Designing an Investment Research System for Asset Management Based on Natural Language Processing
Name
chen-chenyz31-msms-sloan-2023-thesis.pdf
Description
Thesis PDF
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725.31 KB
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Adobe PDF
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1b695b6c92d00fdbad2fb29cff16dabe
Author(s)
Chen, Yanzhang
Advisor(s)
Johnson, Simon
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
In recent years, the asset management industry has experienced rapid growth, with the global asset management scale continuously increasing. Conventionally, investment research in asset management entails the acquisition of data and information from a myriad of sources, which is then manually processed and analyzed. However, in the face of macroeconomic volatility, fierce competition, and a deluge of fragmented information, this traditional approach to investment research increasingly struggles to manage the sheer volume of financial market data and information.
Natural Language Processing (NLP), an essential subset of artificial intelligence, has achieved significant breakthroughs in recent years. It facilitates automatic processing, analysis, and text generation to specific tasks, aiding investment institutions in swiftly assimilating and dissecting massive volumes of information, and consequently formulating investment research results. NLP can assist investment institutions in rapidly integrating and analyzing vast information and automatically generating investment reports. This paper aims to trace the evolution of NLP, evaluate its prospective positive impact on asset management, and deliberate on designing an investment research system grounded in NLP technology.
MIT Department
Sloan School of Management
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