A Integrated Approach for Asset Price Forecasting via Prophet Model and Optimizing Investment Strategies through Genetic Algorithms


  • Janitha Senadheera Faculty of Computing, General Sir John Kotelawala Defence University, Ratmalana
  • Pavithra Madushanka Faculty of Computing General Sir John Kotelawala Defence University Ratmalana, Sri Lanka
  • Wijendra Gunathilake Faculty of Computing General Sir John Kotelawala Defence University Ratmalana, Sri Lanka


forecasting models, optimizing strategy, price prediction, gold price, oil price


This research presents an in-depth exploration of a wide array of algorithms, techniques, methods and models used for forecasting asset values. Significantly, the study introduces an unprecedented approach, featuring a dedicated model for precise price forecasting and another for recommending optimized strategies. By assessing and contrasting the approaches and outcomes of asset value prediction across different fields, this paper study aims to harness the power of Artificial Intelligence (AI) in forecasting asset prices and tailoring investment strategies. Implemented system integrates the Prophet Model for precise price forecasting and employs Genetic Algorithms for investment strategy generation. Through a systematic evaluation of the system, we demonstrate its capacity to provide accurate asset price predictions, outperform traditional investment strategies and mitigate risks effectively. Empirical unit testing showcased impressive results such as gold model with a 4.76% MAPE and an R-squared value of 0.9795 and oil model with notable metrics such as a Mean Absolute Error of 6.80, and Root Mean Squared Error of 10.92. Every single user, across the board, either strongly agreed or agreed that the investment recommendations provided valuable insights and 92.4% perceiving system predictions as very accurate. It further delves into the challenges and limitations, such as the quality of data used and model interpretability, underscoring the imperative for robust, compliant and interpretable forecasting models. Additionally, the study explores future directions in the domain, advocating for the expansion of asset classes and the integration of Natural Language Processing (NLP) into the system


P. Hajek and J. Novotny, “Fuzzy Rule-Based Prediction of Gold Prices using News Affect,” Expert Systems with Applications, vol. 193, p. 116487, May 2022, doi: https://doi.org/10.1016/j.eswa.2021.116487.

I. E. Livieris, E. Pintelas, and P. Pintelas, “A CNN–LSTM model for gold price time-series forecasting,” Neural Comput & Applic, vol. 32, no. 23, pp. 17351–17360, Dec. 2020, doi: 10.1007/s00521-020-04867-x.

Z. H. Kilimci, “Ensemble Regression-Based Gold Price (XAU/USD) Prediction”.

P. Baser, J. R. Saini, and N. Baser, “Gold Commodity Price Prediction Using Tree-based Prediction Models,” International Journal of Intelligent Systems and Applications in Engineering.

F. Weng, Y. Chen, Z. Wang, M. Hou, J. Luo, and Z. Tian, “Gold price forecasting research based on an improved online extreme learning machine algorithm,” J Ambient Intell Human Comput, vol. 11, no. 10, pp. 4101–4111, Oct. 2020, doi: 10.1007/s12652-020-01682-z.

V. G. S and H. V. S, “Gold Price Prediction and Modelling using Deep Learning Techniques,” in 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Thiruvananthapuram, India, Dec. 2020, pp. 28–31. doi: 10.1109/RAICS51191.2020.9332471.

A. Wagh, S. Shetty, A. Soman, and Prof. D. Maste, “Gold Price Prediction System,” IJRASET, vol. 10, no. 4, pp. 2843–2848, Apr. 2022, doi: 10.22214/ijraset.2022.41623.

K. A. Manjula and P. Karthikeyan, “Gold Price Prediction using Ensemble based Machine Learning Techniques,” in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, Apr. 2019, pp. 1360–1364. doi: 10.1109/ICOEI.2019.8862557.

D. Makala and Z. Li, “Prediction of gold price with ARIMA and SVM,” J. Phys.: Conf. Ser., vol. 1767, no. 1, p. 012022, Feb. 2021, doi: 10.1088/1742-6596/1767/1/012022.

S. Dabreo, S. Rodrigues, V. Rodrigues, and P. Shah, “Real Estate Price Prediction,” International Journal of Engineering Research, vol. 10, no. 04.

A. S. Ravikumar, “Real Estate Price Prediction Using Machine Learning”.

H. Yu and J. Wu, “Real Estate Price Prediction with Regression and Classification”.

P.-F. Pai and W.-C. Wang, “Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices,” Applied Sciences, vol. 10, no. 17, p. 5832, Aug. 2020, doi: 10.3390/app10175832.

R. Gupta, A. Sharma, V. Anand, and S. Gupta, “Automobile Price Prediction using Regression Models,” in 2022 International Conference on Inventive Computation Technologies (ICICT), Nepal, Jul. 2022, pp. 410–416. doi: 10.1109/ICICT54344.2022.9850657.

S. Selvaratnam, B. Yogarajah, T. Jeyamugan, and N. Ratnarajah, “Feature selection in automobile price prediction: An integrated approach,” in 2021 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, Sep. 2021, pp. 106–112. doi: 10.1109/SCSE53661.2021.9568288.

F. M. Basysyar, Ferisanti, M. Wulandari, I. Sucitra, D. A. Kurnia, and Solikin, “Prediction of Automobiles Prices Using Exploratory Data Analysis Based on Improved Machine Learning Techniques,” in 2022 Seventh International Conference on Informatics and Computing (ICIC), Denpasar, Bali, Indonesia, Dec. 2022, pp. 1–6. doi: 10.1109/ICIC56845.2022.10006925.

W. Gunathilake and T. Neligwa, “Towards a Quality Assessment Framework for a KMS Software: A Mapping Study“, KIM2013 Conference , School of Computing & Mathematics, Keele University, United Kingdom.

L. K. T. G Liyanarachchi, IA Wijethunga, MKP Madushanka: “Housing price prediction using Machine Learning”, 14th International Research Conference, General Sir John Kotelawala Defence University, Sri Lanka, September 2021.

O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, “Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019,” arXiv:1911.13288 [cs, q-fin, stat], Nov. 2019, Available: https://arxiv.org/abs/1911.13288

B. M. Henrique, V. A. Sobreiro, and H. Kimura, “Literature review: Machine learning techniques applied to financial market prediction,” Expert Systems with Applications, vol. 124, pp. 226–251, Jun. 2019, doi: https://doi.org/10.1016/j.eswa.2019.01.012.

H. Sun and B. Yu, “Forecasting Financial Returns Volatility: A GARCH-SVR Model,” Computational Economics, May 2019, doi: https://doi.org/10.1007/s10614-019-09896-w.

P. Li and R. Feng, “Nested Monte Carlo simulation in financial reporting: a review and a new hybrid approach,” Scandinavian Actuarial Journal, Feb. 2021, doi: https://doi.org/10.1080/03461238.2021.1881809.

J. Ruf and W. Wang, “Neural networks for option pricing and hedging: a literature review,” arXiv.org, May 09, 2020. https://arxiv.org/abs/1911.05620.

I. K. Nti, A. F. Adekoya, and B. A. Weyori, “A systematic review of fundamental and technical analysis of stock market predictions,” Artificial Intelligence Review, vol. 53, no. 1, pp. 3007–3057, Aug. 2019, doi: https://doi.org/10.1007/s10462-019-09754-z.

A. Picasso, S. Merello, Y. Ma, L. Oneto, and E. Cambria, “Technical analysis and sentiment embeddings for market trend prediction,” Expert Systems with Applications, vol. 135, pp. 60–70, Nov. 2019, doi: https://doi.org/10.1016/j.eswa.2019.06.014.

G. Shobana and K. Umamaheswari, “Forecasting by Machine Learning Techniques and Econometrics: A Review,” IEEE Xplore, Jan. 01, 2021. https://ieeexplore.ieee.org/abstract/document/9358514.

K. Mishev, A. Gjorgjevikj, I. Vodenska, L. T. Chitkushev, and D. Trajanov, “Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers,” IEEE Access, vol. 8, pp. 131662–131682, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3009626.

Y. Sun and H. Peng, “A Quantum Evolutionary Algorithm and Its Application to Optimal Dynamic Investment in Market Microstructure Model,” pp. 386–396, Jan. 2022, doi: https://doi.org/10.1007/978-981-19-4546-5_30.

Y. Liang, Y. Lin, and Q. Lu, “Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM,” Expert Systems with Applications, vol. 206, p. 117847, Nov. 2022, doi: https://doi.org/10.1016/j.eswa.2022.117847.

F. Rundo, F. Trenta, A. L. di Stallo, and S. Battiato, “Machine Learning for Quantitative Finance Applications: A Survey,” Applied Sciences, vol. 9, no. 24, p. 5574, Dec. 2019, doi: https://doi.org/10.3390/app9245574.



How to Cite

Senadheera, J., Madushanka, P. ., & Gunathilake, W. (2024). A Integrated Approach for Asset Price Forecasting via Prophet Model and Optimizing Investment Strategies through Genetic Algorithms. International Journal of Research in Computing, 2(2), 34–41. Retrieved from http://ijrcom.org/index.php/ijrc/article/view/125