| Title | Journal | Date | Author | Abstract | Link |
|---|
| Informed Trading Intensity | Journal of Finance | 20240401 | Bogousslavsky, Vincent; Fos, Vyacheslav; Muravyev, Dmitriy | We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data-driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure. | View Infographic |
| The Virtue of Complexity in Return Prediction | Journal of Finance | 20240201 | Kelly, Bryan; Malamud, Semyon; Zhou, Kangying | Much of the extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning. | View Infographic |
| (Re-)imag(in)ing Price Trends | Journal of Finance | 20231201 | Jiang, Jingwen; Kelly, Bryan; Xiu, Dacheng | We reconsider trend-based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock-level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short-term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets. | View Infographic |
| Firm-Level Climate Change Exposure | Journal of Finance | 20230601 | Sautner, Zacharias; Van Lent, Laurence; Vilkov, Grigory; Zhang, Ruishen | We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets. | View Infographic |
| Biased Auctioneers | Journal of Finance | 20230401 | Aubry, Mathieu; Kraussl, Roman; Manso, Gustavo; Spaenjers, Christophe | We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors. | View Infographic |
| Do Municipal Bond Dealers Give Their Customers 'Fair and Reasonable' Pricing? | Journal of Finance | 20230401 | Griffin, John M.; Hirschey, Nicholas; Kruger, Samuel | Municipal bonds exhibit considerable retail pricing variation, even for same-size trades of the same bond on the same day, and even from the same dealer. Markups vary widely across dealers. Trading strongly clusters on eighth price increments, and clustered trades exhibit higher markups. Yields are often lowered to just above salient numbers. Machine learning estimates exploiting the richness of the data show that dealers that use strategic pricing have systematically higher markups. Recent Municipal Securities Rulemaking Board rules have had only a limited impact on markups. While a subset of dealers focus on best execution, many dealers appear focused on opportunistic pricing. | View Infographic |
| Anomalies and the Expected Market Return | Journal of Finance | 20220201 | Dong, Xi; Li, Yan; Rapach, David E.; Zhou, Guofu | We provide the first systematic evidence on the link between long-short anomaly portfolio returns--a cornerstone of the cross-sectional literature--and the time-series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that long-short anomaly portfolio returns evince statistically and economically significant out-of-sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence. | View Infographic |
| Predictably Unequal? The Effects of Machine Learning on Credit Markets | Journal of Finance | 20220201 | Fuster, Andreas; Goldsmith-Pinkham, Paul; Ramadorai, Tarun; Walther, Ansgar | Innovations in statistical technology in functions including credit-screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility. | View Infographic |
| Missing Values Handling for Machine Learning Portfolios | Journal of Financial Economics | 20240501 | 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. | View Infographic |
| Charting by Machines | Journal of Financial Economics | 20240301 | 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. | View Infographic |
| Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha | Journal of Financial Economics | 20231201 | 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. | View Infographic |
| Machine-Learning the Skill of Mutual Fund Managers | Journal of Financial Economics | 20231001 | 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. | View Infographic |
| The Colour of Finance Words | Journal of Financial Economics | 20230301 | 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. | View Infographic |
| Machine Learning in the Chinese Stock Market | Journal of Financial Economics | 20220801 | 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. | View Infographic |
| A Picture Is Worth a Thousand Words: Measuring Investor Sentiment by Combining Machine Learning and Photos from News | Journal of Financial Economics | 20220401 | 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. | View Infographic |
| Price Revelation from Insider Trading: Evidence from Hacked Earnings News | Journal of Financial Economics | 20220301 | 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. | View Infographic |
| The Partisanship of Financial Regulators | Review of Financial Studies | 20231101 | Engelberg, Joseph; Henriksson, Matthew; Manela, Asaf; Williams, Jared | We analyze the partisanship of Commissioners at the SEC and Governors at the Federal Reserve Board. Using recent advances in machine learning, we identify partisan phrases in Congress, such as "red tape" and "climate change," and observe their usage among regulators. Although the Fed has remained relatively nonpartisan throughout our sample period (1930-2019), we find that partisanship among SEC Commissioners rose to an all-time high during the 2010-2019 period, driven by more-partisan Commissioners replacing less-partisan ones. Partisanship at the SEC appears in both the language of new SEC rules and the voting behavior of SEC Commissioners. | View Infographic |
| Option Return Predictability with Machine Learning and Big Data | Review of Financial Studies | 20230901 | Bali, Turan G.; Beckmeyer, Heiner; Morke, Mathis; Weigert, Florian | Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. | View Infographic |
| Man versus Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases | Review of Financial Studies | 20230601 | van Binsbergen, Jules H.; Han, Xiao; Lopez-Lira, Alejandro | We introduce a real-time measure of conditional biases to firms' earnings forecasts. The measure is defined as the difference between analysts' expectations and a statistically optimal unbiased machine-learning benchmark. Analysts' conditional expectations are, on average, biased upward, a bias that increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings forecasts. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly used linear earnings models do not work out-of-sample and are inferior to those analysts provide. | View Infographic |
| Credit Building or Credit Crumbling? A Credit Builder Loan's Effects on Consumer Behavior and Market Efficiency in the United States | Review of Financial Studies | 20230401 | Burke, Jeremy; Jamison, Julian; Karlan, Dean; Mihaly, Kata; Zinman, Jonathan | A randomized encouragement design yields null average effects of a credit builder loan (CBL) on consumer credit scores. But machine learning algorithms indicate the nulls are due to stark, offsetting treatment effects depending on baseline installment credit activity. Delinquency on preexisting loan obligations drives the negative effects, suggesting that adding a CBL overextends some consumers and generates negative externalities on other lenders. More favorably for the market, CBL take-up generates positive selection on score improvements. Simple changes to CBL practice, particularly to provider screening and credit bureau reporting, could ameliorate the negative effects for consumers and the market. | View Infographic |
| The Party Structure of Mutual Funds | Review of Financial Studies | 20220601 | Bubb, Ryan; Catan, Emiliano M. | We investigate the structure of mutual funds' corporate governance preferences as revealed by how they vote their shares in portfolio companies. We apply unsupervised learning tools from the machine learning literature to analyze mutual funds' votes and find that a parsimonious two-dimensional model can explain the bulk of mutual fund voting. The dimensions capture competing visions of corporate governance and are related to the leading proxy advisors' recommendations. Cluster analysis shows that mutual funds are organized into three "parties"--the Traditional Governance Party, Shareholder Reform Party, and Shareholder Protest Party--that follow distinctive philosophies of corporate governance and shareholders' role. | View Infographic |
| The Use and Misuse of Patent Data: Issues for Finance and Beyond | Review of Financial Studies | 20220601 | Lerner, Josh; Seru, Amit | Patents and citations are powerful tools increasingly used in financial economics (and management research more broadly) to understand innovation. Biases may result, however, from the interactions between the truncation of patents and citations and the changing composition of inventors. When aggregated at the firm level, these patent and citation biases can survive popular adjustment methods and are correlated with firm characteristics. These issues can lead to problematic inferences. We provide an actionable checklist to avoid biased inferences and also suggest machine learning as a potential new way to address these problems. | View Infographic |
| Thousands of Alpha Tests | Review of Financial Studies | 20210701 | Giglio, Stefano; Liao, Yuan; Xiu, Dacheng | Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation. | View Infographic |
| Measuring Corporate Culture Using Machine Learning | Review of Financial Studies | 20210701 | Li, Kai; Mai, Feng; Shen, Rui; Yan, Xinyan | We create a culture dictionary using one of the latest machine learning techniques--the word embedding model--and 209,480 earnings call transcripts. We score the five corporate cultural values of innovation, integrity, quality, respect, and teamwork for 62,664 firm-year observations over the period 2001-2018. We show that an innovative culture is broader than the usual measures of corporate innovation--R&D expenses and the number of patents. Moreover, we show that corporate culture correlates with business outcomes, including operational efficiency, risk-taking, earnings management, executive compensation design, firm value, and deal making, and that the culture-performance link is more pronounced in bad times. Finally, we present suggestive evidence that corporate culture is shaped by major corporate events, such as mergers and acquisitions. | View Infographic |
| Selecting Directors Using Machine Learning | Review of Financial Studies | 20210701 | Erel, Isil; Stern, Lea H.; Tan, Chenhao; Weisbach, Michael S. | Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted by algorithms to perform poorly indeed do perform poorly compared to a realistic pool of candidates in out-of-sample tests. Predictably bad directors are more likely to be male, accumulate more directorships, and have larger networks than the directors the algorithm would recommend in their place. Companies with weaker governance structures are more likely to nominate them. Our results suggest that machine learning holds promise for understanding the process by which governance structures are chosen and has potential to help real-world firms improve their governance. | View Infographic |
| Microstructure in the Machine Age | Review of Financial Studies | 20210701 | Easley, David; Lopez de Prado, Marcos; O'Hara, Maureen; Zhang, Zhibai | Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes. | View Infographic |
| Bond Risk Premiums with Machine Learning | Review of Financial Studies | 20210201 | Bianchi, Daniele; Buchner, Matthias; Tamoni, Andrea | We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels. | View Infographic |
| Empirical Asset Pricing via Machine Learning | Review of Financial Studies | 20200501 | Gu, Shihao; Kelly, Bryan; Xiu, Dacheng | We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility. | View Infographic |
| Artificial Intelligence, Education, and Entrepreneurship | Journal of Finance | 20240201 | Gofman, Michael; Jin, Zhao | We document an unprecedented brain drain of Artificial Intelligence (AI) professors from universities from 2004 to 2018. We find that students from the affected universities establish fewer AI startups and raise less funding. The brain-drain effect is significant for tenured professors, professors from top universities, and deep-learning professors. Additional evidence suggests that unobserved city- and university-level shocks are unlikely to drive our results. We consider several economic channels for the findings. The most consistent explanation is that professors' departures reduce startup founders' AI knowledge, which we find is an important factor for successful startup formation and fundraising. | View Infographic |
| Artificial Intelligence, Firm Growth, and Product Innovation | Journal of Financial Economics | 20240101 | 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. | View Infographic |
| Goal Setting and Saving in the FinTech Era | Journal of Finance | 20240601 | Gargano, Antonio; Rossi, Alberto G. | We study the effectiveness of saving goals in increasing individuals' savings using data from a Fintech app. Using a difference-in-differences identification strategy that randomly assigns users into a group of beta testers who can set goals and a group of users who cannot, we find that setting goals increases individuals' savings rate. The increased savings within the app do not reduce savings outside the app. Moreover, goal setting helps those individuals previously identified as having the lowest propensity to save. Matching App user survey responses to their behavior highlights the relative merits of monitoring and concreteness channels in explaining our findings. | View Infographic |
| Lender Automation and Racial Disparities in Credit Access | Journal of Finance | 20240401 | Howell, Sabrina T.; Kuchler, Theresa; Snitkof, David; Stroebel, Johannes; Wong, Jun | Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision making. We document these findings in the context of the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black-owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black-owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, preexisting banking relationships, firm performance, or fraud rates. | View Infographic |
| Did FinTech Lenders Facilitate PPP Fraud? | Journal of Finance | 20230601 | Griffin, John M.; Kruger, Samuel; Mahajan, Prateek | In the $793 billion Paycheck Protection Program, we examine metrics related to potential misreporting including nonregistered businesses, multiple businesses at residential addresses, abnormally high implied compensation per employee, and large inconsistencies with jobs reported in another government program. These measures consistently concentrate in certain FinTech lenders and are cross-verified by seven additional measures. FinTech market share increased significantly over time, and suspicious lending by FinTechs in 2021 is four times the level at the start of the program. Suspicious loans are being overwhelmingly forgiven at rates similar to other loans. | View Infographic |
| Attention-Induced Trading and Returns: Evidence from Robinhood Users | Journal of Finance | 20221201 | Barber, Brad M.; Huang, Xing; Odean, Terrance; Schwarz, Christopher | We study the influence of financial innovation by fintech brokerages on individual investors' trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention-induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high-attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it is also driven in part by the app's unique features. Consistent with models of attention-induced trading, intense buying by Robinhood users forecasts negative returns. Average 20-day abnormal returns are -4.7% for the top stocks purchased each day. | View Infographic |
| Regulatory Arbitrage or Random Errors? Implications of Race Prediction Algorithms in Fair Lending Analysis | Journal of Financial Economics | 20240701 | 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. | View Infographic |
| Open Banking: Credit Market Competition When Borrowers Own the Data | Journal of Financial Economics | 20230201 | 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. | View Infographic |
| Measuring the Welfare Cost of Asymmetric Information in Consumer Credit Markets | Journal of Financial Economics | 20221201 | 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. | View Infographic |
| Can FinTech Reduce Disparities in Access to Finance? Evidence from the Paycheck Protection Program | Journal of Financial Economics | 20221001 | 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. | View Infographic |
| Consumer-Lending Discrimination in the FinTech Era | Journal of Financial Economics | 20220101 | 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. | View Infographic |
| Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks | Journal of Financial Economics | 20181201 | 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%. | View Infographic |
| When FinTech Competes for Payment Flows | Review of Financial Studies | 20221101 | Parlour, Christine A.; Rajan, Uday; Zhu, Haoxiang | We study the impact of FinTech competition in payment services when a monopolist bank uses payment data to learn about consumers' credit quality. Competition from FinTech payment providers disrupts this information spillover. The bank's price for payment services and its loan offers are affected. FinTech competition promotes financial inclusion, may hurt consumers with a strong bank preference, and has an ambiguous effect on the loan market. Both FinTech data sales and consumer data portability increase bank lending, but the effects on consumer welfare are ambiguous. Under mild conditions, consumer welfare is higher under data sales than with data portability. | View Infographic |
| Small Bank Lending in the Era of Fintech and Shadow Banks: A Sideshow? | Review of Financial Studies | 20221101 | Begley, Taylor A.; Srinivasan, Kandarp | Amid the emerging dominance of nonbanks, small banks use key financing advantages to persist in the mortgage market. We provide evidence of the heterogeneous impact of two shocks to the supply of mortgage credit: postcrisis regulatory burden and GSE financing cost changes. Small banks exploit regulation disproportionately affecting the largest four banks (Big4) and their ability to lend on balance sheet to strongly substitute for the retreating Big4. The erasure of guarantee fee (g-fee) discounts for large lenders facilitates small bank growth in GSE lending. Small banks also grow balance sheet loans in areas more exposed to g-fee hikes. | View Infographic |
| The Rise of Finance Companies and FinTech Lenders in Small Business Lending | Review of Financial Studies | 20221101 | Gopal, Manasa; Schnabl, Philipp | We document that finance companies and FinTech lenders increased lending to small businesses after the 2008 financial crisis. We show that most of the increase substituted for a reduction in bank lending. In counties in which banks had a larger market share before the crisis, finance companies and FinTech lenders increased their lending more. We find no effect of reduced bank lending on employment, wages, and new business creation by 2016. Our results suggest that finance companies and FinTech lenders are major suppliers of credit to small businesses and played an important role in the recovery from the 2008 financial crisis. | View Infographic |
| Regressive Mortgage Credit Redistribution in the Post-crisis Era | Review of Financial Studies | 20220101 | D'Acunto, Francesco; Rossi, Alberto G. | We document four secular trends about U.S. mortgage origination by traditional and FinTech lenders after the 2008-2009 financial crisis. First, since 2011, the overall number, size, and approval rate of small and medium-sized loans have been decreasing over time, relative to large loans. Second, the largest lenders redistribute their lending the most. Third, this loan-size redistribution of credit increases in the size of the lender. Fourth, the effects are stronger for mortgages further away from the conforming loan limit(s) in both directions. We argue that the supply of credit drives these secular trends, and we assess several potential economic mechanisms. | View Infographic |
| Fintech Borrowers: Lax Screening or Cream-Skimming? | Review of Financial Studies | 20211001 | Di Maggio, Marco; Yao, Vincent | We study the personal credit market using unique individual-level data covering fintech and traditional lenders. We show that fintech lenders acquire market share by lending first to higher-risk borrowers and then to safer borrowers, and rely mainly on hard information to make credit decisions. Fintech borrowers are significantly more likely to default than neighbor individuals with the same characteristics borrowing from traditional financial institutions. Furthermore, they tend to experience a short-lived reduction in the cost of credit, because their indebtedness increases more than non-fintech borrowers after loan origination. However, fintech lenders' pricing strategies are likely to take this into account. | View Infographic |
| On the Rise of FinTechs: Credit Scoring Using Digital Footprints | Review of Financial Studies | 20200701 | Berg, Tobias; Burg, Valentin; Gombovic, Ana; Puri, Manju | We analyze the information content of a digital footprint--that is, information that users leave online simply by accessing or registering on a Web site--for predicting consumer default. We show that even simple, easily accessible variables from a digital footprint match the information content of credit bureau scores. A digital footprint complements rather than substitutes for credit bureau information and affects access to credit and reduces default rates. We discuss the implications for financial intermediaries' business models, access to credit for the unbanked, and the behavior of consumers, firms, and regulators in the digital sphere. | View Infographic |
| How Valuable Is FinTech Innovation? | Review of Financial Studies | 20190501 | Chen, Mark A.; Wu, Qinxi; Yang, Baozhong | We provide large-scale evidence on the occurrence and value of FinTech innovation. Using data on patent filings from 2003 to 2017, we apply machine learning to identify and classify innovations by their underlying technologies. We find that most FinTech innovations yield substantial value to innovators, with blockchain being particularly valuable. For the overall financial sector, internet of things (IoT), robo-advising, and blockchain are the most valuable innovation types. Innovations affect financial industries more negatively when they involve disruptive technologies from nonfinancial startups, but market leaders that invest heavily in their own innovation can avoid much of the negative value effect. | View Infographic |
| To FinTech and Beyond | Review of Financial Studies | 20190501 | Goldstein, Itay; Jiang, Wei; Karolyi, G. Andrew | FinTech is about the introduction of new technologies into the financial sector, and it is now revolutionizing the financial industry. In 2017, when the academic finance community was not actively researching FinTech, the editorial team of the Review of Financial Studies launched a competition to develop research proposals focused on this topic. This special issue is the result. In this introductory article, we describe the recent FinTech phenomenon and the novel editorial protocol employed for this special issue following the Registered Reports format. We discuss what we learned from the submitted proposals about the field of FinTech and which ones we selected to be completed and ultimately come out in this special issue. We also provide several observations to help guide future research in the emerging area of FinTech. | View Infographic |
| The Role of Technology in Mortgage Lending | Review of Financial Studies | 20190501 | Fuster, Andreas; Plosser, Matthew; Schnabl, Philipp; Vickery, James | Technology-based ("FinTech") lenders increased their market share of U.S. mortgage lending from 2% to 8% from 2010 to 2016. Using loan-level data on mortgage applications and originations, we show that FinTech lenders process mortgage applications 20% faster than other lenders, controlling for observable characteristics. Faster processing does not come at the cost of higher defaults. FinTech lenders adjust supply more elastically than do other lenders in response to exogenous mortgage demand shocks. In areas with more FinTech lending, borrowers refinance more, especially when it is in their interest. We find no evidence that FinTech lenders target borrowers with low access to finance. | View Infographic |
