Abstract: The earnings yield and long-term bond yields have been widely used to predict asset returns. In this paper, I focus on the predictive role of the stock-bond “yield gap” – the difference between the earnings yield and the 10 year Treasury bond yield – also know as the FED model, and which can be interpreted as a long term yield spread of stocks relative to bonds. Conditional on other forecasting variables, the yield gap forecasts positive excess stock returns, both at short and long forecasting horizons, although the forecasting power is greater at the near horizons. On the other hand, the yield gap forecasts negative excess returns for bonds, at both short and long horizons. A VAR variance decomposition for stock market returns, shows that shocks in the yield gap are highly positively correlated with innovations in both future discount-rate and cash flow news, confirming that the spread conveys information about future earnings and returns. An investment strategy based on the forecasting ability of the Yield gap produces higher Sharpe ratios than passive strategies in both the market index and long–term bond. In the context of an equilibrium multifactor ICAPM, the yield gap has some explanatory power over the cross section of stock returns.

Abstract: The Reuters NewsScope Event Indices Project is an integrated framework for incorporating real-time news from the Reuters NewsScope subscription service into systematic investment and risk-management protocols. The framework consists of a set of real-time event indices— each one taking on numerical values between 0 and 100—designed to capture the occurrence of unusual events of a particular kind. For example, the Macro index measures the real-time quantity of macroeconomic news, and the NatDist index measures the real-time quantity of natural-disaster news. Each index is constructed by applying disciplined pattern-recognition algorithms to real-time newsfeeds, and calibrated using econometric methods applied to historical data. In this first release, we construct indices that are calibrated to foreign exchange markets, and future releases will focus on other markets. In this paper, we describe the procedures for constructing and validating the Reuters/ AlphaSimplex Event Indices. Section 2 introduces the historical data sets used to calibrate the indices. Section 3 contains the algorithms used to construct the indices. In Section 4, we describe the event-study methodology for validating the indices, and in Section 5 we explore the connection between realized volatility (our metric for market impact) and implied volatility. We conclude in Section 7.