Blog

I am not a professional blogger but a true passionate about finance research. As various projects are progressing, there is a lot of information which I find to be relevant not only to academics but also to professionals in the finance industry.

The purpose of this blog is to share this information in a concise way and make research knowledge more accessible, with the result of not necessarily using academic references, citations or specific vocabulary. The blog covers topics as varied as FinTech and its applications in finance to the outcomes of my various academic involvements. Enjoy the reading!

Blog

I am not a professional blogger but a true passionate about finance research. As various projects are progressing, there is a lot of information which I find to be relevant not only to academics but also to professionals in the finance industry.

The purpose of this blog is to share this information in a concise way and make research knowledge more accessible, with the result of not necessarily using academic references, citations or specific vocabulary. The blog covers topics as varied as FinTech and its applications in finance to the outcomes of my various academic involvements. Enjoy the reading!

Research Update

My recent research is focused on applications of machine learning (ML) and AI in asset pricing.

We are quickly realizing that applying big data and AI/ML to such standard problems like asset allocation, optimal portfolio construction, risk management and investments overall provides significant incremental value compared to using traditional tools.

Yet off-shelf AI algorithms are not well suited for low signal-to-noise ratio of financial data. In my research, with my co-authors, we show how incorporating economic assumptions and restrictions into AI algorithms helps improving investment decision making processes.

Long Horizon Multifactor Investing with Reinforcement Learning

with  Chengyu Zhang (McGill)

We provide a novel approach to multifactor investing for long term investors who take into consideration medium- to long-term volatility and liquidity risks, while minimizing long-term portfolio level rebalancing needs.  We find that training the model under explicit long-horizon holding investment period considerations and low frequency rebalancing, which can only be accommodated via RL, dramatically changes perspective of long-term investors’ portfolio performance vis-à-vis their short-term peers.

Asset Pricing with Attention Guided Deep Learning

with   Philippe Chatigny (University of Sherbrooke) and Chengyu Zhang (McGill)

The Joint Cross Section of Option and Stock Returns Predictability with Big Data and Machine Learning

with  Chengyu Zhang (McGill)

Liquidity Guided Machine Learning: The Case of the Volatility Risk Premium

with Eric Ghysels (UNC) and Chengyu Zhang (McGill)

Volatility and the Cross-Section of Equity Returns: The Role of Short-Selling Constraints

with Paul Schultz (University of Notre Dame)

Demand Pressure and Option Returns

with Chengyu Zhang (McGill)