Warning: strtotime(): It is not safe to rely on the system's timezone settings. You are *required* to use the date.timezone setting or the date_default_timezone_set() function. In case you used any of those methods and you are still getting this warning, you most likely misspelled the timezone identifier. We selected 'America/New_York' for 'EST/-5.0/no DST' instead in /home/mathfcon/public_html/libraries/joomla/utilities/date.php on line 56 Warning: date(): It is not safe to rely on the system's timezone settings. You are *required* to use the date.timezone setting or the date_default_timezone_set() function. In case you used any of those methods and you are still getting this warning, you most likely misspelled the timezone identifier. We selected 'America/New_York' for 'EST/-5.0/no DST' instead in /home/mathfcon/public_html/libraries/joomla/utilities/date.php on line 198

The Mathematics of Adaptive Execution, Robert Almgren Algorithmic execution of large transactions in equity and other markets is a large and growing business. The goal is to optimize the overall execution results relative to some benchmark specified by the client, generally involving some combination of minimum market impact and exposure to volatilty risk. An increasingly important trend in recent years is dynamically adaptive algorithms, that adjust execution in response to short-term variations in estimated market liquidity and volatility. The mathematical challenge is to combine that instantaneous response with a more strategic point of view that optimizes an overall combination of impact cost and volatility risk. We summarize some recent work using dynamic programming to calculate and implement optimally adaptive strategies. Optimal Order Execution, Jim Gatheral In this talk, we review the models of Algmren and Chriss, Obizhaeva and Wang, and Alfonsi, Fruth and Schied.  We use variational calculus to derive optimal execution strategies in these models, and show that static strategies are dynamically optimal, in some cases by explicitly solving the HJB equation.  We conclude by presenting some new generalizations of the Obizhaeva and Wang model given in a recent paper by Gatheral, Schied and Slynko, again deriving explicit closed-form optimal execution strategies. Technology, Latency and Strategy, Joel Hasbrouck and Gideon Saar We study the economic and empirical importance of automated low-latency market activity.  From an economic perspective, latency reduction beyond current levels is unlikely to significantly affect portfolio decisions that are based on value-relevant real information. To the extent, however, that trading gains and losses are determined by strategic interactions and tournament considerations, any change in latency that may affect the ordering of market participants’ interactions can confer an advantage. We study three empirical features of market data that are likely to be associated with algorithmic activity: short-horizon message arrival intensities, periodicity (time clustering) in message occurrences, and the prevalence of cancel-and-replace/execute strategies. We then relate measures of these features to standard measures of market liquidity, such as effective cost and posted spread. Algorithmic Trading and Information, Terrence Hendershott We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse. AT liquidity demand represents 52% of volume and AT supplies liquidity on 50% of volume. AT act strategically by monitoring the market for liquidity and deviations of price from fundamental value. AT consume liquidity when it is cheap and supply liquidity when it is expensive. AT contribute more to the efficient price by placing more efficient quotes and AT demanding liquidity to move the prices towards the efficient price. Algorithmic Trading: An Investment Management Perspective, Ananth Madhavan Modern algorithmic trading evolved from automated trading tools developed by proprietary traders. From the viewpoint of an investment manager, decisions on trade duration cannot be separated from the investment decision. This talk provides a framework to jointly determine the optimal trading structure given forward looking information. We illustrate the value of the approach with some practical examples. Intraday Patterns in the Cross-Section of Stock Returns, Ronnie Sadka Motivated by the literature on investment flows and optimal trading, this paper examines intra-day predictability in the cross-section of stock returns. We find a striking pattern of return continuation at half-hour intervals that are exact multiples of a trading day, and this effect lasts for at least forty trading days. Changes in volume, order imbalance, volatility, and bid/ask spreads exhibit similar patterns, but do not explain the return patterns. We also show that short-term return reversal is driven by two components: temporary liquidity imbalances lasting less than an hour, and bid-ask bounce. Timing trades based on the observed periodicity can reduce execution costs significantly, on average, by the equivalent of a one-way effective spread of a typical algorithmic trade.