Big Data Analytics and Investment

Sabri Boubaker, Z. Liu and Y. Mu

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Abstract: 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\textendashshort 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\textendashshort 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. \textcopyright 2023 Elsevier Inc.

Keywords: Analysis models; Big data; capital flow; capital market; China; China A-share market; China's A-shares market; Commerce; Data analytics; Data Analytics; decision analysis; Financial markets; High stake decision forecasting; Instrumented principal component analyse; investment; Investment decisions; Investments; IPCA; Long-short portfolio; policy making; Predictive analytics; Principal component analysis; Principal-component analysis; Share market; Sharpe ratios; stock market (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

Published in Technological Forecasting and Social Change, 2023, 194, ⟨10.1016/j.techfore.2023.122713⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04435554

DOI: 10.1016/j.techfore.2023.122713

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