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The purpose of this paper is to examine the difference between the daytime (open-to-close) and overnight (close-to-open) returns of CSI 300 index and its derivative futures.

The paper explores the difference between the daytime and overnight time returns by using nonparametric techniques. Moreover, investigation on some factors such as short selling, trading rules, risks are made to seek the sources of the day and night effects based on a large number of empirical analysis. In the end, further analyses on daytime and overnight returns are given by the use of high-frequency data and linear regression technique.

The authors show that the daytime returns of CSI 300 index are no less than its overnight returns, while the daytime returns of CSI 300 index futures are no more than its overnight returns, even after removing the heteroscedasticity of the researched time series. Specifically, the PM returns (13:05 to close) play a quite important role in the intra-day time. The findings also suggest that the unique “T+1 trading rule” in China may be a reason that incurs the lower opening price in the morning and the higher closing price in the afternoon, resulting in the statistically significant differences between the daytime and overnight returns.

The findings are of great importance for investors to decide when to buy and sell stock and futures portfolios in Chinese financial markets.

This study empirically analyzes why there the higher daytime returns and the lower overnight returns exist in the Chinese stock markets from different aspects and contributes the existing literature on day and night effects because of periodic market closures.

At the end of the exercise, students will be able to identify the type of entrepreneur, apply Big Five Personality Traits characteristics of the successful entrepreneur, use the Porter five forces to define the company’s attractiveness, describe David’s three-stage framework, use David’s (2015) strategy formulation framework to propose appropriate strategies for a company, explain the interdependencies of the nine key elements of a business model and create the business model canvas.

The case focuses on Posh Nail Beauty (POSH), one of the leading manicure and pedicure companies in Malaysia. The case concentrates on the discussion of business development, business strategies and challenges of POSH.

The case study is suitable to be used by undergraduate students who are taking the courses such as entrepreneurship, business strategy and marketing related courses.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Pearce and Robinson (2013). Strategic management: Planning for domestic & global competition, (13th ed.). McGraw-Hill/Irwin, New York. • Posh Nail Spa. (2017), available at www.poshnailspa.my/ • Posh! Nail Spa Presents The First Nail Art Fashion Show in Malaysia. (2016), available at http://femalemag.com.my/beauty/posh-nail-spa-presents-first-nail-art-fashion-show-malaysia/ • Scarborough and Cornwall (2015). Entrepreneurship and effective small business management, (11th ed.). Pearson, England. • Siaw (2015). “How to nail it: Plus the do’s and don’ts,” The Star, Malaysia. • This Local Nail Salon Is Going Beyond Mere Manicures. (2017), available at http://marieclaire.com.my/beauty/local-nail-salon-posh-nail-spa/ • Torlak and Şanal (2007). David’s strategy formulation framework in action: the example of Turkish Airlines on domestic air transportation. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 6(12), 81-114. • David (2011). Strategic management (Concepts and cases)(Global Edition 13e). Pearson, Upper Saddle River, New Jersey.

CSS 3: Entrepreneurship.

This paper aims to investigate the primary motivations for China’s outward foreign direct investment (ODI) decisions.

Using a panel data sample covering the period 2003–2012 and a comprehensive set of 176 host countries.

This study finds that market size, trade variables and natural resource variables are strongly related to the Chinese ODI stocks. This indicates that Chinese ODI decisions are driven by both market- and resource-seeking motives. The subperiod sample test results lend even stronger support to the market-seeking motive for ODI.

These results seem to emerge from the policy changes that were undertaken during the sample period. Consistent with subgroup tests, this study finds that the main purposes of China’s ODI in the top 100 countries are natural resource explorations and production line replacements.

China’s outward foreign direct investment (ODI) has become a recent phenomenon in that China is now rated as the world’s third largest country for ODI. Previous studies have found that China’s ODI is driven by the attractions of natural resources and overseas markets. Yet these studies have ignored the role of corporate governance at a national level, the paper aims to discuss these issues.

The Kaufmann et al. (1999) data set is used in our study and the data sample have covered the period from 2003 to 2012 for a comprehensive set of 171 host countries. Random effects model are applied in the paper and population average model is used to check the robustness of the results.

The authors find that the effects of macro-corporate governance are distinct in different sample periods, as well as in geographical and economic regions, when attracting China’s ODI. Indicators such as political stability, the absence of violence, regulatory effectiveness, regulatory quality, the rule of law and the control of corruption are found to be positively related to China’s ODI.

This is one of the first papers to investigate the relationship between macro-corporate governance indicators and China’s ODI. 171 countries are included in the data sample and sub-sample tests are also conducted.

The purpose of this paper is to investigate the frictional behavior of polytetrafluoroethylene (PTFE) composites under oil‐free sliding conditions.

The friction force and power consumption of pressure packing seals, which were, respectively, made of common filled PTFE, 30 wt% CF (carbon fiber) + PTFE and C/C (carbon/carbon) + PTFE, are studied in a reciprocating oil‐free compressor arrangement. Their coefficient of friction is tested on a block‐on‐ring type tribometer.

The results indicate that influence of mean sliding velocity on filled PTFE composites is apparently more predominant than the others. The friction force curvilinear path of 30 wt% CF+PTFE is hardly influenced by changing crankshaft turn angle. For C/C+PTFE, the effect of mean piston velocity on friction force is not evident. The results also indicate that the friction coefficient of C/C+PTFE is lower than that of 30 wt% CF+PTFE if their applied normal force exceeds 9.8 N. Furthermore, their variation curve of friction force is little different and the power consumption of C/C+PTFE is slightly higher than that of 30 wt% CF+PTFE.

Neither the effect of real contact area on friction coefficient measured in a tribometer nor the influence of the temperature on friction force and power tested in a compressor is not taken into consideration here.

Owing to its good mechanical performances and frictional behaviors, C/C+PTFE is an optimum and promising material under conditions with sealing pressure up to 10 MPa and sliding velocity exceeding 4.0 m/s.

A novel material called C/C+PTFE is considered to make packing rings for oil‐free reciprocating compressors and its friction behaviour is tested on a refitted compressor.

Accurate estimation or prediction of the resource required for a project is very important for construction. The more accurate the prediction model, the greater the potential for cost savings will be through elimination of any redesign and the minimization of the maintenance expenses. Contractors can also make use of the models for last‐minute bid estimation. In the past the estimators perform the task by analogy with similar previous projects. This approach highly relies on their experience and knowledge. Owing to the lack of a scientific and easily apprehensible method in resource estimation, prediction outcomes are mainly based on humans’ perception, which is inconsistent and exhibits large variations. This paper proposes the use of multiple Group Method of Data Handling (GMDH) models in developing models for resource estimation. The illustrative example has demonstrated the high accuracy of the approach which is superior to other architectures based on artificial neural networks.

The purpose of this paper is to exploit a new and robust method to forecast the long-term extreme dynamic responses for wave energy converters (WECs).

A new adaptive binned kernel density estimation (KDE) methodology is first proposed in this paper.

By examining the calculation results the authors has found that in the tail region the proposed new adaptive binned KDE distribution curve becomes very smooth and fits quite well with the histogram of the measured ocean wave dataset at the National Data Buoy Center (NDBC) station 46,059. Carefully studying the calculation results also reveals that the 50-year extreme power-take-off heaving force value forecasted based on the environmental contour derived using the new method is 3572600N, which is much larger than the value 2709100N forecasted via the Rosenblatt-inverse second-order reliability method (ISORM) contour method.

The proposed method overcomes the disadvantages of all the existing nonparametric and parametric methods for predicting the tail region probability density values of the sea state parameters.

It is concluded that the proposed new adaptive binned KDE method is robust and can forecast well the 50-year extreme dynamic responses for WECs.

Thin-walled structures inevitably always have manufacturing deviations, which affects the assembly quality of mechanical products. The assembly quality directly determines the performances, reliability and service life of the products. To achieve the automatic assembly of large-scale thin-walled structures, the sizing force of the structures with deviations should be calculated, and its assembling ability should be studied before assembly process. The purpose of this study is to establish a precise model to describe the deviations of structures and to study the variation propagation during assembly process.

Curved thin-walled structures are modeled by using the shell element via the absolute nodal coordinate formulation. Two typical deviation modes of the structure are defined. The generalized elastic force of shell elements with anisotropic materials is deduced based on a continuum mechanics approach to account for the geometric non-linearity. The quasi-static method is introduced to describe the assembly process. The effects of the deviation forms, geometrical parameters of the thin-walled structures and material properties on assembly quality are investigated numerically.

The geometric non-linearity of structure and anisotropy of materials strongly affect the variation propagation and the assembly quality. The transformation and accumulation effects of the deviations are apparent in the multiple assembly process. The constraints on the structures during assembly can reduce assembly deviation.

The plate element via the absolute nodal coordinate formulation is first introduced to the variation propagation analysis. Two typical shape deviation modes are defined. The elastic force of structures with anisotropic materials is deduced. The variation propagation during the assembly of structures with various geometrical and material parameters is investigated.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.

The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.

The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.

The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.