Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [
Lecture 6: Means — [📝Lecture Notebooks] [
Lecture 7: Variance — [📝Lecture Notebooks] [
Lecture 8: Statistical Moments — [📝Lecture Notebooks] [
Lecture 9: Linear Correlation Analysis — [📝Lecture Notebooks] [
Lecture 10: Instability of Estimates — [📝Lecture Notebooks] [
Lecture 11: Random Variables — [📝Lecture Notebooks]
Lecture 12: Linear Regression — [📝Lecture Notebooks] [
Lecture 13: Maximum Likelihood Estimation — [📝Lecture Notebooks]
Lecture 14: Regression Model Instability — [📝Lecture Notebooks] [
Lecture 15: Multiple Linear Regression — [📝Lecture Notebooks]
Lecture 16: Violations of Regression Models — [📝Lecture Notebooks] [
Lecture 17: Model Misspecification — [📝Lecture Notebooks] [
Lecture 18: Residual Analysis — [📝Lecture Notebooks]
Lecture 19: The Dangers of Overfitting — [📝Lecture Notebooks] [
Lecture 20: Hypothesis Testing — [📝Lecture Notebooks]
Lecture 21: Confidence Intervals — [📝Lecture Notebooks]
Lecture 22: p-Hacking and Multiple Comparisons Bias — [📝Lecture Notebooks] [
Lecture 23: Spearman Rank Correlation — [📝Lecture Notebooks] [
Lecture 24: Leverage — [📝Lecture Notebooks]
Lecture 25: Position Concentration Risk — [📝Lecture Notebooks] [
Lecture 26: Estimating Covariance Matrices — [📝Lecture Notebooks]
Lecture 27: Introduction to Volume, Slippage, and Liquidity — [📝Lecture Notebooks]
Lecture 28: Market Impact Models — [📝Lecture Notebooks]
Lecture 29: Universe Selection — [📝Lecture Notebooks] [
Lecture 30: The Capital Asset Pricing Model and Arbitrage Pricing Theory — [📝Lecture Notebooks]
Lecture 31: Beta Hedging — [📝Lecture Notebooks] [
Lecture 32: Fundamental Factor Models — [📝Lecture Notebooks] [
Lecture 33: Portfolio Analysis — [📝Lecture Notebooks]
Lecture 34: Factor Risk Exposure — [📝Lecture Notebooks] [
Lecture 35: Risk-Constrained Portfolio Optimization — [📝Lecture Notebooks]
Lecture 36: Principal Component Analysis — [📝Lecture Notebooks]
Lecture 37: Long-Short Equity — [📝Lecture Notebooks]
Lecture 38: Example: Long-Short Equity Algorithm — [📝Lecture Notebooks]
Lecture 39: Factor Analysis with Alphalens — [📝Lecture Notebooks] [
Lecture 40: Why You Should Hedge Beta and Sector Exposures (Part I) — [📝Lecture Notebooks]
Lecture 41: Why You Should Hedge Beta and Sector Exposures (Part II) — [📝Lecture Notebooks]
Lecture 42: VaR and CVaR — [📝Lecture Notebooks]
Lecture 43: Integration, Cointegration, and Stationarity — [📝Lecture Notebooks] [Video]
Lecture 44: Introduction to Pairs Trading — [📝Lecture Notebooks] [
Lecture 45: Example: Basic Pairs Trading Algorithm — [📝Lecture Notebooks]
Lecture 46: Example: Pairs Trading Algorithm — [📝Lecture Notebooks]
Lecture 47: Autocorrelation and AR Models — [📝Lecture Notebooks] [
Lecture 48: ARCH, GARCH, and GMM — [📝Lecture Notebooks]
Lecture 49: Kalman Filters — [📝Lecture Notebooks] [
Lecture 50: Example: Kalman Filter Pairs Trade — [📝Lecture Notebooks]
Lecture 51: Introduction to Futures — [📝Lecture Notebooks]
Lecture 52: Futures Trading Considerations — [📝Lecture Notebooks]
Lecture 53: Mean Reversion on Futures — [📝Lecture Notebooks]
Lecture 54: Example: Pairs Trading on Futures — [📝Lecture Notebooks]
Lecture 55: Case Study: Traditional Value Factor — [📝Lecture Notebooks]
Lecture 56: Case Study: Comparing ETFs — [📝Lecture Notebooks]
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