Yuyu Sun, Yuchen Zhang and Zhiguo Zhao
Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to…
Abstract
Purpose
Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.
Design/methodology/approach
Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.
Findings
In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.
Practical implications
The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.
Originality/value
Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.
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A combined approach of additive Holt–Winters, support vector regression, simple moving average and generalized simulated annealing with error correction and optimal parameter…
Abstract
Purpose
A combined approach of additive Holt–Winters, support vector regression, simple moving average and generalized simulated annealing with error correction and optimal parameter selection techniques emphasizing optimal smoothing period in residual adjustment is developed and proposed to predict datasets of container throughput at major ports.
Design/methodology/approach
The additive Holt–Winters model describes level, trend and seasonal patterns to provide smoothing values and residuals. In addition, the fitted additive Holt–Winters predicts a future smoothing value. Afterwards, the residual series is improved by using a simple moving average with the optimal period to provide a more obvious and steady series of the residuals. Subsequently, support vector regression formulates a nonlinear complex function with more obvious and steady residuals based on optimal parameters to describe the remaining pattern and predict a future residual value. The generalized simulated annealing searches for the optimal parameters of the proposed model. Finally, the future smoothing value and the future residual value are aggregated to be the future value.
Findings
The proposed model is applied to forecast two datasets of major ports in Thailand. The empirical results revealed that the proposed model outperforms all other models based on three accuracy measures for the test datasets. In addition, the proposed model is still superior to all other models with three metrics for the overall datasets of test datasets and additional unseen datasets as well. Consequently, the proposed model can be a useful tool for supporting decision-making on port management at major ports in Thailand.
Originality/value
The proposed model emphasizes smoothing residuals adjustment with optimal moving period based on error correction and optimal parameter selection techniques that is developed and proposed to predict datasets of container throughput at major ports in Thailand.
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Hui Ma, Shenglan Chen, Xiaoling Liu and Pengcheng Wang
To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.
Abstract
Purpose
To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.
Design/methodology/approach
Using a sample of listed firms from 2010 to 2020, this paper exploits text analysis of annual reports to construct a proxy for enterprise digital development.
Findings
Results show that enterprise digital development not only improves their own capacity utilization but also generates a positive spillover effect on the capacity utilization of peer firms and firms in the supply chain. Next, based on the incomplete information about market demand and potential competitors when making capacity-building decisions, the mechanism tests show that improving the accuracy of market forecasts and reducing investment surges are potential channels behind the baseline results. Cross-sectional tests show the baseline result is more pronounced when industries are highly homogeneous and when firms have access to less information.
Originality/value
This paper contributes to the research related to the economic consequences of digital development. With the development of the digital economy, the real effects of enterprise digital development have also triggered extensive interest and exploration. Existing studies mainly examine the impact on physical operations, such as specialization division of labor, innovation activities, business performance or total factor productivity (Huang, Yu, & Zhang, 2019; Yuan, Xiao, Geng, & Sheng, 2021; Wang, Kuang, & Shao, 2017; Li, Liu, & Shao, 2021; Zhao, Wang, & Li, 2021). These studies measure the economic benefits from the perspective of the supply (output) side but neglect the importance of the supply system to adapt to the actual market demand. In contrast, this paper focuses on capacity utilization, aimed at estimating the net economic effect of digital development by considering the supply-demand fit scenario. Thus, our findings enrich the relevant studies on the potential consequences of digital development.
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Jindong Song, Jingbao Zhu and Shanyou Li
Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.
Abstract
Purpose
Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.
Design/methodology/approach
In the range of 0.5–10.0 s after the P-wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM.
Findings
The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τc method and Pd method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P-wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P-wave arrival, respectively, which is better than the realization rate norm of multiple stations.
Originality/value
At the latest, 1.5 s after the P-wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.
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Łukasz Kryszak, Katarzyna Świerczyńska and Jakub Staniszewski
Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed…
Abstract
Purpose
Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.
Design/methodology/approach
The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.
Findings
Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).
Originality/value
This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.
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Junru Zhang, Yumeng Liu and Bo Yan
This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.
Abstract
Purpose
This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.
Design/methodology/approach
First, taking the Liuyuan Tunnel of Huanggang-Huangmei High-Speed Railway as an example and taking deflection of the third principal stress of the surrounding rock at a vault after tunnel excavation as the criterion, the critical buried depth of the large section tunnel was determined. Then, the strength reduction method was employed to calculate the tunnel safety factor under different rock classes and thickness-span ratios, and mathematical statistics was conducted to identify the relationships of the tunnel safety factor with the thickness-span ratio and the basic quality (BQ) index of the rock for different rock classes. Finally, the influences of thickness-span ratio, groundwater, initial stress of rock and structural attitude factors were considered to obtain the corrected BQ, based on which the stability of a large cross-section tunnel with a depth of more than 100 m during mechanized operation was analyzed. This evaluation method was then applied to Liuyuan Tunnel and Cimushan No. 2 Tunnel of Chongqing Urban Expressway for verification.
Findings
This study shows that under different rock classes, the tunnel safety factor is a strict power function of the thickness-span ratio, while a linear function of the BQ to some extent. It is more suitable to use the corrected BQ as a quantitative index to evaluate tunnel stability according to the actual conditions of the site.
Originality/value
The existing industry standards do not consider the influence of buried depth and span in the evaluation of tunnel stability. The stability evaluation method of large section tunnel considering the correction of overburden span ratio proposed in this paper achieves higher accuracy for the stability evaluation of surrounding rock in a full or large-section mechanized excavation of double line high-speed railway tunnels.
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Waqas Bin Khidmat, Man Wang and Sadia Awan
The purpose of this paper is to investigate the value relevance of Research and development (R&D) and free cash flow (FCF) in an efficient investment setup. Most importantly, this…
Abstract
Purpose
The purpose of this paper is to investigate the value relevance of Research and development (R&D) and free cash flow (FCF) in an efficient investment setup. Most importantly, this paper examines whether the value relevance of R&D and FCF is associated with life cycle stages. Furthermore, this paper reports whether the market response to R&D and FCF is different in competitive market as compared to the concentrated market.
Design/methodology/approach
The analysis is based on the Ohlson (1995) model for the determination of value relevance of earnings and book value. Capitalized R&D and FCF data comprising of the Chinese A-listed firms from the year 2008 to 2016 are selected for this study. Following Anthony and Ramesh (1992), the authors divided the firm life cycle into different stages. HHI index is used to measure the product market competition.
Findings
The main result shows that R&D and FCF are value relevant in Chinese A-listed firms. The impact of R&D and FCF on the value relevance of earnings and book value is also positive and significant. The findings of the effect of R&D and FCF on the value relevance of accounting information signify that the information content (R2=0.46) of the mature stage is higher than that of the growth and stagnant stage. The explanatory power measured by R2 value for competitive industries (0.47) is much higher than the concentrated industries (0.33).
Research limitations/implications
Despite taking into account all the possible available variables, there are few limitations of the study. This study only studies the effect of EPS, BPS, R&D and FCF on the value relevance of accounting information. Other determinant such as size, growth, leverage and firm age is ignored. Since the R&D expenditure is discretionary, therefore the findings cannot be generalized to all the sectors. A sector wise comparative study can be done in future, to understand the differences in the information contents of R&D and FCF. Also, the tax effect of R&D is ignored in this study. For future call, the value relevance of tax effect on R&D can be explored.
Practical implications
The investors can now determine the present value of all the future cash flows of investing activities. The results of the study are significant for the Chinese investors who should incorporate the R&D and FCF along with investment efficiency. The investors should keep in mind the life cycle stage while investing in a certain stock. The competitive markets have more information content than the concentrated markets. The corporate managers can benefit from this study while issuing new shares. The market responds positively to the stock having investment efficient R&D and FCF investment. For the policy implication perspective, the security market regulator should devise the effective pro-effective product market regulations.
Originality/value
The contribution of this study is manifold. First, according to the authors’ knowledge, this is the first study that incorporates investment efficiency with R&D and FCF and explores its effect on the value relevance of accounting information. Second, the impact of R&D on the value relevance is studied by numerous researchers (Lev and Sougiannis, 1996; Han and Manry, 2004). Similarly, FCF-agency cost effect has also been investigated by (Rahman and Mohd-Saleh, 2008; Chen et al., 2012) but the value relevance of R&D and FCF during different life cycle stages still needs to be answered. Finally, this study also tries to answers the question if the market response to R&D and FCF is different in a competitive market as compared to the concentrated market.