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FinTechs and the Market for Financial Analysis

Published online by Cambridge University Press:  11 September 2020

Jillian Grennan*
Affiliation:
Duke University Fuqua School of Business
Roni Michaely
Affiliation:
University of Geneva and Swiss Finance [email protected]
*
[email protected] (corresponding author)

Abstract

Hundreds of equity market intelligence financial technology firms (FinTechs) have formed in the last decade. We assemble novel data to describe their capabilities, users, and consequences. Our data suggest that these FinTechs i) aggregate many data sources, including nontraditional ones (e.g., Twitter, blogs), and synthesize such data using artificial intelligence to make investment recommendations, and ii) change Internet users’ information discovery by serving as substitutes for traditional information providers. We evaluate some nontraditional data and find evidence suggesting that such data contain valuable information or “crowd wisdom” that links to informational efficiency. Overall, our findings are consistent with this innovation benefiting investors and markets.

Type
Research Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We thank Brad Barber, Andriy Bodnaruk, Sudheer Chava, Zhi Da (the referee), Itay Goldstein, Jerry Hoberg, Harrison Hong, Russel Jame, Qian Jun, Adair Morse, Marina Niessner, Nagpurnanand Prabhala, David Robinson, and Paola Sapienza for helpful comments as well as seminar participants at the American Finance Association (AFA) Meeting, the Review of Financial Studies (RFS) FinTech Conference, the Swiss Conference on FinTech, the Credit and the Future of Banking Conference, the Federal Reserve Bank of Chicago, the Federal Reserve Bank of Philadelphia, the Bank of Ireland, Northeastern University, the University of Miami, the University of Washington, Temple University, the University of Utah, and Duke University. We thank William Song for excellent research assistance. Some of the data used in this study come from TipRanks, a firm in which Michaely has an equity interest and serves on the Board of Directors.

References

Amihud, Y.Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.” Journal of Financial Markets, 5 (2002), 3156.CrossRefGoogle Scholar
Angrist, J.; Lavy, V.; and Schlosser, A.. “Multiple Experiments for the Causal Link between the Quantity and Quality of Children.” Journal of Labor Economics, 28 (2010), 773824.CrossRefGoogle Scholar
Antweiler, W., and Frank, M. Z.. “Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards.” Journal of Finance, 59 (2004), 12591294.CrossRefGoogle Scholar
Asquith, P. A.; Mikhail, M. B.; and Au, A. S.. “Information Content of Equity Analyst Reports.” Journal of Financial Economics, 75 (2005), 245282.CrossRefGoogle Scholar
Bai, J.; Philippon, T.; and Savov, A.. “Have Financial Markets Become More Informative?Journal of Financial Economics, 122 (2016), 625654.CrossRefGoogle Scholar
Banerjee, S.; Davis, J.; and Gondhi, N.. “When Transparency Improves, Must Prices Reflect Fundamentals Better?Review of Financial Studies, 31 (2018), 23772414.CrossRefGoogle Scholar
Barber, B.; Lehavy, R.; McNichols, M.; and Trueman, B.. “Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns.” Journal of Finance, 56 (2001), 531563.CrossRefGoogle Scholar
Berg, T.; Burgand, V.; Puri, M.; and Gombović, A.. “On the Rise of FinTechs: Credit Scoring Using Digital Footprints.” Review of Financial Studies, 33 (2020), 28452897.CrossRefGoogle Scholar
Boudoukh, J.; Feldman, R.; Kogan, S.; and Richardson, M.. “Information, Trading, and Volatility: Evidence from Firm-Specific News.” Review of Financial Studies, 32 (2018), 9921033.CrossRefGoogle Scholar
Bradley, D.; Clarke, J.; and Zeng, L.. “The Speed of Information and the Sell-Side Research Industry.” Journal of Financial and Quantitative Analysis, 55 (2020), 14671490.CrossRefGoogle Scholar
Célérier, C., and Vallée, B.. “Catering to Investors through Security Design: Headline Rate and Complexity.” Quarterly Journal of Economics, 132 (2017), 14691508.CrossRefGoogle Scholar
Chen, H.; De, P.; Hu, Y.; and Hwang, B.-H.. “Wisdom of Crowds: The Value of Stock Opinions Transmitted through Social Media.” Review of Financial Studies, 27 (2014), 13671403.CrossRefGoogle Scholar
Chen, Q.; Goldstein, I.; and Jiang, W.. “Price Informativeness and Investment Sensitivity to Stock Price.” Review of Financial Studies, 20 (2007), 619650.CrossRefGoogle Scholar
Coleman, B.; Merkley, K. J.; and Pacelli, J.. “Man versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations.” Working Paper, available at https://ssrn.com/abstract=3514879 (2020).CrossRefGoogle Scholar
Cong, L. W.; Liang, T.; and Zhang, X.. “Textual Factors: A Scalable, Interpretable, and Data-Driven Approach to Analyzing Unstructured Information.” Working Paper, available at https://ssrn.com/abstract=3307057 (2020).Google Scholar
Conley, T. G.; Hansen, C. B.; and Rossi, P. E.. “Plausibly Exogenous.” Review of Economics and Statistics, 94 (2012), 260272.CrossRefGoogle Scholar
Cookson, A. J., and Niessner, M.. “Why Don’t We Agree? Evidence from a Social Network of Investors.” Journal of Finance, 75 (2020), 173228.CrossRefGoogle Scholar
Da, Z., and Huang, X.. “Harnessing the Wisdom of Crowds.” Management Science, 66 (2020), 18471867.CrossRefGoogle Scholar
Das, S. R., and Chen, M. Y.. “Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web.” Management Science, 53 (2007), 13751388.CrossRefGoogle Scholar
Detweiler, G. “20 Financial Bloggers Share Their Secrets to Running a Successful Blog.” Forbes, November 2 (2019).Google Scholar
Dugast, J., and Foucault, T.. “Data Abundance and Asset Price Informativeness.” Journal of Financial Economics, 130 (2018), 367391.CrossRefGoogle Scholar
Durnev, A.; Morck, R.; and Yeung, B.. “Value-Enhancing Capital Budgeting and Firm-Specific Stock Return Variation.” Journal of Finance, 59 (2004), 65105.CrossRefGoogle Scholar
Durnev, A.; Morck, R.; Yeung, B.; and Zarowin, P.. “Does Greater Firm-Specific Return Variation Mean More or Less Informed Stock Pricing?Journal of Accounting Research, 41 (2003), 797836.CrossRefGoogle Scholar
Easley, D.; Engle, R.; O’Hara, M.; and Wu, L.. “Time-Varying Arrival Rates of Informed and Uninformed Trades.” Journal of Financial Econometrics, 6 (2008), 171207.CrossRefGoogle Scholar
Easley, D.; Kiefer, N. M.; O’Hara, M.; and Paperman, J. B.. “Liquidity, Information, and Infrequently Traded Stocks.” Journal of Finance, 51 (1996), 14051436.CrossRefGoogle Scholar
Efron, B.; Hastie, T.; Johnstone, I.; and Tibshirani, R.. “Least Angle Regression.” Annals of Statistics, 32 (2004), 407499.CrossRefGoogle Scholar
Farboodi, M.; Matray, A.; and Veldkamp, L.. “Where Has All the Big Data Gone?” Working Paper, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3164360 (2020).CrossRefGoogle Scholar
Farrell, M.; Green, T. C.; Jame, R.; and Markov, S.. “The Democratization of Investment Research and the Informativeness of Retail Investor Trading.” Working Paper, available at https://ssrn.com/abstract=3222841 (2019).Google Scholar
Fedyk, A. “Front Page News: The Effect of News Positioning on Financial Markets.” Working Paper, available at https://sites.google.com/berkeley.edu/fedyk/research?authuser=0 (2019).Google Scholar
Ferreira, M. A., and Matos, P.. “The Colors of Investors’ Money: The Role of Institutional Investors around the World.” Journal of Financial Economics, 88 (2008), 499533.CrossRefGoogle Scholar
Frankel, R.; Kothari, S.; and Weber, J.. “Determinants of the Informativeness of Analyst Research.” Journal of Accounting and Economics, 41 (2006), 2954.CrossRefGoogle Scholar
Franks, J. R.; Serrano-Velarde, N.; and Sussman, O.. “Marketplace Lending, Information Aggregation, and Liquidity.” Review of Financial Studies, (2020).CrossRefGoogle Scholar
Grennan, J., and Musto, D.. “Who Benefits from Bond Market Modernization?” Working Paper, available at https://ssrn.com/abstract=2713865 (2020).Google Scholar
Grossman, S. J., and Stiglitz, J. E.. “On the Impossibility of Informationally Efficient Markets.” American Economic Review, 70 (1980), 393408.Google Scholar
Imbens, G. W., and Rubin, D. B.. Causal Inference in Statistics, Social, and Biomedical Sciences., New York, NY: Cambridge University Press (2015).CrossRefGoogle Scholar
Jame, R.; Johnston, R.; Markov, S.; and Wolfe, M. C.. “The Value of Crowdsourced Earnings Forecasts.” Journal of Accounting Research, 54 (2016), 10771110.CrossRefGoogle Scholar
Kerr, W. R.; Nanda, R.; and Rhodes-Kropf, M.. “Entrepreneurship as Experimentation.” Journal of Economic Perspectives, 28 (2014), 2548.CrossRefGoogle Scholar
Kippersluis, H. V., and Rietveld, C. A.. “Beyond Plausibly Exogenous.” Journal of Econometrics, 21 (2018), 316331.CrossRefGoogle Scholar
Konnikova, M.How Headlines Change the Way We Think.” New Yorker, December 17 (2014).Google Scholar
Lee, C. M. C., and Ready, M. J.. “Inferring Trade Direction from Intraday Data.” Journal of Finance, 46 (1991), 733746.CrossRefGoogle Scholar
Lehavy, R.; Li, F.; and Merkley, K.. “The Effect of Annual Report Readability on Analyst Following and the Properties of Their Earnings Forecasts.” Accounting Review, 86 (2011), 10871115.CrossRefGoogle Scholar
Loh, R. K., and Mian, G. M.. “Do Accurate Earnings Forecasts Facilitate Superior Investment Recommendations?Journal of Financial Economics, 80 (2006), 455483.CrossRefGoogle Scholar
Loh, R. K., and Stulz, R. M.. “When Are Analyst Recommendation Changes Influential?Review of Financial Studies, 24 (2011), 593627.CrossRefGoogle Scholar
Martin, I., and Nagel, S.. “Market Efficiency in the Age of Big Data.” Working Paper 26586, National Bureau of Economic Research (2019).CrossRefGoogle Scholar
Michaely, R.; Rubin, A.; Segal, D.; and Vedrashko, A.. “Lured by the Consensus.” Working Paper, Swiss Finance Institute (2020).Google Scholar
Philippon, T. “The FinTech Opportunity.” Working Paper 22476, National Bureau of Economic Research (2016).CrossRefGoogle Scholar
Robinson, P.Writing a Good Headline Isn’t as Simple as It Sounds.” ACES: The Society for Editing, August 12 (2019).Google Scholar
Roll, R.R 2.” Journal of Finance, 43 (1988), 541566.Google Scholar
Schneider, R. D., and Karmiol, J.. Discover Big Data Today, Hoboken, NJ: John Wiley & Sons (2019).Google Scholar
Stock, J., and Yogo, M., Testing for Weak Instruments in Linear IV Regression, New York, NY: Cambridge University Press (2005).Google Scholar
Tumarkin, R., and Whitelaw, R. F.. “News or Noise? Internet Postings and Stock Prices.” Financial Analysts Journal, 57 (2001), 4151.CrossRefGoogle Scholar
Vallée, B., and Zeng, Y.. “Marketplace Lending: A New Banking Paradigm?Review of Financial Studies, 32 (2019), 19391982.CrossRefGoogle Scholar
Veldkamp, L. Information Choice in Macroeconomics and Finance, Princeton, NJ: Princeton University Press (2011).CrossRefGoogle Scholar
Verrecchia, R. E.Information Acquisition in a Noisy Rational Expectations Economy.” Econometrica, 50 (1982), 14151430.CrossRefGoogle Scholar
Weller, B. M.Does Algorithmic Trading Reduce Information Acquisition?Review of Financial Studies, 31 (2018), 21842226.CrossRefGoogle Scholar
Wolfers, J., and Zitzewitz, E.. “Prediction Markets.” Journal of Economic Perspectives, 18 (2004), 107126.CrossRefGoogle Scholar
Zhu, C.Big Data as a Governance Mechanism.” Review of Financial Studies, 32 (2019), 20212061.CrossRefGoogle Scholar
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