Date |
Topic |
Instructor |
Scriber |
04/09/2023, Mon |
Lecture 01: Overview and History of Artificial Intelligence in Fintech. [ slides ]
[ Project 1 ]
- Warm-up project description [ pdf ]
- Kaggle: Home Credit Default Risk [ link ]
- GitHub Repository for reports of Project 1
[ GitHub ]
|
Y.Y. |
|
11/09/2023, Mon |
Lecture 02: Supervised Learning: Linear Regression and Classification [ slides ]
[ Guest Talk ]
- Title: An Overview of Quant Modeling and Trading: Theory and Practice [ slides ]
- Speaker: Prof. Michael Zhang, CUHK
- Bio: Professor Michael Zhang is the Irwin and Joan Jacobs Chair Professor at the School of Economics and Management, Tsinghua University.
He was previously at the Chinese University of Hong Kong, where he served as the Associate Dean of Innovation and Impact, Co-Executive Director
of Asia Pacific Institute of Business, Co-Director of Hong Kong-Shenzhen Finance Research Centre at the CUHK Business School. Externally, he is also
affiliated with MIT Initiative on Digital Economy and Leibniz Centre for European Economic Research (ZEW).
He has a PhD in Management from MIT Sloan School of Management, an MSc in Management, a BE in Computer Science and a BA in English
from Tsinghua University. Before joining the academia, he worked as an analyst for an investment bank, and as an international marketing manager
for a high-tech company. He holds a US patent, and cofounded several companies in Social Networking (Unknown Space, MITBBS, the biggest
social network for Chinese in America) and FinTech (Super Quantum Fund).
|
Y.Y. |
|
18/09/2023, Mon |
Lecture 03: Model Assessment and Selection: Subset, Ridge, Lasso, and PCR [ slides ]
|
Y.Y. |
|
25/09/2023, Mon |
Lecture 04: Decision Tree, Bagging, Random Forests and Boosting [ slides ]
[Reading Material]:
- Shihao Gu, Bryan Kelly and Dacheng Xiu
"Empirical Asset Pricing via Machine Learning", Review of Financial Studies, Vol. 33, Issue 5, (2020), 2223-2273. Winner of the 2018 Swiss Finance Institute Outstanding Paper Award.
[ link ]
- Jingwen Jiang, Bryan Kelly and Dacheng Xiu
"(Re-)Imag(in)ing Price Trends", Chicago Booth Report, Aug 2021
[ link ]
- Tracy Ke, Bryan Kelly and Dacheng Xiu
"Predicting Returns with Text Data", Mar. 2022. Winner of the 2019 CICF Best Paper Award.
[ link ]
|
Y.Y. |
|
09/10/2023, Mon |
Lecture 05: An Introduction to Convolutional Neural Networks [ slides ]
|
Y.Y. |
|
16/10/2023, Mon |
Lecture 06: Topics on CNN: Neural Style and Adversarial Examples [ slides ] and Support Vector Machines [ slides ]
[ Project 2 ]
- Project description: paper replication study [ pdf ]
- Asset Pricing paper replication [ pdf ]
- Reimaging paper replication [ pdf ]
- Shihao Gu, Bryan Kelly and Dacheng Xiu
"Empirical Asset Pricing via Machine Learning", Review of Financial Studies, Vol. 33, Issue 5, (2020), 2223-2273. Winner of the 2018 Swiss Finance Institute Outstanding Paper Award.
[ link ]
- Jingwen Jiang, Bryan Kelly and Dacheng Xiu
"(Re-)Imag(in)ing Price Trends", Chicago Booth Report, Aug 2021
[ link ]
- GitHub Repository for reports of Project 2
[ GitHub ]
|
Y.Y. |
|
30/10/2023, Mon |
Lecture 07: An Introduction to Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) [ slides ]
|
Y.Y. |
|
06/11/2023, Mon |
Lecture 08: Attention, Transformer, GPT, and BERT [ slides ]
[ Presentation ]
- 1. XUE Wenjing, CHEN Hongxi, DING Guobin, QIAO Guan [ Best Report of Project 1 ]
[ report (pdf) ] [ slides (pptx) ]
- 2. Aoran LI, Tianying ZHOU, Langting WENG, Yijia MA [ Best Technique of Project 1 ]
[ report (pptx) ] [ slides (pdf) ]
|
Y.Y. |
|
13/11/2023, Mon |
Lecture 09: An Introduction to Reinforcement Learning with Applications in Quantitative Finance [ slides ]
[ Reference ]:
- Google DeepMind's Deep Q-learning playing Atari Breakout:
[ youtube ]
- To view .ipynb files below, you may try [ Jupyter NBViewer]
- Deep Q-Learning Pytorch Tutorial: [ link ]
- A Tutorial of Reinforcement Learning for Quantitative Trading:
[ Tutorial ]
[ Replicate ]
- FinRL: Deep Reinforcement Learning for Quantitative Finance
[ GitHub ]
- Reinforcement Learning and Supervised Learning for Quantitative Finance: [ link ]
- Prof. Michael Kearns, University of Pennsyvania, Algorithmic Trading and Machine Learning,
Simons Institute at Berkeley [ link ]
|
Y.Y. |
|
20/11/2023, Mon |
Lecture 10: Final Project and Talks.
[ Reference ]
- Kaggle: G-Research Crypto Forecasting. [ link ]
- Kaggle: M5 Forecasting - Accuracy, Estimate the unit sales of Walmart retail goods. [ link ]
- Kaggle: M5 Forecasting - Uncertainty, Estimate the uncertainty distribution of Walmart unit sales. [ link ]
[ Presentation ]
- 1. LI Aoran, MA Yijia, WENG Langting, ZHOU Tianying [ Best Technique of Project 2 ]
[ report (pdf) ] [ slides ]
[ Guest Talk ]
- Title: Sequential Predictive Conformal Inference for Time Series [ slides ]
- Speaker: Chen XU, Georgia Institute of Technology
- Abstract: We present a new distribution-free conformal prediction algorithm for sequential data (e.g., time series), called the sequential predictive conformal inference (SPCI).
We specifically account for the nature that time series data are non-exchangeable, and thus many existing conformal prediction algorithms are not applicable. The main idea is to adaptively re-estimate the conditional quantile of non-conformity scores (e.g., prediction residuals), upon exploiting the temporal dependence among them. More precisely, we cast the problem of conformal prediction interval as predicting the quantile of a future residual, given a user-specified point prediction algorithm. Theoretically, we establish asymptotic valid conditional coverage upon extending consistency analyses in quantile regression. Using simulation and real-data experiments, we demonstrate a significant reduction in interval width of SPCI compared to other existing methods under the desired empirical coverage. Source codes can be found at [ GitHub ].
- Bio: Chen Xu is currently a 4th year Operations Research PhD at Georgia Tech ISyE, where he is supervised by Prof. Yao Xie. His current research interests are two-fold. (1) Uncertainty quantification for machine learning models. Specifically, advance conformal prediction as a distribution-free method for arbitrarily complex deep models, especially in the context of time-series modeling. (2) Generative models through flow-based neural networks. Specifically, develop scalable computational tools for problems at the intersection of statistics and optimization, including extensions to high-dimensional optimal transport, distributionally robust optimization, and differential privacy. He has published in top machine learning conferences (e.g., ICML 2021 oral, NeurIPS 2023 spotlight) and journals (e.g., IEEE TPAMI 2023, IEEE JSAIT).
|
Y.Y. |
|
27/11/2023, Mon |
Lecture 11: Artificial Intelligence in Equity Investment
[ Guest Talk ]
- Title: Artificial Intelligence in Equity Investment [ slides ]
- Speaker: Prof. Haifeng You, HKUST and Tsinghua University
- Bio: Haifeng You is a Professor of Accounting and Co-Director of the Center for Securities Analysis with Financial Technology at
Hong Kong University of Science and Technology (HKUST). His research focuses on the role of financial information and financial technology
in equity investment. He has published on leading academic and professional journals such as Journal of Financial Economics, Journal of
Accounting and Economics, and Financial Analyst Journal. Previously, he served as the Head of Quantitative Equity Research at China Investment
Corporation and Quantitative Researcher at Barclays Global Investors. He has also served as advisors and consultants for investment firms
such as GSA capital in the UK and China Pacific Asset Managemen and Bosera Asset Management in China, helping them to build Asian and global
quantitative equity strategies. Professor You holds a PhD degree in Accounting from University of California, Berkeley and a BA degree in Finance from Peking University.
[ Presentation ]
- 1. HUANG Yuxin, LEI Yunxin, AN Tianyuan, LIN Fengshan, LIU Zongxuan [ Best Writing of Project 2 ]
[ report (pdf) ] [ slides (pptx) ]
- 2. JIA Yaoyao, JIANG Xiaoyue, YANG Tianhao, HUANG Yuxuan [ Best Report of Project 2 ]
[ report (pdf) ] [ slides (pptx) ]
|
Y.Y. |
|