Regression techniques for the prediction of stock price trend

Han Lock Siew, Md. Jan Nordin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pre-transformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. The outcomes of both processes are examined and appraised. The primary design is based on regression analysis from WEKA machine learning software. The stock price movement in Bursa Malaysia is used as our research setting. The data sources are corporate annual reports which included balance sheet, income statement and cash flow statement. The variables included in the data set were formed based on stock market trading fundamental analysis approach. Classifiers in WEKA were used as algorithms to produce the outcomes. This study showed that the outcomes of regression techniques can be improved for the prediction of stock price trend by using a dataset in standardized ordinal data format.

Original languageEnglish
Title of host publicationICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences"
Pages99-103
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012 - Langkawi, Kedah
Duration: 10 Sep 201212 Sep 2012

Other

Other2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012
CityLangkawi, Kedah
Period10/9/1212/9/12

Fingerprint

Stock Prices
Regression
Prediction
Ordinal Data
Currency
Malaysia
Trends
Stock Market
Regression Analysis
Annual
Ranking
Machine Learning
Classifier
Software

Keywords

  • classifiers
  • fundamental analysis
  • linear regression
  • machine learnin
  • ordinal data type
  • regression techniques

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Siew, H. L., & Nordin, M. J. (2012). Regression techniques for the prediction of stock price trend. In ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences" (pp. 99-103). [6396535] https://doi.org/10.1109/ICSSBE.2012.6396535

Regression techniques for the prediction of stock price trend. / Siew, Han Lock; Nordin, Md. Jan.

ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences". 2012. p. 99-103 6396535.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Siew, HL & Nordin, MJ 2012, Regression techniques for the prediction of stock price trend. in ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences"., 6396535, pp. 99-103, 2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012, Langkawi, Kedah, 10/9/12. https://doi.org/10.1109/ICSSBE.2012.6396535
Siew HL, Nordin MJ. Regression techniques for the prediction of stock price trend. In ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences". 2012. p. 99-103. 6396535 https://doi.org/10.1109/ICSSBE.2012.6396535
Siew, Han Lock ; Nordin, Md. Jan. / Regression techniques for the prediction of stock price trend. ICSSBE 2012 - Proceedings, 2012 International Conference on Statistics in Science, Business and Engineering: "Empowering Decision Making with Statistical Sciences". 2012. pp. 99-103
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