cMDA Feature selection can improve both predictive accuracy and intuition about regime-dependence of your trading strategy.

• Cluster together features are that are similar and  should receive the same importance rankings
• Rank these Clusters by importance scores.
• Use only clusters with high scores to predict profitability.
• Improves out-of-sample predictive accuracy by building more parsimonious models. (See our recent paper.)

Example: S&P 500 index excess monthly returns

• Using a public dataset as described in this Paper. The dataset is available under the “Get Database” tab as SPX train and test data.
• Based on the paper, the dependent variable is equity premium, that is, the total rate of return on the stock market minus the prevailing short-term interest
  rate.
• Independent variables are stock characteristics including fundamental and technical specifications like:
  • dfy : Default yield spread
  • infl : Inflation
  • svar : Stock variance
  • d/e : Dividend payout ratio
  • lty : Long term yield
  • tms : Term spread
  • tbl : Treasury-bill rate
  • dfr : Default return spread
  • d/p : Dividend price ratio
  • d/y : Dividend yield
  • ltr : Long term return
  • e/p : Earning price ratio
  • and more…
• cMDA Feature selection as you can see from the feature plot tells us:
  • 2 clusters (C_1, C_2) were found to be most important in terms of feature importance
  • On closer inspection, two clusters can clearly be interpreted as fundamental vs technical indicators.
  • Due to robust nature of cMDA, the feature rankings don’t change: fundamental indicators are always found to be more important than technical indicators in all 100 runs with different random seeds.