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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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

The study applies a recent novel methodology advanced by Hansen (2017) that allows modelling a regression kink with an unknown threshold.

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.

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.

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.