USA – Sweeping reforms proposed for securities offerings

Article by Kimberly Anne Summe (Investment Banking Legal Division, Morgan Stanley Dean Witter, New York) looking at proposals from the US Securities and Exchange Commission (the “SEC”) to modernise the regulatory structure for securities offerings. Published in the Letter from … section of Amicus Curiae - Journal of the Institute of Advanced Legal Studies and its Society for Advanced Legal Studies. The Journal is produced by the Society for Advanced Legal Studies at the Institute of Advanced Legal Studies, University of London

USA – Sweeping reforms proposed for securities offerings

Article by Kimberly Anne Summe (Investment Banking Legal Division, Morgan Stanley Dean Witter, New York) looking at proposals from the US Securities and Exchange Commission (the “SEC”) to modernise the regulatory structure for securities offerings. Published in the Letter from … section of Amicus Curiae - Journal of the Institute of Advanced Legal Studies and its Society for Advanced Legal Studies. The Journal is produced by the Society for Advanced Legal Studies at the Institute of Advanced Legal Studies, University of London

An Examination of Lehman Brothers\u27 Derivatives Portfolio Postbankruptcy Would Dodd- Frank Have Made a Difference?

Chapter 4 in Bankruptcy Not Bailou

Lessons Learned from the Lehman Bankruptcy

This document is a chapter out of the Ending Government Bailouts As We Know Them , which highlights the perils of relying bailouts to bring about financial stability and offers other solutions to obtain that overarching objective

Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs

Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data streams in real-time. Although forward-mode differentiation enables online learning, its computational requirements restrict scalability. This is typically addressed through approximations that limit learning in deep models. In this study, we propose Online Training with Postsynaptic Estimates (OTPE) for training feed-forward SNNs, which approximates Real-Time Recurrent Learning (RTRL) by incorporating temporal dynamics not captured by current approximations, such as Online Training Through Time (OTTT) and Online Spatio-Temporal Learning (OSTL). We show improved scaling for multi-layer networks using a novel approximation of temporal effects on the subsequent layer's activity. This approximation incurs minimal overhead in the time and space complexity compared to similar algorithms, and the calculation of temporal effects remains local to each layer. We characterize the learning performance of our proposed algorithms on multiple SNN model configurations for rate-based and time-based encoding. OTPE exhibits the highest directional alignment to exact gradients, calculated with backpropagation through time (BPTT), in deep networks and, on time-based encoding, outperforms other approximate methods. We also observe sizeable gains in average performance over similar algorithms in offline training of Spiking Heidelberg Digits with equivalent hyper-parameters (OTTT/OSTL - 70.5%; OTPE - 75.2%; BPTT - 78.1%)

Should derivatives be privileged in bankruptcy?

Derivatives enjoy special status in bankruptcy: they are exempt from the automatic stay and effectively senior to virtually all other claims. We propose a corporate finance model to assess the effect of these exemptions on a firm's cost of borrowing and incentives to engage in derivative transactions. While derivatives are value‐enhancing risk management tools, seniority for derivatives can lead to inefficiencies: it transfers credit risk to debtholders, even though this risk is borne more efficiently in the derivative market. Seniority for derivatives is efficient only if it provides sufficient cross‐netting benefits to derivative counterparties that provide hedging services

Mutual inclusivity improves decision-making by smoothing out choice’s competitive edge

Decisions form a central bottleneck to most tasks, one that people often experience as costly. Previous work proposes mitigating those costs by lowering one’s threshold for deciding. Here we test an alternative solution, one that targets the basis of most choice costs: the idea that choosing one option sacrifices others (mutual exclusivity). Across 6 studies (N = 565), we test whether this tension can be relieved by framing choices as inclusive (allowing selection of more than 1 option, as in buffets). We find that inclusivity makes choices more efficient by selectively reducing competition between potential responses as participants accumulate information for each of their options. Inclusivity also made participants feel less conflicted, especially when they could not decide which good option to keep or which bad option to get rid of. These inclusivity benefits were also distinguishable from the effects of manipulating decision threshold (increased urgency), which improved choices but not experiences thereof