How should we estimate value-relevance models? Insights from European data

We study the consequences of unobserved heterogeneity when employing different econometric methods in the estimation of two major value-relevance models: the Price Regression Model (PRM) and the Return Regression Model (RRM). Leveraging a large panel data set of European listed companies, we first demonstrate that robust Hausman tests and Breusch-Pagan Lagrange Multiplier tests are of fundamental importance to choose correctly among a fixed-effects model, a random-effects model, or a pooled OLS model. Second, we provide evidence that replacing firm fixed-effects with country and industry fixed-effects can lead to large differences in the magnitude of the key coefficients, with serious consequences for the interpretation of the effect of changes in earnings and book values per share on firm value. Finally, we offer recommendations to applied researchers aiming to improve the robustness of their econometric strategy.     

The dynamics of leveraged ETFs returns: a panel data study

Leveraged exchange-traded funds (LETFs) are limited liability securities that allow investors to take daily constant leverage bets on a reference index. This work proposes a new empirical design to investigate the dynamics of quarterly LETFs returns. Rather than relying on fund-by-fund overlapping regressions, as in existing literature, the paper exploits a large panel of non-overlapping data covering the whole universe of Proshares, the US primary LETFs provider. Overall, it is found that the variables prescribed by theory broadly explain cross-sectional variability. It is also found that inverse LETFs and more generally, leveraged funds operating in asset classes like international equity, bonds and commodities underperform theoretical predictions. This underperformance is mainly attributed to frictions in the process of implementing the required daily leverage.     

Intraday pairs trading strategies on high frequency data: the case of oil companies

This paper introduces novel ‘doubly mean-reverting’ processes based on conditional modelling of model spreads between pairs of stocks. Intraday trading strategies using high frequency data are proposed based on the model. This model framework and the strategies are designed to capture ‘local’ market inefficiencies that are elusive for traditional pairs trading strategies with daily data. Results from real data back-testing for two periods show remarkable returns, even accounting for transaction costs, with annualized Sharpe ratios of 3.9 and 7.2 over the periods June 2013–April 2015 and 2008, respectively. By choosing the particular sector of oil companies, we also confirm the observation that the commodity price is the main driver of the share prices of commodity-producing companies at times of spikes in the related commodity market.     

24-Hour realized volatilities and transatlantic volatility interdependence

This paper proposes an innovative econometric approach for the computation of 24-h realized volatilities across stock markets in Europe and the US. In particular, we deal with the problem of non-synchronous trading hours and intermittent high-frequency data during overnight non-trading periods. Using high-frequency data for the Euro Stoxx 50 and the S&P 500 Index between 2003 and 2011, we combine squared overnight returns and realized daytime variances to obtain synchronous 24-h realized volatilities for both markets. Specifically, we use a piece-wise weighting procedure for daytime and overnight information to take into account structural breaks in the relation between the two. To demonstrate the new possibilities that our approach opens up, we use the new 24-h volatilities to estimate a bivariate extension of Corsi et al.’s [Econom. Rev., 2008, 27(1–3), 46–78] HAR-GARCH model. The results suggest that the contemporaneous transatlantic volatility interdependence is remarkably stable over the sample period.     

Efficiency in Turkish banking: post-restructuring evidence

Turkish banking sector went through a significant restructuring process in the aftermath of the country's financial crisis of 2000–2001. In this paper, we analyze the evolution of banking performance using a novel approach due to Ray [(2007). “Shadow Profit Maximization and a Measure of Overall Inefficiency.” Journal of Productivity Analysis 27, 231–236]. We derive ‘shadow unrealized profit scores’ as well as ‘shadow input–output prices’ for each year and bank in the sector from 2002 to 2011. We argue these scores operationalize the Hicksian concept of ‘monopolistic quiet life’. We provide some evidence the sector came closer to the ‘zero profit condition’ as well as displaying a closer approximation to the ‘law of one price’ over time. We show the variability of these ‘shadow prices’ essentially coincides with that of corresponding actual prices. We utilize shadow price information to show that business models and competitive choices of banks differ across ownership types with foreign banks competing on the broadest front compared to state-owned and privately owned Turkish banks.     

A GPS data-based analysis of built environment influences on bicyclist route preferences

This study examines the effects of built environment features, including factors of land use and road network, on bicyclists' route preferences using the data from the city of Seattle. The bicycle routes are identified using a GPS dataset collected from a smartphone application named “CycleTracks.” The route choice set is generated using the labeling route approach, and the cost functions of route alternatives are based on principal component analyses. Then, two mixed logit models, focusing on random parameters and alternative-specific coefficients, respectively, are estimated to examine bicyclists' route choice. The major findings of this study are as follows: (1) the bicycle route choice involves the joint consideration of convenience, safety, and leisure; (2) most bicyclists prefer to cycle on shorter, flat, and well-planned bicycle facilities with slow road traffic; (3) some bicyclists prefer routes surrounded by mixed land use; (4) some bicyclists favor routes which are planted with street trees or installed with street lights; and (5) some bicyclists prefer routes along with city features. This analysis provides valuable insights into how well-planned land use and road network can facilitate efficient, safe, and enjoyable bicycling.     

消费领域的量化自我:研究述评与展望

量化自我是指消费者对自我数据进行收集并经由数据获得自我知识的过程。量化自我能增强消费者的自我精准管控和行为理性,驱动消费者行为方式的转变。数据驱动的量化自我时代正在来临,全新的商业逻辑正在被建立起来。消费者的行为正从非标准化向精准化转变。鉴于此,越来越多学者开始围绕量化自我展开研究,在清晰界定量化自我概念的基础上,分析量化自我的参与动机与阶段性过程,关注量化自我带来的消费者精准化消费方式转变。本文系统回顾了量化自我相关研究,阐释了量化自我的概念与本质,从量化自我的类型与效用、动机与过程等方面对相关研究进行了述评,整理提炼了量化自我在消费领域的应用现状,并在此基础上明确了量化自我研究尚需解决的问题和未来的研究方向。     

Atypical work: a threat to labour productivity growth? Some evidence from Italy

Various theories suggest the existence of a negative relationship between the use of atypical employment contracts and productivity growth, arguing that firms’ utilisation of atypical contracts may reduce the incentive to innovate and internal training, inducing firms to follow a ‘low-road’ to competitiveness, based upon cost-cutting strategies.

This paper aims to provide new evidence on the occurrence of these effects in the Italian economy, where changes in labour legislation from the mid-Nineties onwards, associated with an ‘institutional’ wage moderation period, have brought about a significant process of job creation, but also an appreciable slowdown in labour productivity.

This issue is investigated using a microeconomic approach, taking a rich source of microdata for firms and estimating a dynamic model for labour productivity on a pseudo-panel of firms for the period 2003-2008.

The results support the hypothesis of a negative impact of external labour flexibility on labour productivity growth at firm level, such effect proving stronger for small and medium than for large enterprises and of varying magnitude for the different atypical contracts.     

FDI是否促进了中国城市的郊区化?--基于卫星夜间灯光数据的实证检验

城市郊区化是国内外城市发展过程中不可避免的现象。伴随着城市化的不断发展,中国的城市郊区化问题也受到了越来越多的关注,但FDI 作为各个地方旧城改造和新区开发的重要力量,其对中国城市空间的形态塑造作用却被忽视了。文章基于单中心城市模型的理论假设,将FDI 因素纳入分析城市郊区化的框架,通过全球夜间灯光数据、Landscan 人口分布数据和中国行政区域矢量数据构造了中国城市市辖区层面的郊区化指数和蔓延指数,并利用2003-2013年中国地级及以上城市市辖区面板数据检验了FDI 对城市郊区化的影响。研究表明：(1)FDI 是促进我国城市郊区化的关键因素,FDI 占GDP 的比重每提高1%,我国城市郊区化的水平会相应地提高0.1%;(2)由于FDI 在我国存在区域分布的不平衡性,因此其对不同区域的城市郊区化的促进作用有所差异,外资进入显著地促进了东部地区的城市郊区化发展,但外资进入对中西部地区的城市郊区化的影响并不显著。因此,各地方政府应格外关注FDI 可能带来的城市低密度蔓延现象,并检视自身的城市发展战略。