An empirical analysis of herd behavior in the Singapore stock market

1 online resource ( v, 30 p.) : col. ill.Includes abstract.Includes bibliographical references (p. 27-30).This paper examines herd behavior in the Singapore markets by using daily data from January 2002 to December 2012. Evidence of herd behavior is found in the Singapore market and is present in both the bull and bear markets, but it is more significant in the falling market. During periods of financial crisis, I find particularly strong evidence of herd behavior in the Singapore market. Among 10 industries, the impact of herd behavior on the financial industry is the most significant, and that on the health care and consumer services is relatively significant

In-Context Operator Learning with Prompts for Differential Equation Problems

This paper introduces a new neural-network-based approach, namely In-Context Operator Networks (ICON), to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update. Existing methods are limited to using a neural network to approximate a specific equation solution or a specific operator, requiring retraining when switching to a new problem with different equations. By training a single neural network as an operator learner, we can not only get rid of retraining (even fine-tuning) the neural network for new problems, but also leverage the commonalities shared across operators so that only a few demos in the prompt are needed when learning a new operator. Our numerical results show the neural network's capability as a few-shot operator learner for a diversified type of differential equation problems, including forward and inverse problems of ordinary differential equations (ODEs), partial differential equations (PDEs), and mean-field control (MFC) problems, and also show that it can generalize its learning capability to operators beyond the training distribution.Comment: The second and third authors contributed equall

The effect of mindfulness and job demands on motivation and performance trajectories across the workweek: an entrainment theory perspective

Employee performance is commonly investigated as a static, one-time snapshot of prior employee behaviors. For the studies that do acknowledge that performance fluctuates over time, the timeframe decision is disconnected from theoretical underpinnings. To make this connection clearer, we draw on entrainment theory and investigate trajectories in motivation and performance across the 5-day workweek. We hypothesize that both motivational control (i.e., staying on course and sustaining effort in pursuit of goals through the redirection of attention) and performance have a declining trajectory across the workweek. Drawing on self-determination theory, we also hypothesize that trait-based mindfulness (i.e., nonjudgmental present moment attention and awareness) negatively relates to the downward trajectory in performance across the workweek via its effect on the trajectory of motivational control. Finally, we take a trait activation theory perspective, hypothesizing that mindfulness is relevant as an indirect influence on performance trajectories through motivational control trajectories only when job demands are high. We test our model using 151 full-time employees in a medical device company. We collected data from participants twice daily across the 5-day workweek. We then use these daily scores to create between-person (e.g., person-centric) trajectories to investigate the proposed relationships. The hypotheses are generally supported. There is a downward trajectory of both motivational control and performance across the workweek. Furthermore, job demands conditionally moderate the indirect effect of mindfulness on performance trajectories through motivational control trajectories. Theoretical and practical implications specific to dynamic motivation and performance, entrainment, and mindfulness literature are discussed

High order computation of optimal transport, mean field planning, and mean field games

Mean-field games (MFGs) have shown strong modeling capabilities for large systems in various fields, driving growth in computational methods for mean-field game problems. However, high order methods have not been thoroughly investigated. In this work, we explore applying general high-order numerical schemes with finite element methods in the space-time domain for computing the optimal transport (OT), mean-field planning (MFP), and MFG problems. We conduct several experiments to validate the convergence rate of the high order method numerically. Those numerical experiments also demonstrate the efficiency and effectiveness of our approach

A primal-dual approach for solving conservation laws with implicit in time approximations

In this work, we propose a novel framework for the numerical solution of time-dependent conservation laws with implicit schemes via primal-dual hybrid gradient methods. We solve an initial value problem (IVP) for the partial differential equation (PDE) by casting it as a saddle point of a min-max problem and using iterative optimization methods to find the saddle point. Our approach is flexible with the choice of both time and spatial discretization schemes. It benefits from the implicit structure and gains large regions of stability, and overcomes the restriction on the mesh size in time by explicit schemes from Courant--Friedrichs--Lewy (CFL) conditions (really via von Neumann stability analysis). Nevertheless, it is highly parallelizable and easy-to-implement. In particular, no nonlinear inversions are required! Specifically, we illustrate our approach using the finite difference scheme and discontinuous Galerkin method for the spatial scheme; backward Euler and backward differentiation formulas for implicit discretization in time. Numerical experiments illustrate the effectiveness and robustness of the approach. In future work, we will demonstrate that our idea of replacing an initial-value evolution equation with this primal-dual hybrid gradient approach has great advantages in many other situations

How do leaders react when treated unfairly? Leader narcissism and self-interested behavior in response to unfair treatment

In this article we employ a trait activation framework to examine how unfairness perceptions influence narcissistic leaders’ self-interested behavior, and the downstream implications of these effects for employees’ pro-social and voice behaviors. Specifically, we propose that narcissistic leaders are particularly likely to engage in self-interested behavior when they perceive that their organizations treat them unfairly, and that this self-interested behavior in turn decreases followers’ pro-social behavior and voice. Data from a multisource, time-lagged survey of 211 team leaders and 1,205 subordinates provided support for the hypothesized model. Implications for theory and practice are discussed