[1] Chain structured neural architecture search for financial time series forecasting金融时间序列预测的链结构神经结构搜索来源:ARXIV_20240325[2] Teamwork and Spillover Effects in Performance Evaluations绩效评估中的团队合作和溢出效应来源:ARXIV_20240325[3] Robust Utility Optimization via a GAN Approach基于GAN方法的鲁棒效用优化来源:ARXIV_20240325[4] Deep Learning Based Measure of Name Concentration Risk基于深度学习的姓名集中风险测度来源:ARXIV_20240326
[1] Chain structured neural architecture search for financial time series forecasting
标题:金融时间序列预测的链结构神经结构搜索作者:Denis Levchenko, Efstratios Rappos, Shabnam Ataee, Biagio Nigro, Stephan Robert来源:ARXIV_20240325Abstract : We compare three popular neural architecture search strategies on chain structured search spaces Bayesian optimization, the hyperband method, and reinforcement learning in the context of financial time series forecasting. ......(摘要翻译及全文见知识星球)Keywords :
[2] Teamwork and Spillover Effects in Performance Evaluations
标题:绩效评估中的团队合作和溢出效应作者:Enzo Brox, Michael Lechner来源:ARXIV_20240325Abstract : This article shows how coworker performance affects individual performance evaluation in a teamwork setting at the workplace. We use high quality data on football matches to measure an important component of individual performance, shooting performance, isolated from collaborative effects. Employing causal machine learning methods, we address the assortative matching of workers and estimate both average and heterogeneous effects. There is substantial......(摘要翻译及全文见知识星球)Keywords :
[3] Robust Utility Optimization via a GAN Approach
标题:基于GAN方法的鲁棒效用优化作者:Florian Krach, Josef Teichmann, Hanna Wutte来源:ARXIV_20240325Abstract : Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst case outcome. In this work, we propose a generative adversarial network (GAN) approach to (approximately) solve robust utility optimization problems in general and realistic settings. In particular, we model both the investor and the market by neural networks (NN)......(摘要翻译及全文见知识星球)Keywords :
[4] Deep Learning Based Measure of Name Concentration Risk
标题:基于深度学习的姓名集中风险测度作者:Eva Lütkebohmert, Julian Sester来源:ARXIV_20240326
Abstract : We propose a new deep learning approach for the quantification of name concentration risk in loan portfolios. Our approach is tailored for small portfolios and allows for both an actuarial as well as a mark to market definition of loss. The training of our neural network relies on Monte Carlo simulations with importance sampling which we explicitly formulate for the CreditRisk......(摘要翻译及全文见知识星球)Keywords :
