The Journal of Prediction Markets

Machine Learning Models Comparison for Bankruptcy Predication for Indian Companies

Manish Meena — 2025-04-01

It is essential to recognize that dynamics of bankruptcy events vary across regions and legal frameworks. In this context, the paper aims to fill the critical gap in literature by presenting an analysis of machine learning (ML) models for early detection of bankruptcy probability among Indian companies operating under the Insolvency and Bankruptcy Code (IBC) of 2016. This study distinguishes itself by leveraging an extensive dataset covering the period from FY 2016 to FY 2022, encompassing 65,583 entries for 7,008 unique corporations, including 257 bankrupt entities. This paper employs various predictive variables, including traditional financial ratios, Altman Z-scores, and comprehensive financial statement data, employing a scenario-based approach over a one-year forecasting horizon. The findings support the notion that ML models, particularly XGBoost, outperform traditional logistic regression models and Altman Z-scores in accurately predicting bankruptcy among Indian corporates. These findings align with the trend in the literature favoring ML models for enhanced predictive power, offering valuable insights for financial institutions and policymakers in India’s corporate landscape.

Predictive Dynamics in Cryptocurrency Trading: Unraveling Behavioral and Psychological Influences

Ananya Hadadi Raghavendra — 2025-04-01

The rapid expansion of cryptocurrency trading has become a defining feature of contemporary financial markets, attracting a constantly growing group of participants, now surpassing 106 million worldwide. This research focuses on the psychological and behavioral foundations of trading behaviors, investigating how individual psychological states and lifestyle choices impact cryptocurrency trading activities. Using Ordinary Least Squares (OLS) regression, we examine the influence of various factors such as Loneliness, Negative Emotions, Fear of Missing Out (FOMO), Socialization, Healthy Lifestyle Habits, Entertainment Spending, and Sense of Achievement on the frequency of cryptocurrency trades. Our study also includes an analysis of gender differences through Levene’s T-test, thereby increasing the depth of our predictive model. The results of this study aim to fill a gap in existing literature by quantifying the degree to which individual psychological profiles and behaviors can predict trading activities, thereby providing detailed insights into the emotional and cognitive dimensions of the digital trading world. This research not only contributes to the field of behavioral finance but also provides a foundation for developing strategic interventions tailored to various trader segments, ultimately fostering a deeper understanding of the complex dynamics that characterize the crypto market’s volatile landscape.

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

Gianmarco Mendiola Colan — 2025-04-01

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.

Blockchain Based Prediction Markets

Patrick Buckley — 2025-04-01

Prediction markets are a form of collective intelligence that leverage market mechanisms to incentivise large numbers of individuals to make forecasts about future uncertain events. Since their origin in the 1980’s, they have been the subject of a small but steady stream of academic research. Proponents suggest that they have several advantages over comparable information aggregation mechanisms such as polls or expert groups. More recently the rise of blockchain, cryptocurrencies and decentralised finance (DeFi) has excited new interest in prediction markets. The characteristics of this triad of technologies has particular resonances with prediction markets. This research identifies the potential impact of blockchain technology on prediction market design and performance with a view to informing a research agenda to investigate those potential impacts.

International Evidence on Managerial Skills of Green Mutual Funds

Lagan Jindal — 2025-04-01

Using an international sample of 60 green funds from 01 January 2010 to 31 December 2022, this study compares the managerial skills of green and matching conventional funds. Additionally, the study separately assesses how fund size and age affect the managerial abilities of both types of funds. The results suggest that fund’s green characteristic impacts managerial skills since green fund managers show better managerial skills than their conventional counterparts. Fund size influences managerial skills as small mutual funds are mainly responsible for the positive stock-selection skills in green and conventional fund managers. This paper also controls the results for the fund age effect and concludes that young and green funds possess better managerial skills than their older and conventional counterparts. The study reflects that green mutual fund, along with other green financial instruments, contribute to the global movement called “Sustainable Finance”, which aims to invest in public and private projects, therefore supporting mitigation and containment of climate change risks.