The integration of Machine Learning (ML) in the pricing of a diverse range of energy commodities highlights a paradigm shift towards advanced analytical methodologies in the energy sector. In this project, we aim to investigate the dynamics of various energy futures products using machine learning methods to better capture their dynamic behavior and improve forecasting accuracy. Specifically, we focus on energy products listed on Yahoo Finance and examine five futures products including WTI crude oil, Brent crude oil, natural gas, heating oil and Gasoline with contracts expiring on February 24, 2024. To identify determining factors, we focus on five critical trading indicators: Open, High, Low, Close, and Volume, and augment our analysis by incorporating a wide array of macroeconomic indicators. Importantly, we provide an ensemble of various ML approaches including Elastic Net Regularization, Principal Component Regression (PCR), Partial Least Squares (PLS), Decision Tree, Random Forest, Gradient Boosted Regression Trees (GBRT) and Neural Network and compare their accuracy of the prediction. The result will provide novel evidence on the pivotal role of ML in enhancing forecasting accuracy, and market analysis across various energy commodities.

This project comprises of the development of a new class of mechanisms, which we call “Incontestable Mechanisms” in market design, integrating legal and social considerations with economic efficiency and fairness. This project proposes mechanisms that use a novel approach for designing the information requirements in mechanism as a way for respecting complex socio-legal constraints. The project consists of two interlinked parts: theoretical development and empirical application of incontestable mechanisms, and an experimental exploration of behavioral dynamics in strategy-proof mechanisms. In the first part, we will develop a theoretical framework for incontestable mechanisms, tested empirically using data from the Indian Administrative Service. This approach aims to enhance the practicality and applicability of allocation mechanisms in real-world scenarios, particularly in public services and education. The second part involves experimental economics, examining how reversing traditional preference elicitation methods affects strategic decision-making. This approach seeks to explore the interlink between theoretically-equivalent message spaces and cognitive processes influencing economic choices when interacting with these mechanisms.

This project aims to study how trade policy uncertainty (TPU) affects firms’ investment and innovation decisions. First, we will compile a unique dataset on the measurements of TPU at both the macro and micro levels for China and the US, using consistent textual analysis techniques. Second, we will investigate the transmission mechanisms of TPU from the aggregate level to the firm level, with an emphasis on firm characteristics and regional institutional settings. Our findings will enrich the literature on uncertainty and investment as well as offer practical implications on firm behaviors against the backdrop of rising global tensions and protectionism. Additionally, we will contribute to the burgeoning field of textual analysis by comparing applications on Chinese and English source materials concerning the same topic.

The project aims to analyze the effects of information disclosure on the competition in contests. We approach this problem from three different but related angles. First, we consider the public disclosure of private value or ability type of contestants. Specifically, in a private value all-pay auction, we assume that the contest designer can release signals on the contestants’ private types. Moreover, the contest designer may control the informativeness of the signals to manipulate the upcoming competition. We are interested in information structures that maximize the total effort or winner’s effort in the contest. Second, we consider the interactions of information disclosure and scoring bias in shaping contest competition. We first study the contestants’ equilibrium effort provision under various information disclosure policies and arbitrary scoring biases, and then investigate whether the two instruments can play complementary roles to enhance the contest’s performance. Third, we focus on the information content of being invited to participate in a contest and its effects on follow-up competition. In many situations, potential contestants receive invitations to join contests. Receiving an invitation per se may be informative about the competition environment, as is the case when the contest designer is privately informed about the prize.

The proposed project studies the cross-sectional jump tail risk and asset pricing implications. Power law patterns have been observed in diverse domains, ranging from city sizes, income distributions of companies, macroeconomic disasters, and stock trading volume. Understanding the power-law tail behavior is crucial for comprehending key mechanisms in economics and finance. We will investigate the cross-sectional tail behavior in returns of a large number of assets. Theoretically, we will develop estimators of the power law tail index for the cross section of systematic jumps and idiosyncratic jumps using high-frequency returns from a large number of stocks and establish statistical inference theories. Empirically, we will analyze the pricing implication of the tail risk in systematic jumps and idiosyncratic jumps.

Research and development (R&D) policy differs from other policies in its various forms and easy implementation. R&D policy may also vary substantially across countries and regions. There is no consensus in the literature about the effectiveness of R&D policy on promoting technology transfer and stimulating economic growth. Exploring this problem not only contributes to the theoretical literature, but also helps designing long-run policy systems that increase technological innovations and facilitate the growth process. This project focuses on two regimes of R&D policy: patent policy and subsidy policy, to systematically study the mechanisms behind which these policy regimes affect technology transfer and economic growth. First, based on cross-country data, this project will analyze summary statistics regarding R&D policy, technology transfer, and economic growth to identify the important roles of R&D policy under different growth frameworks. Second, according to the steady-state and dynamic features of R&D policy, dynamic general equilibrium frameworks with endogenous growth will be constructed to characterize the behaviors of households, firms, and governments. Then by using methods of numerical dynamic programming and empirical moments matching, combined with macroeconomic database, the model is solved analytically and numerically in addition to calibrating parameters. Finally, the calibrated parameters will be used to perform quantitative simulations about the impacts of patent design and subsidization setup on technology transfer, economic growth, and social welfare, respectively. The simulated outcomes will provide qualitative implications that evaluate policy alternatives for their implementation.

Motivated by casino concessions in Macau, this project examines the socially optimal design for concession agreements by governments. Concessionaires, namely firms that receive concessions, earn profits by operating but are expected to perform socially desirable activities during their operations. These firms might face little incentives to exert effort to perform these activities for several reasons. First, exerting effort is costly. Second, it is difficult for governments to monitor the firms’ efforts, as their efforts are typically unobservable; governments only partially infer their efforts via some observable outcomes. Finally, it can be difficult for governments to assess these observable outcomes because they might not be familiar with the firms’ private information, such as their operating costs.

Thus, the design for concession agreements that provides incentives for firms to reveal their private information and to exert efforts to perform socially desirable activities is of paramount importance. This project develops a game-theoretic analysis of such design, elucidating the optimal structure such as the number of concessions to grant and the length of concessions, and how governments should screen the concession applicants and decide the winning applicants. The results provide policy implications for concession agreements of casinos and other public utilities in Macau.

Using official regional and prefecture-level data of the Guangdong- Hong Kong-Macao Greater Bay Area, we identify the sources of output growth and study the technical efficiency with its determinants across cities and sectors over time. Accordingly, implications for appropriate industrial policy, investment on human capital and R&D will be examined.

During 2018–2019, the Trump administration imposed unexpected punitive tariffs on China, raising the average U.S. tariff on Chinese products from 3.57% in early 2018 to 26.3% by the end of 2019. This triggered a series of retaliatory tariff actions from China. Although the tariff spikes most immediately impacted exporting activities and international trade configuration (Fajgelbaum et al., 2021), the core argument for the initial U.S. tariff escalations, as documented in its Section 301 investigation report, concerns the “technology transfer, intellectual property, and innovation” of Chinese entities (USTR, 2018a,b). It is therefore imperative to ask whether and how the U.S.–China trade war affects China’s innovation activities. Specifically, did the Trumpian tariffs discourage innovation by leading Chinese firms? In this research, we plan to use balanced panel data for publicly listed Chinese manufacturing companies to provide comprehensive evidence on the innovation responses of Chinese firms to the U.S.–China trade war by exploiting the tariff variations during the U.S.–China trade war. Moreover, we plan to use triple difference-in-differences and textual analysis to further explore the mechanisms behind.  Specifically, we aim to evaluate and provide evidence about how the market size effect, the induced-competition effect and the government intervention affect the innovation response of Chinese firms to the US-China trade war.

Using official regional and prefecture-level data of the Guangdong-Hong Kong-Macao Greater Bay Area, the project studies the efficiency of R&D technical efficiency with its determinants across cities and sectors over time. Policy implications for appropriate industrial policy are examined. The objectives of the project include: (a) identifying the sources of innovation growth in GBA; (B) examining the R&D efficiency across the GBA cities; and (c) assessing the R&D efficiency change in GBA over time.

The project aims to examine individual gambling behavior by investigating one important question: can the seemingly contradicting fallacies: Gambler’s Fallacy (GF) and the Lucky Store Effect co-exist in the same context? If so, what is the behavioral theory to reconcile these two seemingly contradicting phenomenon?

The project aims to explore this issue by exploiting a unique large peer-to-peer online lottery marketplace for the Chinese national lottery. The project wants to investigate whether lottery players exhibit Gambler’s Fallacy beliefs when picking lottery numbers and meanwhile whether they believe in ‘lucky stores’ when choosing which online lottery store to purchase their tickets from. More importantly, the project focuses the co-existence of these fallacies and if so, plans to propose a simple behavioral theory to reconcile these two seemingly contradicting phenomenon.

The tourism industry has been the largest and one of the fastest growing industry in the world. Tourism expansion has been actively sought by economies around the globe as an engine for economic development and growth. Yet, the restricted mobility of people cross borders during the novel coronavirus period has called for a near standstill of international tourist flows, thereby bringing colossal economic losses to the tourism industry. To devise strategies for reinventing the industry in the post-pandemic new normal, this project purports to examine the global tourism development dynamics and analyze the steady-state condition along the long-run growth trajectory. To this aim, global tourism demand growth will be decomposed into its structural and cyclical components, the region(s) of centroid for global tourism development will be identified, while the contributions of economic, social and political forces that drive international tourism demand will also be scrutinized.

This project aims to explore the impacts of two policy instruments on innovation and economic growth in a data economy. The policy instruments in consideration include monetary policy and subsidy policy. Therefore, this project will consist of two research topics, including how (a) monetary policy (in terms of inflation) and (b) subsidy policy (in terms of research subsidies) on innovation and economic growth in a dynamic general equilibrium model with a data-provision process.

Data has become an important factor in the process of consumer behavior and knowledge accumulation, with the development of technologies in modern economies. Inflation places an extra cost burden in consumption, manufacturing, and research and development (R&D) investment, whereas subsidization is one crucial policy instrument that governments implement to steer the market economy. Therefore, it is important to explore how these policy tools affect the use of data, leading to implications on innovation, economic growth and social welfare. This project expects to make significant contributions in terms of theoretical exploration and policy implications.

Macao has the foundation to become a financial and trade bridge between companies in Mainland China and foreign markets, and has long struggled to become the next International Financial Centre. This project aims to explore the specific path of international commercial mediation in Guangdong-Hong Kong-Macao Greater Bay Area (hereinafter referred to as the GBA) in response to the need of financial dispute resolution in the GBA. International commercial mediation, as a moderate and efficient way of dispute resolution, plays an important role in the international community and is also an important part of the innovation of the rule of law in the GBA. In the process of applying international commercial mediation to solve cross-border financial disputes in the GBA, it is inevitable to face the conflicts of legal systems of mediation in different jurisdictions. Therefore, it is necessary to study the effective interface among the mediation systems of the Mainland China, Hong Kong and Macao, so as to provide some guiding principles and methods for the use of international commercial mediation in resolving cross-border financial disputes in the GBA. Specifically, this project compares the mediation rules of Mainland China, Hong Kong and Macao, and draws lessons from international treaties and international judicial practice.

Outsourcing is increasingly recognized as an important strategic decision for high-tech firms. This study empirically estimates the impact of government outsourcing contracts on high-tech vendors. Employing the earnings-return analyses framework, we conjecture that, for high-tech vendors engaged in government outsourcing contracts, the stock market places a higher value on each unit of unexpected earnings compared to other firms. Additionally, we conjecture that, this impact becomes stronger for contracts with longer terms, for contracts outsourced by the U.S. government or by countries with better political and economical stability. We plan to obtain causal evidence through difference-in-differences of high-tech firms’ initiations of government contracts. Mechanism analyses focus on two primary drivers behind this impact: increased persistence of future earnings and improved alignment between accrual earnings and cash flows. Overall, our research will shed insights into the following question, i.e., does stock market incorporates information from supply-chain networks, especially that related to government customers, in the valuation of high-tech firms?

For financial index tracking, a sparse tracking portfolio with only a small number of assets is often desirable in practice in order to avoid small and illiquid positions and large transaction costs. The tradition way of using Cardinality constraints to directly to limit the number of stocks is if often difficult and computationally intensive as the resulting optimization problem is NP hard. Owing to its computational efficiency and variable selection properties, this project employs the regularization technique originating from high-dimensional statistics for sparse index tracking in high dimensions.

As a platform for economic and trade cooperation between China and Portuguese-speaking countries, Macau takes a unique position in the field of international commercial dispute resolution. According to the long-term policy of the SAR government, Macau is intended to be built up as an international arbitration center for resolving commercial disputes between enterprises from China and Portuguese-speaking countries (Brazil, Portugal, Mozambique, Angola, Sao Tome and Principe, Guinea-Bissau, Cape Verde, and East Timor). In the internet age, a lot of information has to be transmitted, stored and handled electronically. In the case of arbitration, on the one hand, arbitration institutions need to extract, store and make use of electronic data; on the other hand, they need to prevent cyber attacks and ensure the security of data. Unfortunately, arbitral institutions has so far rarely utilized advanced technology such as Blockchain. They also can take advantage of the advanced technology for their case management and trials. Through analyzing the application of Blockchain technology, this project will identify how Blockchain can really help arbitration institutions in Macau e.g. in ensuring cyber security and data confidentiality and managing cases so as to promote Macau to become a popular arbitration center.

This project integrates top research teams and professors in Mathematics and Computer Science of FST and Finance of FBA in UM to develop a very high-speed asset movement forecasting system with Independent Intellectual Property Rights (IIPRs), which focus on the future demand of financial industry in Greater Bay Area, supporting market benchmark, commodities, foreign exchanges, high-speed trading strategy selection and pricing for financial products. The innovative idea of the project is that to develop the implied volatility calculation model, with the scope of integration of traditional numerical analysis and artificial intelligence calculation by parallel processing on CPU and on hardware accelerator with high energy computing environment (e.g. GPU, ASIC, FPGA), to forecast asset movement.

Motivated from the hedge relationships revealed by the inverse conveyance matrix of asset recturns, the graphical least absolute shrinkage and selection operator (Glasso) has been proposed by Goto and Xu (2015) to estimate a sparse inverse covariance matrix for improving portfolio performance in high-dimensional settings. The Glasso approach achieves significant risk reduction and boosts certainty-equivalent returns (CER) by overcoming the multicollinearity issue of highly correlated assets in hedge regressions. However, the sample covariance matrix is used as input for Glasso analysis, which is susceptible to data outliers that can make the Glasso polluted by estimation error and become numerically unstable. Owing to its computational efficiency and portfolio allocation properties, this project employs robust treatment with Glasso strategy for portfolio selection in high dimensions. In particular, we replace the traditional sample covariance matrix with a cellwise robust estimator of covariance matrix as input tor the Glasso.

Arbitrators are generally obliged to perform their duty to solve commercial disputes independently and impartially. When they fail to comply with this duty they may be legally liable for their misconduct. This liability usually assumes the nature of contractual or tort liability. By comparison, criminal liability of arbitrators is rarer, at least at the practical level. In this aspect, China stands as an outlier, not only having established a set of criminal provisions to regulate arbitrators, including specific provisions regarding bribery of arbitrators and a crime named “perversion of law” in 2006, but indeed there have been a number of cases where defendants have been convicted as a result of these provisions. This approach was received with skepticism by some Chinese and foreign legal practitioners who warned that this approach could discourage the usage of arbitrators in China and that it would give police and court authorities overbroad powers to intervene in arbitral proceedings.

Considering the importance of arbitration in China and world trade, this research will focus on reviewing the application of the aforementioned legislation in the period 2006-2023. A combination of data analysis and judgement content analysis is performed to discover how arbitrators in China are criminalized, including an identification of key players in prosecuted cases, crime patterns and punishments. Based on the data, the study offers insights into the impacts of criminal legislation on arbitrators and to what extent, if at all, this legislation has been used to disrupt arbitral proceedings.

Disease detection significantly influences prevention outcomes by affording individuals better control over a disease’s trajectory. Detecting a disease in its early stages enables more effective management and intervention. However, prior research shows that procrastination in disease detection is prevalent among individuals, presumably due to the fear of negative outcomes. Given the critical role of disease detection throughout consumers’ lifespans and the significant health and economic costs associated with not doing so, this research aims to investigate the relative effectiveness of the gain and loss frames in the context of disease detection communication messaging.

As the number of patients seeking international sources for medical services has increased over the years, the secure, efficient transmission of personal health data has become a vital facet of medical tourism. At the same time, advanced technologies, such as mobile health, telehealth, blockchain, cloud technology, the Internet of Medical Things (IoMT), and artificial intelligence have seen increased deployment in smart healthcare systems. These innovations not only expand the scope of medical tourism, but also raise legal and ethical concerns. This project seeks to explore how smart technologies aid medical tourists and discuss the regulatory issues concerning the use of such technologies, emphasizing the protection of personal health data and transnational health data transfer. We will use Mainland China and Macau as a case study, to analyze current data governance laws and policies in both jurisdictions and their influence on the development of smart medical tourism. It aims to identify the legal challenges in managing cross-border health data transfer in the context of start medical tourism and propose suggestions for data governance policy improvements.

This research proposal aims to develop an AI chatbot that collects data on individuals with disabilities and their specific needs and preferences related to travel, in order to enhance the inclusivity and accessibility of the tourism industry for people with disabilities. The AI chatbot will gather information from users on accessibility requirements, dietary restrictions, medical conditions, and other relevant details. The collected data will then be analyzed to identify patterns and characteristics that can be utilized to create personalized travel experiences for individuals with disabilities.

To ensure user-friendliness and ease of interaction, the AI chatbot will engage users in interactive conversations, allowing them to provide detailed information about their travel needs and preferences. It will also be equipped with a knowledge base that includes information on accessible accommodations, transportation options, attractions, activities, and services tailored to individuals with disabilities. This research will involve collaboration with key stakeholders, including individuals with disabilities, disability advocacy organizations, tourism industry professionals, and AI experts. Through pilot testing and iterative feedback cycles, the chatbot’s functionalities will be refined to ensure its effectiveness and accuracy in gathering data and providing personalized travel recommendations.

The question “What makes a tourist destination competitive?” is one of the central questions in tourism and hospitality management. Understanding how tourist destinations perform and what makes them competitive is important for tourists and all the stakeholders involved, including residents, tourism practitioners, and policymakers (Andrades et al., 2017). Significant efforts in helping tourism destinations evaluate and measure their competitive advantage compared with that of other destinations worldwide were exerted over the past three decades (Xia et al., 2019; 2020). Yet, a number of theoretical and methodological issues remain, despite the great interest in the topic by tourism scholars. One key question relates to the epistemological underpinnings of “Who defines what makes the tourist destination competitive “?
This project aims to address this question to better understand tourist destination competitiveness to ultimately improve strategic positioning of destinations. We use Macao as a Case Study context and propose to adopt innovative AI methods, using user-generated data to both conceptually and methodologically advance the understanding of ‘what makes tourist destinations competitive,’ which can then be extended with complementary qualitative analysis to create an impact on improving tourist destination competitiveness. Importantly, we also address the current highly significant real-world problem to ensure long-term recovery to ensure the prosperity of Macao businesses, residents, and future tourists alike.

A part of the building’s cultural history preserved in oral form has disappeared recently. In this project, we build a smart guided tour platform to collect and share the models and introductions of many buildings with historical significance and characteristics. the real-world buildings are digitally recorded based on 3D modeling. The platform shows the 3D models, structural materials, and internal details with virtual reality (VR) technology.

With the building of infrastructure for the region, such as new transportation, new flight routes, new highway and metro, the tourism demand of the region becomes more depending on nearby cities and countries. For example, the Hong Kong-Zhuhai-Macau bridge could connect Macao, Hong Kong, and Zhuhai, three major regions on the Pearl River Delta. The building on this bridge not only promotes economic development of the whole Pearl River Delta, but also creates a tourism clique with significant reducing on traveling time (Tian & Jiang, 2018). Accordingly, tourism demand of these three regions could be deeply changed and influenced by every city within the tourism clique, and existing tourism demand forecasting methods on single destination is insufficient to allow the public and private sectors to make the collective plan within the tourism clique. Hence, this project aims to explore the predictivity of tourism demand forecasting for the tourism clique, build the graph deep learning model to forecast the multi-destination tourism demand for the tourism clique and analysis the competitiveness circumstance within the tourism clique.

The presence of strangers, socially unrelated but physically near, is often an integral part of a travel and tourism experience. The COVID- 19 pandemic has abruptly and artificially changed the spatial relations among strangers. Mandatory and voluntary social distancing may lead to estrangement behavior in post-pandemic era travel and tourism such as distancing behavior, open hostility and micro-aggression towards travelers. Guided by the theoretical framework of strangership, this research examines the phenomenon of estrangement and tourism in the post- pandemic era through a socio-psychological lens. Using a sequential mixed method approach comprised of a quantitative study and a qualitative study, the objectives of this research are to evaluate the extent of estrangement in the post-COVID travel and tourism behavior in the aspects of emotional estrangement, self-estrangement and other-imposed estrangement and offer a phenomenological account of estrangement behavior in travel and tourism. Important theoretical and practical implications are also discussed.

Smart tourism has been adopted in many countries. However, despite the advantages of using digital tools for smart tourism, potential risks to privacy and data protection have raised increasing attentions. This paper analyses smart tourism-related mobile applications (apps) used in selected Asian countries, with a focused exploration on the following perspectives. 1) What services do they offer?; 2) How do they portray the services? 3) What benefits and risks of using smart tourism Apps are mentioned? 4) how do they address the informed consent issues? And 5) what strategies used to ensure the protection of users’ information? Based on the study findings, this study will then discuss potential privacy challenges for the adoption of smart tourism. It will make recommendations for regulatory improvements that meet the development of the smart tourism market and the increasing demand for privacy protection.

Sense of place, what a tourist thinks and feels about a geographically-defined region or community, has been a central concept of tourism. The traditional conceptualization of sense of place is based on the assumption that a constellation of place-related cognitions and affects is contained within the physical boundaries of a place. However, the advent of metaverse, a confluence of multiple advanced technologies, has challenged the long-held assumption of clear geographical demarcation of a place. Metaverse also extends the temporal, spatial and experiential dimensions of a place. In other words, what constitutes a place is being redefined. In addition, latest sensory technologies have significantly augmented our senses, redefining what constitutes senses. It is not a stretch to suggest that metaverse significantly alters and expands the meanings of place, sense, and sense of place. Given this context, this research attempts to explore how metaverse as a confluence of technologies and as a new paradigm is redefining sense of place. Specifically, the objectives of the research are to: (a) conceptually redefine sense of place in a metaverse realm; (b) identify key attributes of sense of place in a metaverse realm; and (c) empirically investigate sense of place in a metaverse context.