%0 Book Section %1 Becker:2006:GPTP %A Becker, Ying %A Fei, Peng %A Lester, Anna M. %B Genetic Programming Theory and Practice IV %C Ann Arbor %D 2006 %E Riolo, Rick L. %E Soule, Terence %E Worzel, Bill %I Springer %K 500, Arbitrage Asset Capital Information Model, Pricing Quantitative S&P Stock Technical algorithms, asset coefficient, equity genetic management market, models, programming, quantitative ratio, rules, selection selection, stock trading %P - %T Stock Selection : An Innovative Application of Genetic Programming Methodology %V 5 %X One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information. The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon SSgA's previous research on using genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection models in a large investable stock universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that the GP process can generate a stock selection model for an low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical backtest results indicate that portfolios based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating various market regimes and have more consistent performance than the traditional model. %& 12 %@ 0-387-33375-4

@incollection{Becker:2006:GPTP, abstract = {One of the major challenges in an information-rich financial market is how effectively to derive an optimum investment solution among vast amounts of available information. The most efficacious combination of factors or information signals can be found by evaluating millions of possibilities, which is a task well beyond the scope of manual efforts. Given the limitations of the manual approach, factor combinations are typically linear. However, the linear combination of factors might be too simple to reflect market complexities and thus fully capture the predictive power of the factors. A genetic programming process can easily explore both linear and non-linear formulae. In addition, the ease of evaluation facilitates the consideration of broader factor candidates for a stock selection model. Based upon SSgA's previous research on using genetic programming techniques to develop quantitative investment strategies, we extend our application to develop stock selection models in a large investable stock universe, the S&P 500 index. Two different fitness functions are designed to derive GP models that accommodate different investment objectives. First, we demonstrate that the GP process can generate a stock selection model for an low active risk investment style. Compared to a traditional model, the GP model has significantly enhanced future stock return ranking capability. Second, to suit an active investment style, we also use the GP process to generate a model that identifies the stocks with future returns lying in the fat tails of the return distribution. A portfolio constructed based on this model aims to aggressively generate the highest returns possible compared to an index following portfolio. Our tests show that the stock selection power of the GP models is statistically significant. Historical backtest results indicate that portfolios based on GP models outperform the benchmark and the portfolio based on the traditional model. Further, we demonstrate that GP models are more robust in accommodating various market regimes and have more consistent performance than the traditional model.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Ann Arbor}, author = {Becker, Ying and Fei, Peng and Lester, Anna M.}, biburl = {https://www.bibsonomy.org/bibtex/29fec610cd789f915c6108d441426c7f4/brazovayeye}, booktitle = {Genetic Programming Theory and Practice {IV}}, chapter = 12, editor = {Riolo, Rick L. and Soule, Terence and Worzel, Bill}, interhash = {82e8ed1cff1abf6412b49c1b8195088f}, intrahash = {9fec610cd789f915c6108d441426c7f4}, isbn = {0-387-33375-4}, keywords = {500, Arbitrage Asset Capital Information Model, Pricing Quantitative S&P Stock Technical algorithms, asset coefficient, equity genetic management market, models, programming, quantitative ratio, rules, selection selection, stock trading}, month = {11-13 May}, notes = {part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop Principal, Head of US Active Equity Research, Advanced Research Center, State Street Global Advisors, Boston, MA 02111;}, pages = {-}, publisher = {Springer}, series = {Genetic and Evolutionary Computation}, size = {16 pages}, timestamp = {2008-06-19T17:36:23.000+0200}, title = {Stock Selection : An Innovative Application of Genetic Programming Methodology}, volume = 5, year = 2006 }