Chapter 6 · Section 2 practice

Forecast Benchmarks and Walk-forward Learning

0 of 3 answered

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

Question 1

Which metric is unusable for forecasting minute-by-minute price changes?

  • MAE — absolute errors are not differentiable
  • RMSE — it over-penalises large errors
  • MAPE — it divides by actual values that are near or exactly zero
  • Directional accuracy — the sign of a change is meaningless

Question 2

Target is y = r (today’s return). A colleague builds X[“std21”] = r.rolling(21).std() with no shift. What is wrong?

  • Nothing — volatility is a legitimate feature
  • The window at day \(t\) contains \(r_t\) itself: the target leaks into the feature
  • Rolling std needs at least 60 days to be stable
  • The feature is non-stationary

Question 3

Your LSTM beats the GBM by 4 % accuracy in-sample. What do you check first?

  • Increase the hidden size — the LSTM is under-fitting
  • Walk-forward out-of-sample on the same features and folds, plus a leakage audit of the sequence windows
  • Switch the loss from MSE to directional accuracy
  • Add more epochs