The paper investigates the impact of corporate sustainability on asset prices. For that purpose, we develop a novel corporate sustainability factor and test the extent to which this factor is priced in an augmented four-factor version of the traditional Fama & French (1993) asset pricing model. The corporate sustainability factor is based on a zero-investment portfolio which is long in stocks with high sustainability and short in stocks with low sustainability. We use data on the Brazilian stock market to estimate alternative model specifications with different combinations of four explanatory variables: the corporate sustainability premium, the market risk factor premium, the size factor premium and the book-to-market factor premium. Our results indicate that corporate sustainability is priced and helps to explain the variability in the cross-section of expected stock returns.
We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8–2.0%, and over 80% of the models yield returns equal to or larger than our linearly constructed baseline factor. For the best performing models, the risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.
We propose a novel method to forecast corporate earnings, which combines the accuracy of analysts' forecasts with the unbiasedness of a cross-sectional model. We build on recent insights from the earnings forecasts literature to improve analysts' forecasts in two ways: reducing their sluggishness with respect to information in recent stock price movements and improving their long-term performance. Our model outperforms the most popular methods from the literature in terms of forecast accuracy, bias, and earnings response coefficient. Furthermore, using our estimates in the implied cost of capital calculation leads to a substantially stronger correlation with realized returns compared to earnings estimates from extant cross-sectional models.
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