Forecasting expected shortfall: Should we use a multivariate model for stock market factors?

Is univariate or multivariate modeling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead expected shortfall of a stock portfolio based on its exposure to the Fama–French and momentum factors. Applying extensive tests and comparisons, we find that in most cases there are no statistically significant differences between the forecasting accuracy of the two approaches. This result suggests that univariate models, which are more parsimonious and simpler to implement than multivariate factor-based models, can be used to forecast the downside risk of equity portfolios without losses in precision.     

What governs firm-level R&D: Internal or external factors?

Two parallel streams of research investigating the determinants of corporate R&D exist: one from economics and the other from management. The economists’ variables tend to reflect the firm’s external environment while the explanatory variables used by management scientists are commonly internal to the firm. This paper combines both approaches to test for the relative importance of each type of factor using firm-level data on large Australian companies from 1990 to 2005. Our evidence suggests that most of a firm’s R&D activity can be explained by time-invariant factors which we believe relate to internal and specific characteristics such as the firm’s managerial dimensions, competitive strategy and how it communicates with employees. Of the remaining time-varying portion, we find that past profits, the rate of growth of the industry and the level of R&D activity over the firm’s industry is pertinent. These results are suggestive since we cannot clearly identify the extent to which the firm’s internal behaviour is conditioned by its external environment.     

Who is more important,parents or children? Economic and environmental factors and health insurance purchase

The existing research indicates that insurance demand is affected by the policyholder’s occupation, family economy, urbanization level, and insurance coverage. Using micro data of insurance purchase at the individual level, this study investigates the influence of economic and environmental factors on the priorities of health insurance purchase decision. The results reveal that the more developed the economy and the more financial knowledge people possess, the more the latter tend to prioritize health insurance for their children. Furthermore, the study investigates the influence of air pollution on the decision to purchase health insurance. If the air pollution level is high, policyholders prioritize obtaining insurance for their children. Our research on the order of insurance purchase indicates that Chinese families have a lack of support from their children, and the function of family pension is gradually weakening. Our research also reveals that families are willing to spend money on the insurance of their children. Therefore, to solve the problem of health and old-age security for the elderly, it is more effective to promote health insurance by considering families as a unit     

Supplier selection or collaboration? Determining factors of performance improvement when outsourcing manufacturing

An empirical study was designed to determine factors of performance improvement when outsourcing manufacturing. Findings from a survey of 136 manufacturing plants in Sweden show that most of them achieve their outsourcing motives, but not without trade-offs. Factors of performance improvements such as economies of scale or operations in low-cost countries can improve one performance dimension, such as product cost, yet negatively impact volume flexibility, speed or product innovation. The results show part characteristics and supplier operating capabilities are more important than supplier relationship strategies when outsourcing manufacturing, meaning that supplier selection trumps supplier collaboration in the make-or-buy decision.     

Antecedents of work–life involvement in work–life issues: institutional pressures,efficiency gains or both?

This paper contributes to the literature by integrating relevant insights from institutional theory and expected efficiency gains to explain organisational responsiveness to work–life issues. In times of global crisis it seems relevant to explore both the question of whether institutional pressures influence organisational behaviour and the positive and negative consequences implementing work–life practices. We tested the model using survey data from 146 private Spanish firms in two different industries. Hierarchical regression analyses tested the relationship between institutional pressures and organisational responsiveness and the potential moderator effect of the expected efficiency gains. The results reveal that mimetic and particular normative pressures significantly influence work–life practices, while coercive and global normative pressures appear not to do so. The perception of certain negative consequences related to the implementation of work–life practices inhibits this responsiveness. The positive outcomes are only relevant for the moderating effect they have on the relationship between institutional pressures and work–life responsiveness.     

Industrial Relations, Manipulative or Democratic?

Summary: Three historical periods of motivating workers toward increasing productivity still "coexist" today: negative incentives, external rewards, forms of workers' participation in industry. Varying modes of joint consultation at top levels existing in many countries are contrasted with American industrial research emphasizing workers' participation on the workshop level. The problems of "common goals" and "manipulation" in industry are considered as contributing factors to the gap between those two levels. To fill it, industrial sociologists should follow Mayo and others in the use of 'clinical' research to find new institutional forms for a democratic work process based on collective bargaining.     

Weibull, RRSB or extreme-value theorists?

The Weibull distribution was discovered by Rosin, Rammler, Sperling and Bennett between 1932 and 1936 in the context of particle measurement. Weibull found the same distribution a little later while investigating the strength of materials. More than 10 years after, in 1951, he finally showed that this distribution has the potential for wide applications in statistics. However, does this justify that only his name is used to denote this important probability distribution? A neutral technical name like “powered exponential distribution” might be more suitable. This paper discusses the papers by Rosin, Rammler, Sperling and Bennett as well as Weibull’s work.     

楼市走势,涨or跌?

年年岁岁花相似,岁岁年年景不同。到了年关,楼市“泡沫”论又一次甚嚣尘上。关于“泡沫”的口水仗年年打,年年泡沫满天飞,2004年可谓杭州楼市“不跌的神话”依然是涛声依旧。那么,2005年的楼市到底何去何从?“悬念”挥之不去,也无法说清。     

Shrinking Cities,Shrinking Households,or Both?

Household size decline accounts for a substantial portion of population loss in shrinking cities, yet little research has focused on it. Much of the literature presents a simple growth/decline binary that is largely determined via population figures. In this paper, we highlight the importance and assess the impact of household size changes on population decline, and determine what types of household size declines are more acute in shrinking cities than other locales. We find that elderly households and households with school-aged children are under-represented in shrinking cities, while households with pre-school-aged children are over-represented. More tellingly, we find the biggest source of household-related loss in shrinking cities to be the growth of single-person households now living in houses that were once home to entire families. These findings puncture the binary on which much of the shrinking cities discourse is based. The population dynamics of most cities are subtler than either practitioners or critical scholars assert. We argue that plans and development strategies for shrinking cities should reflect a range of demographic changes, including outmigration and internal household restructuring.     

When does virtuality help or hinder teams? Core team characteristics as contingency factors

In this paper, we seek to encourage scholars to consider how reliance on technology-mediated communications can bring both promises and perils to team-based work structures. Specifically, we argue that a team's core characteristics (including skill differentiation, temporal stability, and authority differentiation) will differentially affect the challenges and opportunities presented by the team's reliance on virtual means of communication. First, we will discuss how varying degrees of each core characteristic can affect outcomes when teams rely on virtual communication. We then propose how configurations of the three characteristics and virtuality can enhance understanding in both research and practice. We advance propositions that we hope will serve as a starting point for scholarly discussion about how the literature on virtual teams can leverage the existing theories and knowledge on team structure and interdependencies.     

Forecasting football results and exploiting betting markets: The case of “both teams to score”

The continuous growth of available football data presents unprecedented research opportunities for a better understanding of football dynamics. While many research works focus on predicting which team will win a match, other interesting questions, such as whether both teams will score in a game, are still unexplored and have gained momentum with the rise of betting markets. With this in mind, we investigate the following research questions in this paper: “How difficult is the ‘both teams to score’ (BTTS) prediction problem?”, “Are machine learning classifiers capable of predicting BTTS better than bookmakers?”, and “Are machine learning classifiers useful for devising profitable betting strategies in the BTTS market?”. We collected historical football data, extracted groups of features to represent the teams’ strengths, and fed these to state-of-the-art classification models. We performed a comprehensive set of experiments and showed that, although hard to predict, in some scenarios it is possible to outperform bookmakers, which are robust baselines per se. More importantly, in some cases it is possible to beat the market and devise profitable strategies based on machine learning algorithms. The results are encouraging and, besides shedding light on the problem, may provide novel insights for all kinds of football stakeholders.     

The macro-financial factors behind the crisis: Global liquidity glut or global savings glut?

It has been argued that the global financial crisis 2007–2009 was intrinsically related to two largely unprecedented phenomena in the global economy: (i) exceptionally benign financial market conditions as mirrored in historically low risk premia and buoyant asset price developments as well as (ii) an unprecedented widening of external imbalances. This paper explores to what extent these global trends can be understood as a reaction to three structural shocks to the macro-financial environment of the global economy: (i) monetary shocks (“excess liquidity” hypothesis), (ii) preference shocks (“savings glut” hypothesis), and (iii) investment shocks (“investment drought” hypothesis). In order to uniquely identify these shocks in an integrated framework, we estimate structural VARs for the two main regions with widening imbalances, the United States and emerging Asia, using sign restrictions that are compatible with standard New Keynesian and Real Business Cycle models. Our results show that (US) monetary policy shocks explain the largest part of the variation in imbalances and financial market prices. We find that savings shocks and investment shocks explain less of the variation. Hence, a “liquidity glut” may have been a more important driver of real and financial imbalances in the US and emerging Asia that ultimately triggered the global financial crisis.     

Forecasting commodity prices out-of-sample: Can technical indicators help?

Economic variables are often used for forecasting commodity prices, but technical indicators have received much less attention in the literature. This paper demonstrates the predictability of commodity price changes using many technical indicators. Technical indicators are stronger predictors than economic indicators, and their forecasting performances are not affected by the problems of data mining or time changes. An investor with mean–variance preference receives utility gains of between 104.4 and 185.5 basis points from using technical indicators. Further analysis shows that technical indicators also perform better than economic variables for forecasting the density of commodity price changes.     

Human resource management and firm performance in Europe through the lens of business systems: best fit,best practice or both?

This study uses the lens of Business Systems theory to explore the importance of geographic context on the link between human resource management and organizational performance. Basing the analysis on ‘HRM bundles of competitive advantage’, drawing evidence from a large-scale survey of European private sector businesses, and using multiple methodologies, we find three distinct geographic regions and 21 ‘HRM bundles of competitive advantage’. Of those bundles 10 were significantly related to performance in one or more regions. The results raise issues about the universal applicability of HRM-performance research and have implications for the standardization of HRM policies and practices within internationally operating organizations.     

Complements or substitutes? Task specialization by gender and nativity in Spain

Learning about the impact of immigration on the labor market outcomes of natives is a topic of major concern for immigrant-receiving countries. Using data from Spain, where the immigrant population has risen from 4% to 13% within a decade, we find that immigration appears to have affected the task specialization of natives without affecting their employment levels. However, the impact of immigration on the relative task supply of natives is twice as great in Spain as in the United States. The magnitude of the immigration impact in a country with a large share of immigrants originating from Spanish-speaking countries suggests that host country language proficiency is not the sole factor driving the observed impact. Additionally, the analysis reveals significant gender differences in the impact of immigration on the relative task supply of natives, possibly resting on the occupational concentration of immigrants and native occupational segregation patterns by gender, among other factors.     

Is inflation driven by survey-based,VAR-based or myopic expectations? An empirical assessment from US real-time data

The relative importance of survey-based, VAR-based or myopic expectations is evaluated in accounting for US inflation dynamics in a New Keynesian Phillips Curve (NKPC) setting. Our contribution is threefold. First, we estimate the NKPC with both final and real-time vintage data in order to control for large revisions in the real GDP data. Second, we distinguish between two different series for VAR-based inflation forecasts—derived by a recursive or rolling-window method—to account for changes in the conduct and transmission mechanisms of US monetary policy after World War II. Third, joint restrictions are tested in the NKPC to assess whether one of the expectational variables is able, on its own, to capture inflation dynamics. On a statistical basis, we find that there is no clear-cut winner between VAR- and survey-based inflation expectations. Most of our estimated NKPC variants conclude that survey inflation expectations tend to have the largest numerical weight. Nevertheless, the difference between VAR- and survey-based expectations’ estimated coefficients is not statistically significant. Moreover, myopic expectations do not play any significant role in the majority of the estimated NKPC variants.     

Forecasting Bitcoin with technical analysis: A not-so-random forest?

This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.     

大学生“回炉”,前进or倒退?

本期话题:近日,武汉铁路桥梁高级技工学校宣布,今年将重新开设停办了两年的大学生班,并且还扩招成两个班。大学生回炉这一现象再次引起人们热议。有人称这是高等教育的伤疤,是教育资源的再度浪费,然而也有人对此表示支持和呼吁,有争议,有认可。大学生回炉,前进or倒退?     

Heterogeneous treatment effects: Instrumental variables without monotonicity?

Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a local average treatment effect parameter using instrumental variables. More recently, Heckman and Vytlacil (1999) suggested the estimation of a variety of treatment effect parameters using a local version of their approach. We investigate the sensitivity of the respective estimates to random departures from monotonicity. Approximations to the respective bias terms are derived. In an empirical application the bias is calculated and bias corrected estimates are obtained. The accuracy of the approximation is investigated in a Monte Carlo study.