Billy Yu's Homepage

Billy is an Associate Professor in the Faculty of Business (FCG) in Macao Polytechnic University. He acquired a doctoral degree from the University of Hong Kong and started his career as an e-business consultant. His major clients were banks, multinational cooperation and the HKSAR government. He then switched to the academy and administered various government-funded and commercial research projects. The course he lectures is recognized by SAS®*. His IT background and network allow him to see the picture in the industry. His research interests were broad in Economics & Business, with great inclination to quantitative methods. It makes possible scientific conclusions for practical business implications. He also lectured executive short course and engaged in keynote speaking for professionals in the industry. He is currently an editor of the journal PLOS ONE. For community service, he volunteers as a board member of the Information Systems Audit and Control Association (ISACA) Macao Chapter for over 10 years now.

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Doctor of Philosophy (Ph.D.), the University of Hong Kong.

Bachelor of Engineering (B.Eng.), the University of Hong Kong.


Research interests

My interests are many. Behavioral economics and monetary economics are my most recent pursuits. Yet, I have strong favors in empirical investigation. I seek truths by means of statistics or even analytics, and do not limit myself to any quantitative methods. This is my school of thoughts – quantitative management. It is an inheritance from Peter Drucker:

"If you can't measure it, you can't improve it."

I even instill this idea to my students and they are strong in data mining. Well, that is where e-commerce comes into play after the millennium.


Student Projects

Recent graduation projects

The application of blockchain to the retail industry

The general public use paper-based receipts in the Macao retail industry; and customer always collect stickers for various kinds of retail promotions and gifts. Losses, handling and frauds in operations are major problems for residential applications. This project proposes an electronic means for that, and with better trust and reliability. Blockchain technology is used to achieve the goals for E-receipt and E-sticker. Blockchain technology provides a decentralized and open mechanism to support next-generation Internet technologies, including anonymous online payments, remittances and other digital asset transactions. By using industrial standard development tools, the students implemented a prototype for the identified operations involved. They proved their solution concepts by programming in blockchain. Their ideas not only improve security, reduce paper use, but also automate the process of receipt-to-stickers, and make possible the transfers and the tasks much more convenient.

The application of blockchain to Supply Chain Management

Supply Chain Management (SCM as Michael Porter described) refers to the activities and processes of planning, coordinating, operating, controlling and optimizing the whole supply chain system, including all the stakeholders like suppliers, manufacturers, distributors, and consumers. In it, a critical challenge is the bullwhip effect, in which demand order variances are amplified as they move upstream. By identifying the major characteristics and solution paths in the literature, a blockchain framework is proposed to explicitly solve the problems. By using industrial standard development tool, the students programmed a prototype to illustrate the solutions they proposed. It proved the concepts of solving the SCM problems by using blockchain technology with its unique features. Not only it increases efficiency just as other electronic means, it also benefits the whole supply chain by improving information sharing, the inventory policy and the demand forecast and ultimately reducing costs.

Applied Natural Language Processing in Tourism

Reviews are important for all consumers nowadays. This user-generated content has got serious attentions for all marketing businesses, from simple brand management, customer acquisition or even risk management. Manual process is not scalable for the massive textual data. This project deployed multiple deep learning language models to assist their analysis on over 400,000 reviews messages in TripAdvisor. Simple as basic RNN (recurrent neural network) like LSTM (long short-term memory) and sophisticated as stat-of-the-art BERT (Bidirectional Encoder Representations from Transformers) were used with different preprocessing treatments. With those NLP techniques, they could accurately estimate the sentiment of a visit from the user comment or sharing. More, the study identified important aspects for both positive and negative reviews. It provided insights for marketing practitioners to adjust their services from the massive user reviews without sweat.

Natural Language Progressing For News Archive

Information overloading is an issue executives are faced with in today's business settings. Tons of news are to be digested for prudent decision making every day. While time is a constraint, the fullness of information in news articles should not be discounted. Summarization should be able to help. This study deployed multiple methods for the job: clustering, transformer, BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-Training Transformer). To assess the models, the author also investigate the loss functions, with consideration on feasibility and practicality. They did a straight-forward and relatively easy solution with extractive summarization by BERT. By tailor-making a pre-trained model with clustering technique for Chinese tokenization and embedding, extractive summarization could yield very human readable messages. However, it only picked portions of the original articles and failed to effectively convey the richness of the original notions; and missing some in the making. The study turned to abstractive summarization to take full advantage of deep learning language models. Due to hardware and time limitations, only 72,000 article-summery pairs were used. The resulting model with GPT, although not perfect, was the best and satisfactory measured with multiple ROUGE scores.

Machine Learning for Live Streaming Commerce Market Analysis

E-commerce live streaming was a new key battleground at the time of their writing. The logistics behind have a lot of complications; Key Opinion Leaders (KOLs) were only the front-end. Despite failures in the West, Multi-Channel Networks (MCNs) became important strategic marketing assets. MCNs managed KOLs and supported related dealings, from styling, sales platform management to product promotion contracts. For sales optimization, a strategic decision was on the influencer-platform-product mix. Tracking over 4000 KOLs, attributes on 4.3 million streaming sessions selling 4.6 million products were obtained. The students investigated the problem with thorough analyses, from basic descriptive statistics to hierarchical clustering in Python and various classification models with SAS Enterprise Miner. They found effective promotion strategies, and elaborated results with visualization of maps and word clouds.

Machine Learning for Analysing Digital Media under COVID-19 Era

Social media popularity skyrocketed during and after Covid-19 lockdowns in China. The cultural preferences also changed. This study investigated the changes of user sentiments based on regular comments and danmu on video sharing. The comment-rich media helped business understand their customers. The students collected over 2 billion live comments for over 86 thousand of videos before and during the pandemic (two full years of 2019-2020). While naive word counting could be misleading, as some words are naturally more frequently used, they deployed Jieba and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization in Python as the feature extraction mean. On top of basic descriptive statistics presented with word clouds and usual charts, they used various classification models with SAS Enterprise Miner to uncover the patterns of changes. They helped promotion tactics by identifying effective and trendy words for titling, tags and keywords.

Deep Learning on Volatility of Cryptocurrencies

Cryptocurrencies have become one of the mainstream investment vehicles. However, notorious is their volatility. Traditional daily data unnecessarily distorted the actual trading information as a result of aggregation by time or other bins. Tick-to-tick data give the most truthful view of the market's trading information (e.g., the timestamp, the transaction type, the trading volume etc.). However, early econometric models were not designed for tick-to-tick volatility analysis upon irregular time intervals. Thus, this study aims to adopt a deep learning approach to assess the volatility measures for price prediction. New manual features were derived from the principles of close-to-close, Garman-Klass and Parkinson volatility. The cryptocurrencies under study were Cardano (ADA), Bitcoin (BTC), Dogecoin (DOGE), Polkadot (DOT), EOS, Ethereum (ETH), Litecoin (LTC), Ripple (XRP). Typical models of LSTM and GRUs were used. Results show that the manual features to deal with irregular time intervals and volatility can effectively help prediction performance. Ironically, the simple close-to-close volatility generally performs the best, based on the measure of mean absolute errors.

Backtesting systematic risk: a deep learning approach

Value at Risk (VaR) has been widely used as a measure of market risk. It is considered a prudent statistical tool to quantify possible losses of an investment asset and has become a means for risk management in the mainstream. It is particularly important for the volatile market. However, the conventional methods were unable to handle the fluctuations in the market, especially in high-frequency trading. With the development of deep learning technology and the popularity of quantitative investment strategies, this study aims to validate the effectiveness of VaR with a backtesting strategy, and hence develop an effective automatic stock trading system. The key component is built upon deep reinforcement learning. With high-frequency data on 30 blue chips traded in the Hong Kong Stock Exchange, the students tested the 4 major deep reinforcement learning algorithms (SAC, PPO, A2C, and DDPG). Their model validated the effectiveness of VaR and found that SAC performed the best. Backtesting results showed that their developed automatic trading system could beat the bear market by almost 3% in 5 days on February 2022.

Deep learning on anomaly detection for crypto-assets

Cryptocurrencies have been accepted as digital assets in property law. However, notorious is its use as money-laundry vehicle. Anomaly detection methods have been around to fulfill anti-money laundry regulations. However, their trading volumes and frequencies invalidate the effectiveness. Daily or any aggregated data with traditional statistical econometric models were not able to identify anomalies under such conditions. Tick-to-tick data reflects the most truthful view of the market's trading information (e.g., actual transaction price, trading volume etc.). This study aims to adopt a deep learning approach to detect trading anomalies. Models were built and tested with basic principles in anomaly detection. Under study were five top cryptocurrencies in Binance including Bitcoin (BTC), Ethereum (ETH), BNB, Ripple (XRP), and Cardano (ADA). Typical models of LSTM and GRUs were used to analyze over 120 million transactions from 2020/01/01 to 2020/07/01. Results show that these strategies could supersede calendar anomalies and LSTM effectively narrowed down the detected anomalies in most cases.

Machine learning analysis on the value of NFT

Non-Fungible Tokens (NFTs) revolutionize the art industry as shareable collectibles with definite ownerships. They have become a major investment asset in the digital world; but like all other fine-art artifacts, valuation is difficult. To explore and facilitate the valuation process, this study attempted to estimate based on the contemporary market values of over 15,000 NFT collections. Collection characteristics like popularity, rarity and trading frequency were considered. With repeated use of decision trees on some meaningful variable transformations, it reveals that the floor prices of NFT collections have strange and non-linear complex relations with multiple characteristics. It provides valuable insights in estimating fair market prices of art pieces to researchers, investors, and NFT professionals.

Using Derivatives to Forecast Market depth: A Deep Learning Study on High-frequency Crypto Trading

The introduction of derivatives was supposed to stabilize the market, but instead, they have only added complexity. High-frequency crypto trading is all the more unpredictability. It is unsure if those principles that govern traditional financial markets still work in the crypto market. By leveraging derivatives metrics and tick-to-tick level transaction data, this study investigated market conditions like volatility, liquidity, market depth and etc. Typical deep learning models were used to analyze aggregated data of over 167 million data points with carefully engineered features. Results reconfirm some of the old principles and provides actionable insights to market regulators and market makers about pathways to identify key factors that affect market depth in the crypto derivative market.

How liquidity helps predict market volatility: A deep learning investigation on high-frequency crypto trading

The cryptocurrency market is notorious for its volatility, dramatic even in a short term. It challenges investors and traders to make informed decisions. Nevertheless, traditional wisdom in high finance says liquidity plays a crucial role in predicting volatility in the present of futures. This study aims to analyze some unique phenomena in the cryptocurrency market. It used tick-to-tick transaction data from Binance exchange and engineered 19 trading features for some deep learning analyses. The phenomena were reconfirmed. Results help smart money to enter or exit the market at a reasonable price; and also provide insights and references for regulators to stabilize the market and help confidence in the cryptocurrency market.

Course projects

Other interesting students' works


Student Achievement

Professional Qualification

Data Mining - Predictive Modeling

In the world of data analytics, there is a professional examination recognized worldwide. Some of our students headed for that and acquired that professional qualitification. They all got very good starts at the early career.



Advices to undergradutes

Techical aids

A story

How to work a good work?

Take minutes to guide progress (your supervisor may have other template, as required)



Last Semester

This Semester

Other courses

  • Cyber Organization and Regulation of Cyberspace
    • A course in an MSC program about organizational management, re-engineering and emerging regulations in e-Commerce.
  • Electronic Marketing
    • A course in an MSC program about the capabilities of the Internet for marketing purposes.
  • Customer Relationship Management
    • An executive programme for the gaming industry outlining operational CRM.



M534, Faculty of Business, Meng Tak Building, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macau

Tel: (853) 8599 3312

Fax: (853) 2872 7653



Selected publications


Google Scholar records are more updated.

*. Yu, B. (2023). Deep Learning Applications for Interactive Marketing in the Contemporary Digital Age. The Palgrave Handbook of Interactive Marketing, 705-728. (Book chapter)

*. Yu, B. (2022). How consumer opinions are affected by marketers: an empirical examination by deep learning approach. Journal of Research in Interactive Marketing, 16(4), 601-614. (SCI, impact factor in 2021: 9.45 Q1)

*. To, W. M., & Yu, B. T. (2021). Effects of Difficult Coworkers on Employees’ Responses in Macao’s Public Organizations—The Mediating Role of Perceived Stress. Administrative Sciences, 12(1), 6. (SCI, impact factor in 2020: 0.57 Q3)

*. Yu, B. T. W., & To, W. M. (2021). The effects of difficult co-workers on employee attitudinal responses and intention to leave among Chinese working adults. SAGE Open, 11(2), 21582440211015723. (SSCI, impact factor in 2021: 2.0 Q2)

*. To, W. M., Lee, P. K., & Billy, T. W. (2020). Sustainability assessment of an urban rail system–The case of Hong Kong. Journal of Cleaner Production, 253, 119961. (SCI, impact factor in 2020: 9.45 Q1)

*. To, W. M., & Yu, B. T. (2020). Rise in higher education researchers and academic publications. Emerald Open Research, 2, 3.

*. To, W., Yu, B., & Lee, P. (2018). How Quality Management System Components Lead to Improvement in Service Organizations: A System Practitioner Perspective. Administrative Sciences, 8(4), 73.

*. To, W.M. and Yu, T.W. (2016). Characterizing the urban temperature trend using seasonal unit root analysis: Hong Kong from 1970 to 2015, Advances in Atmospheric Sciences, 33(12), 1376 - 1385. (SCI, impact factor in 2016: 1.363)

*. To, W. M., Martin, E. F., & Yu, Billy T. W. (2015). Effect of management commitment to internal marketing on employee work attitude, International Journal of Hospitality Management, 45, 14 - 21. (SSCI, impact factor in 2014: 1.939)

*. Yu, Billy T.W. and To, W.M. (2013). The effect of internal information generation and dissemination on casino employee work related behaviors, International Journal of Hospitality Management, 33, 475 - 483. (SSCI, impact factor in 2013: 1.837)

*. Lee, K.C., To, W.M. and Yu, T.W. (2013). Team attributes and performance in operational service teams: An empirical taxonomy development, International Journal of Production Economics, 142(1), 51 - 60. (SCI, impact factor in 2012: 2.081)

*. Yu, T.W., To, W.M. and Lee, K.C. (2012). Quality management framework for public management decision making, Management Decision, 50(3), 420 - 438 (SSCI, impact factor in 2012: 3.787)

*. W.M. To, Peter K.C. Lee, Billy T.W. Yu, (2012) Benefits of implementing management system standards: A case study of certified companies in the Pearl River Delta, China, TQM Journal, 24(1), 17 - 28 (EI)

*. Yu, T.W. and To, W.M. (2011). The Importance of Input control to work performance under the agency theory framework, International Journal of Human Resource Management, 22(14), 2874 - 91. (SSCI, impact factor in 2011: 1.043)

*. To, W.M., Lee, K.C. and Yu, T.W. (2011). ISO9001:2000 Implementation in the Public Sector: A Survey in Macao SAR, TQM Journal, 23(1), 59 - 72. (EI)

*. Lee, K.C., To, W.M. and Yu, T.W. (2009). The Implementation and Performance Outcomes of ISO 9000 in Service Organizations: an Empirical Taxonomy Development, International Journal of Quality & Reliability Management, 26(7), 646 - 662 (INSPEC).

*. Yu, T.W. and To, W.M. (2008). Effects of Control Mechanisms on Positive Organizational Change, Journal of Organizational Change Management, 21(3), 385 - 404. (SSCI, impact factor in 2008: 0.520)

*. W.M. To, T.W. Yu, T.M. Lai, S.P. Li (2007) Characterization of commercial clothes dryers based on energy-efficiency analysis, International Journal of Clothing Science and Technology, 19(5), 277 - 290. (SCI, impact factor in 2007: 0.47)

*. Wong, Ngan Hong and Yu, Tat Wai (2006) A historical Perspective on Modern Casino Management, Journal of Macau Studies, 36, 86 - 91.


My disappearance in Facebook

The intended purpose of my Facebook Messenger was for leisure chatting and non-academic development. Nevertheless, not much actual demand for what I intended. Instead, forgive me, I am too slow to recognize that I might have invited possible unfairness and misunderstandings that students may skip classes, not take notes on classes, be forgetful, not read course materials, not read books and not discuss with their peers, etc. Some young students use instant messaging to ask for personal privileges, like examination tips and extra private tutoring. It must be too tempting.

However, I should be fair to all and give no favor to individual requests. Such questions should be raised in class while all can benefit without partiality. Thus, to minimize conflict of interests, I would delink students in my current teaching courses and refuse friend invitations for them during that semester. We can meet face-to-face regularly in class anyway. The official channels are good enough. Of course, after the course, you may add me again as your friend if you choose to.

Enjoy studying, and be a real university student! Have fun!