Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Revisiting Targeted Factors

This paper proposes new methods for ‘targeting’ factors estimated from a big dataset. We suggest that forecasts of economic variables can be improved by tuning factor estimates: (i) so that they are both more relevant for a specific target variable; and (ii) so that variables with considerable idiosyncratic noise are down‐weighted prior to factor estimation. Existing targeted factor methodologies are limited to estimating the factors with only one of these two objectives in mind. We therefore combine these ideas by providing new weighted principal components analysis (PCA) procedures and a targeted generalized PCA (TGPCA) procedure. These methods offer a flexible combination of both types of targeting that is new to the literature. We illustrate this empirically by forecasting a range of US macroeconomic variables, finding that our combined approach yields important improvements over competing methods, consistently surviving elimination in the model confidence set procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

The trefoil factor family – small peptides with multiple functionalities

The trefoil factor family (TFF) comprises a group of small peptides which are highly expressed in tissues containing mucus-producing cells – especially in the mucosa lining the gastrointestinal tract. The peptides seem crucial for epithelial restitution and may work via other pathways than the conventional factors involved in restitution. In vitro studies have shown that the TFFs promote restitution using multiple mechanisms. The peptides also have other functionalities including interactions with the immune system. Moreover, therapeutic effects of the TFFs have been shown in several animal models of gastrointestinal damage. Still it is not clear which of their in vitro properties are involved in the in vivo mode of action. This review describes the TFF family with emphasis on their biological properties and involvement in mucosal protection and repair. Received 10 October 2008; received after revision 07 November 2008; accepted 10 November 2008     

成本视角下全要素能源生产率评价与解构

本文依据中国"低成本"竞争模式,基于"要素价格"和"多产出"的双约束,构建一种新型的全要素能源生产率指数,并沿着"要素价格-成本权衡-区域能源效率变化"的逻辑链条,从成本视角对全要素能源生产率指数解构,探寻中国区域能源效率的时空分布特征以及根本动因,研究表明:1)要素价格扭曲的"低成本"策略通过技术效率和要素配置效率影响全要素能源生产率的变动,且后者逐渐成为影响我国区域全要素能源生产率指数变动的关键因素.2)就全国而言,能源利用效率和能源配置效率在节能减排政策实施前后有显著的跳跃性增长,说明市场化进程加快、节能减排政策实施能显著提高我国整体能源利用效率和配置效率.3)东部地区能源利用效率表现较好,但因要素市场扭曲程度较高,能源配置效率相对较低;中西部地区技术水平较低,但要素市场扭曲程度相对较小,短期内市场化改革决定的要素配置效率对区域全要素能源生产率提升更重要。     

基于条件蒙特卡罗方法的信用违约互换合约定价

多标的资产违约相关性结构的度量及其联合违约时间的模拟是信用违约互换合约定价的关键.Copula函数和蒙特卡罗模拟是解决此关键问题的有力工具,被广泛应用于信用衍生品定价.本文基于因子t-copula模型,结合条件蒙特卡罗模拟,构建了计算第7n次信用违约互换合约的条件蒙特卡罗算法.该算法能够捕捉多标的资产违约的尾部相关性,更准确地度量标的资产组合的违约风险及提高违约事件的模拟效率.数值结果表明,在考虑尾部相关性的情形下,采用重要抽样技术的JK算法和改进的JK算法是不稳定的,不能达到减方差的目的;而本文新构建的定价算法更稳定,在高斯copula和t-copula模型下,都能够有效减小估计量的方差,提高信用违约互换合约的定价精度和可靠性.     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

风险投资与高技术产业R＆D全要素生产率增长——基于中国数据的实证

利用1995—2011年风险投资与高技术产业R＆D的相关数据,采用DEA-Malmquist指数法、典型相关分析法和多元回归分析法等计量方法,对风险投资与高技术产业R＆D全要素生产率关系进行实证研究。结果显示：风险投资与技术进步指数、技术效率改善指数及全要素生产率指数具有很强相关性,风险投资对R＆D全要素生产率有显著的正向影响;整体上,风险投资对全要素生产率贡献程度在0．1个单位,年度风险投资额对全要素生产率的影响大于风险投资总规模;R＆D内部经费和新产品开发经费对R＆D全要素生产率具有显著的促进作用,是技术进步与创新直接推动因素。     

恶性肿瘤中IGFBP-rP1的抑癌基因作用机制及其可能的临床实用价值

胰岛素样生长因子结合蛋白相关蛋白1(IGFBP-rP1)是近年来恶性肿瘤的研究热点.本文主要综述IGFBP-rP1在恶性肿瘤中的抑癌基因作用机制及可能的临床实用价值.IGFBP-rP1在恶性肿瘤中的作用广泛涉及细胞的增殖、衰老、凋亡、分化、血管生成等多方面,研究指出IGFBP-rP1可缩短细胞增殖周期并影响非停泊性生长从而抑制增殖,降低致瘤能力;调节BRAF-MEKERK信号通路及pRB、HSP60等相关蛋白的表达从而影响衰老及凋亡;主要通过IGF依赖方式抑制血管生成;而且IGFBP-rP1表达下降跟肿瘤细胞分化程度降低有关.研究显示IGFBP-rP1有一定的临床实用价值,如其表达量跟恶性肿瘤的进展相关,低表达提示某些化疗药物抵抗,可提示预后.而在恶性肿瘤中特异性地上调IGFBP-rP1,可抑制肿瘤增殖及血管生成、诱导细胞衰老凋亡、提高肿瘤分化程度及化疗敏感性,具有治疗意义,但研究者们还在努力探究,争取早日找到一种临床有效的靶向IGFBP-rP1的基因治疗方法.     

钨含量对铜基摩擦材料性能的影响

为了探究钨含量对制动用铜基摩擦材料性能的影响,采用热压烧结工艺制备了不同钨含量的铜基摩擦材料,对其物理性能、力学性能和摩擦磨损性能进行了测试。研究表明,铜基摩擦材料的密度随着钨含量的增加而增大,而剪切强度和硬度先增大后减小。钨提高了材料磨损表面微凸体接触的结点强度,从而提高了材料的摩擦因数。适量的钨可以有效地减少磨损表面剥离,有助于在磨损表面形成连续完整的摩擦膜,从而稳定摩擦因数,减小磨损质量。当钨含量过高时,在高温摩擦过程中会产生严重的犁沟或微裂纹,导致摩擦因数剧烈波动和磨损质量增大。钨的质量分数为3%时,铜基摩擦材料在不同温度和压力下具有较为稳定的摩擦因数和较小的磨损质量。