{"title":"Behavioral Attributes Influencing Decision Making of Indian Derivative Market Investors","authors":"LG Honey Singh, Amar Kumar Chaudhary","doi":"10.9734/ajeba/2024/v24i61374","DOIUrl":null,"url":null,"abstract":"Aim: The retail investors of the Indian investment landscape are found to base their investment decisions on several factors that may not be entirely attributed to price movement and information availability. Various studies have been conducted to capture these behavioral attributes that may invariably have an influencing effect either knowingly or unknowingly on the investors. This study aims to identify these behavioral biases influencing investor’s decision-making when they are active participants in the Indian Derivative market. \nMethodology: The study is conducted using primary data over 200 derivative investors within the northern Indian subcontinent aged between 18 years to 50 years. A questionnaire has been adapted from the defined scales from literature and uses the Likert scale to measure the behavioral patterns of investors. A cross-sectional survey method is used to distribute the questionnaire both online and offline. Factor analysis is then employed to identify the latent variables impacting the decision-making. \nResults: The sample under study generated six factors that have a significant influence on decision-making. These factors include Herding bias, Overconfidence bias, Risk Aversion, Market Responsiveness, Information processing Style and Information Reliance. All these factors have a significant influence on the investors buying and selling behavior in the Indian derivative market. A reliability test was run to assess the reliability of the factors where the Cronbach alpha was found to be above 0.8 for all the factors which shows a strong internal coherence among the factors.","PeriodicalId":505152,"journal":{"name":"Asian Journal of Economics, Business and Accounting","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Economics, Business and Accounting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/ajeba/2024/v24i61374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Aim: The retail investors of the Indian investment landscape are found to base their investment decisions on several factors that may not be entirely attributed to price movement and information availability. Various studies have been conducted to capture these behavioral attributes that may invariably have an influencing effect either knowingly or unknowingly on the investors. This study aims to identify these behavioral biases influencing investor’s decision-making when they are active participants in the Indian Derivative market.
Methodology: The study is conducted using primary data over 200 derivative investors within the northern Indian subcontinent aged between 18 years to 50 years. A questionnaire has been adapted from the defined scales from literature and uses the Likert scale to measure the behavioral patterns of investors. A cross-sectional survey method is used to distribute the questionnaire both online and offline. Factor analysis is then employed to identify the latent variables impacting the decision-making.
Results: The sample under study generated six factors that have a significant influence on decision-making. These factors include Herding bias, Overconfidence bias, Risk Aversion, Market Responsiveness, Information processing Style and Information Reliance. All these factors have a significant influence on the investors buying and selling behavior in the Indian derivative market. A reliability test was run to assess the reliability of the factors where the Cronbach alpha was found to be above 0.8 for all the factors which shows a strong internal coherence among the factors.