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 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.

scholarly journals Disposition Effect and Return Predictability in the Tunisian Stock Market

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Cheïma Hmida ◽  
Ramzi Boussaidi
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Stock Prices ◽  
Stock Price ◽  
Return Predictability ◽  
Capital Gains ◽  
Disposition Effect ◽  
Individual Investors ◽  
Long Run ◽  
Return Reversal

The behavioral finance literature has documented that individual investors tend to sell winning stocks more quickly than losing stocks, a phenomenon known as the disposition effect, and that such a behavior has an impact on stock prices. We examined this effect in the Tunisian stock market using the unrealized capital gains/losses of Grinblatt & Han (2005) to measure the disposition effect. We find that the Tunisian investors exhibit a disposition effect in the long-run horizon but not in the short and the intermediate horizons. Moreover, the disposition effect predicts a stock price continuation (momentum) for the whole sample. However this impact varies from an industry to another. It predicts a momentum for “manufacturing” but a return reversal for “financial” and “services”.

A study of prominence for disposition effect: a systematic review

2019 ◽  
Vol 11 (1) ◽  
pp. 2-21 ◽  
Author(s):  
Syed Aliya Zahera ◽  
Rohit Bansal
Keyword(s):  
Mutual Funds ◽  
Behavioral Finance ◽  
Mutual Fund ◽  
Institutional Investors ◽  
Disposition Effect ◽  
Individual Investors ◽  
Content Type ◽  
Experimental Market ◽  
The Individual ◽  
The Impact

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.

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 Investor Sentiment in the Stock Market

2007 ◽  
Vol 21 (2) ◽  
pp. 129-151 ◽  
Author(s):  
Malcolm Baker ◽  
Jeffrey Wurgler
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Stock Prices ◽  
Investor Sentiment ◽  
Cash Flows ◽  
Individual Investors ◽  
Individual Investor ◽  
Top Down ◽  
Limits To Arbitrage ◽  
Future Cash Flows

Investor sentiment, defined broadly, is a belief about future cash flows and investment risks that is not justified by the facts at hand. The question is no longer whether investor sentiment affects stock prices, but how to measure investor sentiment and quantify its effects. One approach is “bottom up,” using biases in individual investor psychology, such as overconfidence, representativeness, and conservatism, to explain how individual investors underreact or overreact to past returns or fundamentals The investor sentiment approach that we develop in this paper is, by contrast, distinctly “top down” and macroeconomic: we take the origin of investor sentiment as exogenous and focus on its empirical effects. We show that it is quite possible to measure investor sentiment and that waves of sentiment have clearly discernible, important, and regular effects on individual firms and on the stock market as a whole. The top-down approach builds on the two broader and more irrefutable assumptions of behavioral finance—sentiment and the limits to arbitrage—to explain which stocks are likely to be most affected by sentiment. In particular, stocks that are difficult to arbitrage or to value are most affected by sentiment.

scholarly journals Measuring Stock Market Investor Sentiment

2012 ◽  
Vol 29 (1) ◽  
pp. 51 ◽  
Author(s):  
Francisca Beer ◽  
Mohamed Zouaoui
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Behavioral Finance ◽  
Hybrid Approach ◽  
Composite Index ◽  
Investor Sentiment ◽  
Predictive Ability ◽  
Relative Performance ◽  
Relative Efficacy ◽  
Noise Traders

Recently, investor sentiment measures have become one of the more widely examined areas in behavioral finance. A number of measures have been developed in the literature without having been fully validated, and therefore leaving in question which measure should be used for empirical exploration. The purpose of this study is to examine the relative performance of a number of popular measures in predicting stock returns and to test the relative efficacy of a hybrid approach. Using a panel of investor sentiment measures, we develop a new measure of sentiment which combines direct and indirect sentiment measures. Our results show that our composite sentiment index affects the returns of stocks hard to value and difficult to arbitrage consistent with the predictions of noise traders models. Finally, we find that our composite index has a better predictive ability than the alternative sentiment measures largely used in the literature.

Developing an instrument for measuring the effects of heuristics on investment decisions

Kybernetes ◽  
2016 ◽  
Vol 45 (7) ◽  
pp. 1052-1071 ◽  
Author(s):  
Kadir C. Yalcin ◽  
Ekrem Tatoglu ◽  
Selim Zaim
Keyword(s):  
Behavioral Finance ◽  
Investment Decisions ◽  
Small Sample ◽  
Equation Modeling ◽  
Individual Investors ◽  
Moderating Effects ◽  
Measurement Scales ◽  
Content Type ◽  
The Usa ◽  
The Impact

Purpose Based on a thorough review and synthesis of the literature in behavioral finance, the purpose of this paper is to develop three measures of heuristics that tend to influence investment decisions of individual investors. Design/methodology/approach Using perceptual data collected from a sample of 167 individual investors in the USA, the reliability and validity of heuristics measures are assessed by confirmatory factor analysis with structural equation modeling. Then, the second-order model is executed in order to indicate the paths among the study’s constructs. Finally, a multiple-group analysis is conducted to analyze the moderating effects of demographic factors on the relationship between the perceived level of heuristics and their constituent dimensions. Findings Of the three groups of heuristics, salience is found to be the most important followed by mental accounting, while representativeness features as relatively less important. Regarding the moderating effects, only investment experience is noted to have a significant moderating impact. Research limitations/implications The data utilized for testing and validating this instrument was acquired from a relatively small sample of individual investors in the USA, which makes the generalization of findings somewhat limited. Practical implications Both researchers and practitioners in behavioral finance can use these measurement scales to better understand the impact of heuristics on individual investment decisions and also to develop models that relate the critical factors of heuristics to the performance of individual investment decisions. Originality/value To date, there has been no systematic attempt in the extant behavioral finance literature to develop a valid and reliable instrument on heuristics which would aid to improve the quality of decision making in investment analysis.

The Impact of Air Pollution on the Stock Market in South Korea

2021 ◽  
Author(s):  
◽  
Seongcheol Paeng
Keyword(s):  
Air Pollution ◽  
Stock Market ◽  
South Korea ◽  
Financial Market ◽  
Behavioral Finance ◽  
Negative Impact ◽  
Impulse Response Function ◽  
Weather Factors ◽  
Momentum Strategy ◽  
The Impact

Recently, behavioral finance researchers have produced many articles about moods effect on financial market. Weather factors and sports sentiments have a significant impact on moods and then the moods affect financial market. Air pollution also has an effect on financial market. This paper's hypothesis is that air pollution has a meaningful negative impact on the stock market in South Korea. This paper uses the Granger Causality for checking the significances and the Vector Auto-Regression model and the Impulse Response Function for investigating its impact according to time. Furthermore, this paper uses the 2SLS method for resolving endogeneity problems and checking robustness. If the level of air pollution increases 100 ??/?3, then the stock return reduces 0.42 after one day, and then recovers. The effect is significant at the 1% level and similar with the 2SLS method. Finally, this paper introduces air pollution momentum strategy that maximizes the cumulative return and measures the key variable's performance.

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