This paper studies the stock market's reaction to aggregate earnings news and better understand the relationship among earnings, stock prices, and discount rates Prior research shows that, for individual firms, stock prices react positively to earnings news but require several quarters to fully...

Prior studies use fundamental earnings forecasts to proxy for the market's expectations of earnings because analyst forecasts are biased and are available for only a subset of firms. We find that as a proxy for market expectations, fundamental forecasts contain systematic measurement errors...

We examine the time variations of expected momentum profits using a two-state Markov switching model with time-varying transition probabilities to evaluate the empirical relevance of recent rational theories of momentum profits. We find that in the expansion state the expected returns of winner...

An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the...

We provide evidence that equity investors with limited attention are slow to incorporate how current oil price changes affect future earnings announcements. A cross-sectional equity trading strategy that exploits this inefficiency yields an annualized Sharpe Ratio of 0.57. Stock prices respond...

The purpose of this paper is to analyse the predictability of earnings information before the quarterly disclosure date. Two categories of firms are contrasted: the firms that announce better quarterly earnings than the prior period and the firms that do not. The paper uses a sample of 67...

I hypothesize that the stock market overreacts to management earnings forecasts because of the uncertainty surrounding them. I find that negative management forecast surprises lead to a –5.9% abnormal return around the forecast and a 1.9% correction in the 2-month period after earnings are...

We apply state-of-the-art financial machine learning to assess the return-predictive value of more than 45,000 earnings announcements on a majority of S&P1500 constituents. To represent the diverse information content of earnings announcements, we generate predictor variables based on various...