I exploit unsupervised machine learning and natural language processing techniques to elicit the risk factors that firms themselves identify in their annual reports. I quantify the firms’ exposure to each identified risk, design an econometric test to classify them as either systematic or idiosyncratic, and construct factor mimicking portfolios that proxy for each undiversifiable source of risk.…Read More
The research objective is to determine how quickly product innovation translates into higher stock market valuations. The novelty in the research approach is using online consumer reviews on websites including Amazon, eBay; combined with stock market data.…Read More
Wharton doctoral candidate Alejandro Lopez-Lira used machine learning to analyze companies’ annual reports and assess the top four systematic risks that affect most firms. …Read More
