To approach the optimzation problem of achieving a higher return while lower risk, we go back to these classic models:
Black-Litterman Model
Markowitz Model
Hierachical risk parity
We used three types of models to generate the best view matrix and uncertainty matrix for Black-Litterman Model.
We picked stock price return as the proxy for the two matrices mentioned above.
- Statistical Finance Models: ARIMA, GARCH
- Machine Learning Models: Linear Regression, Random Forest, XGBoost
- Neural Network Model: Monte Carlo Dropout
Companies preferences Stock preferences Expected Return Acceptable Risk time??
https://github.com/robertmartin8/PyPortfolioOpt https://nbviewer.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb
- closing price trends
- daily high
- trading volume
- moving averages
- exponential moving averages
- Moving Averages (SMA, EMA, Usage - Identifies trends and potential reversal points. Commonly used moving averages are 50-day and 200-day SMAs.)
- Momentum Indicators: (Relative Strength Index (RSI), Stochastic Oscillator)
- Trend Indicators: Moving Average Convergence Divergence (MACD), Average Directional Index (ADX):
- Volatility: Bollinger Bands, Average True Range (ATR)
- Volume: On-Balance Volume (OBV), Volume Moving Average