Investobuddy

Traditional retail investors lack the time and expertise to assess whether a stock is undervalued or overvalued. Existing approaches often rely on short-term signals, ignoring fundamentals, and offer limited predictive power for long-term decision-making.

The development strategy focused on a phased implementation:

• Integrate financial fundamentals with machine learning forecasting
• Provide investors with a simple webapp that outputs whether a stock is fairly priced and predicts its near-term trend
• Blend classical valuation (DCF + Monte Carlo Simulation) with modern ML (LSTM, Random Forest)

Data Collection

• Pulled 5 years of financial and stock market data using Alpha Vantage and Yahoo Finance APIs.
• Extracted key financial ratios (P/E, P/B, Free Cash Flow, D/E, PEG)

Valuation

• Applied Discounted Cash Flow (DCF) model
• Ran Monte Carlo simulations to calculate intrinsic value distributions.

Forecasting

• Implemented stacked LSTM in PyTorch for 6-month price prediction.
• Benchmarked against Random Forest and logistic regression.
• Evaluated using MAE, RMSE, Accuracy, Precision, Recall, F1.

Deployment

• Built a Flask backend + Angular frontend
• Delivered intuitive buy/sell signals and dashboards to end users

• Intrinsic Value Analysis: Correctly flagged Apple and Microsoft as overvalued during evaluation period.
• Forecasting: LSTM - MAE 0.0239, RMSE 0.0317 (best performer); Random Forest: Less accurate, but interpretable baseline.
• End-User Impact: Provided actionable buy/sell recommendations in a webapp, simplifying complex financial analysis for non-technical investors.