Funded Projects for APAEM Members.
Our research team strives to have societal impact with our projects. If your organization is interested in becoming one of our collaborators, please contact us at email@example.com.
Monetary Policy and Subsidy Policy in a Global Economy with Innovation and Technology Transfer
Principal Investigator: Prof. Yibai YANG
This project explores the impacts of two policy regimes on innovation and technology transfer in a global economy. The policy regimes 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 technology transfer in multinational firms’ foreign direct investment (FDI).
Technological innovation becomes increasingly important to sustain long-run economic growth in an open economy, especially for Asian economies. Inflation generates an extra cost burden in manufacturing and research and development (R&D) investments, whereas subsidization is one crucial policy instrument affecting technological improvement and economic growth. Therefore, these policy tools play a crucial role in transferring multinationals’ technologies across countries. This project expects to make significant contributions in terms of theoretical exploration and policy implications.
This project will systematically describe phenomenal observations. Then it will analytically study the effects of two policy instruments on innovation and technology transfer by setting up dynamic general equilibrium frameworks with optimization and endogenous economic growth. Finally, this project will quantify the effects of policy instruments by calibrating theoretical models to realistic data.
Optimal Design for Concession Agreements
Principal Investigator: Dr. Allen VONG
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.
Tourism Development in the Greater Bay Area
Principal Investigator: Prof. Priscilla TAM
* Co-funded by Education Fund of the Macao SAR Government (2021-2022)
This project purports to study the course of tourism development in the Greater Bay Area from the economic perspective. It focuses on examining the spillover effects of tourism development among the regional cities so as to assess the extent of regional cooperation or competition with regards to their complementarity and substitutability. It also investigates the main determinants of regional tourism development for their implications on the regional allocation of resources. Results would provide insights into the current and future development and growth of the region’s tourism industry.
The Evolution of Productivity and Efficiency in the GD-HK-MO Greater Bay Area
Principal Investigator: Prof. Fung KWAN
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.
External Political Shocks and Corporate Innovation in China –Evidence from the 2016 US Presidential Election
Principal Investigator: Prof. Brenda ZHANG
The unanticipated presidential election outcome in 2016 allows us a unique opportunity to estimate the effect of exogenous political shock in the US on the financial market and corporate strategy in China –its largest trade partner and creditor. We identify firms’ exposure to the shock by examining the stock market reaction and investigate the implication on corporate innovation among listed firms. Our results show that how the surprise victory of Donald Trump influences stock returns and its implications on firms’ innovation outcome. We further test if such relation continues to hold in a battery of robustness tests and is moderated by some firm level characters such as size and ownership. Our findings shed light on the broader implication on corporate innovation of US-China trade war.
The Effect of the U.S.–China Trade War on Chinese Corporate Innovation: A Curse or a Blessing?
Principal Investigator: Prof. Jia YUAN
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.
The Application of Robo-Advisor in Macau
Principal Investigator: Prof. Jinjuan REN
Industry Collaborator: BNU
Motivated by the urgent need for inclusive finance in Macau, we plan to develop a Robo-advisor system to provide Macau residents with low-threshold, low-cost, and effective investment advice and financial planning services. This system is based on big data and machine learning technologies. We plan to collect and analyze data on investors’ personal traits, assess their risk attitude, evaluate their portfolios, optimize portfolios based on modern asset pricing theory, and further provide comprehensive and whole-life financial planning for households. We will further utilize the advantage of university education and build in financial literacy and education into the system.
Application of Blockchain Technology in Macau International Commercial Arbitration
Principal Investigator: Prof. Guangjian TU
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.
Augmentation of Digital Economy and Governance Through Deployment and Legal Recognition of Distributed Ledger Technology Applications in Macao SAR and Greater Bay Area
Principal Investigator: Prof. Guangjian TU
The scope and potential of Distributed Ledger Technologies (DLTs) including the Block Chain in the enhancement of economic development and effective governance are increasingly realized in various countries and markets around the world. The nature of the technology, warranting the development and use of different nodes of a network that could span across various borders, creates numerous technological and legal challenges, which needs to be comprehensively addressed in order to ensure the adoption of any transparent, reliable and efficient DLT applications. The proposed interdisciplinary project intends to
- assess the potential of specific DLT applications for enhancement of economic development and effective governance,
- identify relevant technological and legal challenges facing its prospective deployment in Macau SAR and Greater Bay Area (GBA) and
- propose relevant remedial and facilitation measures for wider adoption of the technology. Firstly, the project will systematically study the technology, infrastructure and legal environment necessary for the potential deployment DLT applications in various facets of the economy and public governance in Macau SAR in a comparative assessment with China, Hong Kong and other relevant jurisdictions. Secondly, the project will explore specific cross-border technological and legal cooperation/harmonization measures essential for facilitating economic transactions in the GBA region.
High-Speed Financial Asset Movement Forecasting System
Principal Investigator: Prof. Jerome YEN
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.
Problems and Opportunities of Fintech as Sources and Platform of Financing
Principal Investigator: Prof. Rose Neng LAI
Financial Technology (Fintech) has brought in swift evolution to the financial system, and Fintech finance has grown rapidly to become new sources of funding, which distinguishes from traditional banking by using a lot more information through, for example, Big Data, particularly on credit checks of borrowers, while allowing small investors new choices of flexible and attractive investments. On the payment side, Fintech has facilitated financial inclusion that allows people in even remote areas to be able to use sources of funding as well as change consumption behaviour. This powerful source is however not without downside. When Fintech finance is offered outside of the banking sector, it becomes a new source of shadow banking beyond the scrutiny of the macroprudential banking system, creating a new source of financial fragility. This project attempts to include a collection of studies that analyze issues of Fintech. The first study covers theoretical and empirical analyses on the potential financial risks that Fintech can create to the financial system. The second is an analysis of the opportunities and risks created by the P2P platforms. The third one is to analyze the impact of Fintech financing and online payment platforms on patterns of consumption.
Improving Portfolio Performance based on Robust Hedge Regression
Principal Investigator: Prof. Lianjian SHU
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.
A Smart Guided Tour Via VR for Historic Buildings
Principal Investigator: Prof. Alfred WONG
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.
A Custom Tour Itinerary Robot based on Deep Reinforcement Learning
Principal Investigator: Prof. Alfred WONG
Making a tour itinerary is an obstacle to visitors due to the unfamiliarity of the destination. This project aims to provide a custom tour itinerary robot using deep reinforcement learning. Tour itinerary is considered as the orienteering problem (OP) with search space. Therefore, we decide to train a graph model for predicting the route with reinforcement learning and create a mobile app for a custom tour itinerary in Macau.
Advancing Tourist Destination Competitiveness via Leveraging User-Generated Data
Principal Investigator: Prof. Rob LAW
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.
Tourism Demand Forecasting for Multi-Destinations
Principal Investigator: Prof. Rob LAW
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.
Detecting Fake Hospitality Reviews Using Linguistic Cues
Principal Investigator: Prof. Rob LAW
Online hospitality reviews provide peer opinions on hospitality and tourism products or services, which can assist reducing information asymmetry (Law et al., 2020). Consumers consider online reviews as trustworthy and useful in their decision-making process (Liang et al., 2019). In view of the commercial value of online reviews, fake reviews are often generated to manipulate consumers’ attitudes towards products and services (Huang et al., 2021). As an example, TripAdvisor recently found nearly one million fake reviews in 2020 (TripAdvisor, 2021). Fake hospitality reviews prevent the consumers from obtaining relevant or accurate information, which in turn, damage perceived credibility of reviews and constitute unfair competitions among businesses (Wang et al., 2021). To protect consumers and businesses from biased reviews, identification of fake reviews is crucial. Prior studies in hospitality research mainly focused on using algorithms to optimize the identification approach (Wu et al., 2020). To complement the algorithm development, this project aims to investigate the psychological process underlying review fabrication. Based on interpersonal deception theory, language usage in fake reviews can be different from authentic reviews (Li et al., 2020). Hence, this project is to detect fake reviews using linguistic cues. Specifically, the main objectives of this project include:
- to analyze the linguistic divergences or differences between authentic and fake reviews;
- to examine the interplay of linguistic cues to detect fake reviews; and
- to compare the dynamics of linguistic cues by longitudinal design.
Estrangement Behavior in Post-COVID Travel and Tourism
Principal Investigator: Prof. Li MIAO
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.
Using Smart Technology to Develop Medical Tourism in Macao
Principal Investigator: Prof. Yuanjia HU
Macao has a unique foundation for tourism development, and in response to the demand for moderate economic diversification, the Macao government is also vigorously promoting the development of a large health industry. Medical tourism is a useful exploration of the organic combination of the two for Macao. However, due to various constraints, Macao needs to overcome key bottlenecks with the help of smart technologies. However. Macao has less research in this area. and in this context, this project focuses on using smart technologies to develop medical tourism in Macao. The research aims to: 1) identify the factors and resources that affect the development of medical tourism in Macao, 2) explore the barriers that can be addressed through smart technologies, and 3) propose a pragmatic and actionable framework for the development of medical tourism in Macao. This study will use a combination of interviews, questionnaires, and comprehensive analysis to conduct a systematic study, with the expectation that it will inform and assist governmental decisions, industry development, and individual behavior in this field.
Smart Tourism Apps: Opportunities and Privacy Challenges
Principal Investigator: Prof. Li DU
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.