城市轨道交通开通运营阶段的溢价回收方法研究

城市轨道交通的外部性导致项目盈利困难,解决这种困难的方案之一是将轨道交通溢价返还给建设企业。然而,作为传统计算溢价大小的方法,地价函数模型缺乏时间序列分析。本文在传统土地溢价计算模型的基础上,引入时间维度,建立城市轨道交通开通运营阶段溢价的面板数据计量模型,使得溢价计算的精度得以提高。     

Oil risk contracts,business conduct and performance patterns: Data panel analysis

Using the economic and financial performance data of international companies for the exploration, and exploration–extraction (E&E) of oil, as well as the patterns of institutional situation and orientation with the government market and national oil companies or NOCs that receive project offers, we analyze the institutional development and behavior patterns by type of E&E contract, following the strategic actor approach, or the so-called agency theory. Additionally, in light of Mexico's energy reform being implemented between 2015 and 2019, we analyzed the types of license contracts compared to those for production and shared profit. Subsequently, it was determined-through panel data methods in the analysis of 17 companies between 2005 and 2015-that global companies present bigger yields and commitments compared to specialized companies, confirmed by their net income and returns on equity or ROE.     

Interest rate dynamic effect on stock returns and central bank transparency: Evidence from emerging markets

Interest rate dynamic effect on stock returns is examined under different levels of central bank transparency under an asset pricing context. Using a large set of emerging countries in a panel data framework, we provide evidence for a negative link between stock returns and interest rate differences. However, this negative effect is reduced significantly under a transparent central bank, underlying a non-linear impact on stock returns. Our study is focused on a period from 1998 to 2008 where fundamental changes in the level of central banks’ transparency were occurred. Our findings imply that restrictive monetary policies under high levels of transparency lead to smoother reductions on stock returns with significant benefits for financial stability.     

Liquidity,information, strategic trading in an electronic order book: New insights from the European carbon markets

The electronic limit order book (LOB hereafter) has rapidly become the primary way of trading European carbon assets over the 4 years of the EU ETS programme (2008–2012). In this first attempt of examining the informational content of an electronic order book, we evidence that order flow imbalances have a moderate capacity to predict short term price changes. However, we find that both LOB slope and immediacy costs help to forecast quote improvements and volatility in the next 30 min. Further, we explain why informed trading is highly influential and show that it consists in mixing order splitting strategies and posting fleeting orders once the asymmetric information is reduced (Rosu, 2009). Overall, the consolidated status of the order book mirrors a high level of market uncertainty and a low degree of informational efficiency. In this way, strategic trading can in itself explain some of order book properties, independently of the degree of traders’ sophistication and market competition.     

Determining the integrated volatility via limit order books with multiple records

The integrated volatility plays an important role in risk management and portfolio selection, the estimation methods regarding the quantity have been widely investigated, either under low-frequency data or high-frequency data, or a combination of both. In this paper, we propose a measure for the integrated volatility via limit order book data with possible presence of multiple records. The estimator is valid under mild conditions and it is easily implemented. The finite sample performance of the proposed estimator has been verified by simulation studies and we apply the method to some real high-frequency data-sets as well.     

Application of graph theory to mining the similarity of travel trajectories

Travel mobility has attracted considerable attention from tourism scholars. Studies have extensively discussed discovering key (i.e., collective) movement patterns. Recently, the advancement of information technology has allowed tourism researchers to obtain detailed information regarding travel digital footprints. This study, which analyzes mobile sensor big data, proposes a data mining approach to measure the similarity of travel trajectories by performing a pair comparison of individual trajectory. This method considers the spatial and temporal dimensions of travel flow to help identify trajectory similarity across individual travelers. Considering graph theory, this research also applies graph-based spatiotemporal analytics to identify important insights from complex travel mobility networks. As a result, this study suggests an innovative approach to assess travel trajectory similarity, which can be regarded as a type of data-driven clustering method. This paper also demonstrates the applicability of network science in travel mobility.     

Financial components of FDI and choice of capital structure: an analysis for 30 OECD countries

Even though the choice of capital structure depends on the three different financial components of foreign direct investment (FDI), previous research has regarded FDI as unidimensional rather than multidimensional. This study addresses new findings in the FDI area and investigates the relevant determinants of capital structure in 30 OECD countries from 2006 to 2014 within the framework of a simultaneous equation model. Our primary findings reveal that each component has its own deterministic features driven by relevant policy variables and risks in the market. While an increase or decrease in equity capital shows the ability of the host country to attract new investments, the subsequent components are mostly used to adjust the equity capital investment exposure.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Consistency between Sakernas and the IFLS for Analyses of Indonesia’s Labour Market: A Cross-Validation Exercise

This article assesses the consistency between Indonesia’s National Labour Force Survey (Sakernas) and the Indonesia Family Life Survey (IFLS) for analyses of the country’s labour market. I show that Sakernas and the IFLS depict different levels of and changes in labour-market indicators. My estimates of the labour-force participation model, the sector-choice model, and the Mincer model are all statistically different between the two surveys, although the magnitudes are similar for labour-force participation and sector choice. The IFLS shows a much higher return to education than Sakernas, according to the Mincer model. In addition, I find that the cross-sectional sample of the IFLS and the panel sample of the IFLS corrected for attrition yield similar coefficient estimates from all equations. The findings in this article provide an important reference for researchers interested in using Sakernas and the IFLS—either individually or combined—to analyse labour-market issues in Indonesia.     

Experts,firms, consumers or even hard data? Forecasting employment in Germany

In this article, we forecast employment growth for Germany with data for the period from November 2008 to November 2015. Hutter and Weber (2015) introduced an innovative unemployment indicator and evaluated the performance of several leading indicators, including the Ifo Employment Barometer (IEB), to predict unemployment changes. Since the IEB focuses on employment growth instead of unemployment developments, we mirror the study by Hutter and Weber (2015). It turns out that in our case, and in contrast to their article, the IEB outperforms their newly developed indicator. Additionally, consumers’ unemployment expectations and hard data such as new orders exhibit a high forecasting accuracy.