Financial Analysis. Lesson 27. Quantitative Finance and Data-Driven Investing
Financial Analysis. Lesson 27. Quantitative Finance and Data-Driven Investing
Quantitative finance applies mathematical and statistical methods to financial problems.
Algorithmic trading uses computer algorithms to execute trades at high speed.
Machine learning models analyze large data sets to identify trading opportunities.
Factor analysis evaluates variables like momentum and value in asset selection.
Backtesting tests investment strategies against historical data for reliability.
Time series analysis predicts future trends based on sequential financial data.
Reinforcement learning enables algorithms to optimize strategies through trial and error.
Neural networks process data in layers, uncovering complex financial patterns.
Optimization algorithms refine investment strategies for maximum returns with constraints.
Monte Carlo methods simulate multiple outcomes to evaluate investment risk.
Portfolio rebalancing algorithms adjust asset weights to maintain optimal allocation.
Statistical arbitrage identifies mispriced assets for potential profit opportunities.
High-frequency trading (HFT) involves rapid trades to capitalize on tiny price differences.
Principal component analysis (PCA) reduces dimensionality, focusing on key data features.
Sentiment analysis gauges investor mood by analyzing news or social media.
Clustering algorithms group similar assets for targeted portfolio diversification.
Risk-neutral valuation calculates asset prices without factoring in risk premiums.
Financial engineering designs structured products and derivative instruments.
Stochastic processes model random variables to predict asset price movement.
Kalman filter updates estimates by combining predictions with new data.
Genetic algorithms evolve investment strategies by simulating natural selection.
Data normalization adjusts data scale for compatibility across models.
Outlier detection identifies data points that deviate significantly from norms.
Non-linear optimization solves problems where relationships are non-linear.
Support vector machines (SVMs) classify data for identifying financial trends.
Regularization techniques prevent overfitting in machine learning financial models.
Decision trees segment data to support predictive financial analysis.
Markov chains model probabilistic transitions between different market states.
Dynamic programming solves complex problems by breaking them into simpler stages.
Factor rotation realigns factors for clearer interpretation in financial models.
Technical Examples:
Backtesting helps validate if strategies could perform well under real conditions.
Principal component analysis highlights important variables for asset selection.
Reinforcement learning trains algorithms to improve trading strategies autonomously.