LQ45 Stock Index Prediction using k-Nearest Neighbors Regression
Julius Tanuwijaya1, Seng Hansun2
1Julius Tanuwijaya, Informatics Department, Universitas Multimedia Nusantara, Tangerang, Indonesia.
2Seng Hansun, Informatics Department, Universitas Multimedia Nusantara, Tangerang, Indonesia.
Manuscript received on 5 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 2388-2391 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4663098319/2019©BEIESP | DOI: 10.35940/ijrte.C4663.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The capital market is an organized financial system consisting of commercial banks, intermediary institutions in the financial sector and all outstanding securities. One of the benefits of the capital market is creating opportunities for the community to participate in economic activities, especially in investing. In daily stock trading activities, stock prices tend to have fluctuated. Therefore, stock price prediction is needed to help investors make decisions when they want to buy or sell their shares. One asset for investment is shares. One of the stock price indices that attracts many investors is the LQ45 stock index on the Indonesian stock exchange. One of the algorithms that can be used to predict is the k-Nearest Neighbors (kNN) algorithm. In the previous study, kNN had a higher accuracy than the moving average method of 14.7%. This study uses kNN regression method because it predicts numerical data. The results of the research in making the LQ45 stock index prediction application have been successfully built. The highest accuracy achieved reaches 91.81% by WSKT share.
Index Terms: k-Neighbors Regressor, LQ45, Prediction, Stock, Time Series.
Scope of the Article: Regression and Prediction