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This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.

We examine how the outbreak of the Russia-Ukraine war affected specific stock indices in the Asia-Pacific region, the United States, and Europe. Using event study methodology, we find that the event day’s abnormal returns indicate that the war’s outbreak had a significantly negative impact on most indices. For example, countries like Australia, Germany, Pakistan, India, Singapore, Vietnam, Mongolia, Malaysia, Bangladesh, and the Philippines show significantly negative returns the day after the war’s outbreak. All indices continue to have significantly negative returns in the rest of the post-event window, except Australia, Bangladesh, India, and the Philippines. Our analysis enables us to identify which countries in the Asia Pacific region along with selected markets from the Europe region, the counties directly bordering Ukraine were most vulnerable to war news and when the immediate consequences of the war were better than expected, confirming these cross-sectional implications.