Paravee Maneejuk, Binxiong Zou and Woraphon Yamaka
The primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved…
Abstract
Purpose
The primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.
Design/methodology/approach
The authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.
Findings
The results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.
Originality/value
This study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.
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The primary purpose of this study is to unveil the relationship between oil prices and exchange rates, with a specific focus on five major oil-importing countries. By examining…
Abstract
Purpose
The primary purpose of this study is to unveil the relationship between oil prices and exchange rates, with a specific focus on five major oil-importing countries. By examining this relationship, the research aims to provide valuable insights for policymakers, investors and stakeholders operating in the global economic landscape.
Design/methodology/approach
The study employs a methodological approach to ensure robust and reliable findings. First, we assess the stationarity of the time series data to establish a solid analytical foundation. Subsequently, we construct GARCH(1,1) models to capture the persistence of the volatilities inherent in the data. Building upon this, we propose the novel application of the Markov-switching R-vine copula approach, which enables us to capture structural changes and measure the dependencies between oil prices and exchange rates.
Findings
Our findings uncover significant negative relationships between oil prices and exchange rates across the examined economies while revealing varying degrees of interdependency among these variables. Notably, we elucidate distinct tail dependence structures, encompassing both symmetric and asymmetric aspects, which hold profound implications for risk assessment and portfolio management strategies. Furthermore, this study confirms the presence of regime-switching dynamics, elucidating how the co-movement patterns between oil prices and exchange rates evolve across different states or regimes, reflecting the dynamic nature of these interconnected markets.
Originality/value
The originality and value of this study lie in its comprehensive approach to understanding the relationship between oil prices and exchange rates. By accounting for structural changes and regime-switching behaviors, the research provides a nuanced understanding of the complex dynamics at play. The novel application of the Markov-switching R-vine copula approach contributes to the methodological advancement in this field of study. Furthermore, the insights derived from this research offer practical implications for policymakers, investors and stakeholders navigating the complexities of the global economic landscape, enabling them to make informed decisions and develop effective strategies to mitigate risks and capitalize on opportunities.
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Kansuda Pankwaen, Woraphon Yamaka and Paravee Maneejuk
The primary purpose of this study is to explore the effects of demographic transition toward aging populations on the performance of stock market indices across various economic…
Abstract
Purpose
The primary purpose of this study is to explore the effects of demographic transition toward aging populations on the performance of stock market indices across various economic developments. The research aims to provide valuable insights into the life-cycle hypothesis on savings patterns, investment behavior and the potential reverberations on global financial markets.
Design/methodology/approach
The study adopts a comprehensive global perspective, scrutinizing the effects of aging populations on stock market indices across developed, developing and transitional economies through the panel data analysis. Using annual data spanning the period from 1991 to 2020, encompassing a sample of 10 countries from each economic development level, the study employs the panel autoregressive distributed lag (ARDL) model with fixed effect estimation.
Findings
The findings unveil a statistically significant positive impact of the elderly population proportion on global stock market indices. However, the magnitude and contours of this impact exhibit considerable heterogeneity across different country groups. Specifically, the study finds that while the aging population significantly influences stock market performance in developed nations, its effect is overshadowed by other economic factors, such as consumer price indices and interest rates, in developing countries and economies in transition.
Originality/value
The originality and value of this study lie in its comprehensive global perspective, which encompasses a diverse array of economies at varying developmental stages. The research contributes to an understanding of the effects of demographic transitions on stock market performance on a global scale. The insights derived from this study hold significant implications for policymakers, financial institutions and investors seeking to navigate the challenges and opportunities posed by aging societies in an increasingly interconnected global economy. Additionally, the findings highlight the need for specific strategies and policies that account for the unique economic characteristics and developmental stages of different nations.
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Vicente Ramos, Woraphon Yamaka, Bartomeu Alorda and Songsak Sriboonchitta
This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a…
Abstract
Purpose
This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.
Design/methodology/approach
This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.
Findings
The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).
Practical implications
A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.
Originality/value
High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.
Plain Language Summary
This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.
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South African public debt has recently increased significantly and has reached worrying levels. This study aims to examine the debt threshold effects on economic growth in South…
Abstract
Purpose
South African public debt has recently increased significantly and has reached worrying levels. This study aims to examine the debt threshold effects on economic growth in South Africa, with an objective of suggesting a debt threshold as South African policymakers will seek to reduce debt to a sustainable level in the coming years.
Design/methodology/approach
The study applies a recent novel methodology advanced by Hansen (2017) that allows modelling a regression kink with an unknown threshold.
Findings
The findings of this study indicate a robust debt threshold of 37% of gross domestic product (GDP). Below this threshold, debt is growth-enhancing, but above 37% of GDP, debt is harmful to growth in South Africa.
Practical implications
Among other things, to reduce the debt-to-GDP ratio, South Africa will need a fiscal consolidation policy by undertaking reforms to state-owned companies to reduce their reliance on public funds, as well as putting in place economic measures to boost long-term growth. The country should also improve tax collection in order to realize additional tax revenue through enhancing compliance and other revenue collection measures.
Originality/value
Most of the existing studies on debt threshold effects in Africa are panel data studies, which assume parameter homogeneity, by determining a single debt threshold value applicable to all countries. This can be misleading as the debt-growth nexus is country-specific, being conditional on several factors, such as institutional quality. The present study applies a recent novel methodology, which allows to model a regression kink with an unknown threshold, for the case of South Africa. The methodology endogenously determines the debt threshold while also allowing a country-specific analysis.