Trend Forecast of Shanghai Stock Exchange Composite Index Based on Monetary Supply and Consumer Price Index

Abstract

Stock market indices often serve as indicators of a country's economic conditions. Therefore, analysing the trends of stock market indices can assist individuals, institutions, and even governments in comprehending the state of the economy and developing suitable investment strategies or economic policies. However, accurately predicting these indices poses a significant challenge. In recent years, machine learning has displayed remarkable learning capabilities in various industries, making it an intriguing and viable avenue for trend prediction. In this article, we have selected two closely linked data sources, namely monetary supply and consumer price index, which are highly correlated with economic operations. By combining these data with regression models, we have developed an algorithm for predicting China's Shanghai Stock Exchange Composite Index (SSECI). Experimental results illustrate a strong correlation between the collected data and the index, highlighting their value in indicating economic conditions.