Relaxed Wasserstein with Applications to GANs, Xin Guo, Johnny Hong, Tianyi Lin, and Nan Yang, 2017. Preprint.
Ambiguity set and learning via Bregman and Wasserstein, Xin Guo, Johnny Hong, and Nan Yang, 2017. Preprint.
Machine learning techniques for price change forecast using the limit order book data (with J. Han, N. Sutardja, S.F. Wong, 2015)
Abstract: In the setting of high-frequency trading, stock price movements are fairly predictable. We use support vector machines (SVMs) with various kernels and random forests to predict the midprice movements of SPY (SPDR S&P 500 Trust ETF). We find that the machine learning approach provides good prediction accuracy. This work is based on the paper “Modeling high-frequency limit order book dynamics with support vector machines” by Kercheval and Zhang.
- An introduction to the use of hidden Markov models for stock return analysis (with Y. Pitcan, 2015)
Abstract: We construct two HMMs to model the stock returns for every 10-day period. Our first model uses the Baum-Welch algorithm for inference about volatility, which regards volatility as hidden states and uses a mean zero Gaussian distribution as the emission probability for the stock returns. Our second model uses a spectral algorithm to perform stock returns forecasting. We analyze the tradeoffs of these two implementations as well. Code
- Sage program for computation and experimentation with the 1-row Gomory–Johnson infinite group problem (with M. Köppe, Y. Zhou, 2014-)