1. Algorithmic trading
1.1. Types and Models
1.1.1. High-frequency trading
1.1.1.1. Statistical Arbitrage
1.1.1.2. Market Making
1.1.1.3. News-Based Trading
1.1.1.4. Algo-Driven Execution
1.1.2. Algorithmic execution
1.1.2.1. Smart Order Routing
1.1.2.2. Direct Market Access (DMA)
1.1.2.3. Twap and Vwap Trading
1.1.2.4. Dark Pool Execution
1.1.3. Quantitative trading
1.1.3.1. Mathematical modeling
1.1.3.2. Algorithmic trading strategies
1.1.3.2.1. Statistical Arbitrage
1.1.3.2.2. Index Arbitrage
1.1.3.2.3. Trend Following
1.1.3.2.4. Mean Reversion
1.1.3.3. Quantitative portfolio management
1.1.3.4. Risk management using quantitative methods
1.1.4. Statistical Arbitrage
1.1.4.1. Pair trading
1.1.4.2. Statistical arbitrage using options
1.1.4.3. Risk arbitrage
1.1.4.4. Statistical arbitrage using futures
1.2. Factors that are affected by algorithmic trading
1.2.1. Market liquidity
1.2.2. Volatility
1.2.3. Execution speed
1.2.4. Order book depth
1.2.5. Trading costs
1.2.6. Market efficiency
1.2.7. Market manipulation
1.2.8. Market microstructure
1.2.9. Trader behavior
1.2.10. Market fragmentation
1.2.11. Market access
1.2.12. Fairness
1.2.13. Market stability
2. Portfolio Optimization
2.1. Types and Models
2.1.1. Deep Learning-based Optimization
2.1.2. Behavioral Finance-based Optimization
2.1.3. Mean-Variance Optimization
2.1.4. Risk-Constrained Optimization
2.1.5. Robust Optimization
2.1.6. Multi-Objective Optimization
2.1.7. Algorithmic Portfolio Optimization
2.1.8. Black-Litterman Model Optimization
2.1.9. Bayesian Optimization
2.1.10. Stochastic Optimization
2.1.11. Sparse Representation-based Optimization
2.1.12. Hedge Fund Optimization
2.1.13. Real-time Portfolio Optimization
2.1.14. Factor-based Optimization
3. Stock price prediction
3.1. Generic Process and techniques of Stock Price Market Prediction
3.1.1. Statistical
3.1.1.1. ARIMA
3.1.1.2. Echo State Network
3.1.1.3. Regression
3.1.2. Pattern Recognition
3.1.2.1. Template Matching
3.1.2.2. PIP
3.1.3. Machine Learning
3.1.3.1. Genetic Algorithms (GA)
3.1.3.2. Fuzzy Algorithms (FA)
3.1.3.3. Genetic Algorithms (GA)
3.1.3.4. Shallow Model
3.1.3.4.1. Supervised Learning
3.1.3.4.2. Unsupervised Learning
3.1.3.5. Deep Learning Model
3.1.3.5.1. Supervised Learning
3.1.3.5.2. Unsupervised Learning
3.1.3.6. Hybrid Approaches
3.2. Approaches for Stock Market Prediction
3.2.1. Modern
3.2.1.1. Machine Learning Approach
3.2.1.2. Sentiment Analysis Approach
3.2.2. Classical
3.2.2.1. Fundamental Analysis
3.2.2.2. Technical Analysis
3.3. Element Under Prediction
3.3.1. Equity
3.3.1.1. Equity Price
3.3.1.2. Equity Trend
3.3.1.3. Equity price direction
3.3.2. index
3.3.2.1. Index Price
3.3.2.2. Index Trend
3.3.2.3. Index Price direction
3.3.3. Financial derivatives
3.3.3.1. Futures Price
3.3.3.2. Options Price
3.3.3.3. Forwards Price
3.3.3.4. Swaps Price
3.4. Time Horizon
3.4.1. Long term
3.4.2. Mid term
3.4.3. Short term
3.4.4. Daily
3.5. Factors affecting stock prices
3.5.1. Stock market data
3.5.1.1. Stock prices
3.5.1.2. Market capitalization
3.5.1.3. Trading volume
3.5.1.4. Earnings per share (EPS)
3.5.1.5. Price-to-earnings ratio (P/E ratio)
3.5.1.6. Dividend yield
3.5.1.7. Return on Equity (ROE)
3.5.1.8. Price-to-book ratio (P/B ratio)
3.5.1.9. Market indices (e.g. Dow Jones, S&P 500)
3.5.1.10. Analyst ratings and recommendations
3.5.1.11. Insider trading activity
3.5.2. Economic variables
3.5.2.1. Gross Domestic Product (GDP)
3.5.2.2. Consumer Price Index (CPI)
3.5.2.3. Unemployment rate
3.5.2.4. Interest rates
3.5.2.5. Inflation rate
3.5.2.6. Balance of trade
3.5.3. Financial performence data
3.5.3.1. Balance sheet
3.5.3.2. Revenue
3.5.3.3. Net income
3.5.3.4. Earnings per share (EPS)
3.5.3.5. Return on equity (ROE)
3.5.3.6. Return on assets (ROA)
3.5.3.7. Return on investment (ROI)
3.5.3.8. Gross margin
3.5.3.9. Operating margin
3.5.3.10. Net margin
3.5.3.11. EBITDA
3.5.4. Textual data
3.5.5. Technical Indicators