We document foreign biases in AI-generated financial predictions: ChatGPT (U.S.-based) is systematically more optimistic about Chinese firms than DeepSeek (China-based), predicting higher end-of-year stock prices and generating more buy recommendations. This AI-specific phenomenon contradicts the traditional home bias in which investors favor domestic assets. We trace this bias to differential information access: ChatGPT's optimism increases when US media coverage of Chinese firms' negative news is scarce relative to Chinese media. Supporting this mechanism, placebo tests with synthetic Chinese firms without such asymmetries show no prediction gap between models. Crucially, providing ChatGPT with Chinese news through prompts—which cannot alter model weights—completely eliminates the prediction gap, demonstrating that the bias stems from missing training data. Our findings imply that the parallel development of LLMs in different countries can create divergent financial forecasts, potentially amplifying rather than reducing cross-border information asymmetries as these tools shape investment decisions globally.
[pre-abstract] This multimedia case should be assigned to students in advance of class. [abstract] This multimedia case study focuses on General Partner and Chief Catalyst Officer Cristina Ventura at White Star Capital, as she builds an ecosystem for investors and startups in Southeast Asia. The case follows how Ventura worked to break down geographic siloes and build connections in the region, while setting up White Star Capital's office in Singapore, building a team both local and global. The case also charts Ventura's path as a leader, with particular focus on her ecosystem building, personal investment philosophy, and purpose-driven leadership. The case ends with White Star Capital looking to expand the firm's strength in Southeast Asia, while turning to new areas of growth and opportunity. Ventura is left wondering how she can apply the catalyst leadership lessons she has learned in the MENA region.
We investigate the impact of observing peers’ information acquisition on financial analysts’ allocation of attention. Using the timely disclosure mandate by the Shenzhen Stock Exchange as a setting, we find that, shortly after analysts observe that a firm has been visited by peer analysts, they reduce short-term attention to that firm, as indicated by a reduced tendency to conduct follow-up visits. Nonvisiting analysts who do not conduct follow-up visits are more likely to discontinue coverage of the visited firm. These findings are consistent with the conjecture that the timely disclosure reveals the first-mover advantage of visiting analysts, leading nonvisiting ones to reallocate their limited attention. We also find that, compared to the pre-mandate period, the information environments of visited firms deteriorate immediately after an analyst’s visit but not over the longer term. Further evidence suggests that the timely disclosure mandate has positive externalities in the form of increased immediate attention to and improved short-term information environments of unvisited peer firms.
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