Application of Genetic Algorithm and Personal Informatics in Stock Market

Khulood Albeladi - King Abdul Aziz University, Jeddah, Saudi Arabia
Salha Abdullah - King Abdul Aziz University, Jeddah, Saudi Arabia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.2.2.115

Abstract


The financial market is extremely attractive since it moves trillion dollars per year. Many investors have been exploring ways to predict future prices by using different types of algorithms that use fundamental analysis and technical analysis. Many professional speculators or amateurs had been analysing the price movement of some financial assets using these algorithms. The use of genetic algorithms, neural networks, genetic programming combined with these tools in an attempt to find a profitable solution is very common. This study presents a prototype that utilizes genetic algorithms (GAs) and personal informatics system (PI) for short-term stock index forecast. The prototype works according to the following steps. Firstly, a collection of input variables is defined through technical data analysis. Secondly, GA is applied to determine an optimal set of input variables for a one-day forecast.  The data is gathered from the Saudi Stock Exchange as being the target market. Thirdly, PI is utilised to create a smart environment, which enables visualisation of stock prices. The outcome indicates that this approach of forecasting the stock price is positive. The highest accuracy obtained is 64.67% and the lowest one is 48.06%.

Keywords


genetic algorithms; personal informatics; stock market; Saudi Stock Exchange.

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References


Hameed, A., Kang, W. and Viswanthan, S. (2010). Stock Market Declines and Liquidity. The Journal of Finance, 65(1), pp.257-293.

Enke, D. and Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), pp.927-940.

Cao, L., Luo, C., Ni, J., Luo, D. and Zhang, C. (2014). Stock Data Mining through Fuzzy Genetic Algorithm. Australian Research Council Discovery Grant, 06S3011(546).

Einhorn, M. (2012). Trademark Valuation and Market Analysis. SSRN Journal.

Sumantyo, R. (2014). Effect Analysis of Fundamental Factors Toward Cigarettes Companies Stock Price That Listed In Indonesia Stock Exchange (IDX) Period 2008-2013. SSRN Electronic Journal.

Drakopoulou, V. (2016). A Review of Fundamental and Technical Stock Analysis Techniques. Journal of Stock & Forex Trading, 05(01).

Yamamoto, R. (2012). Intraday technical analysis of individual stocks on the Tokyo Stock Exchange. Journal of Banking & Finance, 36(11), pp.3033-3047.

Bradic-Martinovic, A. (2006). Stock market prediction using technical analysis. Economic. Annals, 51(170), pp.125-146.

Federici S, Borsci S. 2010. Usability evaluation: models, methods, and applications. In: JH Stone, M Blouin, editors. International Encyclopedia of Rehabilitation. Available online: http://cirrie.buffalo.edu/encyclopedia/en/article/277/ [Accessed 12 April 2016].

Buie, E. (2001). Whiteboard: usability design and testing. Interactions, 8(5), pp.13-17.

Wang, Y., Yu, J. and Wen, S. (2014). Does Fundamental and Technical Analysis Reduce Investment Risk for Growth Stock? An Analysis of Taiwan Stock Market. International Business Research, 7(11).

Budgetsimple.com. (2016). BudgetSimple - Easy Budget Software to Manage Your Personal Finances. [Online] Available at: https://www.budgetsimple.com/ [Accessed 12 April 2016].