Forecasting Financial Stocks Using Data Mining

25 Pages Posted: 6 Mar 2010 Last revised: 11 Mar 2010

See all articles by Ojoung Kwon

Ojoung Kwon

affiliation not provided to SSRN

K.C. Tseng

California State University, Fresno - Department of Finance and Business Law

Jill Bradley

affiliation not provided to SSRN

Luna Christie Tjung

California State University - Fresno

Date Written: March 6, 2010

Abstract

This study presents a Business Intelligence (BI) approach to forecast daily changes in seven financial stocks’ prices from September 1, 1998 to April 30, 2008 with 267 independent variables. The purpose of our paper is to compare the performance of Ordinary Least Squares model and Neural Network model to see which model better predicts the changes in the stock prices and to identify critical predictors to forecast stock prices to increase forecasting accuracy for the professionals in the market.

We used SPSS to perform stepwise regression to create a unique regression model for each company. Then, we ran the neural network with Alyuda NueroIntelligence to create a NN model by performing data analysis, data preprocessing, network design with hyperbolic tangent method, training with batch back propagation, testing, and query. We did data manipulation by using the first derivative and adding 0.1 to the absolute value of the minimum value in each variable to avoid minus sign from the rounding. Finally, we tested the model with the paired t-test in 152 randomly selected data points.

Our result showed that the neural network model (batch back propagation algorithm) outperformed OLS model. The %error for NN and OLS mean ranges from 2.13%-3.27% and 4%-32% and standard deviation ranges from 1.78%-3.39% and 2.46%-8%.

The OLS model is easy to use, validate, and works fast with lower forecasting accuracy because it is a linear model. NN has a better forecasting accuracy with no explanation of the relationship between interacting variables with dynamic results due to the learning setup. Some critical success factors to train NN are the network architecture, network design algorithm, training algorithm, and stop training conditions. Data normalization can make a huge difference to the result. We recommend more forecasting method and independent variables (e.g. expert opinion) to be included for future studies.

Keywords: Financial Forecasting, Financial Modelling, Business Intelligence, Neural Network, Econometrics, Data Mining

Suggested Citation

Kwon, Ojoung and Tseng, K.C. and Bradley, Jill and Tjung, Luna Christie, Forecasting Financial Stocks Using Data Mining (March 6, 2010). Available at SSRN: https://ssrn.com/abstract=1566268 or http://dx.doi.org/10.2139/ssrn.1566268

Ojoung Kwon

affiliation not provided to SSRN ( email )

K.C. Tseng

California State University, Fresno - Department of Finance and Business Law ( email )

United States

Jill Bradley

affiliation not provided to SSRN ( email )

Luna Christie Tjung (Contact Author)

California State University - Fresno ( email )

5241 North Maple Avenue
Fresno, CA 93740
United States

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