The sustainability of stock price fluctuations: Explanation from a recursive dynamic model

The sustainability of stock price fluctuations indicated by many empirical studies hardly reconciles with the existing models in standard financial theories. This paper proposes a recursive dynamic asset pricing model based on the comprehensive impact of the sentiment investor, the information trader and the noise trader. The dynamic process of the asset price is characterized and a numerical simulation of the model is provided. The model captures the features of the actual stock price that are consistent with the empirical evidence on the sustainability of stock price fluctuations. It also offers a partial explanation for other financial anomalies, for example, asset price’s overreaction, asset bubble and the financial crisis. The major finding is that investor sentiment is the key factor to understand the sustainability of stock price fluctuations.

Market Efficiency and Nonlinear Analysis of Soybean Futures

In this paper, the multifractal detrended fluctuation analysis (MF-DFA) method is used to identify the multifractal structure of in the Chicago Board of Trade (CBOT) soybean futures and quantitatively describe the inefficiency and nonlinearity of the market. The data is the daily price of CBOT soybean futures from 3 January 2000 to 20 December 2019, with a total of 5025 trading days. The empirical results also show that the perspective based on MF-DFA can explain the market’s nonlinear, long-range correlation, predictability and other financial anomalies. At the same time, the prediction of price change direction and risk degree of the market are further studied. It is pointed out that multifractal characteristics are generated under the joint action of fat-tail distribution and long-range correlation. Investors can make use of these market characteristics to make arbitrage possible. Finally, based on the empirical results, some policy suggestions are put forward: strengthening rational investment education, strengthening supervision, reducing information asymmetry and other measures to improve market efficiency.

Behavioral Finance Research in 2020: Cui Bono et Quo Vadis?

Emanating from the influential survey of Barberis and Thaler (2003), this systematic literature review examines the significant volume of studies on behavioral finance from 36 reputable finance journals published be-tween 2009 and 2019. The findings are clustered into eight prominent research streams, which indicate the current developments in behavioral finance. Findings show that research intensively focuses on behavioral biases and their influence on economic phenomena. Driven by the impetus to understand the human mind, significant findings originated in the relatively new field of Neurofinance. Additionally, the analysis addresses the influence of market sentiment and its correlation with some of the other findings. Furthermore, implications on the limits to arbitrage in connection with some financial anomalies complete the holistic picture.

A Look at Behavioral Finance

Behavioral theory in finance ties finance theory and practice to human behavior. This paper aims at reviewing behavioral finance principles, concepts and theories. This paper starts with the shift from EMH/CAPM paradigm to behavioral finance. Then, the paper goes through the financial anomalies including the size effect, value effects, momentum effects, weekend effect and turn-of-the year effect. Finally, the paper addresses the key pillars of behavioral finance by explaining the limits to arbitrage and the main behavioral biases.

Autosimilarty, Long Memory and Chaos: Evidence from the Tunisian Market

Fractal Finance came to the rescue of the classical models unable to explain financial anomalies and of linear models inadequate to characterize complex processes. The characterization of financial series is still topical. The calculation of the Hurst exponent, the fractal dimension, the Lyapunov exponent, the window of Theiler and the realization of the determinism test, have allowed us to understand the dynamics of the Tunisian indexes returns. Clearly, findings show that the returns are, on the one hand, nonlinear, follow alpha-stable laws, have a long memory and on the other hand, are not chaotic. Thus, the hypothesis of a Brownian fractal motion is privileged.