When Machines Trade on Corporate Disclosures: Using Text Analytics for Investment Strategies

30 Pages Posted: 26 Sep 2021 Last revised: 26 Oct 2021

See all articles by Hans Christian Schmitz

Hans Christian Schmitz

Deka Investment GmbH

Bernhard Lutz

University of Freiburg

Dominik Wolff

Deka Investment GmbH; Darmstadt University of Technology; Frankfurt University of Applied Sciences

Dirk Neumann

University of Freiburg

Date Written: August 24, 2021

Abstract

In this study, we evaluate several trading strategies based on the textual content of corporate disclosures using machine learning. To obtain a conservative estimate of profitability, we require orders to be placed for the close price of the current trading day and stocks must exhibit high liquidity to ensure proper order execution. Our evaluation based on 354,992 form 8-K filings and 10,204 ad hoc announcements shows that the proposed trading strategies yield up to 7.81% and 9.34% out-of-sample annualized return. More importantly, we find that the prevalent approach in the literature of estimating the stock market reaction of a disclosure based on the closing price of the past trading and omitting liquidity filters substantially overestimates profitability. We also provide useful insights for practitioners by describing feature importance to shed light onto how the machine learning models arrive at decisions.

Keywords: Finance, investment strategies, machine learning, text mining, decision support

Suggested Citation

Schmitz, Hans Christian and Lutz, Bernhard and Wolff, Dominik and Wolff, Dominik and Neumann, Dirk, When Machines Trade on Corporate Disclosures: Using Text Analytics for Investment Strategies (August 24, 2021). Available at SSRN: https://ssrn.com/abstract=3910451 or http://dx.doi.org/10.2139/ssrn.3910451

Hans Christian Schmitz (Contact Author)

Deka Investment GmbH ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325
Germany

Bernhard Lutz

University of Freiburg ( email )

Fahnenbergplatz
Freiburg, D-79085
Germany

Dominik Wolff

Deka Investment GmbH ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325
Germany

Darmstadt University of Technology

Hochschulstraße 1
S1|02 40
Darmstadt, Hessen D-64289
Germany

Frankfurt University of Applied Sciences ( email )

Nibelungenplatz 1
Frankfurt / Main, 60318
Germany

Dirk Neumann

University of Freiburg ( email )

Albert-Ludwigs-Universität Freiburg, Wirtscha.inf.
Kollegiengebäude II, Platz der Alten Synagoge
Freiburg im Breisgau, 79098
Germany

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