Stock Scoring Methods: Regression vs Genetic-Based Models
Explore a comparative study between traditional regression models and machine learning-based linear models for stock scoring, with Genetic Algorithms (GA) optimizing model parameters. Discover how the genetic-based method excels in stock selection, offering a compelling alternative to regression-based approaches in finance.
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A Comparative Study of Stock Scoring using Regression and Genetic-based Linear Models Chien-Feng Huang, Tsung-Nan Hsieh, Bao Rong Chang, Chih-Hsiang Chang National University of Kaohsiung 2011 IEEE International Conference on Granular Computing pp. 268 - 273 Presenter : Tsai Yao-Chou 1
Abstract(1/2) Stock selection has long been a challenging and important task in investment and finance. Researchers and practitioners in this area often use regression models to tackle this problem due to their simplicity and effectiveness. Recent advances in machine learning (ML) are leading to significant opportunities to solve these problems more effectively. In this paper, we present a comparative study between the traditional regression-based and ML-based linear models for stock scoring, which is crucial to the success of stock selection. In ML-based models, Genetic Algorithms (GA), a class of well-known search algorithms in the area of ML, is used for optimization of model parameters and selection of input variables to the stock scoring model. 2
Abstract(1/2) We will show that our proposed genetic-based method significantly outperforms the traditional regression-based method as well as the benchmark.As a result, we expect this genetic-based methodology to advance the research in machine learning for finance and provide an attractive alternative to stock selection over the regression-based approach. 3
Regression Model Type : Liner regression ? training instances with input variable ? and output variable ? at time ?: ?1? ,?1? , ?2? ,?2? , , ??? ,??? ? = regression coefficient ? ? = error term ? ? = ? ? ? + ? ? 4
Fundamental indicators (1/2) PE ratio : Price to earnings ratio = PB ratio : Price to book ratio = PS ratio : Price to sales = 5
Ranking scheme 1. ??? = ? ??? ? = ranking function ??? = ranking of stock ? at time ? 1 ? ?=1 ?????? 2. R1= ??? = ranked stock at time ? ?? = actual return of a stock at time ? Average return over all the me stocks in the portfolio at time ? ? ??= ?=1 3. ?? Evaluate the performance of a stock selection model. 7
GA-based model (1/4) ??,?? : score of stock ? assigned by variable ? at time ?. ??,?? : value of variable ?. ??,?? = ??,?? . 1. ??= ?: ??,?? ??,?? ??? ??,?? ??,?? for ? ?. 2. ??= ?: ??,?? ??,?? ??? ??,?? ??,?? for ? ?. 8
GA-based model (2/4) ??? = ?????,?? . 1. Where ??is weight of the j-th variable. 2. Total score of stock ? at time ?. Basic genetic algorithm steps: 9
GA-based model (3/4) Three sets of parameters: F : The set of input features. 1. 1. 1 select, 0 not select I : The set of stock sorting indicators. 2. 1. Based on page 6 10
GA-based model (4/4) W : The weights of the fundamental variables. 3. Weight for variable 1 Genotypes to Phenotype: ? ? = ????+ 2? 1 ???? ???? Fitness function: ??? ??????? = 11
