Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

商业银行拓展助学贷款的现实条件与策略

本文在介绍开展教育助学贷款现实条件的基础上,着重就商业银行拓展教育助学贷款活动中的经营策略问题,作了一些初步探讨。     

岭南文化嵌入性对珠三角本土企业组织学习的影响

组织学习对企业的自主创新乃至整体进化具有重大影响,而组织学习在很大程度上是嵌入一定文化环境的珠三角本土企业的组织学习不可避免地受到岭南文化嵌入性的影响,而这种影响有很大一部分是"阻碍性"的岭南文化的偏向闭锁凝滞、拙于自我创造、缺乏价值理性、排斥正统正规、忽略正式组织等局限性在珠三角本土企业组织学习的取向上留下了明显烙印,并具体反映在知识来源、知识传播、学习主体、学习幅度和学习效果等方面     

Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.     

地理邻近在区域创新中的作用机理及其启示

由于隐性知识需要直接交流的特性与创新的背景依赖,邻近性在创新研究中越来越受到重视。文章从知识流动和集体学习的视角,探讨了地理邻近在区域创新中的作用机理。地理邻近是"距离产生美",太多或太少的地理邻近都不利于集体学习和区域创新,地理邻近与其它类型的邻近之间相互促进,并可以相互替代。最后给出了研究结论带来的启示。     

浅谈多媒体和网络技术在大学英语自主学习中的作用

在我国推行新一轮大学英语课程改革和高校扩招的大背景下,提高学生的自主学习能力的要求越来越迫切。简要介绍了"自主学习"理论,描述了我国大学生自主学习的现状,并且详细阐述了多媒体和网络技术在大学英语自主学习中的作用。     

基于案例学习的知识管理系统

简单了介绍知识管理的特点以及知识管理系统具有的功能,说明将基于案例的推理技术(CBR)和其它机器学习技术结合起来应用于知识管理系统的必要性和有效性,并在此基础上提出一种基于案例学习的知识管理系统的实现框架。     

基于工程项目知识管理的文化建设

建立以知识为导向的文化是成功实施知识管理的关键因素之一。文章阐述了知识管理与项目文化的关系,提出了建立以信任和学习型文化为基础的知识导向型项目文化,使知识管理在工程项目组织中能得到成功实施。