Transaction Cost Analysis via Label-Spreading Learning

Autor:	Xiaodong Li, Pangjing Wu
Rok vydání:	2019
Předmět:	Transaction cost ComputingMethodologies_PATTERNRECOGNITION Computer science Semi-supervised learning Data mining Algorithmic trading computer.software_genre computer Stock (geology)
Zdroj:	Machine Learning and Intelligent Communications ISBN: 9783030323875 MLICOM
DOI:	10.1007/978-3-030-32388-2_38
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::006ec76b02e6dfe8d3d043d0f5f001a1 https://doi.org/10.1007/978-3-030-32388-2_38 Zobrazit plný text záznamu