Mutual Fund Performance Forecasting

Skills, Tech Stack, and Libraries

1. Skills: Time-Series Analysis, Predictive Modeling, Statistical Analysis, Data Visualization

2. Tech Stack: Python, SQL, AWS

3. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn

Approach

Objective:

I developed a predictive model to forecast the performance of mutual funds by analyzing historical financial data. The project aimed to assist investors in making informed decisions by identifying trends, returns, and risk factors associated with mutual funds.

Approach:

1. Data Collection and Preprocessing:

   • Acquired historical data on mutual funds, including net asset value (NAV), returns, and market indices, using SQL from financial databases.

   • Cleaned the data using Pandas, handling missing values and aligning time-series data for accurate analysis.

2. Exploratory Data Analysis (EDA):

   • Conducted EDA to uncover patterns in mutual fund performance, identifying trends, seasonal effects, and correlations with market indices.

   • Visualized NAV trends and historical returns using Matplotlib and Seaborn.

3. Feature Engineering:

   • Created features like rolling averages, volatility, and Sharpe ratio to capture risk-adjusted returns.

   • Engineered lag features to include the influence of previous NAV values on future performance.

4. Model Development:

   • Trained machine learning models, including:

     • Linear Regression and ARIMA for baseline forecasts.

     • LSTM networks for capturing long-term dependencies in time-series data.

   • Optimized model performance using hyperparameter tuning to reduce prediction errors.

5. Visualization and Reporting:

   • Built an interactive dashboard to present:

     • Predicted NAV and returns over different time horizons.

     • Risk indicators such as volatility and drawdowns.

     • Historical performance comparisons with benchmarks.

6. Automation:

   • Automated data updates and model retraining workflows using Python scripts to ensure the system remained current with market changes.

     
     

Code Flow:

1. Load mutual fund data using Pandas and preprocess it for time-series analysis.

2. Perform EDA to understand trends and patterns in the data.

3. Engineer features like rolling averages and volatility to enhance model accuracy.

4. Train ARIMA and LSTM models to forecast NAV and returns.

5. Deploy predictions and visualizations via an interactive dashboard for users.

   
   

Results

The Mutual Fund Performance Forecasting System achieved significant outcomes:

• Accurate Predictions: Achieved a Mean Absolute Percentage Error (MAPE) below 5% for NAV forecasts, enabling reliable decision-making for investors.

• Enhanced Risk Management: Identified high-volatility funds, empowering users to adjust their portfolios to align with their risk appetite.

• Strategic Insights: Delivered long-term performance forecasts, helping investors identify funds with consistent growth potential.

• User Engagement: The interactive dashboard simplified complex financial data, making it accessible and actionable for users.

This project highlighted the integration of advanced machine learning techniques with financial data to support investment strategies and risk management.

Git Link

For more information and code, visit the Git link.