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  • Writer's pictureMatt DeLong

Why do machine learning models not perform well when used in live stock market prediction?

  • “Machine learning” by definition assumes there is a repeatable pattern that can be trained, but we all know there is some percentage of randomness to the markets that add a twist.

  • Overfit the model & data so backtests works well, but real performance is poor.

  • Not running model through a real-time simulator for some period of time as an “out of sample” test data before going live.

  • Not taking into account the REAL transaction costs of trading: such as ECN, SEC, FINRA, broker fees, borrow fees (if shorting), platform fees, market data fees.

  • Not comparing a backtest to a benchmark ($SPY at the very least).

  • Using backtest market data that doesn’t account for dividend payouts or stock splits, this is a common mistake.

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