Multi-Layer Coupled Hidden Markov Model for Cross-Market Behavior Analysis and Trend Forecasting

The frequent global financial crisis indicates the increasing importance and challenge to analyze and forecast the future trends of stock market for investors and trading agents. Especially with the globalization of the world economy and integration of international financial markets, the complex relationships between markets from different countries should be considered in forecasting market trends, involving multi-layered, interactive, evolutionary, and heterogeneous financial variables and the couplings between variable sets from different countries. A variety of methods have been proposed and implemented for forecasting stock market trends, but there is very limited work reported on predicting a market's movement based on analyzing the multi-layered, hidden coupling relationships between various markets in different countries. This involves the analysis of hierarchical coupled behaviors and their relationships across multiple markets, and the nonlinear market dynamics. To address this critical issue, this paper proposes a new approach Multi-layer Coupled Hidden Markov Model (MCHMM) for Hierarchical Cross-market Behavior Analysis (HCBA), namely exploring the complex coupling relationships between variables of markets from a country (Layer-1 coupling) and couplings between markets from various countries (Layer-2 coupling), to forecast a stock market's movements. Toward capturing the hierarchical coupled market behaviors, a Multi-layered Coupled Hidden Markov Model (MCHMM) is built to infer movements of a stock market in a target country by forecasting its price return probabilities. The experimental results on 11 years of data from two types of markets (stock market and currency market) of 13 countries show that our proposed approach outperforms other four benchmarks from technical and business perspectives.