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Optimize-via-Predict: Realizing out-of-sample optimality in data-driven optimization
We examine a stochastic formulation for data-driven optimization wherein the decision-maker is not privy to the true distribution, but has knowledge that it lies in some hypothesis set and possesses a historical data set, from which information about it can be gleaned.
Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies.
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