Two Research Achievements by Professor Zhang Qunzi's Team Published Online Respectively in International Authoritative Finance Journal Journal of Futures Markets

Recently, two research achievements by Professor Zhang Qunzi's team from the School of Economics, Shandong University, Asymmetric Commodity Tails and Index Futures Returns and ChatGPT and Commodity Return, were published online in Journal of Futures Markets, an international authoritative finance journal.

The authors of Asymmetric Commodity Tails and Index Futures Returns are Professor Zhang Qunzi, doctoral candidate Wang Yuanzhi from the School of Economics, Shandong University, and Lecturer Wei Xinbei from Capital University of Economics and Business. This paper studies the predictive ability of tail risk in commodity futures for S&P 500 index futures returns. The study finds that tail risk in commodity futures can significantly predict S&P 500 index futures returns even after controlling for business cycles, economic factors, investor sentiment, other tail risk factors, and macroeconomic conditions. The paper adopts the method of Kelly and Jiang (2014) to directly estimate tail risk factors from the cross-section of commodity futures returns, effectively capturing the level of tail risk. Further return decomposition analysis shows that tail risk factors in commodities affect index futures returns mainly through the discount rate channel. This study provides in-depth insights for financial market participants and researchers on the relationship between tail risk in commodity futures and S&P 500 index futures returns, and enriches the research literature on tail risk and asset returns.

The authors of ChatGPT and Commodity Return are Professor Zhang Qunzi, doctoral candidate Wang Yuanzhi and master candidate Wang Shijie from the School of Economics, Shandong University, and Researcher Gao Xin from University of Electronic Science and Technology of China. This paper studies the predictive ability of ChatGPT-based indicators for excess returns on commodity futures indices. The study constructs a commodity news sentiment index by extracting information from 2.5 million articles in nine international newspapers through ChatGPT, and finds that this index can significantly predict the future returns of commodity futures, performing well both in-sample and out-of-sample. In addition, this indicator has better predictive performance than traditional text analysis methods and is economically significant in the asset allocation framework. The results emphasize the key role of ChatGPT in forecasting commodity market dynamics and provide valuable references for financial market participants and researchers.

Zhang Qunzi is the Vice Dean, Professor and Ph.D. Supervisor of the School of Economics, Shandong University, the Principal Investigator of the Excellent Young Scientists Fund of the National Natural Science Foundation of China, a High-end Financial Talent of Shandong Province, an Outstanding Young and Middle-aged Scholar and Qilu Young Scholar of Shandong University. She is the person in charge of the "Double Leader" Teacher Party Branch Secretary Studio for National Universities, and the person in charge of Shandong Provincial First-Class Undergraduate Course Corporate Finance.

Links to the original texts of the two research achievements:
https://onlinelibrary.wiley.com/doi/full/10.1002/fut.22564
https://onlinelibrary.wiley.com/doi/full/10.1002/fut.22568