A Comparative Predicting Stock Prices using Heston and Geometric Brownian Motion Models

TL;DR

This paper compares Heston and Geometric Brownian Motion models, enhanced with Ito's lemma and Euler-Maruyama methods, to predict stock prices more accurately than traditional statistical indicators.

Contribution

It introduces a novel application of stochastic volatility models combined with advanced numerical methods for improved stock price prediction.

Findings

Models outperform traditional statistical indicators

Heston and GBM models effectively incorporate volatility and interest rates

Predictions demonstrate high accuracy in stock market forecasting

Abstract

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.