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 focuses on understanding how investors’ demand pressures alter inventory risk management strategies of market makers in the equity options market.

Derivatives, unlike stocks, are in zero net supply. Thus, positive or negative demand pressures, and inability to hedge perfectly, can lead to various deviations from model-implied prices. These deviations are non-trivial and result in different qualities of executions depending on what time of a day you trade. I am currently working with LiveVol/CBOE to attract attention of academics and industry practitioners about the importance of using intra-day trading equity options data rather than end-of-day closing prices.

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

with Chengyu Zhang (McGill)

Using big data, and machine learning we show that investors can quite successfully predict delta-hedged (or delta-neutral straddle) option returns. The predictability is driven by option specific predictors, and commonly used stock predictors play only marginal role. Large set of stock predictors and machine learning also fail to robustly predict stock returns in more recent data. However, options-based predictors and machine learning allows identifying mispricing even for the cross-section of the most liquid S&P500 stocks. Moreover, machines almost unanimously identify options illiquidity as the main predictor of stock returns. Guided by machines, we uncover positive option illiquidity premium in the stock returns. Stock long-short value-weighted portfolio strategies formed on option illiquidity outperform machine learning based portfolios with Sharpe ratio of 2.

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)

Price Pressures and Option Returns

with Chengyu Zhang (McGill)