1
Q
Data-driven investing
A
uses systematic, quantitative strategies to analyze current and historical data to forecast securities’ future performance. It departs from conventional methods that often rely on intuition, qualitative analysis, or historical precedence.
2
Q
Detailed Mechanics of Modeling - Data Collection
A
- Sources
- Types of Data
- Data Cleaning
3
Q
Detailed Mechanics of Modeling - Strategy Definition
A
- Trade Criteria
- Portfolio and Capital allocation
- Risk Management Rules
4
Q
Detailed Mechanics of Modeling - Implementation
A
- Coding the Strategy
- Simulation
5
Q
Detailed Mechanics of Modeling - Performance Analysis
A
- Benchmarking
- Risk metrics
6
Q
The Essence of Modeling Testing
A
- Testing asset returns
- Evaluating Investment Strategies
- Cointegration tests
- Event Studies
7
Q
Challenges & Considerations in Testing
A
- Data Snooping
- Non-normal distributions
- Multiple testing problem
- Model assumptions
8
Q
Benefits of Modeling: A Deeper Insight
a.
A
- Confidence Building
- Strategy evolution
- Capital allocation guidance
9
Q
Modeling Testing
Pitfalls and Considerations: Beyond the Basics
A
- Parameter sensitivity
- Market Regime Changes
- Post- strategy drift
- Adaptative Markets
10
Q
Advanced Best Practices
A
- Walk-Forward Analysis
- Monte Carlo Stimulations
- Stress Testing
