I study how and why the two major types of business investment, equipment investment and structures investment, are differently linked to stock returns. I empirically show that equipment investment has a significantly stronger predictive power for stock market returns than structures investment,...

An increase in the number of asset pricing models intensifies model uncertainties in assetpricing. While a pure "model selection" (singling out a best model) can result in a loss of usefulinformation, a full “model pooling” may increase the risk of including noisy information.We make a...

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...

We propose that innovative originality is a valuable organizational resource, and that owing to limited investor attention and skepticism of complexity, greater innovative originality may be undervalued. We find that firms' innovative originality strongly predicts higher, more persistent, and...

In the paper, we show a significant economic linkage between analyst EPS forecast skewness and cross section stock returns. The effect on stock return of our skewness measure is quite different from that based on skewness calculated from options or high frequency data. Literature shows that,...

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...

This paper finds evidence that stock returns vary with the physical climate change exposure of firms in a predictable manner. We construct measures of exposures to physical climate changes at the firm level, and find that firms with high climate change exposures experience lower future...

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...

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 combine annual stock market data for the most important equity markets of the last four centuries: the Netherlands/U.K. (1629-1812), U.K. (1813-1870) and U.S. (1871-2015). We show that dividend yields are stationary and consistently forecast returns. The documented predictability holds for...