AI in Finance

Develop advanced machine learning models and libraries for finance.

We design deep learning and deep reinforcement learning (DRL) algorithms for financial tasks, including LSTM, DQN, DDPG, PPO, etc; Based on the differential privacy notion, we build more robust models;  We develop a deep reinforcement learning library FinRL for finance.

Scholar data and ESG data as alternative data, we propose a practical machine learning approach and develop trading strategy to capture the scholar data or ESG data driven alpha.

Machine Learning

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. The three basic machine learning paradigms are supervised learning, unsupervised learning, and reinforcement learning.

Reinforcement Learning

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

Alternative Data

Currently, we study two kinds of alternative data sets: scholar data and ESG data.

Robust Machine Learning Models

We exploit the notion of differential privacy to build more robust machine learning models.

TensorLet Team

The achievement of AI in Finance we did by now!
  • SEC – Invited talk, June 2019 [PDF]

  • 10 publications on AI in Finance

  • Wrote 2 chapters for [Book Chapters] Reinforcement learning for cyber-physical systems: with cybersecurity case studies. Chapman & Hall/CRC, 2019.

Related Publications

[BigData] Y. Fang, Xiao-Yang Liu, Hongyang Yang, Practical machine learning approach to capture the scholar data driven alpha in AI industry. IEEE BigData 2019. [PDF]  (AI Publications as Finance Alternative Data).
[ICML Workshop] X. Li, Y. Li, Y. Zhan, Xiao-Yang Liu, Optimistic Bull or Pessimistic Bear: adaptive deep reinforcement learning for stock portfolio allocation. ICML Workshop on Applications and Infrastructure for Multi-Agent Learning, 2019. [PDF]

[ICML Workshop] W. Bao, Xiao-Yang Liu, Multi-agent reinforcement learning for liquidation strategy analysis. ICML Workshop on Applications and Infrastructure for Multi-Agent Learning, 2019. [PDF]

[NeurIPS Workshop] X. Li, Y. Li, Hongyang Yang, L. Yang, Xiao-Yang Liu. DP-LSTM: Differential privacy-inspired LSTM for stock prediction using financial news. Robust ML DP LSTM Financial News, NeurIPS 2019. [PDF]

[NeurIPS Workshop] Z. Xiong, Xiao-Yang Liu, S. Zhong, Hongyang Yang, A. Walid. Practical deep reinforcement learning approach for stock trading. NeurIPS Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, 2018. [PDF] and [Poster]

[NeurIPS Workshop] W. Lu, Xiao-Yang Liu, Q. Wu, Y. Sun, A. Walid. Transform-based multilinear dynamical system for tensor time series analysis. NeurIPS Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, 2018. [PDF] and [Poster]

[KDD Workshop] X. Li, Y. Li, Xiao-Yang Liu, C. Wang, Risk management via anomaly circumvent: Mnemonic deep learning for midterm stock prediction. KDD Workshop on Anomaly Detection in Finance, 2019. [PDF]

[URTC] R. Zhang, Z. Huang, Xiao-Yang Liu, Machine learning approach for art market. MIT URTC 2019. [PDF]

[EdgeBlock] L. Yang, Xiao-Yang Liu, W. Gong, Secure smart home systems: A blockchain perspective. IEEE INFOCOM Workshop on International Symposium on Edge Computing Security and Blockchain (EdgeBlock), 2020.

[EdgeCom] L. Yang, Xiao-Yang Liu, J. S. Kim, Cloud-based livestock monitoring system using  RFID and blockchain technology. IEEE EdgeCom 2020.

[SmartBlock] L. Yang, Xiao-Yang Liu, X. Li, and Y. Li, Price prediction of cryptocurrency: An empirical study. Springer SmartBlock 2019. [PDF]

[TrustCom] Hongyang Yang, Xiao-Yang Liu, Q. Wu. A practical machine learning approach for dynamic stock recommendation. IEEE TrustCom, 2018. [PDF]

Reach the top ending AI science!

A young team, professional in GPU tensor and deep learning technology, commits to creating top AI algorithms and solutions for cooperates, labs, and communities.