Comparative analysis of GEN-A.I. and machine learning models in predictive financial modelling techniques
Sahoo, Shashwat Kumar
Comparative analysis of GEN-A.I. and machine learning models in predictive financial modelling techniques - Ahmedabad Indian Institute of Management 2024 - 39 p. : ill. ; includes reference
Submitted by Shashwat Kumar Sahoo
Anup Itale
Financial modelling and forecasting cashflows is a key aspect of assessing any business opportunity. This task often involves multiple assumptions and judgment calls based on market, industry, and company data. This project proposes to leverage machine learning techniques, including exploring the efficiency of Generative Pre-trained Transformer (GPT) models in building financial models. In this study, we have assessed existing techniques and best practices in discounted cash flow forecasting. We have also conducted primary research to understand the application of GPT-based tools for financial applications from industry experts. We then built our tool: ‘Your Friendly Neighborhood Finbro’ available on the GPT store. This was done by creating a workflow and acceptable output formats for the GPT trainer. This tool was also reviewed with potential users as part of our primary research. We have also presented a comparative analysis of this tool with traditional time series and machine learning models.
Financial modelling is very standardized in its steps and the formulae applicable: CAPM, WACC, FCF, and NPV. The complexity comes from the assumptions and the absence of data points in many cases. These are usually resolved with assumptions and industry averages. Both these require deep financial knowledge and experience. A large language model, like GPT, has 3 benefits: internet access to the latest market data, natural language processing, and contextualizing capabilities. These provide it the potential to be best suited to perform the task of assumption-making. There is a need for a GPT tool that complies with the standard practices of the financial industry. Our study suggests that the GPT tools need to be used along with the traditional tools to arrive at the most accurate results, as each tool excels at a different aspect of forecasting.
Financial forecasting-Automation
Machine learning-Financial applications
Large language models
SP2024/3884 / SP003884
Comparative analysis of GEN-A.I. and machine learning models in predictive financial modelling techniques - Ahmedabad Indian Institute of Management 2024 - 39 p. : ill. ; includes reference
Submitted by Shashwat Kumar Sahoo
Anup Itale
Financial modelling and forecasting cashflows is a key aspect of assessing any business opportunity. This task often involves multiple assumptions and judgment calls based on market, industry, and company data. This project proposes to leverage machine learning techniques, including exploring the efficiency of Generative Pre-trained Transformer (GPT) models in building financial models. In this study, we have assessed existing techniques and best practices in discounted cash flow forecasting. We have also conducted primary research to understand the application of GPT-based tools for financial applications from industry experts. We then built our tool: ‘Your Friendly Neighborhood Finbro’ available on the GPT store. This was done by creating a workflow and acceptable output formats for the GPT trainer. This tool was also reviewed with potential users as part of our primary research. We have also presented a comparative analysis of this tool with traditional time series and machine learning models.
Financial modelling is very standardized in its steps and the formulae applicable: CAPM, WACC, FCF, and NPV. The complexity comes from the assumptions and the absence of data points in many cases. These are usually resolved with assumptions and industry averages. Both these require deep financial knowledge and experience. A large language model, like GPT, has 3 benefits: internet access to the latest market data, natural language processing, and contextualizing capabilities. These provide it the potential to be best suited to perform the task of assumption-making. There is a need for a GPT tool that complies with the standard practices of the financial industry. Our study suggests that the GPT tools need to be used along with the traditional tools to arrive at the most accurate results, as each tool excels at a different aspect of forecasting.
Financial forecasting-Automation
Machine learning-Financial applications
Large language models
SP2024/3884 / SP003884