Behavioral Finance Models and Behavioral Biases in Stock Price Forecasting (original) (raw)
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Buying and selling shares has become one of the most popular and lucrative investment decisions for investors. Unlike before, today's stock price decision has become a more complex task. Profitability of the stock market investment depends on the investor's decisions and is based on a mix of dynamic environmental factors. Stock price trends are repeatedly forecasted to extract useful patterns and predict their movements. Moreover, the stock market forecast always has some appeal. There are different approaches to stock price prediction and different forecasting methods are used by stock market analysts. As a replica of many scientific endeavors, several methods have been found to accurately predict stock prices. Most researchers have been used technical analysis to get more accurate results, while limited researchers used fundamental analysis. From Sri Lanka's perspective, there is no evidence on predicting stock prices using machine learning. The main objective of this paper is to measure the effect of the announcement on the price of the stock and fill in the gaps in the literature. The population of the current study is all listed companies on the Colombo Stock Exchange. Among them, Ceylon Tobacco Company PLC, Dialog Axiata PLC, and John Keells Holdings PLC were selected as the sample for this study by employing simple random sampling method and market capitalization. The analysis was conducted by employing natural language processing mechanisms and Random Trees classifier presented the best results. The findings facilitate to predict the share price of the Colombo Stock Exchange using machine learning techniques. This model was able to predict the stock price with 65% accuracy and would benefit all individual investors in the local stock market.
The Journal of Economic Research & Business Administration, 2020
A high rate of return on the investment is crucially dependent on rational investment decision making because rational investment decision ensures the successful return of an investment, especially in stocks. Investment decision making is affected by many factors; most of them are related to psychological and behavioural. Since it is difficult to make rational decisions about investment, researchers are trying to discover the factors that influence the investor's behaviour about decision making. For the rational estimation of success rate in stocks, investors have tried many traditional methods but reached on unsatisfactory results. However, Behavioral Finance has addressed this issue and discovered the most crucial factors that may affect the investment decision making. Thus, this study aims to evaluate the influence of the factors of behavioural finance that affect decision making in the stock exchange. Three factors have been selected and used to gauge the impact on investment decision making. These factors include; overconfidence bias, representativeness bias, and availability bias. A structured close-ended questionnaire has been used to collect the data, and data was collected from 211 respondents who are investors on Karachi stock exchange. To analyze the collected data, multiple linear regression (MLR) model has been used. The result of this study shows that all three independent variables have a significant impact on investment decision making. Moreover, the relationship is positive between the independent and dependent variables. Therefore, it can be concluded that the null hypothesis is rejected. This study will assist investors to make decisions rationally in the stock market.
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STUDY ON BEHAVIORAL FINANCE, BEHAVIORAL BIASES, AND INVESTMENT DECISIONS
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