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LiteratureDatasetExamples

CASE 5 - SENTIMENT ANALYSIS

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Background

In the constantly evolving investment landscape, having an edge can make the difference between a successful or unsuccessful investment strategy. In the era of information abundance, the challenge often lies in sifting through data to extract valuable insights. The development of a sentiment analysis (SA) tool addresses this challenge, enabling investment firms to make more informed decisions based on vast amounts of unstructured data found in news articles, financial reports, and social media platforms. Here's how sentiment analysis is commonly applied in fintech:

  • Algorithmic Trading: Some trading algorithms incorporate sentiment analysis to predict stock price movements. By assessing the sentiment of news articles, financial reports, and even tweets related to a particular stock, these algorithms can decide whether to buy, sell, or hold a particular asset.
  • Risk Management: Analyzing sentiments from news and social media can help in predicting potential risks associated with an investment. For instance, if there's increasing negative sentiment around a company due to some rumored bad news, it could be a precursor to a potential stock price drop.
  • Customer Insights and Personal Finance: Fintech companies use sentiment analysis to gauge customer sentiment towards their products and services. This can help them in tailoring their offerings, improving customer experience, or even predicting market demands.
  • Regulatory Compliance and Monitoring: Monitoring public sentiment can also be used to detect anomalies or potential cases of market manipulation, which can be crucial for regulatory bodies or institutions to ensure compliance.

You are on a development team that helps a global investment fund in utilizing Artificial Intelligence (think Chat-GPT) to make better decisions. The investment fund aims to analyze the sentiment of news articles, blog posts, social media posts, financial reports, and other sources of textual information that could influence market dynamics.

PROBLEM DESCRIPTION

Your team is tasked to develop a sentiment analysis tool using advanced NLP (Natural Language Processing) techniques. The tool is to be designed to understand the context, recognize the subtleties of language, identify sarcasm, and differentiate between fact and opinion. This is in order to provide a new perspective on investment decision-making, supplementing traditional financial analysis methods. It allowed the client to make more holistic and informed decisions, contributing to improved portfolio performance.

GOALS

  • How “ChatGPT” can be tuned to perform sentiment analysis on alternative data sources eg. financial news articles, social media posts, and other unstructured data sources.

  • Can SA be used to identify positive or negative sentiment about a particular stock or market trend.

SUGGESTED APPROACH

  • Development of a method for sentiment analysis

  • Deployment of the method as a tool – optional

    • Eg. Use the openai API and streamlit to test of the method.

  • Evaluation of the tool/method

  • Evaluate the added business value of this tool

    • Effects of a streamlined and automated sentiment analysis,

      • Does it free up valuable time for their analyst?

    • Does it identify potential investment risks and opportunities that were not immediately apparent from conventional analysis methods?

    • Does it enable earlier detection of shifts in public sentiment that often precede market changes, giving the firm a competitive edge?

    • Does it capture and interpret a vast amount of data quickly? (which is impossible manually).

  • Recommendations and further work

    • How can (or should) this tool be incorporate into the fund investment strategies?

Examples of sentiment analysis and other cases where ChatGPT is used