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A Nonlinear Stock Valuation Using a Hybrid Model of Genetic Algorithm and Cubic Spline

Stock valuation is to calculate the required rate of return that company we are valuing. However, using different traditional valuation models will get unique risk/return combinations, even those in the same stock market or same industry. It suggests that the fair value of a stock should be a range rather than a single value. In this article, we propose a nonlinear stock investment and capital allocation model using a hybrid of genetic algorithm (GA) and cubic spline (CS), where GA is used to optimize the fair range of stock price, and CS is used to estimate nonlinear capital allocations. From our experiments, GACS model could get better investment returns than using buy & hold in most cases. In addition, the mean stock price almost falls into the fair stock price range predicted by GACS.