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

(Team members)

Machine Learning in Energy Future Price Forecasting

Principal Investigator: Prof. Brenda ZHANG

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.

The Gaming Sector of Macao: Wage Premium and Rent Sharing

Principal Investigator: Prof. Fung KWAN

We investigate the latent wage differentials between gaming, a dominant sector of the small open economy of Macao, and non-gaming. It is expected that croupiers received the largest wage premium among occupations. This outcome can be explained by rent sharing and labor supply restrictions. Casino concessionaires are well-capitalized and licensed with strong market power, generating huge profits and sharing rent—advocated by the local government and the public—with their staff by paying higher salaries. In addition, croupier positions are limited to residents, allowing them to enjoy the major wage premia among professions.

Information Elicitation in Matching Mechanisms: Less Is More? Or Is It the Opposite?

Principal Investigator: Prof. Inácio BÓ

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.

How Does the Past Near-Miss Failure Affect Subsequent Entrepreneurship? Evidence from Chinese Crowdfunding Field Data

Principal Investigator: Prof. Jia YUAN 

Failure is an important component of the business activity. A near miss failure is a special kind of failure that comes close to actual success, which shows up frequently in people’s business behavior. Using unique field data that traces 1,459 Chinese individuals’ lottery purchase history with lottery number picking information and the lottery purchase amount information, we aim to examine the effect of near misses on people’s betting strategy. This research has clean identification as the winning numbers are completely random.  We conjecture that people who have experienced near misses would invest more money in buying lottery. Specifically, we want to test whether a near-miss event would motivate people to increase their investment amount.

From Macro to Micro: Trade Policy Uncertainty and Firm Decisions

Principal Investigator: Prof. Leona LI

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.

Economic Globalization and Probability of Export Growth

Principal Investigator: Prof. Priscilla TAM

This project aims to assess the ability of economic globalization to explain and predict the probability of export growth. The dimensions of economic globalization in terms of volume sizes and network sizes of international economic flows and activities are to be distinguished. Employing the dynamic random effects probit model with unobserved heterogeneity, it is expected that volume sizes affect the probability of export growth positively, while network sizes have negative effect. Moreover, both in-sample and out-of-sample analyses will be conducted to demonstrate the power of economic globalization in anticipating export growth. Findings would suggest heterogenous informational content of economic globalization on the probability of export growth, and recommend combined use of them coupled with other export-related factors in practice.

Information Disclosure and Competition in Contests

Principal Investigator: Prof. Shanglyu DENG

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.

China’s Increasing Global Financial Impact

Principal Investigator: Prof. Sili ZHOU

There will be considerable capital outflows when China completely liberalizes its capital account, a typical prediction for a capital-abundant country. Given China’s economic size, it will significantly change the global financial markets. Even without complete financial integration, China has shown its global influence in many aspects such as trade and output. As the central government continues to liberalize the capital account, China is expected to have a larger financial impact globally. Is China’s global financial footprint on the rest of the world different from U.S. and Europe? What are the transmission mechanisms?

In this project, we use various micro-level data to study these questions, focusing on China’s increasing global financial importance. There are two main objectives. First is to document stylized facts about Chinese global financial investment, including portfolio equity and debt, FDI and bank loans. In particular, we want to collect detailed information on both lenders (both public and private) and borrowers (nationality, residence, etc.). We aim to draw a picture of the global footprint of Chinese investors. Second is to study the increasing global financial impact of Chinese investment, focusing on the heterogeneity and transmission mechanism of Chinese specific shocks to global economy through those global financial linkages.

Cross-sectional Stock Jump Tail Risks

Principal Investigator: Prof. Yi DING

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.

Effects of Blocking Patents and Trade Secrecy in a Schumpeterian Economy with Rent Protection

Principal Investigator: Prof. Yibai YANG

This project explores the impacts of two types of intellectual property rights (IPR) protection in a Schumpeterian economy. The policy instruments in consideration that represent these IPR protection regimes include blocking patents and trade secrecy. Therefore, this project will consist of research questions, including how (a) the degree of blocking patents (in terms of the profit-division rule between consecutive innovators) and (b) trade secrecy (in terms of the proportion of secrecy protection versus patent protection) on technological advances and economic growth in a dynamic general equilibrium model with firms’ internal strategies to capture value from innovations.

Rent protection is an important way for firms to exert private efforts to supplement the legal protection provided by patents. Blocking patents capture the overlapping patent rights between subsequent and entitle incumbent firms to use these rights to extract rents from new entrants, whereas trade secrecy provides one way to avoid information disclosure and protects an invention indefinitely as long as it can be kept private. Therefore, it is important to explore how these IPR regimes interact with firms’ rent protecting activities (RPAs). This project expects to make significant contributions in terms of theoretical exploration and policy implications.

Effects of R&D Policy on Technology Transfer, Economic Growth and Social Welfare

Principal Investigator: Prof. Yibai YANG

* Co-funded by the Research Grant of Department of Science and Technology of Guangdong (2022–2024)

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.

Financial Innovation 

(Team members)

International Commercial Mediation: How Could It Be Employed to Resolve Cross-border Financial Disputes in the Guangdong-Hong Kong-Macao Greater Bay Area?

Principal Investigator: Prof. Guangjian TU

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.

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 Impact of Government Outsourcing Contracts on Valuation of High-tech Firms

Principal Investigator: Prof. Jing XIE

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?

From Cooperation to Integration? An Exploration of the Corporative Patterns Between Third-party Funders and Law Firms in the International Arbitration Market

Principal Investigator: Dr. Zhe MA

The legitimization of third-party funding (TPF) in arbitration is a growing trend, leading to the vigorous growth of third-party funders as an emerging financial industry. Existing research focuses on the regulatory approach to oversee the funders but overlooks the roles of law firms within this specific mechanism. This research provides an empirical exploration of the collaborative patterns between law firms and third-party funders. Building upon this foundation, it evaluates the feasibility of integrating the law firms and funders as partners in TPF projects. Consequently, the findings of this study have the potential to offer a fresh perspective on the examination of the roles of the two professional markets and further propose an innovative assumption regarding their future developing direction: from cooperation to integration. Remarkably, this research serves as the first investigation on the application of the previously proposed T-models by Sahini in 2017, which aims to reshape the roles of funders as internal partners of law firms with the goal of mitigating transnational risks.

High-Dimensional Financial Index Tracking based on the Regularization Approach

Principal Investigator: Prof. Jet Lianjie SHU

* Co-funded by the Research Grant of Department of Science and Technology of Guangdong (2022–2024)

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.

Smart Tourism

(Team members)

Language Framing Effects in Disease Detection Communication

Principal Investigator: Prof. Fangyuan CHEN

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.

Artificial Intelligence in Preventing Legal Risks in Tourism

Principal Investigator: Prof. Hanyue LYU

In a world marked by frequent international exchanges, diverse local laws pose significant legal risks for travellers. This project focuses on leveraging Artificial Intelligence (AI), particularly natural language processing, to analyse official data and media reports from major tourist cities in the Guangdong-Hong Kong-Macao Greater Bay Area and all over the world. By examining legal risks across various destinations, the project aims to develop an AI prototype that provides legal risk alerts for businesses and individuals with substantial travel needs.

Smart medical tourism: regulatory issues and challenges for personal health data protection

Principal Investigator: Prof. Li DU

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.

Rethinking Customer Experience in the Physicality-Virtuality Synthetic Reality Paradigm: Conceptualization and Research Directions

Principal Investigator: Prof. Li MIAO

The convergence of advanced technologies is increasingly merging the physical and digital worlds, altering the essence of human experience and demanding a reconsideration of customer experiences traditionally seen as either physical or digital. This research introduces the metaverse as a new paradigm that combines these realities. It explores how customer experience dimensions transform in this novel context, identifying key shifts such as from telepresence to omnipresence and immersion to surreality, among others. These shifts span various aspects, including sensory responses, cognition, emotional engagement, and interaction levels. The study delves into theoretical aspects of these transitions and proposes a future research agenda to further understand the implications of these evolving experiential dynamics.

Enhancing Travel Accessibility: AI-driven Personalization for Individuals with Disabilities

Principal Investigator: Prof. Xin LIN

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.

Tourist Privacy Perception and Mitigation Throughout the Smart Tourism

Principal Investigator: Prof. Ye WANG

The integration of IT technologies in smart tourism has markedly enhanced the travel experience, offering seamless services in pre-travel planning, during-stay activities, and post-travel engagement. Despite the convenience and enhanced experiences offered by these technologies in various stages of travel, the potential for privacy breaches remains a significant concern. This project focuses on the privacy risks associated with the use of tourist data in the realm of smart tourism from tourists’ view. These risks are particularly acute in scenarios marked by transient and unfamiliar interactions. The project aims to investigate the extent of tourists’ awareness and perceptions of their data usage and the potential privacy risks in a wide range of smart tourism scenarios, including reservations, accommodations, transportation, attractions, and social media engagement post-visit. By identifying and addressing these privacy issues, the project is to propose effective strategies for protecting visitor privacy, thereby fostering a more secure and trustful environment in the smart tourism industry.

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.

Predictivity in Tourism Demand Forecasting: a Bayesian interpretation approach

Principal Investigator: Prof. Rob LAW

Tourism contributes significantly to a region’s economic and business development, while the growth infrastructure of the region can also influence the tourism industry indirectly. Recently, with the methodological development on tourism demand forecasting, the interests of researchers have been shifted from traditional time series forecasting and econometric models to Artificial Intelligence (AI) models. Many works have incorporated deep learning models into tourism demand forecasting by analyzing bid data collected from the Internet. However, these techniques are either predetermined on the selected data or directly leveraging the forecasting practice without clear understanding on the impacts of data characteristics. As such, it remains unclear for the relationship between data characteristics and the maximum predictivity in tourism demand. In the tourism industry, demand forecasting is an important way to support the practitioners in decision making, for which the interpretation on the forecasting at micro and macro levels is also important. This study aims to fill these two gaps by using the information theory and the Bayesian networks. We will propose an explainable predictivity tourism demand forecasting framework, which can provide an analysis of multi-variate predictivity and the interpretation while maintaining accurate forecasting.