Estimating the potential impact of requiring a stand-alone board-level risk committee

Motivated by the requirement under the Dodd-Frank Act that all large bank holding companies create a stand-alone, board-level risk committee, this paper investigates the association between such a committee and regulatory risk both before and during the financial crisis. I focus the analysis on the set of banks that did not have a risk committee in place prior to the Dodd-Frank Act, as these are the banks that were most affected by the regulation. I find that matched control banks with a risk committee in place had higher capital ratios during the financial crisis, but lower capital ratios during more stable economic conditions relative to the banks without a risk committee. This paper contributes to the literature by narrowly investigating the effects a board-level risk committee, by focusing on a risk measure that is of interest to the regulators who implemented the new regulation, and by documenting that this association changes over time which highlights the importance of estimating the effects of new regulations across different economic conditions.     

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

微波加热对叶绿素水溶液的影响

本文通过对叶绿素水溶液进行微波加热,分析微波加热对叶绿素水溶液的温度、色度、叶绿素含量的影响。结果表明,微波加热处理的水溶液温度显著高于对照沸水浴,1~2 min时,1000 W功率下的水溶液温度显著高于200 W(P<0.05)。与沸水浴相比,微波加热显著降低了叶绿素水溶液的绿色程度,且高功率微波加热显著加剧了水溶液亮度的变化。200 W微波加热使水溶液中叶绿素a、b含量降低,但1000 W微波加热则提高了水溶液中叶绿素a、b含量。     

Broadband׳s contribution to economic growth in rural areas: Moving towards a causal relationship

This paper uses recent data on both broadband availability and adoption to empirically gauge the contribution of broadband to the economic growth of rural areas of the United States over the past decade. Availability data from the National Broadband Map aggregated to county level is used in conjunction with county-level adoption data from Federal Communication Commission. Economic variables of interest include median household income, number of firms with paid employees, total employed, percentage in poverty, and the percentage of employees classified as either creative class or non-farm proprietors. A propensity score matching technique (between a “treated” group associated with various broadband thresholds and a control group) is used to make preliminary causal statements regarding broadband and economic health. Growth rates between 2001 and 2010 for different economic measures are tested for statistical differences between the treated and non-treated groups, restricting the analysis to non-metropolitan counties. Results suggest that high levels of broadband adoption in rural areas positively (and potentially causally) impacted income growth between 2001 and 2010, and negatively influenced unemployment growth. Similarly, low levels of broadband adoption in rural areas lead to declines in the number of firms and total employment numbers in the county. Broadband availability measures (as opposed to adoption) demonstrate only limited impacts, suggesting that future broadband policies should be more demand-oriented.     

NTRU格上高效的身份基线性同态签名方案

针对现有的格上身份基线性同态签名方案密钥存储量大、结构复杂导致方案实际运行效率相对偏低的问题，提出了一个NTRU（Number Theory Research Unit）格上高效的身份基线性同态签名方案。首先在密钥生成阶段利用NTRU密钥生成算法产生主密钥，接着采用格基委派算法给出身份签名私钥，最后运行NTRU格上原像抽样算法产生出线性同态签名。对方案的安全性证明与性能分析结果表明，新方案满足正确性，具有弱内容隐私性。在随机预言机模型下，该方案在小整数解问题困难性条件下满足适应性选择身份和选择消息的存在性不可伪造性。同时，由于采用NTRU格的特殊结构，新方案在密钥量与运行效率方面与已有方案相比较均具有显著的优势，这对于计算资源受限环境的同态认证中具有重要的应用价值。     

熔岩台地矮曲林土壤溶液吸光度与土壤碳、氮含量的相关性

通过对五大连池新期火山熔岩台地白桦、香杨、山杨3个林型的矮曲林45个样本进行14个波长(250、254、260、265、272、280、285、300、340、350、365、400、436和465 nm)土壤溶液吸光度以及土壤有机碳和全氮含量的测定,旨在探讨土壤溶液吸光度对土壤碳氮指标的指示作用以及不同林型对该作用的...     

美国金融危机压力的来源及其对中国防范金融风险的启示——基于MIMIC模型的实证分析

党的十九大报告指出：“要坚决打好防范化解重大风险攻坚战,其关键又是防范金融风险,包括防范中国系统性金融风险的发生和抵御国际性金融危机传导所带来的外部冲击。”2007年由美国次贷危机演变成的全球性金融危机,至今还在持续影响着部分国家的经济和金融发展。2015年底开始的美国第六轮加息周期和2018年3月中美贸易战的爆发,使得中国的经济发展面临更大的机遇和挑战。本文以次贷危机为背景,基于MIMIC模型对2005—2016年的美国金融危机压力进行了测度,找到压力的来源为外汇储备增长率、出口增长率和实际有效汇率高估三个方面,进而分析其产生的根本原因,最后从三方面提出中国防范化解重大风险的对策和启示。     

中国养老金隐性债务的模型分析与偿还对策研究

能否解决养老金隐性债务是养老保险制度改革成功的关键性问题。基于养老金隐性债务的含义对养老金隐性债务的理论模型进行了修正与深化,对中国2005年～2050年养老金隐性债务的规模和变动趋势进行了预测与分析,并提出解决弥补隐性债务的综合性方案。     

Commodity price stabilisation: macroeconomic impacts and policy options

A macroeconometric simulation study is undertaken to evaluate the impact of commodity price stabilisation (CPS) schemes for the export tree crop industry in Papua New Guinea. The findings suggest that there is a negligible level of favourable macroeconomic impacts of CPS. Contrary to the expectation, CPS adversely affects the stability of monetary and external sectors (BOP). CPS policy has failed to stabilise the macroeconomy. The price stabilisation policies are no longer appropriate from the macroeconomic point of view. Technical change, futures market and rural savings are the possible alternative policy options to manage the price risk.     

农村集体经济组织收益分配纠纷探析

随着征收农民集体土地行为的大量出现,农村集体经济组织需要对这些收益进行分配,由于相关法律法规缺失和我国农村问题所固有的复杂性,分配纠纷得不到很好地处理。在建设法治、和谐社会的背景下,解决该问题势在必行。