Beyond Time: Accurately Estimating the Fair Value of Stocks with Machine Learning and Fundamental Data
Keywords: Data Analytics, Machine Learning, Stock Market, Value Investing, Data Mining, Deep Learning, Defensive Investing, Data Science, Random Forest, Neural Network
TL;DR: The paper presents a novel machine learning-based methodology for long-term stock valuation, leveraging both qualitative and quantitative data to accurately identify undervalued equities for defensive investing.
Abstract: This paper presents a novel machine learning methodology to assess the intrinsic value of firms, based on both qualitative and quantitative data, and deliberately excluding time series analysis. The employed models demonstrate proficiency in identifying undervalued stocks, highlighting the importance of effective feature engineering and data analysis in finance. This method represents a potential departure from traditional stock valuation techniques, suggesting a new direction in investment strategies. While results require careful interpretation, they indicate a potential shift in investment paradigms.
Submission Number: 113
Loading