Stock Price Prediction Using Brown's Weighted Exponential Moving Average with Levenberg-Marquardt Optimization
Abstract
Stocks are securities as legal proof of company ownership. Such ownership is traded in the stock exchange only for public companies. A capital market is an activity that accommodates the desire to invest. Traders look for profits by taking advantage of stock movements that always experience fluctuating changes using technical analysis. The problem is how traders decide to buy a stock at an uncertain price. Brown's Weighted Exponential Moving Average (B-WEMA) method has the advantage of a better accuracy level, which is considered able to help with stock price prediction problems. The Levenberg-
Marquardt (LM) algorithm has the advantage of optimizing its accuracy; therefore, it is proposed to be used as an optimization method to predict stock prices. The B-WEMA method with LM optimization improves the accuracy of stock price predictions, thereby reducing risks and increasing trading success. Results show the minimum difference of actual prices, and its prediction was 4.03%. The smallest error from MSE and MAPE were 719.56 and 1.99%, respectively.
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