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In the research of stock market efficiency, it is argued that the stock market moves randomly and absorbs all the available information. As a result, it is quite impossible to make predictions about the possible future movement by the investors. But literatures have detected certain calendar anomalies where a day(s) in a week or month(s) in a year or a particular event in a year becomes conducive for investors to earn more than the normal. Hence, the purpose of this study is to find out the month of the year effect in the Indian stock market.

In this study, daily time series data of Sensex and Nifty from 1996 to 2021 is used. The study uses month dummies to capture the effect. Different variants of generalised autoregressive conditional heteroskedasticity (GARCH) models, both symmetric and asymmetric, are used in the study to model the conditional volatility in the presence month effect.

This study found the September effect in the return series of both the stock market. Apart from that, asymmetric GARCH models are found to be the best fit model to estimate conditional volatility.

This study is an endeavour to study month of the year effect in the Indian context. This research will provide valuable insight for studying the different calendar anomalies.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Leveraging signalling theory and institutional environment theory, this study aims to examine how the entrepreneurial orientation of emerging market firms impacts initial public offering (IPO) performance.

The authors conduct regression analysis based on archival data from 312 firms’ IPOs in India.

The results in the Indian context suggest it differs from IPO performance in developed markets. In an emerging market context, the findings suggest that only competitive aggressiveness is valued by investors in IPOs. The findings further show that proactiveness and autonomy negatively influence IPO underpricing.

The research propositions imply that, owing to institutional voids in emerging markets, investors’ risk propensity and, hence, rewarding a firm’s entrepreneurial orientation differ from those in developed markets.

Extant literature has given limited attention to the dynamics of entrepreneurial orientation and the effect of each dimension of entrepreneurial orientation on IPO performance in emerging markets.

The case was developed from secondary sources and interviews with a security analyst. The secondary sources include company annual reports, news reports, analyst reports, industry reports, company websites, stock exchange websites and databases such as Bloomberg and CMIE Prowess.

Increasing competition in product and capital markets has put tremendous pressure on managers to become more cost competitive. To address their firms' uncompetitive cost structures, managers may have to consider dramatic restructuring of their businesses. During 2014–2017, Tata Steel Ltd (TSL) UK considered a series of divestitures and a merger plan to nurse the company back to health. The case considers the economics of the restructuring plan. The case is designed to help students analyze a corporate downsizing program undertaken by a large Indian company in the UK and to highlight the dynamic role of the CFO and governance issues in family firms. It introduces students to issues surrounding a typical restructuring and provides students a platform to practice the estimation of value creation in a restructuring exercise. While some cases on corporate restructuring in the context of developed economies are available, there are very few cases written in an emerging market context. This case bridges that gap. TSL presents a unique opportunity to study corporate restructuring necessitated by a failed cross-border acquisition. It illustrates the potential for value loss in large, cross-border acquisitions. It shows how managerial hubris can prompt family firm owners to overbid in acquisitions and create legacy hot spots. In addition, the case can be used to discuss the causes of governance failures such as weak institutional monitoring and poor legal enforcement in emerging markets that could potentially harm minority shareholders.

The case was developed from secondary sources and interviews with a security analyst. The secondary sources include company annual reports, news reports, analyst reports, industry reports, company websites, stock exchange websites and databases such as Bloomberg and CMIE Prowess.