An interview with Shouyang Wang: research frontier of big data-driven economic and financial forecasting

Abstract

Abstract The development of big data generation, acquisition, storage, processing, and other technologies has greatly enriched our sensory world and fundamentally changed the basis of traditional economic and financial forecasting. Unexpected events in the economic and financial fields challenge our confidence in the performance of forecasting models. Obviously, the big data-driven decision theories and analysis methods are different from the traditional methods. In view of the important role of big data-driven economic and financial forecasting in social stability, innovative development, and sustainability, the research frontiers of big data-driven economic and financial forecasting in the future include: feature mining of complex economic systems with big data representation; accurate real-time correction of theories and methods of dynamic forecasting and early warning; general paradigm of big data forecasting research; formation and process of big data-driven economic and financial system management mechanism, etc. Systematic research on such issues will contribute to the formation of decision-making theories and research systems in the context of big data, thus improving the adaptability and scientificity of management decisions.