Abstract
When the investment institution analyzes the transaction cost of stock orders, it is costly to obtain the transaction cost of the stock orders by trading it. In contrast, many simulated trading orders cannot get the exact transaction cost. Due to the lack of enough labeled data, it is usually hard to use a supervised learner to estimate accurate transaction cost of stock orders. Label-spreading, a graph-based semi-supervised learner, can integrate a small number of labeled real orders and a large number of unlabeled simulated orders, and train a learner simultaneously. Using a RBF kernel, the learner constructs a graph structure through the spatial similarity measure between the transaction cost samples, and propagates the label through edges of graph in high-dimensional space. The results of experiments show that the label-spreading learner can make full use of the information of unlabeled data to improve classification of transaction cost.
This work was supported in part by the National Key R&D Program of China under Grant 2018YFC0407901, in part by the National Natural Science Foundation of China under Grant 61602149, and in part by the Fundamental Research Funds for the Central Universities under Grant 2019B15514.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wu, P., Li, X. (2019). Transaction Cost Analysis via Label-Spreading Learning. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_38
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DOI: https://doi.org/10.1007/978-3-030-32388-2_38
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