Problem Statement
Our client, a US-based hedge fund, wanted us to rank a large number of stocks and bonds targeted for long-term investments and review the relative performance of the assets to manage risks better.
JMI Implementation
- JMI team accessed a set of 2,000+ stocks and fixed-income instruments from the client.
- JMI developed a customized quantamental model that inherited the fundamental data of each stock, such as quarterly financials, institutional holding patterns, daily returns, etc.
- Leveraged ML model to combine core fundamental analysis with state-of-the-art quantitative techniques to predict the performance of a large number of stocks by comparing their relative performance.
- Investigated through three machine learning models using Feed-Forward Neural Networks (FNN), Random Forests (RF), and Adaptive Neural Fuzzy Inference Systems (ANFIS) for stock prediction based on fundamental analysis.
- The JMI model outperformed the standard ranking model used by the client earlier both in terms of accuracy and turnaround time as the JMI machine learning model was back tested against both long and short investment strategies for equities and fixed income assets over a long time.
Collected 2,000+ stocks from client which they wanted to analyze
Inherited fundamental data of each stock such as financials, shareholding pattern, daily return etc.
Applied JMI unique ML model to predict relative performance of each stock
Investigated through different ML models using FNN, RF and ANFIS
Ranked the stocks and analyzed their relative performance
Results
- JMI ML model ranked 2,000+ stocks based on fundamental analysis using quantamental approach.
- Reduced c.70% turnaround time in analyzing large number of stocks and increased accuracy in performance analysis.