This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms...

This paper explores the use of Google trending data as an indicator for market sentiment.The Google query records on keywords including stock, market, correction, and crash areincorporated into an event based trading model for S&P 500 index in an attempt to significantlyenhance the risk-profile...

Retail investors pay over twice as much attention to local companies than non-local ones, based on Google searches. News volume and volatility amplify this attention gap. Attention appears causally related to perceived proximity: first, acquisition by a nonlocal company is associated with less...

I present substantially extended back-testing results of the algorithmic approach to delta-hedging described in my earlier paper. Along with previously researched EURUSD straddles I research USDJPY and GBPUSD straddles as well as call and put spread option combinations. Back-tested period is...