Model of Bias-Driven Trend Followers and Interaction with Manipulators

2017 ◽  
Vol 16 (02) ◽  
pp. 573-590
Author(s):  
Ke Liu ◽  
Kin Keung Lai ◽  
Jerome Yen ◽  
Qing Zhu
Keyword(s):  
Financial Markets ◽  
Behavioral Finance ◽  
Real Data ◽  
Investor Behavior ◽  
Individual Investors ◽  
Investment Performance ◽  
Behavioral Biases ◽  
The Common ◽  
And Performance ◽  
Manipulation Detection

Stock investors are not fully rational in trading and many behavioral biases that affect them. However, most of the literature on behavioral finance has put efforts only to explain empirical phenomena observed in financial markets; little attention has been paid to how individual investors’ trading performance is affected by behavioral biases. As against the common perception that behavioral biases are always detrimental to investment performance, we conjecture that these biases can sometimes yield better trading outcomes. Focusing on representativeness bias, conservatism and disposition effect, we construct a mathematical model in which the representative trend investor follows a Bayesian trading strategy based on an underlying Markov chain, switching beliefs between trending and mean-reversion. By this model, scenario analysis is undertaken to track investor behavior and performance under different patterns of market movements. Simulation results show the effect of biases on investor performance can sometimes be positive. Further, we investigate how manipulators could take advantage of investor biases to profit. The model’s potential for manipulation detection is demonstrated by real data of well-known manipulation cases.

scholarly journals The Relationship of Behavioral Factors with Investment Performance of Individual Investors in the Nepali Stock Market

2021 ◽  
Vol 4 ◽  
pp. 66-83
Author(s):  
Kripa Kunwar
Keyword(s):  
Factor Analysis ◽  
Stock Market ◽  
Investor Behavior ◽  
Individual Investors ◽  
Herd Behavior ◽  
Behavioral Factors ◽  
Investment Performance ◽  
Behavioral Biases ◽  
Market Factors ◽  
The Impact

In recent years, the market anomalies and irrational behavior of investors have influenced the stock market worldwide. The impact of investor behavior on the stock market is more prominent in small and less efficient capital markets. The study is based on the questionnaire survey of 203 investors from Kathmandu and Pokhara. The study uses Exploratory Factor Analysis (EFA) to explore the underlying dimensions of investor behavior employing Principal Component Analysis and Varimax rotation. The suitability of the data for the factor analysis has been examined using KMO and Barlett’s Test of Sphericity. The EFA extracted four factors of investor behavioral dimensions categorized as: heuristics, prospects, market factors and herding effect. The factor scores obtained from the EFA was used to examine the correlation of these behavioral factors with investment performance. The results reveal that behavioral biases like heuristics, prospects, market factor and herding effect are present among individual investors in Nepal. Among the factors, the investment performance of investors is found to be influenced by heuristics and market factors. The heuristic behaviors are found to have the highest and positive influence on the investment performance. Finally, the results depict that following the herd behavior in the market and prospects does not result in the improved investor performance. The findings are helpful to understand the role of investor behavior in the stock market and formulation of appropriate policies that limit the possibility of behavioral biases affecting the stock market adversely.

Institutional Investors

2017 ◽  
Author(s):  
Alexandre Skiba ◽  
Hilla Skiba
Keyword(s):  
Behavioral Finance ◽  
Institutional Investors ◽  
Large Body ◽  
Individual Investors ◽  
Trading Behavior ◽  
Institutional Trading ◽  
Behavioral Biases ◽  
Investor Sophistication ◽  
The Common ◽  
Momentum Trading

A large body of behavioral finance literature focuses on the behavioral biases of individual investors in their trading choices. The research shows that sophistication is related to the level at which these behavioral biases influence investors’ trading choices. This chapter reviews the literature on institutional investors’ trading behavior and finds that, consistent with the level of investor sophistication, institutional investors are less subject to the common behavioral biases. However, some behavioral biases are also present in institutional trading, and more so among less sophisticated practitioners. Evidence also shows that institutional investors engage in some trading choices such as herding, momentum trading, and under-diversification, which could be symptoms of behavioral biases. Based on the reviewed research, these trading behaviors are not value reducing. Overall, evidence indicates that institutional investors are less subject to behavioral biases, making markets more efficient.

scholarly journals The Impact of Financial Information on Noise Traders: Based on the Perspective of Behavioral Finance

2020 ◽  
Vol 9 (1) ◽  
pp. 28
Author(s):  
Miao Jiang
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Investor Behavior ◽  
Individual Investors ◽  
Noise Traders ◽  
Healthy Development ◽  
Noise Trading ◽  
Financial Media ◽  
The Impact ◽  
Excessive Noise

<p>In China's incomplete stock market which mainly consists of retail games and short-term operations, both of the high stock turnover rate and P/E ratios reflect excessive noise trading. This article focuses on the characteristic that individual investors are susceptible to financial media information, combined with the development and characteristics of financial media. From the perspective of behavioral finance, this paper analyzes the impact of financial media on noise trading. Using behavioral finance and psychology-related knowledge, investor behavior can be better understood, so as the motivation behind noise trading. Finally, in order to promote the healthy development of the stock market, this paper makes recommendations to improve the efficiency of the capital market.</p>

scholarly journals GROUP BEHAVIORAL BIASES AFFECT FINANCIAL DECISIONS UNLIKE INDIVIDUAL BEHAVIORAL BIASES

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcelo Henriques de Brito ◽  
Paula Esteban do Valle Jardim
Keyword(s):  
Decision Making ◽  
Group Dynamics ◽  
Behavioral Finance ◽  
Institutional Investors ◽  
Group Behavior ◽  
Individual Investors ◽  
Cognitive Errors ◽  
Financial Decisions ◽  
Behavioral Biases ◽  
Group Members

This work presents a new approach to behavioral finance with a theoretical contribution by suggesting and discussing with examples a list of group behavioral biases along with established individual behavioral biases, bringing, hence, an additional outlook on how behavioral biases affect financial decisions. While individual behavioral biases are detected in individuals acting alone, group behavioral biases require the scrutiny of group behavior. This awareness may be particularly important to institutional investors, whose decisions basically stem from a committee or a group that will exhibit behavioral biases depending on how the group members interact between themselves when making a decision, which may include negotiation activities and not necessarily be related to personality or hierarchy. The focus on individual investors deciding on personal investments explain the need of work already developed in behavioral finance, which focus on individual behavioral biases, which may be a consequence from either cognitive errors or emotional biases. However, decisions from institutional investors basically stem from a committee or a group that will exhibit behavioral biases depending on how the group members interact between themselves when making a decision. To address the challenge of identifying causes and consequences for unexpected or unsuitable financial decision-making within a group, this work initially retrieves previous work on individual behavioral biases, linking emotional biases and cognitive errors to the “system 1” and “system 2” decision-making framework. Then, a conceptual contribution of this paper, which may be particularly relevant for institutional investors, is to explain with examples - after research and experience - which are the group behavioral biases and their impact upon financial decisions. Individual behavioral biases already acknowledged in other works on behavioral finance are contrasted in this work to the suggested group behavioral biases. Furthermore, this work suggests that there are two broad types of group behavioral biases: group dynamics biases and information-acceptance biases. Each broad type is subdivided into biases related to the structure of the group and biases related to how the group decision-making procedure occurs. Group dynamics biases related to the manner the group is structured are the following: kin bias (belonging bias), harmony bias, and competition bias. On the other hand, group dynamics biases may be sorted according to five different decision-making procedures, namely: herding, fad bias, Plato bias (denial bias), scarcity bias, and home bias.

scholarly journals Differential Investment Performance In South Africa Based On Gender And Age

2015 ◽  
Vol 14 (3) ◽  
pp. 537 ◽  
Author(s):  
Gizelle Willows ◽  
Darron West
Keyword(s):  
South African ◽  
Behavioral Finance ◽  
Total Sample ◽  
Individual Investors ◽  
Investment Performance ◽  
Men And Women ◽  
Trading Frequency ◽  
Gender And Age ◽  
Significant Difference ◽  
Different Ages

Behavioral finance shows us that individuals do not always behave rationally, owing to certain behavioural biases. A certain bias known as overconfidence has been found to incite increased trading frequency which in turn, reduces the overall return earned. Behavioral biases manifest differently amongst men and women of different ages. Men and more overconfident and women are more risk averse, whilst the young hold more volatile portfolios and the more experienced display fewer of these biases. A sample of 19,021 individual investors from a South African investment house was analysed over five years in order to draw conclusions on the trading behaviour, returns earned and variances in these returns earned by men and women of different ages. The results showed women over the age of 60 years earning statistically significantly higher returns than men and older investors having lower variances in return. For investors of younger ages, no statistically significant difference in the returns earned by men and women are noted, however men were found to have higher variances of returns. Whilst the trading frequency of men is statistically significantly higher than women for the total sample of investors; this result is not consistent amongst the different age-groupings analysed.

Theory of Behavioral Finance

2015 ◽  
pp. 1-24 ◽  
Author(s):  
Jaya M. Prosad ◽  
Sujata Kapoor ◽  
Jhumur Sengupta
Keyword(s):  
Financial Markets ◽  
Prospect Theory ◽  
Behavioral Finance ◽  
Individual Investors ◽  
Market Conditions ◽  
Market Anomalies ◽  
Behavioral Theories ◽  
Portfolio Managers ◽  
Academic Events

This chapter explores the evolution of modern behavioral finance theories from the traditional framework. It focuses on three main issues. First, it analyzes the importance of standard finance theories and the situations where they become insufficient i.e. market anomalies. Second, it signifies the role of behavioral finance in narrowing down the gaps between traditional finance theories and actual market conditions. This involves the substitution of standard finance theories with more realistic behavioral theories like the prospect theory (Kahneman & Tversky, 1979). In the end, it provides a synthesis of academic events that substantiate the presence of behavioral biases, their underlying psychology and their impact on financial markets. This chapter also highlights the implications of behavior biases on financial practitioners like market experts, portfolio managers and individual investors. The chapter concludes with providing the limitations and future scope of research in behavioral finance.

Algorithmic Models of Investor Behavior

2021 ◽  
Vol 1 (1) ◽  
pp. 1-29
Author(s):  
Andrew Lo ◽  
Alexander Remerov
Keyword(s):  
Behavioral Finance ◽  
Historical Data ◽  
Asset Returns ◽  
Investor Behavior ◽  
Investment Strategies ◽  
Investment Performance ◽  
Evolutionary Forces ◽  
Asset Class ◽  
Functional Forms ◽  
The Mean

We propose a heuristic approach to modeling investor behavior by simulating combinations of simpler systematic investment strategies associated with well-known behavioral biases—in functional forms motivated by an extensive review of the behavioral finance literature—using parameters calibrated from historical data. We compute the investment performance of these heuristics individually and in pairwise combinations using both simulated and historical asset-class returns. The mean-reversion or momentum nature of a heuristic can often explain its effect on performance, depending on whether asset returns are consistent with such dynamics. These algorithms show that seemingly irrational investor behavior may, in fact, have been shaped by evolutionary forces and can be effective in certain environments and maladaptive in others.

ANALYSIS OF THE DISPOSITION EFFECT: ASYMMETRY AND PREDICTION ACCURACY

2013 ◽  
Vol 7 (1) ◽  
pp. 27-42
Author(s):  
Florian Teschner
Keyword(s):  
Decision Making ◽  
Financial Markets ◽  
Market Efficiency ◽  
Behavioral Finance ◽  
Prediction Accuracy ◽  
Disposition Effect ◽  
Prediction Market ◽  
Behavioral Biases ◽  
Individual Traders ◽  
Economic Indices

The disposition effect describes investors’ common tendency of selling a winning investment too soon and holding on to losing investments too long. We analyze the disposition effect in a prediction market for economic indices. We show that the effect for individual traders as well as on an aggregated level. Furthermore we find a significant asymmetry of the disposition effect. The effect can almost exclusively be attributed to the percentage of gains realized (PGR). Additionally we link the aggregated disposition effect and market efficiency. A common hypothesis of the behavioral finance literature is that if participants make systematically biased decisions, market efficiency will suffer. Our setup is well-suited to studying the behavioral aspects of decision making because, in contrast to financial markets (i) the value of shares in our market is ultimately known and (ii) we can measure the participants’ behavioral biases (i.e the disposition effect). Against intuition we find no correlation between the disposition effect and prediction accuracy - a proxy for market efficiency.

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