Stochastic Optimization for Market Return Prediction Using Financial Knowledge Graph

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Fu:2018:ICBK,

• author = "Xiaoyi Fu and Xinqi Ren and Ole J. Mengshoel and Xindong Wu",
• booktitle = "2018 IEEE International Conference on Big Knowledge (ICBK)",
• title = "Stochastic Optimization for Market Return Prediction Using Financial Knowledge Graph",
• year = "2018",
• pages = "25--32",
• address = "Singapore",
• abstract = "Interactive prediction of financial instrument returns is important. It is needed for asset managers to generate trading strategies as well as for stock exchange regulators to discover pricing anomalies. In this paper, we introduce an integrated stochastic optimization technique, namely genetic programming (GP) with generalized crowding (GC), GP+GC, as an integrated approach for a market return prediction system, using a financial knowledge graph (KG). On the one hand, using time-series data for twenty-nine component stocks of the Dow Jones industrial average, we show that our stochastic local search method can give a better prediction performance by providing a comparison of its return performances with two traditional benchmarks, namely a Buy & Hold strategy and the Moving Average Convergence Divergence (MACD) technical indicator. On the other hand, we use features extracted from a time-evolving knowledge graph constructed from fifty component stocks of the SSE50 Index. These features are used to a GP variant and then incorporate the knowledge extracted from the expression learnt from GP into a KG. Overall, this work demonstrates how to integrate GP+GC with KGs in a powerful manner.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICBK.2018.00012",
• month = "17-18 " # nov,
• notes = "Also known as \cite{8588771}",

}

Genetic Programming entries for Xiaoyi Fu Xinqi Ren Ole J Mengshoel Xindong Wu

Citations