The Time-Varying Relation between Stock Returns and Monetary Variables

The nature of the relation between stock returns and the three monetary variables of interest rates (bond yields), inflation and money supply growth, while oft studied, is one that remains unclear. We argue that the nature of the relation changes over time, and this variation is largely driven by shocks, with a change in risk associated with each variable shifting the pattern of behaviour. We show a change in the correlation between each of the three variables with stock returns. Notably, a predominantly negative correlation with bond yields and inflation becomes positive, while the opposite is true for money supply growth. The shift begins with the bursting of the dotcom bubble but is exacerbated by the financial crisis. Results of predictive regressions for stock returns also indicate a switch in behaviour. Predominantly negative predictive power switches temporarily to positive around economic shocks. This suggests that higher yields, inflation and money growth typically depress returns but support the market during periods of stress. However, after the financial crisis, higher inflation and money growth exhibit persistent positive predictive power and suggest a change in the risk perception of higher values.

Option informativeness before earnings announcements and under real activity manipulation

Purpose This paper aims to investigate whether single-name options trading prior to earnings announcements is more informative when there exist real activity manipulations. Design/methodology/approach Using 5,419 earnings announcements during 2004–2018 made by 208 public US companies with relatively high options volumes ranked by the CBOE, the authors uncover two regularities using predictive regressions for stock return. Findings First, the total options volume up to twenty days pre-announcement is significantly higher than that in other periods only for earnings management firms; moreover, after detailing options characteristics, the authors find these intensive pre-announcement trading to be concentrated in transactions of in-the-money call and long-term maturity put options. Second, an increase in the single-name call minus put options volume can positively predict the underlying stock’s next-day excess return much better in real earnings management firms, with a larger magnitude of effect in periods right before regular earnings announcement dates. Originality/value This paper makes a marginal and novel contribution by showing that real earnings management can serve as a proxy for the potential profit from informed trading in options as the return predictability of options volume becomes stronger for firms that have the manipulation motive and indeed perform manipulative actions.

Financial distress, corporate takeovers and the distress anomaly

PurposeThis paper examines the relation between takeover likelihood and the documented underperformance of distressed company stocks while exploring two competing hypotheses. The failure risk explanation predicts lower returns to distressed firms with high probability of being acquired because the acquisition reduces risk and investors' required return. Conversely, the agency conflicts explanation predicts lower returns when acquisition is unlikely.Design/methodology/approachThe likelihood of receiving a takeover bid is estimated, and portfolio tests explore the underperformance of distressed company stocks relative to non-distressed stocks across varying levels of takeover likelihood. Predictive regressions subsequently examine the relation between distress, takeover exposure and future firm operating performance including how the relation is affected by state anti-takeover laws.FindingsDistressed stocks underperform non-distressed company stocks by economically and statistically significant margins when takeover likelihood is low, yet there is no evidence of underperformance among distressed stocks with moderate or high takeover exposure. Consistent with agency conflicts playing a key role, distressed firms that are disciplined by takeover threats invest more, use more leverage and experience higher future profitability. State-level anti-takeover legislation limits this disciplinary effect, however.Originality/valueThe results show that the well-documented distress anomaly is driven by a subset of distressed firms whose managers face limited pressure from the external takeover market. The evidence casts doubt on the failure risk explanation and suggests that agency conflicts play a key role.

LEAST SQUARES AND IVX LIMIT THEORY IN SYSTEMS OF PREDICTIVE REGRESSIONS WITH GARCH INNOVATIONS

The paper examines the effect of conditional heteroskedasticity on least squares inference in stochastic regression models of unknown integration order and proposes an inference procedure that is robust to models within the (near) I(0)–(near) I(1) range with GARCH innovations. We show that a regressor signal of exact order $O_{p}\left ( n\kappa _{n}\right ) $ for arbitrary $\,\kappa _{n}\rightarrow \infty $ is sufficient to eliminate stationary GARCH effects from the limit distributions of least squares based estimators and self-normalized test statistics. The above order dominates the $O_{p}\left ( n\right ) $ signal of stationary regressors but may be dominated by the $O_{p}\left ( n^{2}\right ) $ signal of I(1) regressors, thereby showing that least squares invariance to GARCH effects is not an exclusively I(1) phenomenon but extends to processes with persistence degree arbitrarily close to stationarity. The theory validates standard inference for self normalized test statistics based on the ordinary least squares estimator when $\kappa _{n}\rightarrow \infty $ and $\kappa _{n}/n\rightarrow 0$ and the IVX estimator (Phillips and Magdalinos (2009a), Econometric Inference in the Vicinity of Unity. Working paper, Singapore Management University; Kostakis, Magdalinos, and Stamatogiannis, 2015a, Review of Financial Studies 28(5), 1506–1553.) when $\kappa _{n}\rightarrow \infty $ and the innovation sequence of the system is a covariance stationary vec-GARCH process. An adjusted version of the IVX–Wald test is shown to also accommodate GARCH effects in purely stationary regressors, thereby extending the procedure’s validity over the entire (near) I(0)–(near) I(1) range of regressors under conditional heteroskedasticity in the innovations. It is hoped that the wide range of applicability of this adjusted IVX–Wald test, established in Theorem 4.4, presents an advantage for the procedure’s suitability as a tool for applied research.

Unifying inference for semiparametric regression

In the literature, a discrepancy in the limiting distributions of least square estimators between the stationary and nonstationary cases exists in various regression models with different persistence level regressors. This hinders further statistical inference since one has to decide which distribution should be used next. In this paper, we develop a semiparametric partially linear regression model with stationary and nonstationary regressors to attenuate this difficulty and propose a unifying inference procedure for the coefficients. To be specific, we propose a profile weighted estimation equation method that facilitates the unifying inference. The proposed method is applied to the predictive regressions of stock returns, and an empirical likelihood procedure is developed to test the predictability. It is shown that the Wilks theorem holds for the empirical likelihood ratio regardless of predictors being stationary or not, which provides a unifying method for constructing confidence regions of the coefficients of state variables. Simulations show that the proposed method works well and has favorable finite sample performance over some existing approaches. An empirical application examining the predictability of equity returns highlights the value of our methodology.