Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 22nd Dec 2024, 05:20:02am CET
What Drives Trading in Financial Markets? A Big Data Perspective
Anton Lines1, Shikun Ke2
1Copenhagen Business School, Denmark; 2Yale University
Discussant: Pedro Tremacoldi-Rossi (Columbia University)
We train deep Bayesian neural networks to mimic the trading activity of a large sample of institutional investors. Our methodology allows us to evaluate the predictive power of hundreds of public information signals with potentially complex non-linear effects on trading, and aggregate them into interpretable categories. Deep learning models predict trading decisions with up to 86% accuracy out-ofsample, with macroeconomic data and market liquidity together accounting for
most (66−91%) of the explained variance. Stock fundamentals, corporate news, and analyst forecasts have comparatively low explanatory power. Our results suggest that differences of opinion about macroeconomic conditions or heterogeneous aggregate hedging needs explain most of the observed institutional trading activity, while stock-specific factors other than liquidity are comparatively unimportant.
How important is corporate governance? Evidence from machine learning
Ian Gow2, David Larcker3, Anastasia Zakolyukina1
1University of Chicago Booth School of Business, United States of America; 2University of Melbourne; 3Stanford University
Discussant: Gerard Hoberg (University of Southern California)
We use machine learning to assess the predictive ability of over a hundred corporate governance features for firm outcomes, including financial-statement restatements, class-action lawsuits, business failures, operating performance, firm value, stock returns, and credit ratings. We discover that adding corporate governance features does not improve the predictive accuracy of models over that of models constructed using only firm characteristics. Our results confirm the challenges in constructing measures of corporate governance with predictive value suggested in prior research. These results also raise doubts about the existence of strong causal effects of corporate governance on firm outcomes studied in prior research.
