ESSAYS IN CORPORATE FINANCE AND BEHAVIORAL FINANCE

Abstract

This thesis examines important topics in corporate cash holdings and forecaster overconfidence. First, I provide an in-depth study of the interaction between intra-industry contagion risk and cash holdings. I develop a novel measure of a firm’s exposure to contagion risk that builds on the firm’s stock return comovement with other industry participants and separates contagion and competition effects caused by the expected financial distress of its industry peers. I show that high contagion-risk firms hold more cash because they face higher costs of external finance due to the potential decrease in their collateral values and the increased likelihood of their future financial distress caused by the net contagion effect. Second, in a co-authored paper with Drs. Jiaping Qiu and Chi Wan, we conduct a cross-country analysis to examine how financial development affects the reliance of corporate liquidity management on tangible capital. We find that financial development and better institutions lower the cash-tangibility sensitivity. This supports the view that financial development reduces the collateral role of tangible assets, thereby relaxing financial constraints of firms with low asset tangibility. This provides further firm-level evidence and sheds new light on the link between financial development and economic growth, as financial development promotes more efficient allocations of economic resources and hence facilitates investments and economic growth. Third, in a co-authored paper with Drs. Richard Deaves and Michael Schröder, we document using the ZEW panel of German stock market forecasters that weak forecasters tend to be overconfident in the sense that they provide extreme forecasts and their confidence intervals are less likely to contain eventual realizations. Moderate filters based on forecast accuracy over short rolling windows are somewhat successful in improving predictability. While poor performance can be due to various factors, a filter based on a prior tendency to provide extreme forecasts also improves predictability.