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”.
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References
Graham, Benjamin, and David Dodd. 2008. Securities Analysis. New York, New York, USA. McGraw-Hill.
Korns, Michael F., 2010. Abstract Expression Grammar Symbolic Regression. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice VIII, New York, New York, USA. Springer, pp. 109–128.
Guido Smits, Ekaterina Vladislavleva, and Mark Kotanchek 2010, Scalable Symbolic Regression by Continuous Evolution with Very Small Populations, in Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice VIII, New York, New York, USA. Springer, pp. 147–160.
Flor Castillo, Arthur Kordon, and Carlos Villa 2010, Genetic Programming Transforms in Linear Regression Situations, in Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice VIII, New York, New York, USA. Springer, pp. 175–194.
Trent McConaghy, Pieter Palmers, Gao Peng, Michiel Steyaert, Goerges Gielen 2009, Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach. New York, New York, USA. Springer.
J.A., Nelder, and R. W. Wedderburn, 1972, Journal of the Royal Statistical Society, Series A, General, 135:370–384.
John R Koza 1992, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge Massachusetts, The MIT Press.
Bernstein, J., 2001. Momentum Stock Selection: Using The Momentum Method for Maximum Profits. New York, New York, McGraw Hill
Nicholas, J., 2000. Market-Neutral Investing: Long/Short Hedge Fund Strategies. New York, New York, Bloomberg Press.
Korns, Michael F, 2011b. Accuracy in Symbolic Regression. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice IX, New York, New York, USA. Springer.
Korns, Michael F., 2012a. A Baseline Symbolic Regression Algorithm. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice X, New York, New York, USA. Springer.
Korns, Michael F., 2013. Extreme Accuracy in Symbolic Regression. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XI, New York, New York, USA. Springer.
Korns, Michael F., 2014. Extremely Accurate Symbolic Regression for Large Feature Problems. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XII, New York, New York, USA. Springer.
Korns, Michael F., 2012b. Predicting Corporate Forward 12 Month Earnings, 2012. Theory and New Applications of Swarm Intelligence, ISBN 978-953-51-0364-6, edited by Rafael Parpinelli and Heitor S. Lopes, InTech Academic Publishers.
Kleinbaum, David G., and Klein, Michael, 2010. Logistic Regression: A Self-Learning Text (Statistics for Biology and Health), ISBN 978–1441917416, New York, New York, USA. Springer.
Anderson, David R., Sweeney, Dennis J., and Williams, Thomas A, 2002. Essentials of Statistics for Business and Economics, ISBN 978–0324145809, Southwestern College Publishers.
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Korns, M.F. (2015). Trading Volatility Using Highly Accurate Symbolic Regression. In: Gandomi, A., Alavi, A., Ryan, C. (eds) Handbook of Genetic Programming Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20883-1_21
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DOI: https://doi.org/10.1007/978-3-319-20883-1_21
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