Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

"大沈阳经济区"的构建与辽宁老工业基地的振兴

大沈阳经济区是指沈阳与周边经济社会活动有密切联系的地区所形成的“区域经济共同体”.它的建立对于振兴东北老工业基地具有重要的战略意义.但在建立这一经济区的过程中也不可避免的存在一些问题,这需要通过一体化运作、机制创新等有效途径来加以解决.     

浅析多媒体技术在车辆工程教学中的应用

随着教育的发展 ,传统的教学方法已不能满足时代的需要 ,亟待运用现代教育技术手段 ,特别是多媒体的教学形式进入课堂 ,以发挥其特有的功能来提高学习效率。本文浅淡了如何在车辆工程教学中运用计算机多媒体的图、文、声、像并茂的特点 ,有效的激发学生的学习兴趣。     

机械零件刚度的模糊可靠性设计方法

介绍了机械零件变形分布参数的确定和刚度隶属函数的选择方法，导出了零件刚度模糊可靠度的计算公式，并给出了应用实例。     

论GIS在社会经济领域中的运用

地理信息系统是一种新兴的技术工具,它不仅被应用在现代测绘、军事国防、野外数据采集等自然科学方面,而且被广泛应用在社会经济领域中,在管理和决策中具有重要的作用.随着人类获取空间数据能力的不断提高和信息技术的发展,地理信息系统的应用将提升到一个新的深度和广度,它将在国民经济信息化中发挥重要的作用.本文介绍地理信息系统的基本概念及其在经济、管理、法律等领域中的应用实例,着重强调在社会经济领域中开展地理信息系统应用研究的意义.     

企业家能力与企业成长:基于中国经验的实证研究

本文基于国内277家企业调查的数据,确定了测量中国企业家能力的四个维度:战略能力、管理能力、政府关系能力和社会关系能力,并对企业家能力差异性之根源,以及企业家能力对企业成长的贡献进行了分析。经验数据表明:企业家能力的发挥将受到经济结构和企业家背景因素的影响,尤其是后者的影响更为显著;相对于战略能力和管理能力,企业家的关系能力对企业成长的贡献出现一定程度的弱化;企业家对企业绩效的直接贡献十分有限,其主要功能在于培育组织能力。     

提高钻井机具产品质量的可靠性技术

钻井机具产品的设计质量和应用质量是影响钻井顺利施工的主要因素，从产品的设计到现场应用全过程的质量监控和可靠性实时评价保证了机具产品质量。通过砂泵和减振器的设计制造分析、应用过程、质量管理和风险评估，说明提高该类产品质量的可靠性技术的普遍性。     

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.