We present a dynamic model of venture capital financing, described as a sequential investment problem with uncertain outcome. Each venture has a critical, but unknown threshold beyond which it cannot progress. If the threshold is reached before the completion of the project, then the project...

This paper presents a novel take on the effect of uncertainty on investor learning about managerial skills by examining the fund flow-performance relationship in ESG rated funds in the context of climate uncertainty. Utilizing a large sample of mutual funds domiciled in Australia and New Zealand...

We propose a theory of rational "Rush", emphasizing the quantity of rational over-investment in contrast to the theory of irrational price "Bubble". We illustrate an important friction when financing breakthrough innovations: non-excludability and spillover of uncertain knowledge due to...

We develop a way of ranking and scoring actively managed funds and investment strategies. Our performance measure accounts for the feature that investors may exhibit caution, via the mechanism of ambiguity aversion, when evaluating investment strategies. Linking developed theory to data, we...

This paper studies how investors evaluate themselves and its implications. In my model, investors form subjective beliefs about both the stock currently held in portfolio and their trading talent and update their beliefs through learning with fading memory. I calibrate the memory decay...

Using the mutual fund industry as a laboratory, we demonstrate theoretically and empirically that economic policy uncertainty an affect investment decisions through an information rather than real options channel. Specifically, we find that fund flow-performance sensitivity decreases in...

The behavioural finance literature attributes the persistent market misvaluation observed in real data to the presence of deviations from rational thinking of the actors involved. Cognitive biases and the use of simple heuristics can be described using expected utility maximising agents that...

This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors' information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the...

Neoclassical finance assumes that investors are Bayesian. In many realistic situations, Bayesian learning is challenging. Here, we consider investment opportunities that change randomly, while payoffs are observable only when invested. In a stylized version of the task, we wondered whether...

How do people cope with tail risk? In a lab experiment that removed informational and incentive confounds, subjects overwhelmingly behaved like Bayesian learners. The results of simulations further revealed that if one is to survive under tail risk, one needs to follow the Bayesian approach, as...