Predictive and Prescriptive Analytics for Strategic Financial Decisions: Seasoned Equity Offerings, Stock Splits, Pandemic effects, and Investment Decisions

Abstract

Scholars in the intersection of operational research, strategy, and finance have extensively examined the effects of event studies in finance, especially that of a strategic nature, such as that of planned as well as unexpected corporate events and respective abnormal returns on the stock market. Nonetheless, there is still a research gap on the extent of the forecastability of this abnormal behaviour, especially when predictions may provide crucial information to both investors and issuers, and therefore drive effectively investment decisions. In this study we forecast the value effect of SEOs and Stock Splits, across developed and emerging economies. The selection of these nations, namely the United States (benchmark), Brazil, and India, was based on their Gross Domestic Product (GDP) and the impact of their stock markets on economic growth. Data consist of 2,043 strategic financial decisions with historical information from the New York Stock Exchange (NYSE), Bombay Stock Exchange (BSE), National Stock Exchange of India (NSE) and Brazil Stock Exchange (B3) from 2010 to 2020. Linear regression (benchmark), random forests, gradient boosting machines, support vector regression and neural networks methods are empirically evaluated, with non-linear models performing better than the benchmark. A trading simulation is also incorporated to complement model outcomes and determine whether these predictions could be capitalised through effective decision making in the investment spectrum. Finally, the effects of the COVID-19 pandemic were also analysed for SEOs in the NYSE, and significant differences were discovered in March and April 2020. Results indicate how negative abnormal returns were exacerbated by COVID-19’s systemic impact during March and rebounded in April.