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Predicting Daily Market Direction

Determine whether and how to predict, with reliable accuracy, whether a financial market will go up or down from one day to the next, i.e., to classify the next day’s market movement as upward or downward on a daily horizon.

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Background

The paper evaluates machine-learning approaches (LSTM, Gradient-Boosted Trees, Random Forests) and introduces a dynamic multi-agent deep neural network (Model A) for short-term trading of S&P 500 E-mini futures using limited exchange-based data. Despite achieving profitable performance primarily through dynamic exposure control, the classification accuracy across models remains close to chance, reflecting the inherent difficulty of predicting daily market direction.

Within this context, the authors explicitly note that attempts to forecast whether the market will move up or down from one day to the next remain unsolved, underscoring a fundamental, widely pursued yet unresolved challenge in financial time-series prediction.

References

Attempts to predict intrinsically unpredictable systems such as whether the market goes up or down from one day to the next, remain unsolved.

— Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction (2408.11740 - Finnegan et al., 21 Aug 2024) in Subsection “Gradient-Boosted Trees and Random Forests” (Section 2, Data and Methods)