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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM45585

ML Based Macroeconomic Model Simulator

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM45585

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM45585

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AG

Aditya Gaur1

SB

Shivsen Bhosale2

SC

Shruti Cheulkar3

YG

Yash Gupta4

AD

Anas Dange5

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Abstract

This AutoFi, a cutting-edge AI-driven macroeconomic simulation model, provides a robust framework for forecasting financial markets. Financial markets are deeply influenced by macroeconomic indicators such as GDP, unemployment rates, inflation, and interest rates. Accurate forecasting of these indicators is critical for optimizing investment strategies. This research presents AutoFi, a machine learning-based macroeconomic simulation model that predicts key financial indicators and asset performance over time. The proposed system incorporates historical macroeconomic data, time series forecasting models, and regression-based asset prediction to estimate the future value of investments. Additionally, an interactive financial assistant provides investment insights, enhancing accessibility and decision-making. The system enables investors to input their capital allocation preferences across different asset classes and receive projected portfolio values. The results demonstrate the potential of AI-driven economic modeling to improve financial decision-making and risk assessment.

KEYWORDS

Macroeconomic forecasting financial markets AI-driven investment AutoFi machine learning economic simulation portfolio management asset performance prediction

Cite This Article

Aditya Gaur1, Shivsen Bhosale2, Shruti Cheulkar3, Yash Gupta4, Anas Dange5, "ML Based Macroeconomic Model Simulator," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM45585

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Publication Date

4/23/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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