  This paper presents a novel approach to predicting stock prices using
technical analysis. By utilizing Ito's lemma and Euler-Maruyama methods, the
researchers develop Heston and Geometric Brownian Motion models that take into
account volatility, interest rate, and historical stock prices to generate
predictions. The results of the study demonstrate that these models are
effective in accurately predicting stock prices and outperform commonly used
statistical indicators. The authors conclude that this technical analysis-based
method offers a promising solution for stock market prediction.

Statistical Finance;Numerical Analysis;Numerical Analysis