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The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

The authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).

The authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.

According to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.

The authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This study is motivated in part by the fact that the unfolding 2022 bear market, which has reached the −25% drawdown, has not been preceded by the inverted 10Y-3 m spread or an inverted near-term forward spread.

The authors develop a three-factor probit model to predict/explain the deep stock market drawdowns, which the authors define as the drawdowns in excess of 20%.

The study results show that (1) the rising credit risk predicts a deep drawdown about a year in advance and (2) the monetary policy easing precedes an imminent drawdown below the 20% threshold.

This study three-factor probit model shows adaptability beyond the typical recessionary bear market and predicts/explains the liquidity-based selloffs, like the 2022 and possibly the 1987 deep drawdowns.

Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.

Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.

The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.

Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.