Prediction of Market Behavior for Short Term Stock Prices using Regression Techniques
Tousif Al Rashid1, Vinay Kumar Goyal2
1Tousif Al Rashid, Department of Computer Science and Engineering, Chandigarh University, Mohali, India.
2Vinay Kumar Goyal, Department of Statistical and Mathematical Modeling and Machine Learning Particularly in Health Sciences and Financial Prediction.
Manuscript received on 09 April 2019 | Revised Manuscript received on 16 May 2019 | Manuscript published on 30 May 2019 | PP: 1176-1183 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3491058119/19©BEIESP
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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: Stock price Prediction always been a desired area for many institutions of finance. As price prediction in finance has long been a challenging task due to volume and speed of the data, investors are always looking for good algorithm to know the future price. The various machine learning algorithms (MLR, SVM, Random Forest etc.) used to predict and make further decision on stock market. The errors of predicted prices may be minimized, if the labeled dataset is mined in a efficient way. As the technical analysis always plays a major role to put profit in a investors pocket, a very simple algorithm is proposed for short term closing price prediction after analyzing similar types of movements of last few days prices to the historical data of that stock. A novel approach using Correlation Coefficients, Euclidian Distance and machine learning techniques is proposed to forecast a meaningful price based on the SBI data, fetched from the Yahoo Finance.
Index Terms: Multiple Linear Regression, Random Forest, Technical Analysis, Short Term Stock Prediction, Data Mining.
Scope of the Article: Data Mining.