Field Of Behavioral Finance Research Articles

Individual Investments Biased by the Size of a Foreign Investor: An Experimental Study


 
 
 The field of behavioral finance has actively researched behavioral elements influencing the choices of individual investors. This study also contributes to the behavioral finance and examines the effect of an increase in a foreign firm’s partial ownership in a domestic firm on the local individual investments in that domestic firm. Specifically, using a controlled lab experiment the study examines the investments of Pakistani individual investors between a purely Pakistani firm and a Pakistani firm with three different levels of Chinese ownership (portfolio, minority, majority). The experimental results show that with reference to Chinese minority ownership in a Pakistani firm, the potential investors are 47% (61%) less likely to invest in a Pakistani firm with Chinese portfolio (majority) ownership than in a purely Pakistani firm. The study uncovers an important non-monetary factor in the form of a foreign firm’s partial ownership that can significantly influence the choices of individual investors. It also makes an important contribution to the growing literature on the Chinese foreign investments specifically in Pakistan by exploring how potential individual Pakistani investors are likely to react to an increase in Chinese investments in Pakistani firms.
 
 

A COMPARATIVE STUDY ON INVESTMENT PATTERN OF SELF-EMPLOYED & START-UP ENTREPRENEURS WITH SPECIAL REFERENCE TO PAN INDIA

This study connotes the influence of demographical factors on the behavior of two categories of investors especially when they are pre-occupied with commitments and constraints. It also includes the subsequent effects on their risk appetite. It focuses on the fact that investors are not always rational in nature, have limits to their self-control and are influenced by their own limitations and bounded knowledge. This study is a comparative study between the Investment pattern of Self-Employed and Entrepreneurs. For the purpose of Study, we have taken 100 samples from Start-up entrepreneurs and Self-Employed. The Data used in the study are both primary and secondary. Using random Sampling technique, the responses were collected and analyzed using graphs and tables. Many developments were seen during the analysis. The analysis was carried by connecting the factors which are influential on one another and Chi-Square Test tools were utilized. Overall, the study has given new perspectives of the Financial Behavior of both Self-Employed and Entrepreneur which can further be analyzed by expanding the scope of the study and samples collected. There is still lot of scope to study and research in the area for contributing to the field of Behavioral Finance with various biases influencing the investing behavior.
 JEL Classification Codes: G40, G41.

Do Google Trends forecast bitcoins? Stylized facts and statistical evidence

In early 2018 Bitcoin prices peaked at US$ 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between Bitcoin price and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Bitcoin Trends for the behavior of Bitcoin price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.

Чи відрізняються поведінкові упередження інституційних та індивідуальних інвесторів?

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.

Behavioral Finance – Impact of Artificial Intelligence and Social Media Analytics & Impact of Swarms and Human Crowds on the Stock Market

Artificial intelligence and social media analytics have played a critical role in avoiding the errors investors make when making investment decisions. There is a need to rely on data, and this study addresses the various ways that data can be obtained, analyzed, and utilized in making essential investment decisions. The study utilizes a qualitative research model that is guided by research questions. Secondary sources of data are used to identify the impact of social media analytics and artificial intelligence at large in the field of behavioral finance. The crowd-based method and the swarm method are based on the stock market because ​researchers in the field of Collective Intelligence show that groups can outperform individuals when making decisions, predictions, and forecasts. The most common method for harnessing a group's intelligence treats the population as a “crowd” of independent agents that provide input in isolation in the form of polls, surveys, and market transactions. While such crowd-based methods can be effective, they are markedly different from how natural systems harness group intelligence. The present study compares the predictive ability of crowds and swarms when tapping the intelligence of human groups. More specifically, the present study tasked a of 469 football fans and a swarm of 29 football fans in a challenge to predict 20 Prop Bets during the 2016 Super Bowl. Although 16 times larger in size, results revealed that the was significantly less accurate (at 47% correct) than the swarm (at 68% correct). Further, the swarm outperformed 98% of the individuals in the full study. These results suggest that swarming, with closed-loop feedback, is potentially a more effective method for tapping groups' insights than traditional polling. Researchers in Collective Intelligence has shown that groups can outperform individuals when making decisions, predictions, and forecasts. The most common method for harnessing the intelligence of a group threats the population as a crowd of independent agents that provide input in isolation in the form of polls, surveys, and action. While such crowd-based methods can be effective, they are markedly different from how natural systems harness group intelligence. The present study compares the predictive ability of crowds and swarms when tapping the intelligence of human groups. More specifically, the present study tasked a of 469 football fans and a swarm of 29 football fans in a challenge to predict 20 Prop Bets during the 2016 Super Bowl. Although 16 times larger in size, results revealed that the was significantly less accurate (at 47% correct) than the swarm (at 68% correct). Further, the swarm outperformed 98% of the individuals in the full study. These results suggest that swarming, with closed-loop feedback, is potentially a more effective method for tapping groups' insights than traditional polling.

Capital structure construct: a new approach to behavioral finance

Within the framework of behavioral finance, this research shows that financial behavior can be assessed as a cognitive construct. Using certain variables, a multidimensional “cognitive finance” construct can thus be established. Through a technological – psychometric type design with descriptive data analysis, a factor analysis is presented to determine which latent variables tend to charge significantly in order to assess the validity of the dimensions comprising the construct of capital structure and explore its dimensions in relation to financial theory. A 44-item questionnaire is adapted and applied to a sample of chief financial officers from diverse public and nonpublic companies in Mexico. The analysis reveals the existence of four construct dimensions consistent with corporate financial theory. The model helps to explain how decision-makers react to uncertainty and environmental conditions, directly affecting the valuation of firm’s losses or earnings. As evidenced by the results, application of the Item Response Theory to the field of behavioral finance could open up new avenues to the study of cognitive biases, involved in the financial decision-making process. Thus, this implies that behavioral finance can also be treated as “cognitive finance.”

Исторически корени на поведенческата финансова мисъл

The rise of the first implicit ideas in the field of behavioural finance is examined from a historical point of view, taking into consideration the evolution of economic theories. The first documented manifestation of irrational behaviour on a market level, which took place during the 17th century, is presented. The foundations of Daniel Bernoulli’s Decision theory from the 18th century are systemized in regard to the implementation of behavioural aspects and the reference to the contemporary Prospect theory. The classical and early neoclassical schools of economic thought, where the idea that economic decisions are also being influenced by emotional stimulus can be found in the works of economists like Hermann Heinrich Gossen, Adam Smith, Jeremy Bentham, William Stanley Jevons, Léon Walras and Edgeworth, are examined. The neoclassical school of economics, where the representatives of marginalism limit the idea of the complexity of human nature, is also analysed. In conclusion, a short description of some of the contemporary aspects of the science of behavioural finance is made.

Investor sentiment in the theoretical field of behavioural finance

Investor sentiment is a research area in the theoretical field of behavioural finance that analyses the sentiment of investors and the way it influences stock market activity. Recently, there has been an increase in the number of publications in this area, which indicates its incremental relevance. To date, there is no consensus on the theoretical structure of behavioural finance nor on the investor sentiment research area. We have used co-citation, bibliographic coupling and co-occurrence analysis to provide an overview of the structure of investor sentiment. Therefore, this study contributes to defining the theoretical structure of investor sentiment by identifying the foundations of the research area and main journals, references, authors, or keywords, which represent the core of knowledge of this research area. The results obtained suggest that investor sentiment is related to efficient market theory and behavioural finance theories. Furthermore, investor sentiment is a relevant research field, especially since 2014. Advances in computer science or theories based on physics or mathematics can help to better define the influence of investor sentiment on stock markets. This study advances research on investor sentiment within the field of behavioural finance, thus showing its relevance.

Framing Effect and Financial Wellbeing: Role of Investment Behaviors as Mediator

Based on the premise that human beings are not rational agents and are frame dependent while making investment decisions, this study has found a significant impact of framing effect on investment behaviors and financial wellbeing. Structural Equation Modelling is employed in Stata, on the data collected from 344 respondents from Pakistan (299 salaried persons and 45 businessmen), having more than three years’ experience. We supported the view of behavioral finance that framing effect could influence the financial wellbeing negatively. We extended the analysis further by validating the mediating role of investment behaviors between framing effect and financial wellbeing, which is a contribution to the body of knowledge in the field of behavioral finance.

Using Text Mining to Predicate Exchange Rates with Sentiment Indicators

Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioral finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioral finance. This study explores the efficacy of using novel sentiment indicators from Market Psych, which analyses social media in addition to newsfeeds to quantify various levels of individual’s emotions, as a predictor for financial time series returns of the Australian Dollar (AUD)-US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioral finance, combining technical and behavioral aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares Multivariate Linear Regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation.

“Psychological factors in investment decisions: A review of literature”

The field of behavioral finance is a multidisciplinary field of study which applies the theories of behavioural sciences to describe the irrational financial behavior of investors. Initially the finance field was hesitant to accept the views of psychologists who had suggested the behavioural finance model but later on financial economists also started to believe that the investors do behave irrationally. The present article focuses to highlight the existing reviews of literature to understand the preconceptions in behavioural finance. Moreover it also tries to highlight different types of relationships between human psychology and its impact ion decision making while investing in the market.

The framing effect among executive officers and board members

The objective of this study is to investigate the framing effect in the Brazilian corporate environment. Thus, this paper focuses on loss aversion related to different groups within organizations. Board members and executive officers are at first qualified, well prepared, and hold positions in which they are frequently called upon to decide, advise, or evaluate critical decisions. Interviews were held with executives, members of the Boards of Directors (henceforth simply board members) and Audit Committee members (henceforth AC members) from publicly traded companies listed on the São Paulo Stock Exchange (BMFBovespa). The results suggest that the framing effect occurs differently among AC members and the other respondents. With respect to the loss, 30.28% of the board members opt to spend 8% or more, however only 22.02% would do the same to avoid reducing a profit, a drop of 8.26 percentage points. This research contributes towards the field of behavioral finance by demonstrating how the framing effect manifests even among a group of experts. The main findings of this study suggest the presence of the framing effect even among qualified professionals.

BEHAVIORAL ECONOMICS AND BEHAVIORAL FINANCE: A BIBLIOMETRIC ANALYSIS OF THE SCIENTIFIC FIELDS

AbstractThe aim of the study is to conduct a bibliometric analysis of the scientific field of Behavioral Economics and Behavioral Finance. The research was conducted using the Web of Science database, which returned 2617 articles, revealing that the amount of research within these fields has grown over time. Furthermore, the results also prove the relevance of the works of Daniel Kahneman and Amos Tversky for the field of Behavioral Economics and Finance, and Steven Hursh to Behavioral Economics. It is still possible to note that the field of Behavioral Economics encompasses subjects that connect human behavior with demand, consumption and price, with investments and with managerial decisions, as well as with the role played by heuristics and cognitive biases in decision‐making processes. In turn, the field of Behavioral Finance is more focused on the study of errors of judgment and of decision‐making characteristics in financial investments. Additionally, it is inferred that the field of Behavioral Economics is more wide‐ranging than the field of Behavioral Finance, as the latter is a byproduct of Behavioral Economics. Finally, a conclusion is then reached, demonstrating that the fields of Behavioral Economics and Finance have turned into an important field of study.

Aggregate Investor Confidence in the Stock Market

ABSTRACTOverconfidence is one of the most robust findings in the field of behavioral finance, and is associated with excessive trading and risk taking among market participants. Assessment of the level of confidence of individuals in their abilities and skills is well documented. However, the literature lacks an aggregate measure of investor confidence, with this required to test its implications on a macro level. The author introduces a simple measure of aggregate investor confidence by adopting a formal model of overconfidence. The applications of the measure suggest that, in aggregate, higher trading activity occurs when investor confidence soars, particularly for smaller stocks. Subsequently, the effect partially reverses, implying a correction to an initial overreaction. The newly introduced investor confidence index possesses better ability to predict trading activity than past returns, as used in prior studies. Additionally, investors tend to have a higher risk appetite when confident, as shown by increased investment in small stocks with higher risk.

Uticaj određenih psiholoških faktora investitora i menadžera na strukturu kapitala

Psychology represents the basic requirement for the emergence of disciplines such as behavioral finance and behavioral economics. It has contributed to a better understanding of the behavior of economic actors under conditions of risk arising from the imperfections of cognitive abilities of human beings. Consequently, it is necessary to change the economic models based on mathematical laws in favor of descriptive models that consider the cognitive abilities of the human mind. The most common decisions that are being studied in the field of behavioral finance are the decisions regarding capital structure in companies. The methodology in this paper is based on the net operating income approach. This approach is analyzing the financial section of income statement that refers to the financial expenses of the companies. Financial expenses are assumed to be fixed, determining financial break even, as a consequence of use of financial leverage. The main task of this paper is to determine the impact of some psychological factors in the terms of capital structure and financial leverage, through two case studies of comparative analysis of income statements of the companies Puma and Adidas that will consequently affect the achievement of financial break-evens well as the profitability of the companies. Therefore it is possible to conclude that some analyzed psychological aspects in the process of financial decision making of investors and managers can influence the capital structure decisions, which can be a subject of further researches.

Performance in stock transactions by gender: an application with quantile regression models

Our study tries to identify if there are performance differences between men and women in stock investment decisions. We use experimental methodology and count on the participation of 1,050 volunteers who took decisions in virtual environment, similar to a real online home broker for stock trading. Through quantile regression models, we have found evidence that women get results lower than men when decisions involve gains when decisions involve losses, women lose as much as men. These results demonstrate behavioural biases associated with disposition and endowment effects, as well as aspects related to loss aversion. As a result, we present a contribution on the field of behavioural finance, especially on the literature of gender diversity, which presents a controversial debate in several studies.

Institutional investor behavioral biases: syntheses of theory and evidence

PurposeThis 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/approachA literature survey is done to gather and synthesize evidence on behavioral biases of institutional investors.FindingsThe 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/valueThe 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.

Behavioral Finance: History and Foundations

Recent evidence suggests that ideology has the potential to affect academic research in economics and that exposure to a wide range of approaches may increase intellectual diversity, eventually leading to better decisions. Therefore, writing a literature review in behavioral finance, in principle, can bring benefits to a wide range of readers, especially since the field of behavioral finance itself has already grown into a complex web of related but distinct sub-fields and reached a stage when it can guide policy decisions. This review differs from the existent ones as it focuses on the history of the field and its psychological foundations. While the review of psychological foundations is necessary to appreciate the benefits of a behavioral approach and understand its limitations, even a brief historical detour may provide a compelling case against a naive dichotomy between behavioral and classical finance.

EFIKASNOST FINANSIJSKIH TRŽIŠTA / EFFICIENCY OF FINANCIAL MARKETS

Are financial markets efficient is a question on which there is still no clear and complete answer. Position that prices of securities fully reflect available information about securities is called the efficient market hypothesis (EMH). The EMH (on the example of stocks) has three forms (or levels) of efficiency: 1) the ‘’ weak’’ form of the EMH- is the claim that stock prices reflect all information contained in previous transactions; 2) the ‘’ semi- strong’’ form of the EMH- is the claim that stock prices reflect all publicly available information, and 3) the’’ strong’’ form of the EMH- is the claim that stock prices reflect all relevant information (public and private), including the privileged (the insider) information.Numerous studies have confirmed the existence of a’’ weak’’ form of the EMH, and generally supported the existence of a’’ semi-strong’’ form of the EMH, but not of a ‘’ strong’’ form of the EMH. However, the EMH, even if it is a ‘’weak’’ or a ‘’semi-strong’’ form has a number of weaknesses. Some of the the EMH assumptions confront with the reality – there is no perfect information, transaction and information costs can be significant, markets are often imperfect, and investors do not have complete knowledge about the set of all available financial strategies for a given situation.The information ’’overload’’ confuses people and affects their ability to prioritize and make good decisions. On the other hand, electronic trading method significantly affects the information at the disposal of the different market actors. It seems that the greatest threat to the EMH comes from the field of behavioral finance, which is engaged in research on the possible impact of psychological factors (loss aversion, anchoring, overconfidence...) on the behavior of investors. The basic argument of behavioral finance is that ’’standard’’ financial theory is not paying attention to how ordinary people make decisions and that ‘’ human factor’’ can not be ignored.Tha aim of this study was to critically examine the EMH. Apperently, the EMH after numerous studes and identified anomalies, largely remains at the level of (insufficiently confirmed) hypothesis, although it is often given the status, or created an illusion, of confirmed. This is also because the EMH is an important component of the rulling ’’paradigm’’ in finance or ’’standard finance theory’’. Joperdizing the status of the EMH bring into question many other important components of this ’’paradigm’’.The EMH has not offered acceptable answers to some of the specific developments and events in the financial market, including the last global financial crisis. But, the EMH still remains one of the cornerstones of ’’standard’’ finance theory.

Corporate Name Change and the Market Valuation of Firms: Evidence from an Emerging Market

Investors’ responses to a firm’s name change and the determinants of their response are scantly explored areas in the field of behavioral finance. Based on a sample of 415 Indian firms from 2005 to 2014, this study suggests that investors respond positively to the announcement of firm name changes. Furthermore, the study indicates that when firms do not indicate geographical specificity in the name and have a specific rather than generic name, then the firm will experience greater abnormal returns. Also, when firm names are fluent and are associated with the owner’s family name, again, abnormal returns generated are positive. Nevertheless, as a firm ages and investors gain more information about it, then abnormal returns due to name change decrease.