title: Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
abstract: Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
authors.affiliation: Department of Industrial Engineering, Seoul National University, Seoul, South Korea.
authors.forename: Saerom
authors.initials: S
authors.lastname: Park
authors.affiliation: Department of Industrial Engineering, Seoul National University, Seoul, South Korea.
authors.forename: Jaewook
authors.initials: J
authors.lastname: Lee
authors.affiliation: Research Institute of Engineering and Entrepreneurship, Seoul National University, Seoul, South Korea.|Department of Industrial and Systems Engineering, Rutgers University, Piscataway, New Jersey, United States of America.
authors.forename: Youngdoo
authors.initials: Y
authors.lastname: Son
mesh_terms: D003365:Costs and Cost Analysis; D007449:Investments / Q000191:economics* / Q000706:statistics & numerical data*; D000069550:Machine Learning; D012044:Regression Analysis; D018709:Statistics, Nonparametric