By examining managers’ decisions about disclosing updated assessments of firms’ risks, we present evidence that the risk factor disclosures are informative. We use the setting of cybersecurity risk factor disclosures after a data breach because data breaches, especially severe breaches, serve as a natural experiment where an exogenous shock to managers’ assessment of their firm’s cybersecurity risks occurs. We analyze the topic from the perspective of two different theoretical lenses: the economic lens of optimal risk exposure and the ethical lens (...) of stakeholder theory. Using a sample of firms experiencing data breaches, we find that firms experiencing a data breach increase the amount of cybersecurity risk factor disclosures compared to matched firms with no data breach. Further investigation reveals that the severity of data breaches affects the results; cybersecurity risk factor disclosures increase only after severe data breaches. While there is no significant market reaction if breached firms’ subsequent annual reports include increased cybersecurity risk factor disclosures, a significant negative market reaction occurs if breached firms decrease cybersecurity risk factor disclosures, regardless of the severity of the breach, implying that the market anticipates increased disclosures after data breaches. (shrink)

Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test–retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level (...) semantic features from spatial or temporal representations via N-fMRI. However, integrating both spatial and temporal characteristics of brain activities for better interpreting the patterns under naturalistic stimuli is still underexplored. In this work, a novel hybrid learning framework that comprehensively investigates both the spatial (via Predictive Model) and the temporal [via convolutional neural network (CNN) model] characteristics of the brain is proposed. Specifically, to focus on certain relevant regions from the whole brain, regions of significance (ROS), which contain common spatial activation characteristics across individuals, are selected via the Predictive Model. Further, voxels of significance (VOS), whose signals contain significant temporal characteristics under naturalistic stimuli, are interpreted via one-dimensional CNN (1D-CNN) model. In this article, our proposed framework is applied onto the N-fMRI data during naturalistic classical/pop/speech audios stimuli. The promising performance is achieved via the Predictive Model to differentiate the different audio categories. Especially for distinguishing the classic and speech audios, the accuracy of classification is up to 92%. Moreover, spatial ROS and VOS are effectively obtained. Besides, temporal characteristics of the high-level semantic features are investigated on the frequency domain via convolution kernels of 1D-CNN model, and we effectively bridge the “semantic gap” between high-level semantic features of N-fMRI and low-level acoustic features of naturalistic audios in the frequency domain. Our results provide novel insights on characterizing spatiotemporal patterns of brain activities via N-fMRI and effectively explore the high-level semantic features under naturalistic stimuli, which will further benefit the understanding of the brain working mechanism and the advance of naturalistic stimuli clinical application. (shrink)