Institutional Investors

Author(s):  
Alexandre Skiba ◽  
Hilla Skiba

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.

2017 ◽  
Vol 16 (02) ◽  
pp. 573-590
Author(s):  
Ke Liu ◽  
Kin Keung Lai ◽  
Jerome Yen ◽  
Qing Zhu

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.


Author(s):  
Marcelo Henriques de Brito ◽  
Paula Esteban do Valle Jardim

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.


2019 ◽  
Vol 11 (1) ◽  
pp. 2-21 ◽  
Author(s):  
Syed Aliya Zahera ◽  
Rohit Bansal

Purpose The purpose of this paper is to study the disposition effect that is exhibited by the investors through the review of research articles in the area of behavioral finance. When the investors are hesitant to realize the losses and quick to realize the gains, this phenomenon is known as the disposition effect. This paper explains various theories, which have been evolved over the years that has explained the phenomenon of disposition effect. It includes the behavior of individual investors, institutional investors and mutual fund managers. Design/methodology/approach The authors have used the existing literatures from the various authors, who have studied the disposition effect in either real market or the experimental market. This paper includes literature over a period of 40 years, that is, Dyl, 1977, in the form of tax loss selling, to the most recent paper, Surya et al. (2017). Some authors have used the PGR-PLR ratio for calculating the disposition effect in their study. However, some authors have used t-test, ANNOVA, Correlation coefficient, Standard deviation, Regression, etc., as a tool to find the presence of disposition effect. Findings The effect of disposition can be changed for different types of individual investors, institutional investors and mutual funds. The individual investors are largely prone to the disposition effect and the demographic variables like age, gender, experience, investor sophistication also impact the occurrence of the disposition effect. On the other side, the institutional investors and mutual funds managers may or may not be affected by the disposition effect. Practical implications The skilled understanding of the disposition effect will help the investors, financial institutions and policy-makers to reduce the adverse effect of this bias in the stock market. This paper contributes a detailed explanation of disposition effect and its impacts on the investors. The study of disposition effect has been found to be insufficient in the context of Indian capital market. Social implications The investors and society at large can gains insights about causes and influences of disposition effect which will be helpful to create sound investment decisions. Originality/value This paper has complied the 11 causes for the occurrence of disposition effect that are found by the different authors. The paper also highlights the impact of the disposition effect in the decision-making of various investors.


2021 ◽  
pp. 61-73
Author(s):  
Roshani Chamalka Gunathilaka ◽  
◽  
J. M. Ruwani Fernando ◽  

Purpose: The purpose of this paper is to investigate how does the behavioral biases differ among the individual and institutional investors based on Colombo Stock Exchange. The study considers the effect of four behavioral biases; overconfidence bias, representativeness bias, disposition effect and herd mentality bias on the financial investment decision making of individual investors and institutional investors. Design / methodology / approach: A questionnaire was utilized to collect the data and the final sample consisted with 104 individual and 71 institutional respondents. The data of 175 investors was analyzed by using Partial Least Square-Structural Equation Modeling approach. Findings: The study revealed that disposition effect make an impact on the investment decisions of both individual investors and institutional investors whereas overconfidence bias has impact only on the individual investors’ investment decisions. Originality: This study is one of the pioneering studies examining the behavioral biases differences of individual and institutional investors’ decision making. Thus, this study expands the existing literature in the field of behavioral finance particularly in emerging market context. In this sense, the findings of this study could draw important inferences for researchers, investors and policy makers to ensure that they make rational investments decisions.


2017 ◽  
Vol 40 (5) ◽  
pp. 578-603 ◽  
Author(s):  
Zamri Ahmad ◽  
Haslindar Ibrahim ◽  
Jasman Tuyon

Purpose This paper aims to review the theory and empirical evidence of institutional investor behavioral biases in the lenses of behavioral finance paradigm. It surveys the research specifically focusing on behavioral biases among institutional investors in investment management activities worldwide. Design/methodology/approach A literature survey is done to gather and synthesize evidence on behavioral biases of institutional investors. Findings The survey and analysis reveal the following findings. First, the theoretical underpinning of investors’ irrational behavior has been neglected in behavioral finance research. Second, the behavioral heuristics and biases are dynamic and complex. Third, understanding behavioral biases’ origin, causes and effects requires interdisciplinary perspectives from the fields of psychology, sociology and biology. Originality/value The analysis and alternative perspectives drawn in this paper provide new insights into the field of behavioral finance and aims to suggest researchers, practitioners and regulators on the next course of actions.


1998 ◽  
Vol 01 (03) ◽  
pp. 321-353 ◽  
Author(s):  
Anya Khanthavit

This study examines the information and trading behavior of investors in the Thai market. This market is an important emerging market in the Pacific Rim, whose structure is different from that of a more developed market. We propose a vector autoregression model to describe and test action and reaction of the portfolio reallocation of investors and the movement of stock prices over time. Using daily market data from January 3, 1995 to October 27 1997 , this study finds that, in the Thai market, the foreign investors bought stocks when prices had risen. This strategy was consistent with a positive autocorrelation in the stock return. The local individual investors bought stocks when prices had fallen, while the local institutional investors disregarded past price changes. These two investor groups also exhibited herd behavior of both informational cascades and interpersonal communications types. They followed each other and reacted negatively to an innovation in the stock return. It is interesting to find that the foreign investors brought new information into the market. The local individual and local institutional investors brought in noise, but the explanatory share of this noisy information in the stock volatility was small. So, the study concludes that the volatility in the Thai market was not excessive.


2012 ◽  
Vol 15 (1) ◽  
pp. 5-13
Author(s):  
Quan Duc Hoang Vuong ◽  
Phuc Quy Dao

The study aims to determine individual investors’ behavioral biases at individual level in the Vietnamese stock market and investigate the relationships between mutual behavioral biases, between demographic variables and behavioral biases, between stock investment variables and behavioral biases. This is a quantitative research in behavioral finance with the survey conducted in forms of questionnaire. Each question is a problem which requires investors to make decision. The research finds out that there are specific behavioral biases which influence investors’ investment decisions. Furthermore, there are relationships between gender and illusion of control bias, gender and optimism bias, gender and self-control bias. We also realize relationships between average value per trading times and investment experience, average value per trading times and loss aversion bias, trading frequency and optimism bias, investment experience and optimism bias, monthly income and optimism, age and cognitive dissonance bias. Our findings confirm relationships between mutual behavioral biases mentioned in behavioral finance such as relationships between framing bias and mental accounting bias, illusion of control bias and overconfidence bias. Additionally, we find out relationships between ambiguity aversion bias and confirmation bias.


2019 ◽  
Vol 11 (2) ◽  
pp. 128-143
Author(s):  
Leonardo Weiss-Cohen ◽  
Peter Ayton ◽  
Iain Clacher ◽  
Volker Thoma

PurposeBehavioral finance research has almost exclusively investigated the decision making of lay individuals, mostly ignoring more sophisticated institutional investors. The purpose of this paper is to better understand the relatively unexplored field of investment decisions made by pension fund trustees, an important subset of institutional investors, and identify future avenues of further exploration.Design/methodology/approachThis paper starts by setting out the landscape in which pension fund trustees operate and make their decisions, followed by a literature review of the extant behavioral finance research applicable to similar situations.FindingsDespite receiving training and accumulating experience in financial markets, these are limited and sparse; therefore, pension fund trustees are unlikely to be immune from behavioral biases. Trustees make decisions in groups, are heavily reliant on advice and make decisions on behalf of others. Research in those areas has uncovered many inefficiencies. It is still unknown how this specific context can affect the psychological effects on their decisions.Research limitations/implicationsGiven how much influence trustees’ decisions have on asset allocation and by extension in financial markets, this is a surprising state of affairs. Research in behavioral finance has had a marked influence on policy in the past and so we anticipate that exploring the decisions made within pension funds may have wide ramifications for the industry.Originality/valueAs far as the authors are aware, no behavioral research has empirically tested pension fund trustees’ decisions to investigate how the combination of group decisions, advice and surrogacy influence their decisions and, ultimately, the sustainability of our pensions.


2018 ◽  
Vol 10 (2) ◽  
pp. 210-251 ◽  
Author(s):  
Syed Aliya Zahera ◽  
Rohit Bansal

Purpose The purpose of this paper is to study and describe several biases in investment decision-making through the review of research articles in the area of behavioral finance. It also includes some of the analytical and foundational work and how this has progressed over the years to make behavioral finance an established and specific area of study. The study includes behavioral patterns of individual investors, institutional investors and financial advisors. Design/methodology/approach The research papers are analyzed on the basis of searching the keywords related to behavioral finance on various published journals, conference proceedings, working papers and some other published books. These papers are collected over a period of year’s right from the time when the most introductory paper was published (1979) that contributed this area a basic foundation till the most recent papers (2016). These articles are segregated into biases wise, year-wise, country-wise and author wise. All research tools that have been used by authors related to primary and secondary data have also been included into our table. Findings A new era of understanding of human emotions, behavior and sentiments has been started which was earlier dominated by the study of financial markets. Moreover, this area is not only attracting the, attention of academicians but also of the various corporates, financial intermediaries and entrepreneurs thus adding to its importance. The study is more inclined toward the study of individual and institutional investors and financial advisors’ investors but the behavior of intermediaries through which some of them invest should be focused upon, narrowing down population into various variables, targeting the expanding economies to reap some unexplained theories. This study has identified 17 different types of biases and also summarized in the form of tables. Research limitations/implications The study is based on some of the most recent findings to have a quick overview of the latest work carried out in this area. So far very few extensive review papers have been published to highlight the research work in the area of behavioral finance. This study will be helpful for new researches in this field and to identify the areas where possible work can be done. Practical implications Practical implication of the research is that companies, policymakers and issuers of securities can watch out of investors’ interest before issuing securities into the market. Social implications Under the Social Implication, investors can recognize several behavioral biases, take sound investment decisions and can also minimize their risk. Originality/value The essence of this paper is the identification of 17 types of biases and the literature related to them. The study is based on both, the literature on investment decisions and the biases in investment decision-making. Such study is less prevalent in the developing country like India. This paper does not only focus on the basic principles of behavioral finance but also explain some emerging concepts and theories of behavioral finance. Thus, the paper generates interest in the readers to find the solutions to minimize the effect of biases in decision-making.


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