We revisit the apparent historical success of technical trading rules on daily prices of the DJIA index from 1897 to 2011, and use the False Discovery Rate as a new approach to data snooping. The advantage of the FDR over existing methods is that it selects more outperforming rules which allows...

In the data mining and machine learning fields, forecasting the direction of price change can be generally formulated as a supervised classfii cation. This paper attempts to predict the direction of daily changes of the Nasdaq Composite Index (NCI) and of the Standard & Poor's 500 Composite...

Using a comprehensive sample of trades from Schedule 13D filings by activist investors, we study how measures of adverse selection respond to informed trading. We find that on days when activists accumulate shares, measures of adverse selection and of stock illiquidity are lower, even though...

Illiquidity measures appear to be related to monthly realized returns but do they impact long-run costs of capital (CoC) for firms? Using U.S. data, we find cross-sectional evidence that, controlling for market capitalization, the Amihud (2002) measure of illiquidity is negatively related to CoC...

This chapter surveys theoretical research on the long-term performance of fixed-mix investment strategies. These self-financing strategies rebalance the portfolio over time so as to keep constant the proportions of wealth invested in various assets. The main result is that wealth can be grown...

In this study, we examine the rationale that informed traders use in choosing various financial instruments in order to speculate on the volatility of the underlying asset, here a common stock. Using a continuous-time trading model, we demonstrate that the quality of the private information...

We estimate agents' expectations about future fundamentals using a dynamic stochastic generalequilibrium model augmented with anticipated shocks. Accounting for agents' expectations atthe business cycle horizon results in aggregate risk factor innovations that have significant explanatory power...

We classify the sentiment of a large sample of StockTwits messages as bullish,bearish or neutral, and create a stock-aggregate daily sentiment polarity measure.Polarity is positively associated with contemporaneous stock returns. On average,polarity is not able to predict next-day stock returns....

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...