A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

Far from the madding crowd: collective wisdom in prediction markets

We investigate market selection and bet pricing in a repeated prediction market model. We derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information content of prevailing prices in the case of fractional Kelly traders with heterogeneous beliefs. It turns out that, apart some non-generic situations, prices do not converge, neither almost surely nor on average, to true probabilities, nor are they always nearer to the truth than the beliefs of all surviving agents. This implies that, in general, prediction market prices are not maximum likelihood estimators of the true probabilities. However, when more than one agent survives, the average price emerging from a prediction market approximates the true probability with lower information loss than any individual belief.     

Consequences of Debt Forgiveness: Strategic Default Contagion and Lender Learning

I use a unique data set of loans to small business owners to examine whether lenders face adverse consequences when they grant debt forgiveness to borrowers. I provide evidence consistent with borrowers communicating their debt forgiveness to other borrowers, who then more frequently strategically default on their own obligations. This strategic default contagion is economically large. When the lender doubles debt forgiveness, the default rate increases by 10.9% on average. Using an exogenous shock to the lender's forgiveness policy, my findings suggest that as the lender learns about the extent of borrower communication the lender tightens its debt forgiveness policy to mitigate default contagion.     

Failure to learn from failure: The 2008 mortgage crisis as a déjà vu of the mortgage meltdown of 1994

This article traces the developments in the market for residential mortgage-backed securities (MBS) during the period 1970–2008. Drawing on an analysis of trade publications, business press, and interviews with practitioners, it shows that an MBS market meltdown in 1994 provided clear signals of problems with MBS. The market participants did not re-evaluate their use of risk management tools or adjust security design in response to the 1994 crisis, suggesting a lack of understanding of the implications of the crisis. The 1994 meltdown showed that MBS were vulnerable to systematic risks and that these risks could precipitate an MBS market crash. Furthermore, the 1994 meltdown demonstrated that large-scale investment in MBS could affect the primary mortgage market, thereby rendering the MBS risks unpredictable. After 1994, MBS investment shifted to MBS backed by mortgages with default risk – a development that led to the crash of 2008. By drawing parallels between the 1994 and 2008 crises, this article shows how the MBS market failed to self-correct. The results suggest that financial market participants do not always incorporate relevant information in their decision-making and that market participants have difficulties in both foreseeing the effect of financial innovations on markets and interpreting these effects.     

The Effects of Four Dimensions of Strategic Orientation on Firm Innovativeness and Performance in Emerging Market Small- and Medium-Size Enterprises

We investigate the effects of technology, entrepreneurial, market, and learning orientations on firm innovativeness, and the mediating effect of firm innovativeness on these relationships, using a survey of 374 small- and medium-size enterprises in Korea. The results reveal that, while technology, entrepreneurial, and learning orientations significantly influence firm innovativeness, firm innovativeness has a significant effect on firm performance. We also find that firm innovativeness has a statistically significant mediating role in the relationships of technology, entrepreneurial, and learning orientations to firm performance. Our study contributes to strategic management and emerging market literature by identifying the pivotal role of innovativeness for firms that seek to benefit from various types of strategic orientations.     

PBL教学结合多媒体技术在物流专业课堂教学改革中的应用

文章分析了传统理论教学所存在的问题，结合《配送中心运营管理》课程的特点，将PBL教学模式与多媒体技术进行结合改革课堂教学模式，从而达到提高学生积极性和创造性的目的，对实施前准备工作、课堂教学中的组织和课堂后质量考核评价进行了设计和应用，从而达到培养学生职业能力的目的。     

高职院校工学结合“双师型”青年教师队伍建设的探讨

为了满足人才市场的需求,高职院校采用工学结合的教学模式,进一步培养技能型人才,因此需要加大力度建设"双师型"青年教师队伍。没有高水平的教师队伍,就培养不出高素质的技能型人才。本文通过分析高职院校"双师型"青年教师队伍建设的现状,及存在的问题,提出相应的政策建议。     

大数据、人工智能与厂商竞争路径

经济学对市场竞争路径的学理性分析，主要集中在价格确定、产量确定、规模经济、产业组织等方面，而对科技进步引发市场竞争路径的变化并没有足够的关注。其实，市场竞争路径变化的底蕴是科技进步，只是经济学家在分析市场竞争路径时偏好于将科技因素作为外生变量处理。大数据和人工智能等的发展可谓是一场史无前例的科技革命，它对人类经济活动产生广泛而深刻的影响主要表现为：大数据及其运用怎样影响厂商投资经营，大数据与机器学习等人工智能手段相融合会在哪些方面改变厂商竞争路径，厂商如何提高数据智能化和实现网络协同化，在什么样的条件下会出现行业垄断，等等。文章的基本分析观点是：厂商竞争路径变化是贯穿于大数据、互联网和人工智能等相互融合过程的一种现象，这种现象对应于新科技进步和运用的不同层级；微观经济分析需要将新科技因素作为内生变量，通过分析大数据、机器学习与厂商竞争路径之间的关联，揭示厂商竞争路径变化机理以及由此引致的产业组织等问题。