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Recently, there has been a surge of interest in the use of machine learningto help aid in the accurate predictions of financial markets. Despite theexciting advances in this cross-section of finance and AI, many of the currentapproaches are limited to using technical analysis to capture historical trendsof each stock price and thus limited to certain experimental setups to obtaingood prediction results. On the other hand, professional investors additionallyuse their rich knowledge of inter-market and inter-company relations to map theconnectivity of companies and events, and use this map to make better marketpredictions. For instance, they would predict the movement of a certaincompany's stock price based not only on its former stock price trends but alsoon the performance of its suppliers or customers, the overall industry,macroeconomic factors and trade policies. This paper investigates theeffectiveness of work at the intersection of market predictions and graphneural networks, which hold the potential to mimic the ways in which investorsmake decisions by incorporating company knowledge graphs directly into thepredictive model. The main goal of this work is to test the validity of thisapproach across different markets and longer time horizons for backtestingusing rolling window analysis. In this work, we concentrate on the predictionof individual stock prices in the Japanese Nikkei 225 market over a period ofroughly 20 years. For the knowledge graph, we use the Nikkei Value Search data,which is a rich dataset showing mainly supplier relations among Japanese andforeign companies. Our preliminary results show a 29.5% increase and a 2.2-foldincrease in the return ratio and Sharpe ratio, respectively, when compared tothe market benchmark, as well as a 6.32% increase and 1.3-fold increase,respectively, compared to the baseline LSTM model.

Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis