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• W3094237116 abstract "While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it is possible to model individual market participants’ behaviors through observation of publicly available unlabeled market data alone. Several methods have been suggested in the literature using classification methods based on summary trading statistics, as well as using inverse reinforcement learning methods to infer the reward function underlying trader behavior. Our primary contribution is to propose an alternative neural network based multi-modal imitation learning model which performs latent segmentation of stock trading strategies. As a result that the segmentation in the latent space is optimized according to individual reward functions underlying the order submission behaviors across each segment, our results provide interpretable classifications and accurate predictions that outperform other methods in major classification indicators as verified on historical orderbook data from January 2018 to August 2019 obtained from the Tokyo Stock Exchange. By further analyzing the behavior of various trader segments, we confirmed that our proposed segments behaves in line with real-market investor sentiments." @default.
• W3094237116 created "2020-10-29" @default.
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• W3094237116 date "2020-10-23" @default.
• W3094237116 modified "2023-10-16" @default.
• W3094237116 title "Latent Segmentation of Stock Trading Strategies Using Multi-Modal Imitation Learning" @default.
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• W3094237116 doi "https://doi.org/10.3390/jrfm13110250" @default.
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