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Summary

The Anatomy of Trading Algorithms
Author(s):
Tyler Beason, Arizona State University
Sunil Wahal, Arizona State University
Discussant(s):
Gideon Saar, Cornell University
Abstract:

Beason and Wahal study the anatomy of four widely used institutional trading algorithms representing $675 billion in demand from 961 institutions between 2012 and 2016. Parent orders generate hundreds of child orders which strategically employ price, time-in-force, and display priority rules to navigate the tradeoff between the desire to trade and minimizing transaction costs. Child orders incur price impact at the time they are submitted to the book regardless of whether or not they are (ex post) filled, and even when passively priced relative to the prevailing quote. The intra-parent distribution of child orders is non-random, generating strategic runs which oscillate between the aggressive or passive side of the spread. Despite algorithmic attempts to reduce their influence, programmatic child-level price, time-in-force, and display choices aggregate up to parent-level trading costs borne by investors.

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The Good, the Bad, and the Ugly: How Algorithmic Traders Impact Institutional Trading Costs
Author(s):
Talis Putnins, University of Technology, Sydney
Joseph Barbara, Australian Securities and Investments Commission
Discussant(s):
Charles M. Jones, Columbia University
Abstract:

Putnins and Barbara show that behind the aggregate effects of algorithmic and high-frequency traders (AT/HFT) is substantial heterogeneity in how individual algorithms impact institutional trading costs. Using unique trader-identified regulatory data, they find that the cluster of "harmful" algorithmic traders doubles institutional trading costs. "Beneficial" algorithmic traders offset much of this increase. The researchers find no evidence that speed (e.g., being an HFT) is a characteristic of harmful traders. Traders that hold inventory overnight are more likely to benefit institutional investors by providing more sustained liquidity. The heterogeneity explains why AT/HFT appear detrimental to some investors despite being beneficial or benign in aggregate.

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The Value of Differing Points of View: Evidence from Financial Analysts' Geographic Diversity
Author(s):
William C. Gerken, University of Kentucky
Marcus O. Painter, Saint Louis University
Discussant(s):
Christina Zhu, University of Pennsylvania
Abstract:

Gerken and Painter show that analysts incorporate geographically dispersed information about firms into individual forecasts and that limited analyst geographic diversity adversely affects consensus forecasts and firm liquidity. Using satellite imagery of U.S. retailers' parking lots, they find analysts shade their own forecast in the direction of local car counts relative to other analysts covering the same firm at the same time but from different locations. Examining all industries, the researchers find firms with more geographically concentrated analyst coverage have higher consensus forecast errors and are less liquid. Evidence from shocks in geographic coverage due to brokerage closures suggest these relations are causal.

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Imprecise and Informative: Lessons from Market Reactions to Imprecise Disclosure
Author(s):
J. Anthony Cookson, University of Colorado
Katie Moon, University of Colorado
Joonki Noh, Case Western Reserve University
Discussant(s):
Kathleen Weiss Hanley, Lehigh University
Abstract:

Imprecise language in corporate disclosures can convey valuable information on firms' fundamentals during uncertain times. To evaluate this idea, Cookson, Moon, and Noh develop a novel measure of linguistic imprecision based on sentences marked with the "weasel tag" on Wikipedia. For a 10-week window following the 10-K disclosure, the researchers find that the use of imprecise language in 10-Ks predicts 1) positive and non-reverting abnormal returns, 2) improvements to stock liquidity, 3) greater intensities of insider and informed buying, and 4) higher news sentiment. These findings are the strongest when the firm disclosures are more forward looking, and for firms with greater idiosyncratic volatility. Taken together, their findings imply that the imprecise language in 10-Ks contains new information on positive but yet immature prospects of future cash flow.

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Stock-Specific Price Discovery from ETFs
Author(s):
Thomas Ernst, University of Maryland
Discussant(s):
Maureen O'Hara, Cornell University
Abstract:

Conventional wisdom warns that exchange-traded funds (ETFs) harm stock price discovery, either by "stealing" single-stock liquidity or forcing stock prices to co-move. Contra this belief, Ernst develops a theoretical model and present empirical evidence which demonstrate that investors with stock-specific information trade both single stocks and ETFs. Single-stock investors can access ETF liquidity by means of this tandem trading, and stock prices can flexibly adjust to ETF price movements. Using high-resolution data on SPDR and the Sector SPDR ETFs, he exploits exchange latencies in order to show that investors place simultaneous, same-direction trades in both a stock and ETF. Consistent with my model predictions, effects are strongest when an individual stock has a large weight in the ETF and a large stock-specific informational asymmetry. Ernst concludes that ETFs can provide single-stock price discovery.

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Who Benefits from Robo-advising? Evidence from Machine Learning
Author(s):
Alberto G. Rossi, Georgetown University
Stephen Utkus, University of Pennsylvania
Discussant(s):
Tarun Ramadorai, Imperial College London
Abstract:

Rossi and Utkus study the effects of a large U.S. hybrid robo-adviser on the portfolios of previously selfdirected investors. Across all investors, robo-advising reduces idiosyncratic risk by lowering the holdings of individual stocks and active mutual funds and raising exposure to low-cost indexed mutual funds. It further eliminates investors' home bias and increases investors' overall risk-adjusted performance, mainly by lowering investors' portfolio risk. The researchers use a machine learning algorithm, known as Boosted Regression Trees (BRT), to explain the cross-sectional variation in the effects of advice on portfolio allocations and performance. Finally, they study the determinants of investors' sign-up and attrition.

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True Cost of Immediacy
Author(s):
Terrence Hendershott, University of California at Berkeley
Dan Li, Federal Reserve Board
Dmitry Livdan, University of California at Berkeley
Norman Schurhoff, University of Lausanne
Discussant(s):
Hendrik Bessembinder, Arizona State University
Abstract:

Traditional liquidity measures can provide a false impression of the liquidity and stability of financial market trading. Using data on auctions (bids wanted in competition; BWICs) from the collateralized loan obligation (CLO) market, we show that a standard measure of liquidity, the effective bid-ask spread, dramatically underestimates the true cost of immediacy because it does not account for failed attempts to trade. The true cost of immediacy is substantially higher than the observed costs for successful BWICs. This cost gap is higher in lower-rated CLOs and stressful market conditions when failure rates exceed 50%. Across our 2012-2020 sample period for trades in senior CLOs, the observed cost is four basis points (bps) while the true cost of immediacy is 13bps. In stressful periods, such as the COVID-19 pandemic, for junior tranches the observed cost of trading increases from an average of 12bps to 25bps while the true cost of immediacy increases from less than 3% to almost 15%.

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How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI
Author(s):
Sean Cao, Georgia State University
Wei Jiang, Columbia University and NBER
Baozhong Yang, Georgia State University
Alan L. Zhang, Florida International University
Discussant(s):
Lauren Cohen, Harvard University and NBER
Abstract:

In this paper, Cao, Jiang, Yang, and Zhang analyze how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Their findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing. Moreover, firms with high expected machine downloads manage textual sentiment and audio emotion in ways catered to machine and AI readers, such as by differentially avoiding words that are perceived as negative by computational algorithms as compared to those by human readers, and by exhibiting speech emotion favored by machine learning software processors. The publication of Loughran and McDonald (2011) is instrumental in attributing the change in the measured sentiment to machine and AI readership. While existing research has explored how investors and researchers apply machine learning and computational tools to quantify qualitative information from disclosure and news, this study is the first to identify and analyze the feedback effect on corporate disclosure decisions, i.e., how companies adjust the way they talk knowing that machines are listening.

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Vestigial Tails? Floor Brokers at the Close in Modern Electronic Markets
Author(s):
Dermot Murphy, University of Illinois at Chicago
Edwin Hu, New York University
Discussant(s):
Joel Hasbrouck, New York University
Abstract:

The closing auction is the most important event of the trading day, now accounting for over 10% of trading volume. The NYSE closing auction design is highly advantageous to NYSE floor brokers, who have near-exclusive auction access from 3:50pm to 4:00pm. Murphy and Hu show that closing auction quality, as measured by the accuracy of closing auction information feeds and efficiency of closing prices, is significantly worse on NYSE than Nasdaq. However, closing auction quality improved when NYSE halted floor trading during the COVID-19 pandemic. Their findings highlight the tradeoffs associated with designing a single-price call auction that accepts orders during regular trading hours.

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Life Cycles of Firm Disclosures
Author(s):
AJ Yuan Chen, University of Southern California
Gerard Hoberg, University of Southern California
Vojislav Maksimovic, University of Maryland
Discussant(s):
René M. Stulz, The Ohio State University and NBER
Abstract:

Chen, Hoberg, and Maksimovic propose that the product life cycle is important in understanding the firm's disclosure policy and test this hypothesis using a 4-dimensional text-based life cycle model. Mature-stage life cycle firms disclose more, consistent with an outward-focused investment strategy that lowers search costs for finding synergistic alliance partners. Early-stage life cycle firms are secretive, consistent with inward-focused organic investment and mitigating competitive threats. These results obtain across disclosure measures relating to intellectual property, redaction of contracts, and readability. A quasi-natural experiment based on waves of rapid depreciation of protected intellectual property, and analysis of pairwise co-search of peer filings on the SEC EDGAR website, reinforce this interpretation.

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Does Big Data Improve Financial Forecasting? The Horizon Effect
Author(s):
Laurent Fresard, University of Lugano and SFI
Thierry Foucault, HEC School of Management
Olivier Dessaint, INSEAD
Discussant(s):
Laura Veldkamp, Columbia University and NBER
Abstract:

Fresard, Foucault, and Dessaint study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts forecast short-term and long-term earnings and choose how much information to process about each horizon to minimize forecasting error, net of information processing costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information processing strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. The researchers provide empirical support for this prediction using a large sample of forecasts at various horizons and novel measures of analysts' exposure to abundant data. Data abundance can thus impair the quality of long-term financial forecasts.

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Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases
Author(s):
Jules H. van Binsbergen, University of Pennsylvania and NBER
Xiao Han, University of Edinburgh
Alejandro Lopez-Lira, University of Florida
Discussant(s):
Bryan T. Kelly, Yale University and NBER
Abstract:

van Binsbergen, Han, and Lopez-Lira use machine learning to construct a statistically optimal and unbiased benchmark for firms' earnings expectations. They show that analyst expectations are on average biased upwards, and that this bias exhibits substantial time-series and cross-sectional variation. On average, the bias increases in the forecast horizon, and analysts revise their expectations downwards as earnings announcement dates approach. The researchers find that analysts' biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies consist of firms for which the analysts' forecasts are excessively optimistic relative to our benchmark. Managers of companies with the greatest upward biased earnings forecasts are more likely to issue stocks.

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This paper was distributed as Working Paper 27843, where an updated version may be available.

Participants

Senay Agca, George Washington University
James J. Angel, Georgetown University
Meghana Ayyagari, George Washington University
Markus Baldauf, University of British Columbia
Snehal Banerjee, University of California at San Diego
Adam Baybutt, UCLA
Tyler Beason, Arizona State University
Hendrik Bessembinder, Arizona State University
Maxime Bonelli, HEC Paris
Jonathan Brogaard, University of Utah
Celso Brunetti, Federal Reserve Board
AJ Yuan Chen, University of Southern California
Michel Crouhy
Victor DeMiguel, London Business School
Olivier Dessaint, INSEAD
Thomas Ernst, University of Maryland
Laurent Fresard, University of Lugano and SFI
Michael Goldstein, Babson College
Kathleen Weiss Hanley, Lehigh University
Hans G. Heidle, U.S. Securities and Exchange Commission
Edwin Hu, New York University
John Hund, University of Georgia
Paul Irvine, Texas Christian University
Pankaj Jain, University of Memphis
Kose John, New York University
Kevin Khang, Vanguard
Laura Kodres, Massachusetts Institute of Technology
Dan Li, Federal Reserve Board
Marc Lipson, University of Virginia
Bradford Lynch, University of Pennsylvania
Katya Malinova, McMaster University
David Modest, QLS Partners
Joshua Mollner, Northwestern University
Pamela Moulton, Cornell University
Dmitriy Muravyev, Michigan State University
Dermot Murphy, University of Illinois at Chicago
Giang Nguyen, Pennsylvania State University
Joonki Noh, Case Western Reserve University
Shawn O'Donoghue, FINRA
Mark Paddrik, Office of Financial Research
Marcus O. Painter, Saint Louis University
Andreas Park, University of Toronto
Christine Parlour, University of California at Berkeley
Paolo Pasquariello, University of Michigan
Neil Pearson, University of Illinois at Urbana-Champaign
Talis Putnins, University of Technology, Sydney
Adam Reed, University of North Carolina at Chapel Hill
Matthew Ringgenberg, University of Utah
Ryan Riordan, Smith School of Business, Queens University
Zac Rolnik, Now Publishers
Tavy Ronen, Rutgers University
Alberto G. Rossi, Georgetown University
Asani Sarkar, Federal Reserve Bank of New York
Norman Schurhoff, University of Lausanne
Andriy Shkilko, Wilfrid Laurier University
Elvira Sojli, University of New South Wales
Wing W. Tham, University of New South Wales
Stathis Tompaidis, University of Texas at Austin
Tugkan Tuzun, Federal Reserve Board
Liyan Yang, University of Toronto
Chen Yao, The Chinese University of Hong Kong
Gaiyan Zhang, University of Missouri
Christina Zhu, University of Pennsylvania
Wei Zhu, University of Illinois at Urbana-Champaign
Marius A. Zoican, University of Toronto

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