Trading Volatility Using Highly Accurate Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Korns:2015:hbgpa,
  author =       "Michael F. Korns",
  title =        "Trading Volatility Using Highly Accurate Symbolic
                 Regression",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  pages =        "531--547",
  keywords =     "genetic algorithms, genetic programming, Value
                 investing, Symbolic regression, Logistic regression,
                 Nonlinear regression",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_21",
  abstract =     "Research efforts, directed at increasing the accuracy
                 and dependability of Symbolic Regression (SR), have
                 resulted in significant improvements in symbolic
                 regression's range, accuracy, and dependability.
                 Previous research has also demonstrated the
                 practicability of estimating corporate forward 12 month
                 earnings, using advanced symbolic regression. In this
                 paper we put these prior results and techniques
                 together to select a 100 stock semi-passive index
                 portfolio, extracted from the Value Line Timeliness
                 stocks (Value Line), which delivers consistent
                 performance in both bull and bear decades and we will
                 compare its performance to the Standard & Poors 100
                 index.

                 We intend to produce our 100 stock semi-passive index
                 buy list on a weekly basis using automated forward 12
                 month EPS (ftmEPS) prediction involving the analysis of
                 many securities, involving multiple training
                 regressions each on hundreds of thousands of training
                 examples. Plus the timeliness issue will require that
                 our analytic tools be strong and thoroughly matured.
                 The 100 stock buy list will be the foundation for a new
                 semi-passive Value Line 100 index fund which should
                 have great appeal to many high net worth clients, enjoy
                 low management costs, and be easily acceptable to the
                 compliance and regulatory authorities.

                 Valuation of Value Line securities via their forward 12
                 month price earnings ratio (ftmPE) is a very common
                 securities valuation method in the industry. Obviously
                 the ftmPE valuation depends heavily on the estimate of
                 forward 12 month corporate earnings per share (ftmEPS).
                 Several obvious inputs to the ftmEPS prediction process
                 are the past earnings time series plus one or more
                 analyst predictions.

                 Valuation via ftmEPS is a necessary but not a
                 sufficient attraction for a semi-passive index fund. So
                 we will introduce the advantages of trading volatility.
                 Our thesis will be that emotional trading patterns tend
                 to make markets less efficient.

                 The efficient market hypothesis depends upon equal
                 access to information and rational trading patterns.
                 Trading on insider information is illegal in most
                 developed securities markets; however, trading when
                 others are emotional is unregulated. In this paper we
                 will develop a set of factors—all of which
                 incorporate a measure of volatility indicating possible
                 overly emotional trading patterns. The theme of our new
                 semi-passive index fund will be Buy value from those
                 who are selling in a highly emotional state.",
}

Genetic Programming entries for Michael Korns

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