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 

Asian Economics

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.

Financial Innovation

Impact of Financial Technology (Fintech) on Banking and Small-Medium Enterprises (SMEs)

Principal Investigator: Prof. Rose Neng LAI

Industry Collaborator: BOC

Many global research institutes have proven that COVID-19 has profound impact on our livelihoods and lifestyles, shifting how consumers shop, spend and consume. even though the pandemic situation in Macao is much milder than the rest of the world, consumption patterns have still gone through significant changes. In addition, the Macao government has planned to increase the development of the digital economy, including financial technology (Fintech). Mobile payments and money transfers between banks and mobile payment providers are some simple forms of Fintech. Through this study, we attempt to analyze the potential penetration of “Simple Pay” initiated by the Monetary Authority of Macao (AMCM), as well as implications to the small and medium-sized enterprises (SMEs).

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

  1. assess the potential of specific DLT applications for enhancement of economic development and effective governance,
  2. identify relevant technological and legal challenges facing its prospective deployment in Macau SAR and Greater Bay Area (GBA) and
  3. 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-Dimensional Financial Index Tracking based on the Regularization Approach

Principal Investigator: Prof. Jet Lianjie SHU

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.

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.

Smart Tourism

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 addresses 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.