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Summary

Demand for Information, Uncertainty, and the Response of U.S. Treasury Securities to News
Author(s):
Hedi Benamar, Federal Reserve Board
Thierry Foucault, HEC School of Management
Clara Vega, Federal Reserve Board
Discussant(s):
Michael J. Fleming, Federal Reserve Bank of New York
Abstract:

Benamar, Foucault, and Vega conjecture that an increase in investors' information demand about an asset signals that their perceived uncertainty about the value of this asset has increased. One implication is that an increase in investors' demand for information should be predictive of a stronger role of news in price discovery. Consistent with this hypothesis, the researchers find that the impact of non-farm payroll news on U.S. Treasury note futures more than doubles when information demand (measured by the number of people reading related news) is high before the release of the announcement.

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Consumer-Lending Discrimination in the FinTech Era
Author(s):
Robert P. Bartlett, University of California at Berkeley
Adair Morse, University of California, Berkeley
Richard Stanton, University of California at Berkeley
Nancy Wallace, University of California at Berkeley
Discussant(s):
Manju Puri, Duke University and NBER
Abstract:

Ethnic discrimination in lending can occur in face-to-face decisions or in algorithmic scoring. The GSEs' model for pricing credit risk provides us with an identified setting to estimate discrimination for FinTech and face-to-face lenders, as well as to offer a workable enforcement interpretation of U.S. fair -lending laws using the court's justification of legitimate business necessity. Bartlett, Morse, Stanton, and Wallace find that face-to-face and FinTech lenders charge Latinx/African-American borrowers 6-9 basis points higher interest rates for purchase mortgages, consistent with the extraction of monopoly rents in weaker competitive environments and from profiling borrowers on shopping behavior. In aggregate, Latinx/African-American pay $750M per year in extra mortgage interest. FinTech algorithms have not removed discrimination, but two silver linings emerge. Algorithmic lending seems to have increased competition or encouraged more shopping with the ease of applications. Also, while face-to-face lenders discriminate against minorities in application rejection, there are reasons to believe FinTechs may discriminate less.

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Selecting Directors Using Machine Learning
Author(s):
Isil Erel, The Ohio State University and NBER
Léa H. Stern, University of Washington
Chenhao Tan, University of Colorado, Boulder
Michael S. Weisbach, The Ohio State University and NBER
Discussant(s):
Luigi Zingales, University of Chicago and NBER
Abstract:

Can an algorithm assist firms in their nominating decisions of corporate directors? Erel, Stern, Tan, and Weisbach construct algorithms tasked with making out-of-sample predictions of director performance. They run tests of the quality of these predictions and show that directors predicted to do poorly indeed do poorly compared to a realistic pool of candidates. Predictably unpopular directors are more likely to be male, have held more directorships, have fewer qualifications, and larger networks than the directors the algorithm recommends. Machine learning holds promise for understanding the process by which governance structures are chosen, and has potential to help firms improve their governance.

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This paper was distributed as Working Paper 24435, where an updated version may be available.

Predicting Returns with Text Data
Author(s):
Zheng Tracy Ke, Harvard University
Bryan T. Kelly, Yale University and NBER
Dacheng Xiu, University of Chicago
Discussant(s):
Tim Loughran, University of Notre Dame
Institutional Order Handling and Broker-Affiliated Trading Venues
Author(s):
Amber Anand, Syracuse University
Mehrdad Samadi, Southern Methodist University
Jonathan Sokobin, Financial Industry Regulatory Authority
Kumar Venkataraman, Southern Methodist University
Discussant(s):
Gideon Saar, Cornell University
Abstract:

Using detailed order handling data over the life of 330 million institutional orders, Anand, Samadi, Sokobin, and Venkataraman study whether order routing by brokers to Alternative Trading Systems (ATSs) that they own affects execution quality. In a multivariate regression specification that controls for stock attributes, order characteristics and market conditions, orders handled by brokers with high affiliated ATS routing are associated with lower fill rates. Trading costs based on the implementation shortfall approach are higher when clients select a broker with high affiliated ATS routing. Broker outcomes are highly persistent suggesting that improved disclosures on order handling could help institutional clients with broker selection.

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Microstructure in the Machine Age
Author(s):
David Easley, Cornell University
Maureen O'Hara, Cornell University
Zhibai Zhang, NYU Tandon
Discussant(s):
Joel Hasbrouck, New York University
Abstract:

Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. Easley, Lopez de Prado, O'Hara, and Zhang demonstrate how a machine learning algorithm can be applied to microstructural research. They find that simple microstructure measures designed to reflect frictions in a simpler market continue to provide insights into the process of price adjustment. The researchers find that some of these microstructure features with apparent high explanatory power can exhibit low predictive power, and vice versa. They also find that some microstructurebased measures are useful for out-of-sample prediction of various market statistics, leading to questions about the efficiency of markets. The results are derived using 87 of the most liquid futures contracts across all asset classes.

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Stock Compensation and Employee Attention
Author(s):
Bo Cowgill, Columbia University
Eric Zitzewitz, Dartmouth College and NBER
Discussant(s):
Antoinette Schoar, Massachusetts Institute of Technology and NBER
Abstract:

Cowgill and Zitzewitz show that daily stock price movements affect the mood, effort level, and decision making of employees. Positive current-day stock returns are accompanied by greater reported economic confidence and job satisfaction, shorter working hours, more optimistically biased beliefs about firm performance, tougher grading of innovative ideas, and tougher evaluation of interviewees. These effects are very short lived, lasting one or two business days. The effects on mood and many types of behavior are larger for employees with larger prior stock and option grants. The researchers show that the short-term effects of (plausibly exogenous) shock to moods is the opposite sign of cross-sectional correlations. Whereas happier employees in the cross section perform better and are more lenient evaluators, shocks that increase happiness longitudinally are accompanied by lower work effort and tougher evaluation.

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Background Noise? TV Advertising Affects Real Time Investor Behavior
Author(s):
Jura Liaukonyte, Cornell University
Alminas Zaldokas, Hong Kong University of Science and Technology
Discussant(s):
Lauren Cohen, Harvard University and NBER
Abstract:

Using minute-by-minute television advertising data covering approximately 326, 000 ads, 301 firms, and $20 billion in ad spending, Liaukonyte and Zaldokas study the real-time effects of TV advertising on investor search for online financial information and subsequent trading activity. The researchers identification strategy exploits the fact that viewers in different U.S. time zones are exposed to the same programming and national advertising at different times, allowing to control for contemporaneous confounding events. The researchers find that an average TV ad leads to a 3% increase in SEC EDGAR queries and an 8% increase in Google searches for financial information within 15 minutes of the airing of that ad. Such advertising effects spill over through horizontal and vertical product market links to financial information searches on closest rivals and suppliers. The ad-induced queries on the advertiser and its key rival lead to higher trading volumes of their respective stocks. For large advertisers, around 0.8% of daily trading volume can directly be attributed to advertising. This suggests that advertising, originally intended for consumers, has a sizable effect on financial markets.

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High-Frequency Analysis of Financial Stability
Author(s):
Michael Gofman, University of Rochester
Sajjad Jafri, Queen's University
James T. Chapman, Bank of Canada
Discussant(s):
Antoine Martin, Federal Reserve Bank of New York
Abstract:

Gofman, Jafri, and Chapman study empirically efficiency and stability trade off in the design of large value payment systems using $500 trillion CAD of intraday transaction level data from Canadian Large Value Transfer System (LVTS). They develop measures of systemic risk and apply these measures to millions of LVTS payments during 2001-2014. LVTS showed stress during 2007-2009. The main source of fragility of the system are binding collateral and credit constraints that cause delays and rejections of payments. Unprecedented injection of liquidity by the Bank of Canada prevented a spillover of systemic risk to global systemically important payment and settlement systems.

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Participants

James F. Albertus, Carnegie Mellon University
Ayeh Bandeh-Ahmadi
Jonathan Brogaard, University of Utah
Dave Chung, U.S. Department of the Treasury
David Cimon, Bank of Canada
Michael J. Fleming, Federal Reserve Bank of New York
Michael Gofman, University of Rochester
Michael Goldstein, Babson College
Edwin Hu, New York University
Sajjad Jafri, Queen's University
Pankaj Jain, University of Memphis
Abby Kim, U.S. Securities and Exchange Commission
Badrinath Kottimukkalur, George Washington University
Jura Liaukonyte, Cornell University
Tim Loughran, University of Notre Dame
Charles Martineau, University of Toronto
Dmitriy Muravyev, Michigan State University
Jordan Nickerson, Massachusetts Institute of Technology
Shawn O'Donoghue, FINRA
Esen Onur, CFTC
Matthew Ringgenberg, University of Utah
John Ritter, U.S. Securities and Exchange Commission
John S. Roberts, CFTC
Zac Rolnik, Now Publishers
Tavy Ronen, Rutgers University
Renee Tang, Department of the Treasury
Wing W. Tham, University of New South Wales
Chen Yao, The Chinese University of Hong Kong
Alminas Zaldokas, Hong Kong University of Science and Technology
Xing Zhou, Federal Reserve Board
Wei Zhu, University of Illinois at Urbana-Champaign

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