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Our paper examines analyst reports and online stock opinion articles which recommend buying stocks that, based on the literature, trade at high prices and earn low future returns ("short-leg securities"). Using a textual analysis, we test whether the justifications primarily (1) emphasize safe-haven qualities, (2) indicate exuberance, or (3) highlight lottery-like features. Our results strongly point to (3). We subsequently validate our text-based inferences through a survey of institutional and retail investors with long positions in short-leg securities. Overall, perceived upside potential appears to play a material role in driving investor demand for stocks in the short legs of anomalies.

In the digital age, traditional public services face significant challenges, including inefficiency and outdated information. Large language models (LLMs), while impressive, struggle with "hallucinations" (generating fluent but incorrect responses) and a lack of domain-specific data, making them inadequate for high-accuracy demands. To address these issues, the Retrieval-Augmented Generation (RAG) framework has emerged as a transformative solution, offering greater accuracy and efficiency.

While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.

A large literature in neuroscience and social psychology shows that humans are wired to be meticulous about how they are perceived by others. In this paper, we propose that impression management considerations can also end up guiding the content that investors transmit via word of mouth and inadvertently lead to the propagation of noise. We analyze server log data from one of the largest investment-related websites in the United States. Consistent with our proposition, we find that investors more frequently share articles that are more suitable for impression management despite such articles less accurately predicting returns. Additional analyses suggest that high levels of sharing can lead to overpricing.