New PDF release: A branch-and-cut algorithm for nonconvex quadratic programs

By Vandenbussche D., Nemhauser G. L.

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The program searches over a range of fixed-width windows simulating trading of a mean plus or minus standard deviation model; simulation ‘‘profit’’ is the metric used to compare models (data window length, Bollinger bandwidth) and the maximum profit generating model is identified. One can very quickly fit models to many pairs using such tools. The dangers of relying solely on such shallow data analysis should be immediately evident. Tools with similar capabilities are offered by Goldman Sachs, Reynders Gray, and Lehman Brothers, among others.

Every year the response is different. Similar—that is why strategies work more often than not—but different. When selecting a parameter value at which to operate a strategy, it is critical to understand both the form of the response curve and the natural amount of variation and relate these to understanding of the phenomenon under study—reversion in this case— when it is available. Picking the return-maximizing value of the parameter from panel (a) is risky because in some years the response curve shifts sufficiently that model performance falls off the cliff.

77 results. Identifying volume peaks in historical data is scarcely different from the demonstration of peak identification in price histories documented previously. In live trading, however, forward-looking monitoring for patterns of increased trading volume, an important risk management tool, is subtly different. One needs to flag volume increase during the build-up before a peak is identifiable because identification after the fact is usually too late for ameliorating impact on a portfolio. 3 STATISTICAL ARBITRAGE Correlation Search in the Twenty-First Century Several vendors now offer software tools for managing aspects of pairs trading, from identifying tradable pair candidates to execution channels and portfolio management.

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A branch-and-cut algorithm for nonconvex quadratic programs with box constraints by Vandenbussche D., Nemhauser G. L.


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