# LSTM Stock Forecasting: NSE Banking Anomaly & Risk Analysis
> Learn how LSTM networks and Autoencoders are used for stock price prediction, anomaly detection, and Sharpe ratio risk analysis on NSE banking stocks.

Tags: stock-forecasting, lstm, nse-india, deep-learning, anomaly-detection, fintech, risk-analysis, banking-sector
## Stock Prediction & Risk Analysis using LSTM

*   **Objective:** Leveraging Long Short-Term Memory (LSTM) for price forecasting, anomaly detection, and risk analysis in the Indian banking sector (SBI, HDFC, ICICI).
*   **Methodology:** 
    *   Data: OHLCV data from Jan 2021 to March 2026.
    *   Architecture: 3-layer stacked LSTM (128-64-32 units) and LSTM Autoencoders for unsupervised anomaly detection.
    *   Preprocessing: 60-day sliding window with MinMaxScaler.

## Historical Trends and Correlations

*   **SBI:** Long-term uptrend reaching INR 1200+ by early 2026.
*   **HDFC:** Experienced a 50% structural correction in August 2025 (~INR 2000 to INR 1000).
*   **ICICI:** Most consistent compounding growth from INR 600 to INR 1500.
*   **Correlation:** SBI and ICICI show a strong correlation of 0.94.

## Predictive Results & Metrics

*   **Accuracy:** ICICI achieved the best MAPE of 1.86%.
*   **Anomalies:** The system successfully detected the HDFC Aug 2025 crash and the SBI Jan 2026 parabolic surge using a 95th percentile reconstruction error threshold.
*   **Risk Metrics:**
    *   SBI: 18.5% Ann. Return, 0.80 Sharpe Ratio (Best risk-adjusted).
    *   ICICI: 21.1% Volatility (Lowest/Most stable).
    *   HDFC: 14.2% Ann. Return, 0.34 Sharpe Ratio.

## Conclusions

*   Multi-layer LSTM models are commercially viable for bank stock prediction.
*   LSTM Autoencoders effectively capture real market 'black swan' events and technical structural breaks.
*   A diversified portfolio of SBI and ICICI offers an optimal growth-stability tradeoff.
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