This paper studies the impact of productivity on the flow of financial resources to and from firms. To do this we use machine learning methods (Lasso, XGBoost) to derive a new measure of firm productivity using standard corporate accounts. Output is sales revenue and we find that the key inputs...

There is considerable evidence that machine learning algorithms have better predictive abilities than humans in various financial settings. But, the literature has not tested whether these algorithmic predictions are more rational than human predictions. We study the predictions of corporate...

This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in the credit markets. The empirical setting exploits the outburst of the Wells Fargo scandal as a negative shock to the trust in banks. Using a difference-in-differences framework,...

Using a nationwide restaurant sample, we document that minority-owned businesses are more likely to apply to the Paycheck Protection Program through fintech than traditional lenders. We motivate our analysis by a two-sided matching model where this phenomenon can be generated through "technology...

This paper examines the effect of legal bankruptcy protection on small businesses. Using granular agricultural microdata, we use a regression discontinuity design to exploit farm eligibility for a unique bankruptcy code for farms known as Chapter 12. We find that farmers that qualify for this...

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers...