Stock Selection : An Innovative Application of Genetic Programming Methodology

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ying Becker and Peng Fei and Anna M. Lester",
  title =        "Stock Selection : An Innovative Application of Genetic
                 Programming Methodology",
  booktitle =    "Genetic Programming Theory and Practice {IV}",
  year =         "2006",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  volume =       "5",
  series =       "Genetic and Evolutionary Computation",
  pages =        "315--334",
  address =      "Ann Arbor",
  month =        "11-13 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, equity
                 market, stock selection, quantitative asset management
                 Capital Asset Pricing Model, Arbitrage Pricing Model,
                 Technical trading rules, S&P 500, Stock selection
                 models, Information ratio, Information coefficient,
                 Quantitative asset management",
  ISBN =         "0-387-33375-4",
  DOI =          "doi:10.1007/978-0-387-49650-4_19",
  size =         "16 pages",
  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.",
  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",

Genetic Programming entries for Ying Becker Peng Fei Anna M Lester