Quantitative research analysts (Quants) produce in-depth quantitative and econometric modeling of market anomalies to assist sell-side analysts and institutional clients with stock selection strategies. Quants are associated with more efficient analyst forecasting behavior on anomaly predictors...

Sell-side analysts change their stock recommendations when their valuations differ from the market's. These valuation differences can arise from either differences in earnings estimates or the non-earnings components of valuation methodologies. We find that recommendation changes motivated by...

Our study provides evidence on the share price reactions to the announcement of equity issues in Germany, where capital market is characterized by institutional features distinct from the U.S. market. German seasoned equity issues yield a positive market reaction which contrasts to the...

This paper examines short sales transaction volumes on the first trading day of 610 initial public offerings (IPOs) from 2011 to 2015. The tests provide evidence of informed trading immediately at the IPO. Results reveal that short selling volume on the first trading day of the IPO is...

Following the Global Settlement, analysts extensively use a top pick designation to highlight their highest conviction best ideas. Such a designation enables analysts to provide greater granularity of information, but it can potentially be influenced by conflicts of interest. Examining a...

We analyze the market reaction to the sentiment of the CEO speech at the Annual General Meeting (AGM). As the AGM is typically preceded by several information disclosures, the CEO speech may be expected to contribute only marginally to investors' decision-making. Surprisingly, however, we...

Long-short anomaly returns are strongly related to the day of the week. Anomalies for which the speculative leg is the short (long) leg experience the highest (lowest) returns on Monday. The opposite pattern is observed on Fridays. The effects are large; Monday (Friday) alone accounts for over...

The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The...

We develop an agent-based financial market model in which agents follow technical and fundamental trading rules to determine their speculative investment positions. A central feature of our model is that we consider direct interactions between speculators due to which they may decide to change...

This paper is concerned with empirical and theoretical basis of the Efficient Market Hypothesis (EMH). The paper begins with an overview of the statistical properties of asset returns at different frequencies (daily, weekly and monthly), and considers the evidence on return predictability, risk...