application of genetic algorithm and personal informatics in stock market

Clicks: 331
ID: 206798
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #12 of 30 articles by views in intensive care medicine

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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%.
Reference Key
albeladi2018joiv:application Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Khulood Albeladi;Salha Abdullah
Journal intensive care medicine
Year 2018
DOI
10.30630/joiv.2.2.115
URL
http://joiv.org/index.php/joiv/article/view/115
Keywords
computer software genetic algorithms stock market

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.