Regulatory Arbitrage or Random Errors? Implications of Race Prediction Algorithms in Fair Lending Analysis
Journal of Financial Economics, 2024

Greenwald, Daniel L.; Howell, Sabrina T.; Li, Cangyuan; Yimfor, Emmanuel
When race is not directly observed, regulators and analysts commonly predict it using algorithms based on last name and address. In small business lending--where regulators assess fair lending law compliance using the Bayesian Improved Surname Geocoding (BISG) algorithm--we document large prediction errors among Black Americans. The errors bias measured racial disparities in loan approval rates downward by 43%, with greater bias for traditional vs. fintech lenders. Regulation using self-identified race would increase lending to Black borrowers, but also shift lending toward affluent areas because errors correlate with socioeconomics. Overall, using race proxies in policymaking and research presents challenges.

Missing Values Handling for Machine Learning Portfolios
Journal of Financial Economics, 2024

Chen, Andrew Y.; McCoy, Jack
We characterize the structure and origins of missingness for 159 cross-sectional return predictors and study missing value handling for portfolios constructed using machine learning. Simply imputing with cross-sectional means performs well compared to rigorous expectation-maximization methods. This stems from three facts about predictor data: (1) missingness occurs in large blocks organized by time, (2) cross-sectional correlations are small, and (3) missingness tends to occur in blocks organized by the underlying data source. As a result, observed data provide little information about missing data. Sophisticated imputations introduce estimation noise that can lead to underperformance if machine learning is not carefully applied.

Charting by Machines
Journal of Financial Economics, 2024

Murray, Scott; Xia, Yusen; Xiao, Houping
We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal, and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out-of-sample.

Artificial Intelligence, Firm Growth, and Product Innovation
Journal of Financial Economics, 2024

Babina, Tania; Fedyk, Anastassia; He, Alex; Hodson, James
We study the use and economic impact of AI technologies. We propose a new measure of firm-level AI investments using employee resumes. Our measure reveals a stark increase in AI investments across sectors. AI-investing firms experience higher growth in sales, employment, and market valuations. This growth comes primarily through increased product innovation. Our results are robust to instrumenting AI investments using firms' exposure to universities' supply of AI graduates. AI-powered growth concentrates among larger firms and is associated with higher industry concentration. Our results highlight that new technologies like AI can contribute to growth and superstar firms through product innovation.

Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha
Journal of Financial Economics, 2023

DeMiguel, Victor; Gil-Bazo, Javier; Nogales, Francisco J.; Santos, Andre A. P.
Machine-learning methods exploit fund characteristics to select tradable long-only portfolios of mutual funds that earn significant out-of-sample annual alphas of 2.4% net of all costs. The methods unveil interactions in the relation between fund characteristics and future performance. For instance, past performance is a particularly strong predictor of future performance for more active funds. Machine learning identifies managers whose skill is not sufficiently offset by diseconomies of scale, consistent with informational frictions preventing investors from identifying the outperforming funds. Our findings demonstrate that investors can benefit from active management, but only if they have access to sophisticated prediction methods.

Machine-Learning the Skill of Mutual Fund Managers
Journal of Financial Economics, 2023

Kaniel, Ron; Lin, Zihan; Pelger, Markus; Van Nieuwerburgh, Stijn
We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, before and after fees. The outperformance persists for more than three years. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

The Colour of Finance Words
Journal of Financial Economics, 2023

Garcia, Diego; Hu, Xiaowen; Rohrer, Maximilian
Our paper relies on stock price reactions to colour words, in order to provide new dictionaries of positive and negative words in a finance context. We extend the machine learning algorithm of Taddy (2013), adding a cross-validation layer to avoid over-fitting. In head-to-head comparisons, our dictionaries outperform the standard bag-of-words approach (Loughran and McDonald, 2011) when predicting stock price movements out-of-sample. By comparing their composition, word-by-word, our method refines and expands the sentiment dictionaries in the literature. The breadth of our dictionaries and their ability to disambiguate words using bigrams both help to colour finance discourse better.

Open Banking: Credit Market Competition When Borrowers Own the Data
Journal of Financial Economics, 2023

He, Zhiguo; Huang, Jing; Zhou, Jidong
Open banking facilitates data sharing consented to by customers who generate the data, with the regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks' customer transaction data enables better borrower screening for fintechs. Open banking promotes competition if it helps level the playing field for all lenders in screening borrowers; however, if it over-empowers fintechs, it can also hinder competition and leave all borrowers worse off. Due to the credit quality inference from borrowers' sign-up decisions, this remains true even if borrowers have the control of whether to share their banking data. We also study extensions with fintech affinities and data sharing on borrower preferences.

Measuring the Welfare Cost of Asymmetric Information in Consumer Credit Markets
Journal of Financial Economics, 2022

DeFusco, Anthony A.; Tang, Huan; Yannelis, Constantine
Information asymmetries are known in theory to lead to inefficiently low credit provision, yet empirical estimates of the resulting welfare losses are scarce. This paper leverages a randomized experiment conducted by a large fintech lender to estimate welfare losses arising from asymmetric information in the market for online consumer credit. Building on methods from the insurance literature, we show how exogenous variation in interest rates can be used to estimate borrower demand and lender cost curves and recover implied welfare losses. While asymmetric information generates large equilibrium price distortions, we find only small overall welfare losses, particularly for high-credit-score borrowers.

Can FinTech Reduce Disparities in Access to Finance? Evidence from the Paycheck Protection Program
Journal of Financial Economics, 2022

Erel, Isil; Liebersohn, Jack
New technology promises to expand the supply of financial services to small businesses poorly served by banks. Does it succeed? We study the response of FinTech to financial services demand created by the introduction of the Paycheck Protection Program. FinTech is disproportionately used in ZIP codes with fewer bank branches, lower incomes, and more minority households, and in industries with fewer banking relationships. It is also greater in counties where the economic effects of the COVID-19 pandemic were more severe. Substitution between FinTech and banks is economically small, implying that FinTech mostly expands, rather than redistributes, the supply of financial services.

Machine Learning in the Chinese Stock Market
Journal of Financial Economics, 2022

Leippold, Markus; Wang, Qian; Zhou, Wenyu
We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of return prediction factors using various machine learning algorithms. Contrasting previous studies for the US market, liquidity emerges as the most important predictor, leading us to closely examine the impact of transaction costs. The retail investors' dominating presence positively affects short-term predictability, particularly for small stocks. Another feature that distinguishes the Chinese market from the US market is the high predictability of large stocks and state-owned enterprises over longer horizons. The out-of-sample performance remains economically significant after transaction costs.

A Picture Is Worth a Thousand Words: Measuring Investor Sentiment by Combining Machine Learning and Photos from News
Journal of Financial Economics, 2022

Obaid, Khaled; Pukthuanthong, Kuntara
By applying machine learning to the accurate and cost-effective classification of photos based on sentiment, we introduce a daily market-level investor sentiment index (Photo Pessimism) obtained from a large sample of news photos. Consistent with behavioral models, Photo Pessimism predicts market return reversals and trading volume. The relation is strongest among stocks with high limits to arbitrage and during periods of elevated fear. We examine whether Photo Pessimism and pessimism embedded in news text act as complements or substitutes for each other in predicting stock returns and find evidence that the two are substitutes.

Price Revelation from Insider Trading: Evidence from Hacked Earnings News
Journal of Financial Economics, 2022

Akey, Pat; Gregoire, Vincent; Martineau, Charles
From 2010 to 2015, a group of traders illegally accessed earnings information before their public release by hacking several news wire services. We use this scheme as a natural experiment to investigate how informed investors select among private signals and how efficiently financial markets incorporate private information contained in trades into prices. We construct a measure of qualitative information using machine learning and find that the hackers traded on both qualitative and quantitative signals. The hackers' trading caused 15% more of the earnings news to be incorporated in prices before their public release. Liquidity providers responded to the hackers' trades by widening spreads.

Consumer-Lending Discrimination in the FinTech Era
Journal of Financial Economics, 2022

Bartlett, Robert; Morse, Adair; Stanton, Richard; Wallace, Nancy
U.S. fair-lending law prohibits lenders from making credit determinations that disparately affect minority borrowers if those determinations are based on characteristics unrelated to creditworthiness. Using an identification under this rule, we show risk-equivalent Latinx/Black borrowers pay significantly higher interest rates on GSE-securitized and FHA-insured loans, particularly in high-minority-share neighborhoods. We estimate these rate differences cost minority borrowers over $450 million yearly. FinTech lenders' rate disparities were similar to those of non-Fintech lenders for GSE mortgages, but lower for FHA mortgages issued in 2009-2015 and for FHA refi mortgages issued in 2018-2019.

Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks
Journal of Financial Economics, 2018

Buchak, Greg; Matvos, Gregor; Piskorski, Tomasz; Seru, Amit
Shadow bank market share in residential mortgage origination nearly doubled from 2007 to 2015, with particularly dramatic growth among online "fintech" lenders. We study how two forces, regulatory differences and technological advantages, contributed to this growth. Difference in difference tests exploiting geographical heterogeneity induced by four specific increases in regulatory burden-capital requirements, mortgage servicing rights, mortgage-related lawsuits, and the movement of supervision to Office of Comptroller and Currency following closure of the Office of Thrift Supervision--all reveal that traditional banks contracted in markets where they faced more regulatory constraints; shadow banks partially filled these gaps. Relative to other shadow banks, fintech lenders serve more creditworthy borrowers and are more active in the refinancing market. Fintech lenders charge a premium of 14-16 basis points and appear to provide convenience rather than cost savings to borrowers. They seem to use different information to set interest rates relative to other lenders. A quantitative model of mortgage lending suggests that regulation accounts for roughly 60% of shadow bank growth, while technology accounts for roughly 30%.