize daredevil consumers as they spin their wheel of misfortune, which together form a dark financial triangle: overconfidence, use of rationed rationality, and deceitfulness. We provokingly adapt some of the tenets of the Markowitz and Capital Asset Pricing models in the context of the predatory paradigm that consumer financial spinning entails and use modeling principles from the data percolation methodology. We partially test the proposed framework and show under what realistic conditions the relationship between expected returns and risk may depart from linearity. Our analysis and results appear timely and important because a better understanding of the psychological conditions that fuel intense speculation may restrain market frictions, which historically have kept reappearing and are likely to reoccur on a regular basis.