Finance - Research Publications
Now showing items 1-12 of 170
Why Do Option Prices Predict Stock Returns? The Role of Price Pressure in the Stock Market
(Institute for Operations Research and the Management Sciences (INFORMS), 2020-09-01)
Stock and options markets can disagree about a stock’s value because of informed trading in options and/or price pressure in the stock. The predictability of stock returns based on this cross-market discrepancy in values is especially strong when accompanied by stock price pressure, and it does not depend on trading in options. We argue that option-implied prices provide an anchor for fundamental stock values that helps to distinguish stock price movements resulting from pressure versus news. Overall, our results are consistent with stock price pressure being the primary driver of the option price-based stock return predictability.
Wealth Effects of Seasoned Equity Offerings: A Meta-Analysis
We use meta-analysis to review studies on announcement effects associated with seasoned equity offerings. Our sample includes 199 studies from 38 leading finance journals and Social Sciences Research Network working papers. The studies cover different countries, but the US is particularly well-represented with 131 studies. We find a statistically significant mean cumulative abnormal return of -0.98%. Abnormal returns are more negative for equity issues by US companies and for non-US rights issues and are less negative for private placements. In addition, wealth effects are more negative when the proceeds are used for debt reduction, when the SEO is issued shortly after IPO, and for issues by nondividend-paying companies and industrial companies. We identify important avenues for future research.
Watch Your Basket - to Determine CEO Compensation
CEOs (chief executive officers) are paid more if they outperform other firms in their blockholders’ portfolios. For every percentage point by which their own firm's return exceeds the return of the largest blockholder's basket of investments in a year, their compensation increases by over $9,800. Once we benchmark to this portfolio, industry returns and own firm returns are of little importance. When the firm is a larger portion of the blockholder's portfolio and when the blockholder is experienced, the reward for outperforming the blockholder's portfolio is greater. Our results are robust to alternate industry classifications and definitions of blockholders.
Venture capital and career concerns
This paper finds evidence that the market for follow-on capital discourages risk taking by venture capital fund managers. The amount of follow-on capital raised by venture capitalists is concave with respect to current fund performance. In addition, managers with less consistent performance are slower, and less likely, to raise a follow-on fund. Venture capitalists adjust their investment strategy to balance fundraising incentives against the incentive to pursue risk provided by carried interest. The findings are consistent with models of career concerns, where an agent's compensation is designed to (partially) offset the implicit incentives created by future employment opportunities.
Ventral–Dorsal Subregions in the Posterior Cingulate Cortex Represent Pay and Interest, Two Key Attributes of Job Value
(Oxford University Press, 2021-04-01)
Career choices affect not only our financial status but also our future well-being. When making these choices, individuals evaluate their willingness to obtain a job (i.e., job values), primarily driven by simulation of future pay and interest. Despite the importance of these decisions, their underlying neural mechanisms remain unclear. In this study, we examined the neural representation of pay and interest. Forty students were presented with 80 job names and asked to evaluate their job values while undergoing functional magnetic resonance imaging (fMRI). Following fMRI, participants rated the jobs in terms of pay and interest. The fMRI data revealed that the ventromedial prefrontal cortex (vmPFC) was associated with job value representation, and the ventral and dorsal regions of the posterior cingulate cortex (PCC) were associated with pay and interest representations, respectively. These findings suggest that the neural computations underlying job valuation conform to a multi-attribute decision-making framework, with overall value signals represented in the vmPFC and the attribute values (i.e., pay and interest) represented in specific regions outside the vmPFC, in the PCC. Furthermore, anatomically distinct representations of pay and interest in the PCC may reflect the differing roles of the two subregions in future simulations.
Understanding and representing the social prospects of hybrid urban spaces
(PION LTD, 2007-05-01)
As built environments become increasingly hybrid physical, social, and digital spaces, the intersecting issues of spatial context, sociality, and pervasive digital technologies need to be understood when designing for interactions in these hybrid spaces. Architectural and interaction designers need a mechanism that provides them with an understanding of the ‘sociality-places-bits' nexus. Using a specific urban setting as an analytical case study, we present a methodology to capture this nexus in a form that designers of hybrid spaces can effectively apply as a tool to augment digitally sociality in a built environment.
Uncertainty and computational complexity
(Royal Society, The, 2018-12-31)
Modern theories of decision-making typically model uncertainty about decision options using the tools of probability theory. This is exemplified by the Savage framework, the most popular framework in decision-making research. There, decision-makers are assumed to choose from among available decision options as if they maximized subjective expected utility, which is given by the utilities of outcomes in different states weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using Bayes’ Law. The primary concern of the Savage framework is to ensure that decision-makers’ choices are rational. Here, we use concepts from computational complexity theory to expose two major weaknesses of the framework. Firstly, we argue that in most situations, subjective utility maximization is computationally intractable, which means that the Savage axioms are implausible. We discuss empirical evidence supporting this claim. Secondly, we argue that there exist many decision situations in which the nature of uncertainty is such that (random) sampling in combination with Bayes’ Law is an ineffective strategy to reduce uncertainty. We discuss several implications of these weaknesses from both an empirical and a normative perspective.