ONE YEAR STOCK PRICE PREDICTION AND ITS VALIDITY USING LEAST SQUARE METHOD IN MATLAB.
- Departemen of Informatics, Gunadarma University, Jakarta-Indonesia.
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Given time series data of 43 LQ45’s monthly stocks price during the period January 2013 to December 2013; this research interpolates the data into best polynomials as many the stocks, with each RMSE (root mean square error) representing degree of investment risk. The polynomial also is used to extrapolate (i.e. to predict) the next months stock price during the period January 2015 to January 2016; finally the prediction prices is compared with the 13 monthly actual prices and their RMSE (root mean square error) are computed. The computations are run in MATLAB (Matrix Laboratory) programming language implementing the curve fitting procedure based on least square method. The important results of computations are following: (1) degree of polynomials are in the range [28, 97] of maximum 100, with average 73.7209, (2) relative RMSE of interpolation (RMSE-i) are in the range [0.2248%, 2.6804%] with average 0.6719%, (3) relative RMSE of extrapolation (RMSE-e) are in the range [2.1838%, 56.9015%] with average 11.5455%. The average of relative RMSE-e is small enough; it means that the risk for 13 months investment during the period January 2015 to January 2016 is small enough. The value of relative RMSE-e also explains why a stock is removed from or still in LQ45 index, and this is valid for 40 of 43 stocks.
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[Asep Juarna. (2017); ONE YEAR STOCK PRICE PREDICTION AND ITS VALIDITY USING LEAST SQUARE METHOD IN MATLAB. Int. J. of Adv. Res. 5 (Feb). 1641-1648] (ISSN 2320-5407). www.journalijar.com
Departemen of Informatics, Gunadarma University, Jakarta-Indonesia
