Big data analytics and investment

Big data has found extensive applications in various industries, including finance. It is an essential tool for investors to make high-stakes investment decisions. Using China's A-shares Market, this paper employs 76 firm characteristics to conduct descriptive analytics (factor model) and predictive analytics (long-short portfolio) through an Instrumented Principal Component Analysis (IPCA) model. According to our results, the IPCA model outperforms in both description (tangency portfolio Sharpe ratio of 2.91) and forecasting (long-short portfolio Sharpe ratio of 2.38). Moreover, our paper compares the performance of different sets of characteristics in big data analytics and concludes that sentiment is dominant, while fundamental analysis is also important. Our results can provide policymakers with valuable insights into the common trends of the stock market and assist investors in making effective investment decisions.

Automated summary

This paper uses big data to perform descriptive and predictive analytics in the Chinese A-shares Market. The results show that the Instrumented Principal Component Analysis model performs well in both description and forecasting. The study also compares the performance of different sets of characteristics and concludes that sentiment analysis is dominant while fundamental analysis is also important. These findings can provide valuable insights for policymakers and assist investors in making effective investment decisions.