Complete Webinar Recording: https://blog.quantinsti.com/introduction-quantitative-factor-investing-webinar-28-february-2023/ ----------------------------------------- About the Session This section covers the concept of factor investing and different types of factor investing strategies including a discussion of passive vs active investing and due diligence considerations. It also covers the application of quantitative methods such as mathematical modelling and data analysis for factor analysis. ----------------------------------------- Session Outline - What is Quantitative Factor Investing? - Benefits of Factor Investing - How Does Factor Investing Work? - Common Quantitative Factor Investing Strategies - Applications of Quantitative Factor Investing - Key Considerations for Quantitative Factor Investing - Interactive Q&A ----------------------------------------- Pre-Requisites Basic familiarity with investment and trading terminology. ----------------------------------------- About the Speaker Varun Kumar Pothula (Quantitative Analyst at QuantInsti) Varun holds a Masters degree in Financial Engineering. He has experience working as a trader, a global macro analyst, and also an algo trading strategist. Currently, working in the Content & Research Team at QuantInsti as a Quantitative Analyst, his contributions help in creating offerings for learners in the domain of algorithmic & quantitative trading. ----------------------------------------- ✅ Free Resources to Learn Algorithmic Trading https://blog.quantinsti.com/free-resources-list-compilation-learn-algorithmic-trading/ 📄 Blogs and Tutorials https://blog.quantinsti.com/ 🎞️ Complete webinar recordings https://blog.quantinsti.com/tag/webinars/ ----------------------------------------- This event was conducted on: Tuesday, February 28, 2023 8:30 AM EST | 7:00 PM IST | 9:30 PM SGT ----------------------------------------- Like and Follow us on: Our Blog: https://blog.quantinsti.com/ Facebook @ https://www.facebook.com/quantinsti/ Twitter @ https://twitter.com/QuantInsti LinkedIn @ https://www.linkedin.com/school/quantinsti/ Instagram @ https://www.instagram.com/quantinstian/ E-mail us @ sales@quantinsti.com

1. Introduction to Quantitative
Factor Investing

2. Agenda
● Factor investing
● Classification of factors
● Quantitative factor investing
● Application of quantitative methods
● Factor investing vs quantitative factor investing
● Selecting the best factors
● Combining factors

3. What is Factor Investing?

4. Factor Investing
Selecting securities based on specific characteristics, or "factors," that are believed to drive
their returns
Factors: Broad and persistent sources of risk and return
Examples of factors:
● Value
● Momentum
● Quality
● Size
● Volatility

5. Factors
Companies with high-quality characteristics are more likely
to outperform companies with low-quality characteristics
over the long-term
Quality

6. Factors
● Combination of financial ratios
○ Return on equity (ROE)
○ Earnings growth rate
○ Profitability ratios such as gross and net margins
Quality

7. Factors
● Estimating Quality
○ High quality: Companies with high ratios and margins
○ Low quality: Companies with low ratios and margins
● Generate excess returns over long periods of time
Quality

8. Factor Investing
Factors: Broad and persistent sources of risk and return
Broad → Across a wide range of assets within a given market or asset class
Quality factor:
● Across different sectors and industries
● Diversification and risk management

9. Factor Investing
Factors: Broad and persistent sources of risk and return
Persistent → Effectiveness in generating excess returns over long time horizons
Quality factor:
Persistently generate excess returns over long periods of time
● Despite occasional periods of underperformance
● Reliable source of risk and return

10. Total number of factors available for factor investing?

11. Classification of Factors

12. Classification of Factors
Macro
Factors
Style
Sector
ESG
Sentiment
Liquidity
Technical

13. Macro Factors
● Macroeconomic variables
○ GDP growth
○ Inflation rate
○ Interest rates
Example strategy:
Macro factor portfolio of stocks with 70%
capital to stocks that benefit from GDP
growth and 30% capital to stocks
sensitive to rising interest rates
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

14. Style Factors
● Specific investment style
○ Value
○ Momentum
○ Quality
○ Size
Example strategy:
Style factor portfolio of stocks with high
return on equity and low debt-to-
equity ratios
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

15. Sector Factors
● Sector metrics
○ Earnings growth
○ price-to-earnings ratio
○ Dividend yields
○ Market capitalization
Example strategy:
Sector factor portfolio of stocks from the
top 3 sectors based on earnings growth
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

16. ESG Factors
● Environmental, social, and governance
criteria
● Impact on society and the
environment
● Metrics:
○ Carbon footprint
○ Treatment of employees
○ Board composition, executive
compensation, transparency
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

17. ESG Factors
ESG Rating Providers:
● Institutional Shareholder Services (ISS)
● CDP (the Carbon Disclosure Project)
● MSCI
● S&P Global Trucost
Example strategy:
ESG factor portfolio by selecting stocks of
companies that have a high ESG rating
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

18. Sentiment Factors
● Sentiment:
○ News events
○ Earnings reports
○ Social media sentiment
○ Market-related events
Example strategy:
Sentiment factor portfolio by selecting
stocks with top 5 mentions in Twitter with
positive sentiment
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

19. Liquidity Factors
● Liquidity Measures:
○ Bid-ask spread
○ Turnover
○ Trading volume
Example strategy:
Liquidity factor portfolio by selecting stocks
of companies that trade frequently and
have low transaction costs
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

20. Technical Factors
Technical indicators
Example strategy:
Technical factor portfolio by using moving
average crossover to identify stocks that
are in a strong uptrend
Macro
Style
Sector
ESG
Sentiment
Liquidity
Technical

21. Quantitative Factor Investing

22. Quantitative Factor Investing
Investment strategy that uses quantitative methods to identify and exploit specific
factors that are believed to drive returns in the financial markets
Securities with characteristics
of selected factor
Rank securities
and
Create portfolio
● Mathematical models
● Statistical analysis
● Set of rules
&
algorithms

23. Quantitative Factor Investing
Investment strategy that uses quantitative methods to identify and exploit specific
factors that are believed to drive returns in the financial markets
● Remove emotions and biases from investment decisions
● Objective data and analysis
● Identification of less intuitive factors

24. Application of Quantitative
Methods

25. Quantitative Methods
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

26. Statistical Analysis
● Statistical techniques
○ Identify patterns
○ Relationship between securities and
factors
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

27. Factor Modeling
● Estimate the exposure of a security
to specific factors
● Identify factors that are most
predictive of returns
● Construct portfolios that are tilted
towards those factors
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

28. Machine Learning
● Algorithms
○ Factor discovery
○ Factor combination
○ Learn patterns and relationships in
large unstructured datasets
○ Make predictions about future
performance
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

29. Optimisation
● Mathematical models
○ Portfolio construction
○ Maximise factor exposure
○ Minimise risk
○ Minimise transaction costs
○ Optimal order execution
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

30. Time-Series Analysis
● Analysing historical data
○ Identify patterns
○ Trends in factor performance over
time
○ Relationship between factor and
security
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

31. Risk Models
● Quantitative methods
○ Measure risk
○ Manage risk
● Value-at-risk (VaR)
● Conditional value-at-risk (CVaR)
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

32. Monte Carlo Simulation
● Probabilistic models
○ Simulate different market scenarios
○ Estimate the probability of different
outcomes
○ Generate optimal portfolio allocations
based on specific risk and return
criteria
○ Stress tests
■ extreme market conditions
■ market crashes
■ economic recessions
Statistical Analysis
Quantitative
Methods
Factor Modeling
Machine Learning
Optimisation
Time-Series
Analysis
Risk Models
Monte Carlo
Simulation

33. Factor Investing
vs
Quantitative Factor Investing

34. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Factor Investing Approach
1) Identify the factor
○ price-to-earnings ratio (P/E ratio)
2) Determine the universe
○ large-cap stocks
3) Calculate the factor for each stock
4) Sort the stocks by factor
○ Sort the stocks in the universe by their P/E ratio, from lowest to highest.

35. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Factor Investing Approach
1) Identify the factor
2) Determine the universe
3) Calculate the factor for each stock
4) Sort the stocks by factor
5) Construct the portfolio
○ Select the 50 stocks with the lowest P/E ratios from the universe of large-cap stocks

36. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Factor Investing Approach
1) Identify the factor
2) Determine the universe
3) Calculate the factor for each stock
4) Sort the stocks by factor
5) Construct the portfolio
6) Monitor and rebalance
○ Ensure that the portfolio continues to meet the desired factor exposure

37. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
Quantitative Method Used: Machine learning
1. Data Collection
○ Collect historical data on large-cap stocks with price-to-earnings ratios from various sources
like financial databases or APIs.

38. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
○ Clean and preprocess the data to remove any missing or inconsistent data points, and
standardize the data for further analysis.

39. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
3. Feature Selection
○ Identify relevant features that could potentially impact the stock price and factor selection
○ price-to-earnings ratio, dividend yield, earnings growth, and price-to-book ratio.

40. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
3. Feature Selection
4. Model Building
○ decision trees, random forests, or neural networks to train a model that can predict the stock
price based on the selected features.

41. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
3. Feature Selection
4. Model Building
5. Model Evaluation
○ Accuracy, precision, recall, and F1 score.

42. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
3. Feature Selection
4. Model Building
5. Model Evaluation
6. Portfolio Construction
○ Use the predictions from the model to construct a portfolio of stocks with low price-to-
earnings ratios.

43. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
3. Feature Selection
4. Model Building
5. Model Evaluation
6. Portfolio Construction
7. Portfolio Management
○ Make necessary adjustments based on changes in the market or any new information

44. Case Study
“A portfolio manager creates a portfolio that invests in large-cap stocks with low price-to-
earnings ratios”
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
3. Feature Selection
4. Model Building
5. Model Evaluation
6. Portfolio Construction
7. Portfolio Management

45. Case Study
Factor Investing Approach
1) Identify the factor
2) Determine the universe
3) Calculate the factor for each stock
4) Sort the stocks by factor
5) Construct the portfolio
6) Monitor and rebalance
Quantitative Factor Investing Approach
1. Data Collection
2. Data Pre-processing
3. Feature Selection
4. Model Building
5. Model Evaluation
6. Portfolio Construction
7. Portfolio Management

46. How to Select Best Factors ?

47. Selecting the Best Factors
Persistent
Best Factor
Pervasive
Robust
Investable
Intuitive

48. Persistent Factor
● Consistent performance over a long
period of time
Persistent
Best Factor
Pervasive
Robust
Investable
Intuitive

49. Pervasive Factor
● Holds across countries, regions,
sectors, and even asset classes
Persistent
Best Factor
Pervasive
Robust
Investable
Intuitive

50. Robust Factor
● Performs well across
○ Different time periods
○ Different market conditions
● Not overly sensitive to changes in
market dynamics
Persistent
Best Factor
Pervasive
Robust
Investable
Intuitive

51. Investable Factor
● Easily implemented in a portfolio
● Less trading costs
● ETFs or index funds is an added
advantage
Persistent
Best Factor
Pervasive
Robust
Investable
Intuitive

52. Intuitive Factor
● Based on:
○ Fundamental economic concept
○ Financial concept
● Easily understandable
● Accepted by investors
● More likely to be used by investors
Persistent
Best Factor
Pervasive
Robust
Investable
Intuitive

53. How to Combine Factors?

54. Combining the Factors
● Reduce the impact of one factor on the portfolio
● Increase diversification of the portfolio
● Capture benefit of multiple factors
● Reduce the risk of relying on a single factor
● Smoothen the volatility of returns over time

55. Combining the Factors
Equal Weighting
Combining
Factors
Factor Scoring
PCA
Factor Tilting
Machine Learning

56. Equal Weighting
● All factors are given equal
importance
● Equal weights
● Simple and transparent
Equal Weighting
Combining
Factors
Factor Scoring
PCA
Factor Tilting
Machine Learning

57. Factor Scoring
● Score based on its historical
performance
● Combine scores using a weighted
average
● Customization and flexibility
Equal Weighting
Combining
Factors
Factor Scoring
PCA
Factor Tilting
Machine Learning

58. PCA
● Principal component analysis (PCA)
● Combines factors into a smaller set
of uncorrelated components
● Order by variance
○ Top components → portfolio
construction
● Reduces factors
● Removes multicollinearity
Equal Weighting
Combining
Factors
Factor Scoring
PCA
Factor Tilting
Machine Learning

59. Factor Tilting
● Start with benchmark portfolio
● Adjusts weights towards the desired
factor exposure
● Flexible
● Target specific factor exposures
Equal Weighting
Combining
Factors
Factor Scoring
PCA
Factor Tilting
Machine Learning

60. Machine Learning
● Select and combine factors as per
historical performance
● Data driven
● Captures non linear relationships
Equal Weighting
Combining
Factors
Factor Scoring
PCA
Factor Tilting
Machine Learning

61. Complete Webinar Recording