Monetizing Short-Horizon Stock Predictions After Trading Costs
Summary
The document asks how to trade a model’s frequent, short-horizon stock predictions when the predicted moves are smaller than the bid–ask spread. The author reports a model that predicts the direction of a move over the next two minutes with 75% accuracy for a limited set of stocks, but says its accuracy does not hold for larger moves that could clear a typical spread. They ask whether a derivative or another instrument could provide exposure without imposing the same spread burden.
Binary options are raised as a possible alternative, with an example payout used to calculate an apparently positive expected value. The document does not establish that this calculation reflects real contract terms or that the signal would remain predictive for the option’s settlement conditions. It is a question rather than a tested trading method: transaction costs, payout structure, execution, and whether the prediction maps to the contract outcome remain unresolved.
Key ideas
- The author reports short-horizon directional predictions that may be too small to overcome stock spreads.
- The document distinguishes prediction accuracy for small moves from accuracy for moves large enough to cover trading costs.
- It considers binary options as a possible way to express the signal and illustrates a payout-based expected-value calculation.
- The question leaves contract mechanics, costs, and live profitability unverified.
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Full text
# How to monetize ability to predict small stock movements smaller than spread? # How to monetize ability to predict small stock movements smaller than spread? For a relatively small subset of stock symbols I have been able to build a model that is able to 20-100 times per day consistently predict whether a stock is going up within the next 2 minutes, being correct 75% of the time. I have worked both academically and professionally with data science, and dare say I am using proper methodologies, i.e. not testing the model on training data, keeping in mind that I'm working with time series, etc. So for argument's sake, let's assume that this performance would translate into production. But if I was to go trade stocks directly based on this model in real life, e.g. through Interactive Brokers, the commissions and bid-ask-spread would more than eat the profits. As an example, let's say that the typical spread for "SymbolA" is 10 cents, and ignore commissions for now. My model might be able to tell with 75% accuracy whether the stock will move by more than 5 cents within the next 2 minutes. But the model doesn't maintain its accuracy if it is to predict the same for movements of 10 cents, for instance. So for now this is a roadblock for monetizing this. Therefore my question is: How can I monetize this ability? E.g. Is there any way of gearing these movements without gearing the spread too, so to say? From searching, I've stumbled upon (60-second) Binary Options, but I can't figure out of these also incorporate the underlying asset's spread (and commission), by setting the bar for a "win" relatively high? But on the surface it does seem that they forego spreads and commission, by rather paying out just \$0.7 on a win and -\$1 on a loss. So seemingly I could be profitable using these (E[75% * \$0.7 - 25% * \$1.0] = $0.275) But maybe I'm misunderstanding how binary options work. Any input would be appreciated - thanks!
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