Statistical Testing and Validation of Investment Ideas

Question 1

Key Statistical Measures in Finance

Answer 1

• Mean
• Variance and sd dev
• Skewness
• Kurtosis
• Correlation and Covariance
• Beta
• Sharp ratio
• Value at Risk

Question 2

Importance of Statistical Measures in Finance

Answer 2

• Risk Assessment
• Investment Evaluation
• Portfolio Construction
• Performance Benchmarking

Question 3

Challenges and Considerations

Answer 3

• Non - stationary
• Model assumptions
• External Factors

Question 4

Essence of Hypothesis testing

Answer 4

• Null Hypothesis (H0)
• Alternative Hypothesis
• Test Statistic
• Significance level (alpha)
• P-value

Question 5

Applications in Finance

Answer 5

• Teste asset returns
• Evaluating Investment strategies
• Cointegration teste
• Event studies
• Testing Market Efficiency

Question 6

Steps in Hypothesis testing

Answer 6

1. Formulate the Hypotheses
2. Choose the significance level
3. Select the Appropriate Test
4. Compute the test statistic
5. Make a decision

Question 7

Challenges and Considerations in Financial Hypothesis Testing

Answer 7

• Data Snooping
• Non-Normal Distributions
• Multiple Testing Problem
• Model Assumptions

Question 8

back testing

Detailed Mechanics of Backtesting

Answer 8

1.Data Collection
○ Sources
○ Types of Data
○ Data Cleaning
2. Strategy Definition
○ Trade Criteria:
○ Portfolio and Capital Allocation:
○ Risk Management Rules:
3. Implementation
○ Coding the Strategy
○ Simulation
4. Performance Analysis
○ Benchmarking
○ Risk Metrics

Question 9

Expanded Key Metrics in Backtesting

Answer 9

• Annualized returns
• Sortini Ratio (like sharp but only consider downside volatility)
• Calmar ratio
• Percent Profitable Trades
• Profit Factor
• Omega Ratio

Question 10

Benefits of Backtesting: A Deeper Insight

Answer 10

1. Confidence Building
2. Strategy Evolution
3. Capital Allocation Guidance

Question 11

Pitfalls and Considerations: Beyond the Basics

Answer 11

Parameter Sensitivity
Market Regimef Changes
Post- Strategy Drift
Adaptative Markets

Question 12

Advanced Best Practices

Answer 12

Walk Forward Analysis
Monte Carlo Simulations
Stress Testing

Question 13

Why Model Evaluation is Essential

Answer 13

Stakeholder Confidence
Strategic decision making
Regulatoru Compliance
Continuous Improvement

Question 14

Evaluating Forecast Accuracy

Answer 14

Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Mean Absolute Percentage Error (MAPe)
Theil’s Statistic - compares the forecasted model to a (no-change) forecast

Question 15

Classification Model Metrics (For Binary Outcomes, e.g., Default/No
Default)

Answer 15

• Confusion Matrix
• Accuracy
• Precision
• Recall (Sensitivity)
• F1 - Score
• Area under the ROC Curve
• Log Loss

Question 16

Evaluating Time Series Models

Answer 16

Autocorrelation Function (ACF)
Partial Autocorrelation Function (PACF)
Akaike Information Criterion (AIC) and Bayesian Information (BIC)

Question 17

Risk-Adjusted Performance Metrics

Answer 17

Sharp Ratio
Sortino Ratio
Treynor Ratio
Information Ratio

Question 18

Model Robustness and Stability

Answer 18

Out-og-sample Testing
Cross Validation
Rowlling Window analysis

Question 19

Overfitting vs. Underfitting

Answer 19

Bias - Variance Tradeoff
Regularization Techniques
Learning Curve

Question 20

Model Comparisons and Benchmarks

Answer 20

Lift and Gain Charts
Cumulative Accuracy Profile
Benchmark Models

Question 21

Real-world Considerations in Model Evaluation

Answer 21

Implementation Costs
Model Drift
Feedback Loops