Quantifying Backtest Overfitting from Information Leakage: A Walk-Forward and Embargo-Based Diagnostic Framework Applied to Deep Learning Return Forecasts
Machine learning return prediction over 2016–2023, studied by Mir and Shrestha in 2026 with a feedforward neural network. The abstract mentions an out-of-sample test but not a benchmark, transaction costs, multiple testing or the size of the universe.
“These findings indicate that the direction and magnitude of leakage-induced bias cannot be assumed a priori and depends on the interaction between the contamination mechanism and the model's input representation.”
StrategyMachine learning
TechniqueFeedforward networkconvolutional network
FrequencyIntraday
Sample window2016–2023
Also stated: Macro, out-of-sample test
Missing from abstract: benchmark, costs, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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