Machine Learning based Analysis and Effective Visualization of Mutual Funds Through CUSUM and Clustering
Publish place: Journal of Business Data Science Research، Vol: 3، Issue: 1
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JBDSR-3-1_004
تاریخ نمایه سازی: 27 تیر 1403
Abstract:
This research paper aims to present an extensive analysis of Large-cap class-A mutual funds spanning a period of ۲۵ years. Using this historical data, the study presents the readers with the observed patterns and trends in these mutual funds. The data has been sourced from two major data repositories for mutual fund and finance data - WRDS (Wharton Research Data Services) and YF (Yahoo Finance). The study uses statistical methodologies like Sharpe Ratio and Volatility and analytical methodologies like CUSUM and Clustering. Along with this quantitative analysis, the paper also encompasses qualitative data like Assets Under Management, Turnover Ratio, and Management Information for all the funds used. This helps gain insights into the influence of these factors on the performance of the mutual funds. This paper mainly discusses how all the aforementioned factors influence the mutual fund trajectories with the help of effective visualizations and machine learning-based analysis over ۲۵ years, hence developing an efficient pipeline.This research paper aims to present an extensive analysis of Large-cap class-A mutual funds spanning a period of ۲۵ years. Using this historical data, the study presents the readers with the observed patterns and trends in these mutual funds. The data has been sourced from two major data repositories for mutual fund and finance data - WRDS (Wharton Research Data Services) and YF (Yahoo Finance). The study uses statistical methodologies like Sharpe Ratio and Volatility and analytical methodologies like CUSUM and Clustering. Along with this quantitative analysis, the paper also encompasses qualitative data like Assets Under Management, Turnover Ratio, and Management Information for all the funds used. This helps gain insights into the influence of these factors on the performance of the mutual funds. This paper mainly discusses how all the aforementioned factors influence the mutual fund trajectories with the help of effective visualizations and machine learning-based analysis over ۲۵ years, hence developing an efficient pipeline.
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Authors
Aditya Maheshwari
Department of Computer Science, Metropolitan College, Boston University, Boston, USA
Vaidehi Shah
Department of Computer Science, Metropolitan College, Boston University, Boston, USA
Aakansha Sawhney
Department of Computer Science, Metropolitan College, Boston University, Boston, USA
Eugene Pinsky
Department of Computer Science, Metropolitan College, Boston University, Boston, USA
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