Poroflio part 6

Question 1

What is the purpose of backtesting?

Answer 1

To simulate an investment process using historical data and assess the risk and return properties of a strategy.

Question 2

What does backtesting help investors do?

Answer 2

Reassure about strategy performance, refine strategies, and improve investment processes.

Question 3

Why is backtesting intuitive?

Answer 3

It mimics the real-life process of formulating strategies, testing them with historical data, and evaluating outcomes.

Question 4

Who uses backtesting?

Answer 4

Both systematic/quantitative managers and fundamental managers.

Question 5

What key assumption does backtesting rely on?

Answer 5

That the future will resemble the past to some degree.

Question 6

What is a major risk of backtesting?

Answer 6

A model that performs well in backtesting may fail in real-world performance.

Question 7

Why might good real-world models fail in backtesting?

Answer 7

If they do not exhibit predictive power in historical simulations, they are unlikely to be implemented.

Question 8

What technological advances have made backtesting easier?

Answer 8

Availability of large data sets and increased computing power.

Question 9

What are the three fundamental steps in backtesting?

Answer 9

Strategy design, historical investment simulation, analysis of backtesting output.

Question 10

What is the first step in backtesting?

Answer 10

Determine assumptions, investment objectives, and investment universe.

Question 11

What complicates return definition when investing globally?

Answer 11

Currency denomination and whether investments are hedged.

Question 12

Why is long historical data preferred in backtesting?

Answer 12

To maximize confidence in the results.

Question 13

What are common factor categories used in equity strategies?

Answer 13

Defensive value, cyclical value, growth, momentum, analyst sentiment, profitability, leverage, earnings quality.

Question 14

What is data mining trap in backtesting?

Answer 14

Selecting factors based only on good past performance without logical or economic rationale.

Question 15

What is risk parity portfolio construction?

Answer 15

Combining factors so that each contributes equally to overall portfolio risk.

Question 16

What is rolling window backtesting?

Answer 16

Walk-forward system that continually recalibrates and rebalances as new data arrives.

Question 17

What is a key assumption in rolling window backtesting?

Answer 17

Past patterns of performance will repeat over time.

Question 18

What is a potential drawback of rolling window backtesting?

Answer 18

It may not capture the dynamic nature of markets and extreme downside risks.

Question 19

How is a multifactor strategy backtested?

Answer 19

First, create factor portfolios with walk-forward backtesting; then combine factors into BM or RP portfolios and rebalance monthly.

Question 20

What is the Fama and French hedged portfolio methodology?

Answer 20

Long top quintile of stocks by factor scores, short bottom quintile, and rebalance monthly.

Question 21

Why might backtesting methodologies produce different results?

Answer 21

Non-linear relationships between factors and returns, different methodologies capture different aspects.

Question 22

What performance metrics are typically used to analyze backtesting output?

Answer 22

Sharpe ratio, maximum drawdown, Sortino ratio, average return, volatility.

Question 23

What is the advantage of using multiple backtesting methods?

Answer 23

Ideally, similar results from multiple methods increase confidence in the strategy’s robustness.

Question 24

What does VaR measure in backtesting?

Answer 24

Minimum expected loss over a time period at a given probability level.

Question 25

What does Conditional VaR (CVaR) measure?

Answer 25

Average loss given that the loss exceeds VaR.

Question 26

What is maximum drawdown?

Answer 26

Greatest loss from peak to trough in a portfolio's cumulative returns.

Question 27

Why use a log scale for cumulative return plots?

Answer 27

It makes equal percentage changes visually consistent and easier to spot structural breaks.

Question 28

What is survivorship bias?

Answer 28

Error from only considering surviving securities, ignoring those that disappeared over time.

Question 29

How can survivorship bias be avoided?

Answer 29

Using point-in-time data that captures securities active at each moment in history.

Question 30

What is look-ahead bias?

Answer 30

Using information in backtesting that would not have been available at the decision time.

Question 31

How can look-ahead bias be minimized?

Answer 31

Using point-in-time data or applying lag assumptions to reporting periods.

Question 32

What is data snooping?

Answer 32

Testing many strategies and selecting the one with best performance, risking false positives.

Question 33

How can data snooping be addressed?

Answer 33

Use higher critical t-values for significance and apply cross-validation techniques.

Question 34

What is cross-validation?

Answer 34

Testing a model on a different data set than the one used to create it to avoid overfitting.

Question 35

Why is rolling-window backtesting considered a form of cross-validation?

Answer 35

It uses in-sample periods to tune models and out-of-sample periods to test them sequentially.

Question 36

What is historical scenario analysis?

Answer 36

Testing investment strategies over different historical regimes (e.g., recessions, volatility shifts).

Question 37

What defines a high-volatility regime in historical scenario analysis?

Answer 37

Periods when the VIX is above its five-year moving average.

Question 38

What is the main difference between historical simulation and Monte Carlo simulation?

Answer 38

Historical simulation resamples actual past returns; Monte Carlo draws from assumed probability distributions.

Question 39

What advantage does Monte Carlo simulation have over historical simulation?

Answer 39

Monte Carlo can simulate never-before-seen events and account for non-normal distributions.

Question 40

What is bootstrapping in historical simulation?

Answer 40

Sampling historical returns with replacement.

Question 41

Why is fitting the correct distribution crucial in Monte Carlo simulation?

Answer 41

To accurately capture fat tails, skewness, and tail dependence in asset returns.

Question 42

What is tail dependence?

Answer 42

Correlation between extreme outcomes in different assets.

Question 43

What is a multivariate distribution in Monte Carlo simulation?

Answer 43

A distribution that models the joint behavior of multiple correlated variables.

Question 44

How is sensitivity analysis used in simulation?

Answer 44

By changing the assumed distribution (e.g., from normal to skewed t) and rerunning the simulation.

Question 45

Why conduct sensitivity analysis?

Answer 45

To assess how model misspecification affects risk and return estimates.

Question 46

What is the multivariate skewed t-distribution?

Answer 46

A distribution allowing for fat tails and skewness, used to better model real-world asset returns.

Question 47

What are key outputs to compare when interpreting simulation results?

Answer 47

Sharpe ratio, CVaR, and downside risk measures.