Redefining Efficiency: Computational Methods for Financial Models in Python
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© 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, https://doi.org/10.14445/22312803/IJCTT-V71I10P113

Abstract
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.

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

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