Considering the evolution of financial globalization and the impacts of the global economic crisis, stock trading faces unprecedented fluctuations. The inherent volatility in stock prices has resulted in market uncertainty, prompting an interest among investors in reliable pricing models in order to maximize profits. To this end, researchers have continued to diligently refine stock pricing models to mitigate market uncertainty. One notable contender in this arena is the Heston model, conceived to remedy the limitations of the Black-Scholes model. The model embraces stochastic volatility, a departure from the constant volatility assumption underpinning the Black-Scholes model. However, the Heston model itself grapples with certain pivotal constraints, mainly the requisite precision in parameter calibration to produce a reliable estimate. Leveraging the current wave of technological advancement, this study uses an Artificial Neural Network (ANN) as a substitute for simulating different volatility parameters in the Heston model. This approach culminates in the construction of a hybrid semi-parametric forecasting model termed the Heston-ANN model. The study applies this model to datasets of three distinct stocks: BA, IBM, and GOLD. Through graphical analysis and the evaluation of different model performance metrics including Mean Absolute Percentage Error, Mean Absolute Error, and Mean Squared Error, the study compares the hybrid model to the original Heston model. The results reveal that the Heston-ANN model yields more accurate forecasts when juxtaposed with its precursor, the original Heston model. The synergy between the Heston model and ANN makes the hybrid model a more robust solution for forecasting stock prices.
| Published in | International Journal of Data Science and Analysis (Volume 9, Issue 2) |
| DOI | 10.11648/j.ijdsa.20230902.11 |
| Page(s) | 22-33 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Stock Pricing, Artificial Neural Network (ANN), Stochastic Volatility, Stochastic Differential Equation (SDE)
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APA Style
Ann Maina, Samuel Mwalili, Bonface Malenje. (2023). Forecasting Stock Prices Using Heston-Artificial Neural Network Model . International Journal of Data Science and Analysis, 9(2), 22-33. https://doi.org/10.11648/j.ijdsa.20230902.11
ACS Style
Ann Maina; Samuel Mwalili; Bonface Malenje. Forecasting Stock Prices Using Heston-Artificial Neural Network Model . Int. J. Data Sci. Anal. 2023, 9(2), 22-33. doi: 10.11648/j.ijdsa.20230902.11
AMA Style
Ann Maina, Samuel Mwalili, Bonface Malenje. Forecasting Stock Prices Using Heston-Artificial Neural Network Model . Int J Data Sci Anal. 2023;9(2):22-33. doi: 10.11648/j.ijdsa.20230902.11
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Download@article{10.11648/j.ijdsa.20230902.11,
author = {Ann Maina and Samuel Mwalili and Bonface Malenje},
title = {Forecasting Stock Prices Using Heston-Artificial Neural Network Model
},
journal = {International Journal of Data Science and Analysis},
volume = {9},
number = {2},
pages = {22-33},
doi = {10.11648/j.ijdsa.20230902.11},
url = {https://doi.org/10.11648/j.ijdsa.20230902.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20230902.11},
abstract = {Considering the evolution of financial globalization and the impacts of the global economic crisis, stock trading faces unprecedented fluctuations. The inherent volatility in stock prices has resulted in market uncertainty, prompting an interest among investors in reliable pricing models in order to maximize profits. To this end, researchers have continued to diligently refine stock pricing models to mitigate market uncertainty. One notable contender in this arena is the Heston model, conceived to remedy the limitations of the Black-Scholes model. The model embraces stochastic volatility, a departure from the constant volatility assumption underpinning the Black-Scholes model. However, the Heston model itself grapples with certain pivotal constraints, mainly the requisite precision in parameter calibration to produce a reliable estimate. Leveraging the current wave of technological advancement, this study uses an Artificial Neural Network (ANN) as a substitute for simulating different volatility parameters in the Heston model. This approach culminates in the construction of a hybrid semi-parametric forecasting model termed the Heston-ANN model. The study applies this model to datasets of three distinct stocks: BA, IBM, and GOLD. Through graphical analysis and the evaluation of different model performance metrics including Mean Absolute Percentage Error, Mean Absolute Error, and Mean Squared Error, the study compares the hybrid model to the original Heston model. The results reveal that the Heston-ANN model yields more accurate forecasts when juxtaposed with its precursor, the original Heston model. The synergy between the Heston model and ANN makes the hybrid model a more robust solution for forecasting stock prices.
},
year = {2023}
}
TY - JOUR T1 - Forecasting Stock Prices Using Heston-Artificial Neural Network Model AU - Ann Maina AU - Samuel Mwalili AU - Bonface Malenje Y1 - 2023/10/28 PY - 2023 N1 - https://doi.org/10.11648/j.ijdsa.20230902.11 DO - 10.11648/j.ijdsa.20230902.11 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 22 EP - 33 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20230902.11 AB - Considering the evolution of financial globalization and the impacts of the global economic crisis, stock trading faces unprecedented fluctuations. The inherent volatility in stock prices has resulted in market uncertainty, prompting an interest among investors in reliable pricing models in order to maximize profits. To this end, researchers have continued to diligently refine stock pricing models to mitigate market uncertainty. One notable contender in this arena is the Heston model, conceived to remedy the limitations of the Black-Scholes model. The model embraces stochastic volatility, a departure from the constant volatility assumption underpinning the Black-Scholes model. However, the Heston model itself grapples with certain pivotal constraints, mainly the requisite precision in parameter calibration to produce a reliable estimate. Leveraging the current wave of technological advancement, this study uses an Artificial Neural Network (ANN) as a substitute for simulating different volatility parameters in the Heston model. This approach culminates in the construction of a hybrid semi-parametric forecasting model termed the Heston-ANN model. The study applies this model to datasets of three distinct stocks: BA, IBM, and GOLD. Through graphical analysis and the evaluation of different model performance metrics including Mean Absolute Percentage Error, Mean Absolute Error, and Mean Squared Error, the study compares the hybrid model to the original Heston model. The results reveal that the Heston-ANN model yields more accurate forecasts when juxtaposed with its precursor, the original Heston model. The synergy between the Heston model and ANN makes the hybrid model a more robust solution for forecasting stock prices. VL - 9 IS - 2 ER -
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