Financial Market Prediction

Financial market prediction combines technical analysis (price patterns), fundamental analysis (intrinsic value), sentiment analysis (market psychology), and quantitative methods (statistical/ML models) to forecast asset prices, volatility, and market regime changes. While markets are notoriously difficult to predict due to the efficient market hypothesis, exploitable edges exist in volatility forecasting, sentiment-driven mispricings, and machine learning approaches that process alternative data at scale.

## Key Points

1. Technical analysis identifies patterns in price and volume; it works best as a timing tool on top of fundamental views
2. Fundamental analysis (DCF, relative valuation) provides the "what to buy" while technicals provide the "when to buy"
3. Sentiment indicators (VIX, put/call ratio, Fear & Greed) are most valuable as contrarian signals at extremes
4. GARCH models capture volatility clustering and provide calibrated volatility forecasts essential for risk management
5. Options prices contain the market's probability distribution for future prices; the Breeden-Litzenberger formula extracts it
6. Machine learning for financial prediction requires walk-forward validation to avoid look-ahead bias; random train/test splits are invalid
7. The most robust predictive features tend to be simple: momentum, mean reversion, volume, and volatility, not complex patterns
8. Transaction costs, slippage, and market impact are the reality check; many "profitable" strategies evaporate after costs