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.

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.

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.