We examine the predictive power of the CDS-bond basis for future corporate bond returns. We find that residual basis, the part of the CDS-bond basis that cannot be explained by a wide range of market frictions such as counterparty risk, funding risk, and liquidity risk, strongly negatively...

This paper analyzes whether realized higher moments are able to predict out-of-sample sovereign bond returns using high-frequency data from the European bond market. We study bond return predictability over tranquil and crisis periods and across core and periphery markets at the index and...

Inspired by the recent literature on rare events and their impact on asset prices, we investigate the return predictability properties of a set of variables related to the risk of tail events extracted from equity market information and measures based on credit spreads. Our variables outperform...

Non-fundamental demand shocks have significant effects on asset prices, but observing these shocks is challenging. We use the exchange traded fund (ETF) primary market to study non-fundamental demand. Unique to the ETF market, specialized arbitrageurs called authorized participants correct...

According to the dynamic version of the Gordon growth model, the long-run expected return on stocks, stock yield, is the sum of the dividend yield on stocks plus some weighted average of expected future growth rates in dividends. We construct a measure of stock yield as a model-imposed affine...

We investigate the prediction of excess returns and fundamentals by financial ratios – dividend-price ratio, earnings-price ratio, and book-to-market ratio – by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess...

We show that characteristics known to predict returns to U.S. stocks also predict returns for a broad sample of nearly 52,000 stocks from fifty-eight non-U.S. countries, and we evaluate the extent to which six prominent corporate events, including initial and secondary stock offerings, stock...

Long histories of returns are needed but often lacking when estimating the equity premium. This paper studies stock return predictability from the perspective of a Bayesian investor who has access to international data. Learning across countries arises whenever this investor believes that...

We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty...

This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we...