Research

Working Papers

1. “The Demand for Robo-Advising: Adoption and Delegation Intensity” (with Richard Harris and Manuela Pedio) 

SSRN Working Paper Here

Abstract: Empirical studies of robo-advisor adoption report sharply conflicting findings about who uses automated investment advice, which we argue reflects a conflation of two economically distinct decisions: whether to adopt and, conditional on adoption, how much wealth to delegate. Using a proprietary panel of 100,000 Chinese retail investors on the Alipay platform over four years, we estimate Heckman two-stage selection models and document that the determinants of the two margins frequently move in opposite directions. Wealth robustly increases adoption across specifications, while the apparent negative effect of wealth on conditional intensity in baseline specifications collapses once behavioral controls are added. Age does not act uniformly across margins. The prior literature’s demographic disagreements largely reflect uncontrolled behavioral correlates of wealth and age. Across nine behavioral biases, the cross-margin coefficients fall into three sign signatures, which we interpret as awareness, attachment, and cognitive-constraint channels. A within-platform comparison with automatic investment plans, which automate execution but preserve fund-selection authority, suggests that the substantive content of delegation is the transfer of authority rather than automation per se. A within-investor tenure analysis shows that delegation is not permanent for capable adopters; the tenure profile deepens on average but is monotonically attenuated by fund-trading experience and reverses at the upper tail. Selection on unobservables is positive across specifications. Taken together, these findings reconcile conflicting prior evidence, organize the interaction between behavioral biases and corrective technology into three distinct cross-margin signatures, and isolate decision authority as the substantive content of delegation in a class of participation decisions for which the canonical alignment between participation and portfolio efficiency need not hold.

Note: This work is the first chapter of my PhD dissertation.

2. “What's in the Retail Investors' Information Set? Understanding Retail Investor Behaviour with CNNs” (with Manuela Pedio)  

Draft Coming Soon

Abstract: In this paper, we use convolutional neural network (CNN) models to predict retail investors’ trading behaviour on Robinhood. Understanding retail investor trading behaviour is interesting and important, but it is difficult as it can be driven by sentiment and other behavioural factors that are hard to quantify. Among other things, retail investors’ activity has been shown to be driven by the visual features of price charts. CNN models enable us to build a time-series prediction system that exploits both graphical features and a host of stock characteristics to forecast retail trading activity. Specifically, this study seeks to predict investors’ holdings in the stocks that comprise the S&P 500 index over a sample spanning from 2018 to 2020. The CNN models that combine graphical and stock characteristics perform very strongly in predicting aggregate retail investor trading behaviour, achieving low test errors and economically meaningful R^2 at medium horizons, confirming the importance of price-chart information to explain retail investor trading activity. We then apply SHAP (Shapley Additive exPlanations) to help us understand which among the stock characteristics are the most relevant to explain aggregate investor behaviour. We find that technical indicators, such as the volumes and past open and close prices, are the most relevant features to drive retail investors' behaviour.

Note: This work was based on my Master's dissertation under the supervision of Prof. Vincent Han, with a previous title as "Trading by Charts: A Multivariate CNN System to Predict Retail Investor Trading".  Currently, I am working with Manuela to revise the paper significantly and explore the current direction. The work is now at a very early stage and it is my pleasure that this has been accepted by and presented at a series of distinguished economics and finance conferences, including the 45th International Symposium on Forecasting, the Ninth PKU-NUS Annual International Conference on Quantitative Finance and Economics, 4th Digital Economy Forum of the China Society of World Economics, 2nd CUFE International Doctoral Forum, etc.

Selected Work in Progress

"AI and Robo-Advising"

"Whose Loss Is It?" with Tai-lo Yeung

Other Research (for self-interests)

1. “Understanding Financial Markets by Ancient Chinese Wisdom” (Sole Author) 

Highlights: This paper shows that ancient Chinese wisdom can help investors, academics, professionals, and regulators better understand the financial markets and make more feasible decisions accordingly. This is a pioneering study that systematically understands how Chinese wisdom can be used to understand financial markets in different situations. As there are countless experiences and histories that may be used to learn and reflect on the financial market and investor behaviours, this paper provides an example of how to learn these lessons and shows some insights about the future of finance using ancient Chinese wisdom. 

Note: This work has been presented at the 2024 ESRC SWDTP PhD Student Annual Conference. 

Major revisions are expected. 

Here is Jay's story of his Academic Research Journey.

I am currently a PhD student at the University of Bristol. My research interests include FinTech, Retail Investments, Machine Learning, Artificial Intelligence, ESG, Social Welfare, Investor Behaviour, Applied Financial Econometrics, Chinese Financial Market, Financial Theory, etc.

On my academic path, I am grateful to receive funding support from the following supporters.

1. My family (love you Mom and Dad ❤️)

2. The University of Bristol

3. The University of Bristol Business School

4. The University of Bristol Faculty of Social Science and Law

5. The University of Bristol Doctoral College

6. The University of Bristol SEC Alumni Grants Group

7. Jean Golding Institute at the University of Bristol

8. The Alan Turing Institute

9. Bocconi University

10. International Associate of Applied Econometrics

None of my achievements or progress would have happened without these generous supporters. 

I am the first generation to go to university in my family. I am not talented, I am not smart, and I am not even working very hard. I am a normal person who is resilient and determined in his dream, which is the most important thing for the academic journey, and maybe for all journeys. Besides, it is important to put every effort into chasing dreams.

My academic journey begins with my bachelor's degree in Finance at the University of Bristol. When I chose the program, I knew nothing about finance and I thought, similar to many, finance is a major that allows us to make a lot of money in the industry (which is obviously not the case). Luckily, later on, I found my interest in finding the truth behind the cover (it is important to keep curiosity) and in contributing to the future of human well-being. If this is the case for you, research is a good option. I then tried both industrial research and academic research (my first research assistantship with Dr Manuela Pedio and Dr Silvina Rubio) and subjectively concluded that academic research is actually the thing I want. After careful consideration, I decided to start an academic career in finance and applied economics.

The journey is never smooth, due to non-outstanding academic performances in UG, I faced a bunch of difficulties in the application of MSc, Pre-Doc, and PhD programs: Applied, Rejected; Applied, Rejected; etc (Sometimes I can get interviewed or future tasks).  But I am grateful for these experiences, which made me different and, more importantly, resilient and determined. I am even thinking of making a CV of Failures on this webpage, I will do so if I find some time later.

TBC...

What It Takes to Be A Good Scholar?

In this part, I am going to share my naive thoughts about my understanding of 1. what is a good scholar; 2. how to move toward to that goal.

1.  What is a good scholar? 

Before answering this question, it is important to define what is a scholar? I prefer the word "Scholar" to "Researcher" because the previous word contains the meaning of a person who focus on research but goes beyond that. Scholar is a status of lifelong learning and contributing to the field. 

It is important to differentiate Good Scholar from Impactful Scholar.

2. The Economist’s Craft: A Scholar’s Introduction to Research, Publishing, and Professional Development by Michael S. Weisbach

3. Writing Tips for PhD Students by John Cochrane

4. How to Succeed in Academia or Die Trying Have Fun Trying by Lasse Heje Pedersen*

5. Investments Notes by John Cochrane

What It Takes to Be An Impactful Scholar?

An impactful scholar is someone who caused significant effects to participants in the field, sector, and the world. These effects can be academic outputs, personal characteristics, and brilliant thinking.

Influential academic outputs normally are

1. New theories and methodologies that can be widely applied in the field and in other related fields.

2. Important empirical findings in a hot but long-lasting topic. 

3. Widely used textbooks in the field around the world.

TBC.

If you have any questions or suggestions for me, please feel free to get in touch below.