Fair Valuation for Financial Instruments using AI/ML
Keywords:
Artificial Intelligence, Machine Learning, Extreme Gradient Boosting, Support Vector Regression, Artificial Neural Network, Fair ValuationAbstract
The research determines which Artificial Intelligence techniques such as Support Vector Regression (SVR), XGBoost and Artificial Neural Networks (ANN) provide optimal results for fixed-income financial instruments' fair value assessment throughout global markets. The paper examines how the methods function against traditional valuation principles such as DCF and Black-Scholes while showing their weaknesses when working with unclear or scarce data patterns. The research uses July 2024 fixed-income market data to deploy support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) models for comparison. These models receive assessment based on their predictive power of instrument ask prices through evaluation of duration and convexity alongside coupon rate and issuer leverage, and yield metrics. The predictive model utilizing XGBoost delivered superior accuracy than SBVR by producing an RMSE of 0.0069 and R² of 0.9997. SBVR achieved results comparable to XGBoost, having an RMSE of 0.0113 and R² of 0.9990. The ANN model demonstrated poor performance against other models because it produced an RMSE of 0.519 and an R² of 0.662 during financial data predictions. XGBoost, utilized with SHAP (Shapley Additive exPlanations) values, generated explainable models that met the requirements of IFRS 13. The DCF model produces one fixed value of 100, while AI/ML models are adjusted to market conditions during valuation, which results in enhanced accuracy. The study findings demonstrate that XGBoost and SVR models effectively determine exact valuations in developed and emerging economies through SHAP techniques that fulfill the requirements of IFRS 13 reporting requirements. Researchers plan to develop ensemble models together with expanding their approach to the valuation of assets beyond fixed income instruments.
Downloads
References
Abikoye, B.E., Umeorah, S.C., Adelaja, A.O., Ayodele, O. and Ogunsuji, Y.M., 2024. Regulatory compliance and efficiency in financial technologies: Challenges and innovations. World Journal of Advanced Research and Reviews, 23(1), pp.1830-1844.
Akpan, M. and Ukwu, H.U., 2023. Non-fungible Token Valuation Exposed: US Generally Accepted Accounting Principles Compliance and Artificial Intelligence’s Disruptive Role. Akpan, M., & Ukwu, HU (2023). Non-fungible token valuation exposed: US Generally Accepted Accounting Principles Compliance and artificial intelligence’s disruptive role. Risk Governance and Control: Financial Markets & Institutions, 13(4), pp.73-84.
Alao, O.B., Dudu, O.F., Alonge, E.O. and Eze, C.E., 2024. Automation in financial reporting: A conceptual framework for efficiency and accuracy in US corporations. Global Journal of Advanced Research and Reviews, 2(02), pp.040-050.
Albahri, A.S., Duhaim, A.M., Fadhel, M.A., Alnoor, A., Baqer, N.S., Alzubaidi, L., Albahri, O.S., Alamoodi, A.H., Bai, J., Salhi, A. and Santamaría, J., 2023. A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, pp.156-191.
Alharasis, E.E., Tarawneh, A.S., Shehadeh, M., Haddad, H., Marei, A. and Hasan, E.F., 2022. Reimbursement costs of auditing financial assets measured by fair value model in Jordanian financial firms’ annual reports. Sustainability, 14(17), p.10620.
Ali, A.A., Khedr, A.M., El-Bannany, M. and Kanakkayil, S., 2023. A powerful predicting model for financial statement fraud based on optimized XGBoost ensemble learning technique. Applied Sciences, 13(4), p.2272.
Alkhouly, A.A., Mohammed, A. and Hefny, H.A., 2021. Improving the performance of deep neural networks using two proposed activation functions. IEEE Access, 9, pp.82249-82271.
Azzutti, A., 2024. Artificial Intelligence and Machine Learning in Finance: Key Concepts, Applications, and Regulatory Considerations. In The Emerald Handbook of Fintech: Reshaping Finance (pp. 315-339). Emerald Publishing Limited.
Bansal, M., Goyal, A. and Choudhary, A., 2022. A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short-term memory algorithms in machine learning. Decision Analytics Journal, 3, p.100071.
Bao, W., Cao, Y., Yang, Y., Che, H., Huang, J. and Wen, S., 2024. Data-driven stock forecasting models based on neural networks: A review. Information Fusion, p.102616.
Cao, L., 2022. AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys (CSUR), 55(3), pp.1-38.
Cha, J., Eom, S., Lee, S., Lee, C. and Hwangbo, S., 2023. Evaluation on Large-scale Biowaste Process: Spent Coffee Ground Along with Real Option Approach. Clean Technology, 29(1), pp.59-70.
Chadda, N.K. and Vardia, S., 2020. Fair value accounting and valuation of non-financial assets: A study of impact of IFRS adoption. Journal of commerce and accounting research, 9(4), p.63.
Chaudhary, G., 2024. Unveiling the black box: Bringing algorithmic transparency to AI. Masaryk University Journal of Law and Technology, 18(1), pp.93-122.
Chen, W., Hussain, W., Cauteruccio, F. and Zhang, X., 2023. Deep learning for financial time series prediction: A state-of-the-art review of standalone and hybrid models. CMES- Computer Modeling in Engineering and Sciences.
Dostmohammadi, M., Pedram, M.Z., Hoseinzadeh, S. and Garcia, D.A., 2024. A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods. Journal of Environmental Management, 364, p.121264.
Elahi, M.M., 2023. A user-centric exploration of transparency, explanations, and trust in multi-model and single-model decision support systems (Doctoral dissertation, Brunel University London).
Espinoza, D., Morris, J., Baroud, H., Bisogno, M., Cifuentes, A., Gentzoglanis, A., Luccioni, L., Rojo, J. and Vahedifard, F., 2020. The role of traditional discounted cash flows in the tragedy of the horizon: another inconvenient truth. Mitigation and adaptation strategies for global change, 25, pp.643-660.
Espinoza, D., Morris, J., Baroud, H., Bisogno, M., Cifuentes, A., Gentzoglanis, A., Luccioni, L., Rojo, J. and Vahedifard, F., 2020. The role of traditional discounted cash flows in the tragedy of the horizon: another inconvenient truth. Mitigation and adaptation strategies for global change, 25, pp.643-660.
Fritz-Morgenthal, S., Hein, B. and Papenbrock, J., 2022. Financial risk management and explainable, trustworthy, responsible AI. Frontiers in artificial intelligence, 5, p.779799.
Goel, A., Goel, A.K. and Kumar, A., 2023. The role of artificial neural network and machine learning in utilizing spatial information. Spatial Information Research, 31(3), pp.275-285.
Goldberg, A., 2024. AI in finance: Leveraging large language models for enhanced decision-making and risk management. Social Science Journal for Advanced Research, 4(4), pp.33-40.
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M. and Hussain, A., 2024. Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, 16(1), pp.45-74.
Huang, G., Xu, Z., Lin, Z., Guo, X. and Jiang, M., 2024. Artificial Intelligence-Driven Risk Assessment and Control in Financial Derivatives: Exploring Deep Learning and Ensemble Models. Transactions on Computational and Scientific Methods, 4(12).
Huang, J., Algahtani, M. and Kaewunruen, S., 2022. Energy forecasting in a public building: A benchmarking analysis on long short-term memory (LSTM), support vector regression (SVR), and extreme gradient boosting (XGBoost) networks. Applied Sciences, 12(19), p.9788.
Javaid, H.A., 2024. AI-driven predictive analytics in finance: Transforming risk assessment and decision-making. Advances in Computer Sciences, 7(1).
Josephidou, F., 2021. An alternative FX option pricing model: a discrete mixture of normal distributions (Doctoral dissertation, SOAS University of London).
Karami, A. and Igbokwe, C., 2025. The impact of big data characteristics on credit risk assessment. International Journal of Data Science and Analytics, pp.1-21.
Khattak, B.H.A., Shafi, I., Khan, A.S., Flores, E.S., Lara, R.G., Samad, M.A. and Ashraf, I., 2023. A systematic survey of AI models in financial market forecasting for profitability analysis. Ieee Access, 11, pp.125359-125380.
Khattak, B.H.A., Shafi, I., Khan, A.S., Flores, E.S., Lara, R.G., Samad, M.A. and Ashraf, I., 2023. A systematic survey of AI models in financial market forecasting for profitability analysis. Ieee Access, 11, pp.125359-125380.
Khattak, B.H.A., Shafi, I., Khan, A.S., Flores, E.S., Lara, R.G., Samad, M.A. and Ashraf, I., 2023. A systematic survey of AI models in financial market forecasting for profitability analysis. Ieee Access, 11, pp.125359-125380.
Kothandapani, H.P., 2025. AI-Driven Regulatory Compliance: Transforming Financial Oversight through Large Language Models and Automation. Emerging Science Research, pp.12-24.
Kumar, V.S., 2024. Artificial Intelligence in Economic Analysis: An Overview of Techniques, Applications and Challenges. Asian Journal of Economics, Finance and Management, pp.388-396.
Liukkonen, S., 2023. Investigating real options valuation methods for high-value, high-uncertainty investments: applying simulation and fuzzy-based techniques to uncertain R&D projects, complex systems, and inter-stakeholder dynamics.
Lu, Y., Fan, X., Zhang, Y., Wang, Y. and Jiang, X. (2023). Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums. Sensors, [online] 23(4), p.2151. doi:https://doi.org/10.3390/s23042151.
Mesioye, O. and Bakare, I.A., 2024. Evaluating financial reporting quality: Metrics, challenges, and impact on decision-making. Int J Res Public Rev, 5(10), pp.1144-1156.
MManga, R., 2024. Enhancing M&A Valuation Accuracy: A Comparative Analysis of Traditional Financial Models and Machine Learning Approaches.
Mökander, J., Morley, J., Taddeo, M. and Floridi, L., 2021. Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Science and Engineering Ethics, 27(4), p.44.
Moore, G., 2023. The Black Scholes Model and Discounted Cash Flow Valuation. Available at SSRN 4651588.
Moore, G., 2023. The Black Scholes Model and Discounted Cash Flow Valuation. Available at SSRN 4651588.
Olubusola, O., Mhlongo, N.Z., Daraojimba, D.O., Ajayi-Nifise, A.O. and Falaiye, T., 2024. Machine learning in financial forecasting: A US review: Exploring the advancements, challenges, and implications of AI-driven predictions in financial markets. World Journal of Advanced Research and Reviews, 21(2), pp.1969-1984.
Olubusola, O., Mhlongo, N.Z., Daraojimba, D.O., Ajayi-Nifise, A.O. and Falaiye, T., 2024. Machine learning in financial forecasting: A US review: Exploring the advancements, challenges, and implications of AI-driven predictions in financial markets. World Journal of Advanced Research and Reviews, 21(2), pp.1969-1984.
Palepu, K.G., Healy, P.M., Wright, S., Bradbury, M. and Coulton, J., 2020. Business analysis and valuation: Using financial statements. Cengage AU.
Prasad, S., 2024. THE EVOLUTION OF FAIR VALUE ACCOUNTING: IMPLICATIONS FOR FINANCIAL STABILITY AND INVESTOR CONFIDENCE. UNIFIED VISIONS, p.314.
Prathapasinghe, D. and Jayasekara, B.E.A., 2024. Impacts of Competency of Valuers on Accuracy of Fair Value for Financial Reporting Purpose. Journal of Institute of Human Resource Advancement, 11(1), p.34.
Ramli, N.M., Rahman, A.R.A., Marzuki, A., Marzuki, M.M. and Wan, W.A., 2021. Implementation of IFRS 13 Fair Value Measurement: Issues and Challenges faced by the Islamic Financial Institutions in Malaysia. Jurnal Pengurusan, 63.
Rendtorff, J.D., 2020. Corporate citizenship, stakeholder management and Sustainable Development Goals (SDGs) in financial institutions and capital markets. Journal of Capital Markets Studies, 4(1), pp.47-59.
Ridzuan, N.N., Masri, M., Anshari, M., Fitriyani, N.L. and Syafrudin, M., 2024. AI in the financial sector: The line between innovation, regulation and ethical responsibility. Information, 15(8), p.432.
Sahin, E.K., 2020. Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Applied Sciences, 2(7), p.1308.
Sahu, S.K., Mokhade, A. and Bokde, N.D., 2023. An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges. Applied Sciences, 13(3), p.1956.
Sarioguz, O. and Miser, E., 2024. Integrating AI in financial risk management: Evaluating the effects of machine learning algorithms on predictive accuracy and regulatory compliance.
Sarker, I.H., 2021. Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), p.160.
Shivute, A.P., 2019. A comparison of real options and discounted cash flow valuation techniques on a gold mining project (Doctoral dissertation).
Spulbar, C., Birau, R. and Spulbar, L.F., 2021. A critical survey on efficient market hypothesis (EMH), adaptive market hypothesis (AMH) and fractal markets hypothesis (FMH) considering their implication on stock markets behavior. Ovidius University Annals, Economic Sciences Series, 21(2), pp.1161-1165.
Sutiene, K., Schwendner, P., Sipos, C., Lorenzo, L., Mirchev, M., Lameski, P., Kabasinskas, A., Tidjani, C., Ozturkkal, B. and Cerneviciene, J., 2024. Enhancing portfolio management using artificial intelligence: Literature review. Frontiers in artificial intelligence, 7, p.1371502.
Talhartit, I., Jillali, S.A. and El Kabbouri, M., 2022. BEHAVIORAL FINANCE: HOW ARE TRADERS'FINANCIAL DECISIONS AND PERFORMANCE IMPACTED BY BEHAVIORAL BIASES UNDER UNCERTAINTY?. International Journal of Sciences Academic Research, 3(10), pp.4588-4597.
Tobisova, A., Senova, A. and Rozenberg, R., 2022. Model for sustainable financial planning and investment financing using the Monte Carlo method. sustainability, 14(14), p.8785.
Tursunalieva, A., Alexander, D.L., Dunne, R., Li, J., Riera, L. and Zhao, Y., 2024. Making sense of machine learning: a review of interpretation techniques and their applications. Applied Sciences, 14(2), p.496.
Vayas-Ortega, G., Soguero-Ruiz, C., Rojo-Álvarez, J.L. and Gimeno-Blanes, F.J., 2020. On the differential analysis of enterprise valuation methods as a guideline for unlisted companies assessment (I): Empowering discounted cash flow valuation. Applied Sciences, 10(17), p.5875.
Velikova, T., Mileva, N. and Naseva, E., 2024. Method “Monte Carlo” in healthcare. World Journal of Methodology, 14(3), p.93930.
Velikova, T., Mileva, N. and Naseva, E., 2024. Method “Monte Carlo” in healthcare. World Journal of Methodology, 14(3), p.93930.
Viedienieiev, V.A., 2021. Ensemble Forecasting Methods in DCF Modelling of the Fair Value of Enterprises. Universal Journal of Accounting and Finance, 9(4), pp.869-874.
Vuthi, P., Peters, I. and Sudeikat, J., 2022. Agent-based modeling (ABM) for urban neighborhood energy systems: Literature review and proposal for an all integrative ABM approach. Energy Informatics, 5(Suppl 4), p.55.
Wang, Y., Liu, L. and Wang, C., 2023. Trends in using deep learning algorithms in biomedical prediction systems. Frontiers in Neuroscience, 17, p.1256351.
Wang, Y.G., Wu, J., Hu, Z.H. and McLachlan, G.J., 2023. A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition, 133, p.108989.
Weber, P., Carl, K.V. and Hinz, O., 2024. Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 74(2), pp.867-907.
Wolodko, P., 2024. The Future of Business Valuation: How Technological Advancements are Influencing the Valuations Industry (Master's thesis, Universidade Catolica Portuguesa (Portugal)).
Wolodko, P., 2024. The Future of Business Valuation: How Technological Advancements are Influencing the Valuations Industry (Master's thesis, Universidade Catolica Portuguesa (Portugal)).
Wolodko, P., 2024. The Future of Business Valuation: How Technological Advancements are Influencing the Valuations Industry (Master's thesis, Universidade Catolica Portuguesa (Portugal)).
Zammit, K., 2024. Corporate valuation methodologies during an uncertain economic climate in Malta: evidence from COVID-19 (Master's thesis, University of Malta).
Zareef, M., Chen, Q., Hassan, M.M., Arslan, M., Hashim, M.M., Ahmad, W., Kutsanedzie, F.Y. and Agyekum, A.A., 2020. An overview of the applications of typical non-linear algorithms coupled with NIR spectroscopy in food analysis. Food Engineering Reviews, 12, pp.173-190.
Zhang, H., Zhang, M., Liu, F. and Shen, M., 2024. Review of the fractional Black-Scholes equations and their solution techniques. Fractal and Fractional, 8(2), p.101.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
