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.     

高校德育体系研究

高校德育体系作为一个整体,主要包括德育目标体系、德育内容体系、德育运行体系、德育评估体系。深入研究德育体系问题,对于从整体上加强和改进高样德育工作具有重要意义。     

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     

A derivatives trading recommendation system: The mid‐curve calendar spread case

Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work is aimed to develop a trading recommendation system, and to apply this system to the so‐called Mid‐Curve Calendar Spread (MCCS) trade. To suggest that such approach is feasible, we used a list of 35 different types of MCCSs; a total of 11 predictive and 4 benchmark models. Our results suggest that linear regression with l1‐regularisation (Lasso) compared favourably to other approaches from a predictive and interpretability point of views.     

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.     

企业网络强度对企业技术创新能力的影响研究

企业若在竞争残酷的市场中立于不败之地,只有不断从外界获得各种资源,进而帮助企业开展有利于自身发展的创新活动,只有这样才能在市场中站稳脚步。企业网络中的成员随着企业网络的发展,在这个环境中不断加强互动关系,这种互动关系在某种意义上也促进了企业各方面的创新。从现实出发构建了企业的网络关系强度、组织学习能力、创新能力三个变量之间关系的模型,并以河南省平顶山市产业集聚区内的企业为调查对象进行了实证研究,发现了以上三个变量之间的关系：企业的网络关系强度对企业技术创新能力的影响呈正相关,与企业的组织学习能力的关系也是正向的;组织学习能力在企业网络强度和技术创新能力两者之间扮演着不完全中介的角色。     

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。