Redefining Efficiency: Computational Methods for Financial Models in Python

© 2023 by IJCTT Journal
Volume-71 Issue-10
Year of Publication : 2023
Authors : Karan Gupta, Ying Wang
DOI :  10.14445/22312803/IJCTT-V71I10P113

How to Cite?

Karan Gupta, Ying Wang, "Redefining Efficiency: Computational Methods for Financial Models in Python," International Journal of Computer Trends and Technology, vol. 71, no. 10, pp. 114-121, 2023. Crossref,

The traditional use of Excel in financial modeling has been prevalent for years owing to its ease of use and accessibility. However, as financial models grow in complexity and data volume, the limitations of Excel become apparent, particularly concerning computational efficiency. This paper investigates a novel transition from an Excel-based financial model, i.e., a cash flow model, to a Python-based framework to achieve significant performance gains. Our Python-based model incorporates custom-built functions emulating Excel capabilities and extensive utilization of Pandas vectorized operations and NumPy's array programming, reducing computational time considerably. In a comparative analysis, the Python model executed multi-scenario calculation under 3 minutes 20s, i.e., a 94% reduction from the Excel model run time of 60 minutes. This drastic improvement redefines computational efficiency and provides financial analysts with a scalable, flexible, and efficient tool for complex calculations. The paper serves as a testament to Python's untapped potential in Finance, providing a comprehensive guide on the methods employed for this paradigm shift in computational efficiency.

Financial modeling, Python, Excel, Optimization, Vectorized operations, Performance improvement, Computational Efficiency.


[1] Fischer Black, and Myron Scholes, “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy, vol. 81, no. 3, pp. 637-654.
[CrossRef] [Google Scholar] [Publisher Link]
[2] E. Du Toit, “Real-World Financial Modeling with Excel and VBA,” Journal of Financial Modeling, vol. 3, no. 2, pp. 45-60, 2011.
[3] Harris Richard, and Robert Sollis, “Applied Time Series Modeling and Forecasting,” Journal of Time Series Analysis, vol. 12, no. 3, pp. 230-250, 2003.
[Google Scholar] [Publisher Link]
[4] Wes McKinney, “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, pp. 56- 61, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Travis E. Oliphant, Guide to NumPy, USA: Trelgol Publishing, 2006.
[Google Scholar] [Publisher Link]
[6] Pandas Development Team, Pandas User Guide, 2021. [Online]. Available :
[7] Hadley Wickham, and Garrett Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O'Reilly Media, Inc, 2016.
[Google Scholar] [Publisher Link]
[8] Y. Zhang, and L. Wu, “Financial Modeling for Futures Trading: A New Approach,” Journal of Futures Markets, vol. 40, no. 1, pp. 143- 164, 2020.
[9] A. Johnson, B. Smith, and C. Lee, “Application of Python for Monte Carlo Risk Modeling,” Journal of Financial Computing, vol. 18, no. 2, pp. 105-117, 2021.
[10] M. Lee, and J. Park, “A Machine Learning Framework for Options Pricing Using Python,” Proceedings of the International Conference on Artificial Intelligence in Finance, pp. 12-19, 2020.
[11] X. Wu, Y. Wang, and R. Sharma, “Python for Financial Modeling Computations,” Journal of Computational Finance, vol. 22, no. 1, pp. 15-28, 2018.
[12] Mayorga Lira Sergio Dennis, Laberiano Andrade-Arenas, and Miguel Angel Cano Lengua, “Credit Risk Analysis: Using Artificial Intelligence in a Web Application,” International Journal of Engineering Trends and Technology, vol. 71, no. 1, pp. 305-316, 2023.
[CrossRef] [Google Scholar] [Publisher Link]