High-frequency momentum and contrarian strategies in U.S. blue chips

The stock market represents complex systems where multiple agents interact. The complexity of the environment in the financial markets in general has encouraged the use of modeling by multi-agent platforms and particularly in the case of the stock market.
In this paper, an agent-based simulation model is proposed to study the behavior of the volume of market transactions. The model is based on the case of a single asset and three types of investor agents. Each investor can be a zero intelligent trader, fundamentalist trader or traders using historical information in the decision making process. The goal of the study is to simulate the behavior of a stock market according to the different considered endogenous and exogenous variables.

This paper comprehensively evaluates the literature on retail investor overconfidence using a framework-based systematic approach to understand the various dimensions of overconfidence bias, its effect on investing choices, and market dynamics. A systematic review of 137 publications from the Scopus database have been done to detect the research trend concerning investor overconfidence bias from its inception. An integrated ADO-TCM framework has been employed to present a systematic analysis of the theory, context, and methodologies (TCM) employed in the reviewed studies. The ADO (Antecedents, Decisions, and Outcomes) framework thoroughly examines the antecedents, decisions, and results of investor overconfidence.
The study identified four broad sets of factors contributing to investor overconfidence, as found in the existing literature. These factors include demographic characteristics, personality traits of investors, their knowledge and experience, and the features of investments and investor types. The Prospect theory is the most popular theory in the literature, with much research using secondary data and experiment-based analysis. The prospective study directions, based on the gaps in the existing literature, are as follows: further investigation into the decision-making processes of overconfident retail and professional investors is a worthwhile subject. Future research may shift their focus from financial outcome variables to non-financial outcome variables such as the impact of investor overconfidence on individuals’ stress levels, subjective financial well-being, and overall life happiness.

The authors offer evidence for low-risk effect from the Indian stock market using the top-500 liquid stocks listed on the National Stock Exchange (NSE) of India for the period from January 2004 to December 2018. Finance theory predicts a positive risk-return relationship. However, empirical studies show that low-risk stocks outperform high-risk stocks on a risk-adjusted basis, and it is called low-risk anomaly or low-risk effect. Persistence of such an anomaly is one of the biggest mysteries in modern finance. The authors find strong evidence in favor of a low-risk effect with a flat (negative) risk-return relationship based on the simple average (compounded) returns. It is documented that low-risk effect is independent of size, value, and momentum effects, and it is robust after controlling for variables like liquidity and ticket-size of stocks. It is further documented that low-risk effect is a combination of stock and sector level effects, and it cannot be captured fully by concentrated sector exposure. By integrating the momentum effect with the low-volatility effect, the performance of a low-risk investment strategy can be improved both in absolute and risk-adjusted terms. The paper contributed to the body of knowledge by offering evidence for: a) robustness of low-risk effect for liquidity and ticket-size of stocks and sector exposure, b) how one can benefit from combining momentum and low-volatility effects to create a long-only investment strategy that offers higher risk-adjusted and absolute returns than plain vanilla, long-only, low-risk investment strategy.