Introduction
A crucial pillar in successful value investing is acquiring assets at low prices, as the lower the purchase price, the higher the investment returns that can be achieved. Since markets perennially fluctuate and at times swing irrationally, fueled by fear or greed, investors can regularly purchase valuable stocks at depressed prices during times of market anxiety and despondency.
In order to correctly identify periods of market pessimism, NLP (Natural Language Processing) will be used to assess and evaluate market sentiment during the 10-year period considered. It is expected that company valuations during the periods identified as bearish (market downturns) will be lower than normal from a historical point of view. These lower prices will provide great buying opportunities for quality companies, which is expected to contribute to the value investing model delivering superior returns compared to an S&P 500 index fund passive investment strategy (Buffett and Clark, 2011; López de Prado, 2018).
Suggested Relevant Tools and Techniques
When designing an AI model with the Python programming language, the following tools and techniques will be considered to gauge market sentiment:
Data Sources for Market Sentiment
- Social Media and News:
- - X API
- - Reddit API
- - StockTwits API
- - Google Trends
- - Alpha Vantage
- - NewsAPI
- - Web Scraping (BeautifulSoup, Scrapy)
Python Natural Language Processing (NLP) Tools
- - VADER: For social media sentiment analysis.
- - FinBERT: Finance-specific BERT model.
- - Hugging Face Transformers: Pre-trained financial NLP models.
Planning
The planned timescale to develop the stock market sentiment analysis module will run from May 6, 2025, until May 19, 2025.
References
Buffett, M., & Clark, D. (2011). Warren Buffett and the Interpretation of Financial Statements: The Search for the Company with a Durable Competitive Advantage. New York: Scribner.
López de Prado, M. (2018). Advances in Financial Machine Learning. New Jersey: Wiley.
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