This paper considers the impact of UK practices with respect to the measurement and disclosure of intangible assets, focusing on RD activities. We first update prior UK work relating RD activities to market prices. Second, given the clearly identified role of disclosure outside of the financial...

We examine the out-of-sample performance of 240 stock market anomalies enhanced by 49 machine learning algorithms and over 260 individually trained models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant...

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...

Under fairly general assumptions, expected stock returns are a linear combination of two accounting fundamentals ― book to market and ROE. Empirical estimates based on this relation predict the cross section of out-of-sample returns in 26 of 29 international equity markets, with a highly...

We provide the first large-scale study of the performance of expected-return proxies (ERPs) internationally. Analyst-forecast-based ICCs are sparsely populated and not robustly associated with future returns. Earnings-model-forecast-based ICCs are well-populated, but are unreliable outside the...

Analysts often update their recommendations following corporate news. Questions have been raised regarding analysts' ability to generate new information beyond recent corporate events. Employing a comprehensive database on corporate news we show that only a small minority of 27.9% of all...

This paper reviews the literature examining how costs of monitoring for, acquiring, and analyzing firm disclosures – collectively, “disclosure processing costs” – affect investor information choices, trades, and market outcomes. The existence of disclosure processing costs means that...

This study examines the effect of option volume relative to stock volume (O/S) on market response to earnings surprises. The market reaction per unit of earnings surprise is lower for firms that have high O/S prior to earnings announcement than for firms with low O/S prior to earnings...

This paper uses the tools of computational linguistics to analyze the qualitative part of annual reports of UK listed companies. More specifically, the frequency of words associated with different language indicators is measured and used to forecast future stock returns. We find that two of...

We examine the impact of Seasonal Affective Disorder (SAD) on financial analysts. We hypothesize and find that analysts are more pessimistic, less precise, and more asymmetric in their boldness in the fall, as indicated by their forecasts of quarterly earnings. The effects are apparent in all...