SUPPORTED BY UNIGESTION “ Information versus Investment

Firms both make long term investments and reveal information about performance. These activities serve crucial roles in the economy and capital markets, yet they are in direct conflict in the presence of realistic managerial incentives to smooth reported performance. To gauge the quantitative importance of this tradeoff, we estimate a dynamic model that captures the tradeoff between investment efficiency and information accuracy. The model matches a range of observable moments constructed from data on firm investment and periods of detected misreporting by firms. Managers in our model distort reported profits on average by 3% of sales. Counterfactual analysis reveals that eliminating this misreporting through disclosure regulation is possible, but doing so incentivizes managers to distort real investment. The result would be around 1% lower firm value on average, reflecting a quantitatively meaningfully tradeoff. PRELIMINARY DRAFT PLEASE DO NOT QUOTE ANY SPECIFIC RESULTS WITHOUT PERMISSION ∗Terry is at Boston University. Whited is at the University of Michigan, Ross School of Business and the NBER. Zakolyukina is at the University of Chicago, Booth School of Business. A previous version of this paper circulated as “Information Distortion, R&D, and Growth.” We thank Clark Hyde and Hossein Pourreza for outstanding research support. We would also like to thank our discussants Johan Hombert and Massimiliano Croce, as well as seminar participants at the 2017 Stanford Theory and Inference in Capital Market Research Conference, the 2018 SED meetings, Harvard Business School, University of Maryland, University of Minnesota, Stockholm School of Economics, Bocconi University, Wirtschaftsuniversität Wien, Boston College, Boston University, Imperial College London, the London School of Economics, Erasmus University Rotterdam, the 2018 Banque de France Investment Conference, and the 2018 Tepper-LAEF Macro-Finance Conference for helpful comments. This work would have been impossible without the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. Zakolyukina acknowledges financial support from the Fama-Miller Center for Research in Finance and the IBM Corporation Faculty Research Fund and the University of Chicago Booth School of Business, as well all research support from the University of Chicago Research Computing Center.