Topic tones of analyst reports and stock returns: A deep learning approach

We present a novel approach that analyzes topics and tones of analyst reports using a deep neural network in a supervised learning approach. By letting trained classifiers evaluate topics and tones of the reports, we find that incorporation of topic tones significantly enhances the accuracy of predicting cumulative abnormal returns, increasing adjusted ${\mathrm{R}}^2$ from 6.1% without considering textual information to 17.9% with detailed topic tones. This improvement is primarily driven by the inclusion of opinion and corporate fact type of topics. Our findings highlight importance of topic assessment to make the most use of analyst reports for informed investment decisions.     

数字化商业模式研究回顾与展望

在数字经济时代，利用数字技术特性设计和实现能够充分发挥数字化潜能、具有数字化特征的商业模式是企业最重要的能力之一，有利于企业在高度不确定性数字环境中发展壮大。在此背景下，数字化商业模式实践的大量涌现引发了愈来愈多的理论关注，但作为新兴研究议题，数字化商业模式研究尚存在概念不清、研究分散等问题。鉴于此，首先对数字化商业模式进行溯源，解析数字化商业模式概念和类型；其次，对数字化商业模式内在机制、前因后果等文献进行细致梳理，构建数字化商业模式理论框架；最后，基于上述文献梳理，展望数字化商业模式未来研究方向。研究结论可为推进数字化商业模式研究进展、启发数字化商业模式创新实践提供参考。     

The role of timeframes in the retrieval and temporal location judgments of past events

Marketing Letters - Si & Dai (2022) provide evidence for the proposition that a longer memory-search frame (time window from which past events are recalled) leads consumers to retrieve...