{"title":"Turnover tales: Investigating investor biases through the return−volume dynamics in the Indian stock market","authors":"Mirabel Josephine Paul, Madhumathi Rajendran","doi":"10.1016/j.iimb.2026.100672","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates overconfidence and disposition effect biases in the Indian stock market using return−volume relationships as a proxy. Employing vector autoregression and a modified quantile autoregressive model with asymmetric distributed lags, the study examines how behavioural biases vary across trading volume regimes over five years, spanning pre-COVID-19, during COVID-19, and post-COVID-19 crisis periods. Sectoral analysis reveals that overconfidence effects are strongest during high-volume trading in information-intensive sectors such as media and metal, while energy exhibits pronounced loss aversion patterns. Results demonstrate significant evidence of these biases across all periods, with severity influenced by temporal uncertainty, volume-dependent market conditions, information inflow, and increased heuristic usage during market turmoil.</div></div>","PeriodicalId":46337,"journal":{"name":"IIMB Management Review","volume":"38 2","pages":"Article 100672"},"PeriodicalIF":1.6000,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IIMB Management Review","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0970389626000315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/5/14 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigates overconfidence and disposition effect biases in the Indian stock market using return−volume relationships as a proxy. Employing vector autoregression and a modified quantile autoregressive model with asymmetric distributed lags, the study examines how behavioural biases vary across trading volume regimes over five years, spanning pre-COVID-19, during COVID-19, and post-COVID-19 crisis periods. Sectoral analysis reveals that overconfidence effects are strongest during high-volume trading in information-intensive sectors such as media and metal, while energy exhibits pronounced loss aversion patterns. Results demonstrate significant evidence of these biases across all periods, with severity influenced by temporal uncertainty, volume-dependent market conditions, information inflow, and increased heuristic usage during market turmoil.
期刊介绍:
IIMB Management Review (IMR) is a quarterly journal brought out by the Indian Institute of Management Bangalore. Addressed to management practitioners, researchers and academics, IMR aims to engage rigorously with practices, concepts and ideas in the field of management, with an emphasis on providing managerial insights, in a reader friendly format. To this end IMR invites manuscripts that provide novel managerial insights in any of the core business functions. The manuscript should be rigorous, that is, the findings should be supported by either empirical data or a well-justified theoretical model, and well written. While these two requirements are necessary for acceptance, they do not guarantee acceptance. The sole criterion for publication is contribution to the extant management literature.Although all manuscripts are welcome, our special emphasis is on papers that focus on emerging economies throughout the world. Such papers may either improve our understanding of markets in such economies through novel analyses or build models by taking into account the special characteristics of such economies to provide guidance to managers.