Journal of the Association for Information Systems · 2011 · 21 citations · 11 references
Open access
Being equipped with a unique high-frequency dataset that enablesus to precisely identify algorithmic trading (i.e. computergenerated)activity, we provide strong evidence that algorithmictrading does not exceedingly increases volatility, at least not morethan human traders do. Our empirical analyses cover severalpotential reasons why algorithmic trading could increasevolatility. For example, we address whether or not algorithmictraders follow less diverse trading strategies than humans.Moreover, we investigate whether or not algorithmic traderswithdraw liquidity from the market during periods of highvolatility.
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Modeling and Forecasting Realized Volatility
Torben G. Andersen, Tim Bollerslev, Francis X. Diebold et al. · Econometrica · 2003 · 3.9K citations
Forecasting Methodology, Volatility Modeling, Engineering +16
An Investigation of Transactions Data for NYSE Stocks
Robert Wood, Thomas H. McInish, J. Keith Ord · The Journal of Finance · 1985 · 537 citations
Algorithmic Trading and Information
Terrence Hendershott, Ryan Riordan · SSRN Electronic Journal · 2011 · 191 citations · Full text