Chapter 4 quiz

Statistical Predictive Models

0 of 10 answered

These questions review the examples in the companion slides. Choose an answer, then check the explanation.

Question 1

Which statement is justified by the OLS example?

  • Training R² proves the signal will make money
  • A coefficient measures a conditional fitted association, and future forecast quality needs chronological evaluation
  • Normal-looking features guarantee independent errors
  • Pending outcomes should be filled with zero before training

Question 2

Which is a defensible model-selection claim?

  • The subset with the smallest p-value must have the best future return
  • A higher training R² guarantees lower future RMSE
  • Validation can guide a fixed search, but repeated tuning consumes its independence and coefficients still need identification for causal claims
  • A large VIF proves every prediction is wrong

Question 3

Which claim is supported by a validation AUC of 0.544?

  • The model predicts a 54.4% return
  • Its default cutoff must be 0.5
  • The score showed modest event ranking in that sample, while calibration, costs and future performance need separate checks
  • The model should automatically block every trade

Question 4

“Traders who use a risk dashboard have higher risk-control scores.” Which action needs a causal answer?

  • Use adoption as a predictor of a later risk-control score
  • Report the adoption–score association
  • Require every trader to adopt the dashboard, expecting the observed improvement
  • Report the correlation in the annual review

Question 5

What does this survivorship bias do to a backtest run on the file?

  • Nothing — every price is the real price on that day
  • Flatters it: firms that failed are absent, so returns are biased upward and losers are under-represented
  • Hurts it: today’s winners were expensive in 2015
  • Only changes the sector mix

Question 6

One of three 0.33-weight stocks is blocked by the risk rule. What follows from this policy?

  • The other stocks automatically rise to 0.495 each
  • The missing allocation stays in cash, and exposure falls from 0.99 to 0.66 before fees
  • The blocked stock’s return becomes zero in the source file
  • No transaction can occur

Question 7

Which condition is needed to define an identifiable OLS coefficient vector?

  • Every predictor is normally distributed
  • The design matrix has full column rank
  • The training R² exceeds 0.5
  • Every outcome is positive

Question 8

What does a coefficient confidence interval describe?

  • A band containing 95% of future stock returns
  • Uncertainty in a specified coefficient under the estimation and sampling assumptions
  • The probability that every strategy trade succeeds
  • The maximum possible drawdown

Question 9

A logit coefficient is log(2) per unit. What doubles for a one-unit increase, holding other inputs fixed?

  • The event probability at every starting value
  • The conditional odds
  • The expected next-month return
  • The classifier AUC

Question 10

Add \(U\) as a second regressor. The coefficient on \(T\) will:

  • Stay near 2.17 — \(T\) is still strongly correlated with \(Y\)
  • Collapse toward 0 — the true effect
  • Flip to about −2
  • Become undefined because \(T\) and \(U\) are correlated