[1] Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent深度强化学习代理的可解释事后投资组合管理财务策略来源:ARXIV_20240722[2] Explainable AI in Request for Quote询价中的可解释人工智能来源:ARXIV_20240723[3] Deep Learning for Economists经济学家的深度学习来源:ARXIV_20240723[4] Large scale Time Varying Portfolio Optimisation using Graph Attention Networks使用图注意力网络进行大规模时变投资组合优化来源:ARXIV_20240723[5] On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment深度学习计算动态初始保证金和保证金值调整来源:ARXIV_20240724
[1] Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent
标题:深度强化学习代理的可解释事后投资组合管理财务策略作者:Alejandra de la Rica Escudero, Eduardo C. Garrido-Merchan, Maria Coronado-Vaca来源:ARXIV_20240722Abstract : Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set on assumptions that are not supported by data in high volatility markets. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL)......(摘要翻译及全文见知识星球)Keywords :
[2] Explainable AI in Request for Quote
标题:询价中的可解释人工智能作者:Qiqin Zhou来源:ARXIV_20240723Abstract : In the contemporary financial landscape, accurately predicting the probability of filling a Request For Quote (RFQ) is crucial for improving market efficiency for less liquid asset classes. This paper explores the application of explainable AI (XAI) models to forecast the likelihood of RFQ fulfillment. By leveraging advanced algorithms including Logistic Regression, Random Forest, XGBoost and Bayesian Neural Tree, we are able......(摘要翻译及全文见知识星球)Keywords :
[3] Deep Learning for Economists
标题:经济学家的深度学习作者:Melissa Dell来源:ARXIV_20240723Abstract : Deep learning provides powerful methods to impute structured information from large scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression......(摘要翻译及全文见知识星球)Keywords :
[4] Large scale Time Varying Portfolio Optimisation using Graph Attention Networks
标题:使用图注意力网络进行大规模时变投资组合优化作者:Kamesh Korangi, Christophe Mues, Cristián Bravo来源:ARXIV_20240723Abstract : Apart from assessing individual asset performance, investors in financial markets also need to consider how a set of firms performs collectively as a portfolio. Whereas traditional Markowitz based mean variance portfolios are widespread, network based optimisation techniques have built upon these developments. However, most studies do not contain firms at risk of default and remove any firms that drop off indices......(摘要翻译及全文见知识星球)Keywords :
[5] On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment
标题:深度学习计算动态初始保证金和保证金值调整作者:Joel P. Villarino, Álvaro Leitao来源:ARXIV_20240724Abstract : The present work addresses the challenge of training neural networks for Dynamic Initial Margin (DIM) computation in counterparty credit risk, a task traditionally burdened by the high costs associated with generating training datasets through nested Monte Carlo (MC) simulations. By condensing the initial market state variables into an input vector, determined through an interest rate model and a parsimonious parameterization of......(摘要翻译及全文见知识星球)Keywords :
