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The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.
Abstract
Purpose
The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.
Design/methodology/approach
The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.
Findings
The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.
Practical implications
The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.
Originality/value
Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.
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Xin Huang, Ting Tang, Yu Ning Luo and Ren Wang
This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish…
Abstract
Purpose
This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.
Design/methodology/approach
This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.
Findings
The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.
Practical implications
The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.
Originality/value
The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.
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Dan Li, Hualong Yang and Zhibin Hu
Gamification design is considered an effective way of changing users' health behavior and improving their health management performance. Even though numerous studies have…
Abstract
Purpose
Gamification design is considered an effective way of changing users' health behavior and improving their health management performance. Even though numerous studies have investigated the positive effect of gamification competition on users, little research has considered gamification's ineffectiveness and negative effects. In particular, how gamification competition affects users' technological exhaustion remains unclear.
Design/methodology/approach
According to flow theory and related research on gamification, this study discusses the nonlinear relationship between gamification competition and users' technological exhaustion. Furthermore, the authors analyze the moderating effect of user type (socializers and achievers) and users' health condition on this nonlinear relationship. Based on flow theory, the authors propose a series of research hypotheses. To test all research hypotheses, the authors collected information from 407 users via a questionnaire as the data for this study.
Findings
The empirical results found a U-shaped relationship between gamification competition and technological exhaustion. Technological exhaustion gradually decreases as competition increases until reaching the lowest point; after that, technological exhaustion gradually increases as competition increases. Further, being a socializer and health condition play a moderating role in the U-shaped relationship between competition and technological exhaustion.
Originality/value
This study's findings not only enrich the related research in flow theory and gamification, but also contribute to the effective design of gamification in health management platforms.
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John Kwaku Amoh, Abdallah Abdul-Mumuni and Richard Amankwa Fosu
While some countries have used debt to drive economic growth, the asymmetric effect on sub-Saharan African (SSA) countries has received little attention in the empirical…
Abstract
Purpose
While some countries have used debt to drive economic growth, the asymmetric effect on sub-Saharan African (SSA) countries has received little attention in the empirical literature. This paper therefore examines the asymmetric effect of external debts on economic growth.
Design/methodology/approach
The panel nonlinear autoregressive distributed lag (NARDL) approach was employed in the study for 29 sub-Saharan African countries from 1990 to 2021. The cross-sectional dependence test was used to determine the presence of cross-sectional dependence, while the second-generation panel unit root tests was used to examine the unit-root properties.
Findings
The empirical results show that external debt has an asymmetric effect on economic growth in both the short and long run. In the long run, a positive shock in external debts of 1% triggers an upturn in economic growth by 0.216% while a negative shock triggers 0.354% decline in economic growth. This implies that the negative shock of external debts has a much stronger impact on economic growth than the positive shock. In the short run, a positive shock in external debts by 1% triggers a decline in economic growth by 0.641%, while a negative shock of 1% triggers a fall in economic growth of 0.170%.
Originality/value
The paper used the NARDL model to examine the asymmetric impact of external debt on the economic growth of SSA countries, which has not been extensively studied. It is recommended that governments in the selected countries in sub-Saharan Africa should drive economic growth by promoting domestic revenue mobilization since external debts impede economic growth.
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It is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is…
Abstract
Purpose
It is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is an important indicator for its health monitoring. By predicting the changing value of the thrust, it can be judged whether the engine will fail at a certain time. However, the thrust is affected by various factors, and it is difficult to establish an accurate mathematical model. Thus, this study uses a mixture non-parametric regression prediction model to establish the model of the thrust for the health monitoring of a liquid rocket engine.
Design/methodology/approach
This study analyzes the characteristics of the least squares support vector regression (LS-SVR) machine . LS-SVR is suitable to model on the small samples and high dimensional data, but the performance of LS-SVR is greatly affected by its key parameters. Thus, this study implements the advanced intelligent algorithm, the real double-chain coding target gradient quantum genetic algorithm (DCQGA), to optimize these parameters, and the regression prediction model LSSVRDCQGA is proposed. Then the proposed model is used to model the thrust of a liquid rocket engine.
Findings
The simulation results show that: the average relative error (ARE) on the test samples is 0.37% when using LS-SVR, but it is 0.3186% when using LSSVRDCQGA on the same samples.
Practical implications
The proposed model of LSSVRDCQGA in this study is effective to the fault prediction on the small sample and multidimensional data, and has a certain promotion.
Originality/value
The original contribution of this study is to establish a mixture non-parametric regression prediction model of LSSVRDCQGA and properly resolve the problem of the health monitoring of a liquid rocket engine along with modeling the thrust of the engine by using LSSVRDCQGA.
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Muralidhar Vaman Kamath, Shrilaxmi Prashanth, Mithesh Kumar and Adithya Tantri
The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength…
Abstract
Purpose
The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.
Design/methodology/approach
In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.
Findings
The five statistical indicators like coefficient of determination (R2), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.
Originality/value
The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.
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Xiaojie Xu and Yun Zhang
The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important…
Abstract
Purpose
The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.
Design/methodology/approach
The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.
Findings
The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.
Originality/value
The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.
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Dimitrios Panagiotou and Filio Naka
The purpose of this paper is to investigate for symmetries – in sign and size – between spot and futures prices in the markets of energy commodities.
Abstract
Purpose
The purpose of this paper is to investigate for symmetries – in sign and size – between spot and futures prices in the markets of energy commodities.
Design/methodology/approach
The aforementioned objective is pursued using daily observations of spot and futures prices for the commodities of crude oil, Brent, heating oil, gasoline and natural gas, along with local nonlinear regression.
Findings
Symmetry in sign and size cannot be rejected. This means that, shocks of the same absolute magnitude, but of different sign, are transmitted from futures prices to spot prices with the same intensity. In addition, larger absolute value price shocks in the futures are transmitted to the spot markets with the same intensity compared with smaller ones. The findings of symmetry in the comovements among prices reveal a lack of those commodities on diversifying the investors’ investment risk.
Originality/value
To the best of the authors’ knowledge, this is the first study to use local nonlinear regression to test for sign and size symmetry between futures and spot prices in the energy commodities markets.
Zhuang Qian, Charles X. Wang and Haiying Yang
This research aims to empirically investigate the impacts of product and international diversification strategies on firm-level inventory performance.
Abstract
Purpose
This research aims to empirically investigate the impacts of product and international diversification strategies on firm-level inventory performance.
Design/methodology/approach
This study empirically examines the associations between product and international diversification strategies and inventory performance based on a sample of 64,124 observations across 7,367 US publicly traded firms between 1989 and 2019 from the COMPUSTAT Segment, Fundamental Annual and Fundamental Quarterly data files. We employ both linear and nonlinear regression models to perform our empirical analysis.
Findings
This research provides strong evidence that there exists a U-shaped relationship between unrelated product diversification and inventory level and a partially inverted U-shaped relationship between international diversification and inventory level. We also find a positive impact of related product diversification on inventory level, but there is no significant curvilinear relationship between related product diversification and inventory level.
Practical implications
Our research findings offer important insights into top management’s strategic planning for diversification strategies and operations manager’s inventory control policies to achieve the strategic fit between corporate diversification and inventory management.
Originality/value
Product and international diversification strategies not only play an essential role in the firm’s competitive advantage, but also have a significant influence on operations manager’s inventory decision. This research is among the first to systematically investigate how top management’s related product, unrelated product and international diversification strategies may have complex nonlinear impacts on inventory performance.
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Olufemi Gbenga Onatunji, Oluwayemisi Kadijat Adeleke and Akintoye Victor Adejumo
This study reinvestigates the validity of the Phillips curve in Nigeria for the period 1980–2020 by considering the asymmetric nexus between unemployment and inflation.
Abstract
Purpose
This study reinvestigates the validity of the Phillips curve in Nigeria for the period 1980–2020 by considering the asymmetric nexus between unemployment and inflation.
Design/methodology/approach
The nonlinear autoregressive distributed lag (NARDL) technique was used to decompose the unemployment variable into two components: tight and loosened labour markets.
Findings
The empirical outcome shows that unemployment has a significant negative effect on inflation when the labour market is tight and a weakly negative and significant effect on inflation when the labour market is loose. The study confirms an asymmetric Phillips curve in Nigeria since the positive (tight) unemployment rate exerts a greater effect on inflation than the negative (loosened) unemployment rate.
Practical implications
The findings of this study have important implications for implementing monetary policy in Nigeria.
Originality/value
To the best of the authors’ knowledge, this is the first study to investigate the existence of a nonlinear Phillip curve in Nigeria.
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