MAFS 6010Z: AI in Fintech

PKU

MAFS 6010Z. Artificial Intelligence in Fintech
Fall 2023

Course Information

Synopsis

This course offers a comprehensive exploration of the fundamental concepts and underlying principles of artificial intelligence (AI). It delves into the core principles of machine learning and provides valuable insights through case studies of relevant technologies. By providing opportunities for hands-on experimentation with machine learning applications, the course aims to inspire students to devise innovative approaches to address real-life problems in fintech using readily-available AI technologies.
Prerequisite: Some preliminary course on (statistical) machine learning, applied statistics, and deep learning will be helpful.

Instructors:

Yuan Yao

Time and Place:

Monday 19:30-22:20pm, Rm 4619 (Lift 31-32)

Reference (参考教材)

An Introduction to Statistical Learning, with applications in R / Python. By James, Witten, Hastie, and Tibshirani

ISLR-python, By Jordi Warmenhoven.

ISLR-Python: Labs and Applied, by Matt Caudill.

Manning: Deep Learning with Python, by Francois Chollet [GitHub source in Python 3.6 and Keras 2.0.8]

MIT: Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Tutorials: preparation for beginners

Python-Numpy Tutorials by Justin Johnson

scikit-learn Tutorials: An Introduction of Machine Learning in Python

Jupyter Notebook Tutorials

PyTorch Tutorials

Deep Learning: Do-it-yourself with PyTorch, A course at ENS

Tensorflow Tutorials

MXNet Tutorials

Theano Tutorials

The Elements of Statistical Learning (ESL). 2nd Ed. By Hastie, Tibshirani, and Friedman

statlearning-notebooks, by Sujit Pal, Python implementations of the R labs for the StatLearning: Statistical Learning online course from Stanford taught by Profs Trevor Hastie and Rob Tibshirani.

Teaching Assistant:

Email: Mr. CAO, He and Ms. LIU, Xuantong < aifin.hkust (add "AT gmail DOT com" afterwards) >

Schedule

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). [ Reference ] To view .ipynb files below, you may try [ Jupyter NBViewer] Linear Regression Python Notebook [ MAFS6010_Regression.ipynb ] Linear Classification Python Notebook [ MAFS6010_Classification.ipynb ]	Y.Y.
18/09/2023, Mon	Lecture 03: Model Assessment and Selection: Subset, Ridge, Lasso, and PCR [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Model Selection (Subset, Ridge, Lasso, and Principal Component Regression) [ MAFS6010_Selection.ipynb ]	Y.Y.
25/09/2023, Mon	Lecture 04: Decision Tree, Bagging, Random Forests and Boosting [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Decision Trees, Bagging, Random Forests and Boosting [ MAFS6010_tree.ipynb ] [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 ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] LeNet5 for MNIST dataset in Pytorch Notebook [ LeNet5_mnist.ipynb ] LeNet5 for Cifar10 dataset in Pytorch Notebook [ LeNet5_cifar10.ipynb ] AlexNet for Cifar10 dataset in Pytorch Notebook [ AlexNet_cifar10.ipynb ] ResNet for Cifar10 dataset in Pytorch Notebook [ ResNet_cifar10.ipynb ] [ Topics on CNNs ]: Visualizing CNNs [ slides ] To view .ipynb files below, you may try [ Jupyter NBViewer] Visualization of VGG16 in Pytorch Notebook [ vgg16-visualization.ipynb ] Class activation heatmap of VGG16 in Pytorch Notebook [ vgg16-heatmap.ipynb ] Fine-tuning (transfer learning) of ResNet in Pytorch Notebook [ finetuning_resnet.ipynb ] Neural Style of HKUST at Starry Night in Pytorch Notebook [ neural_style_starry-hkust.ipynb ] Adversarial examples of LeNet5 with MNIST [ LeNet5_mnist_fgsm.ipynb ]	Y.Y.
16/10/2023, Mon	Lecture 06: Topics on CNN: Neural Style and Adversarial Examples [ slides ] and Support Vector Machines [ slides ] [ Topics on CNNs ]: Visualizing CNNs [ YY's slides ] To view .ipynb files below, you may try [ Jupyter NBViewer] Visualization of VGG16 in Pytorch Notebook [ vgg16-visualization.ipynb ] Class activation heatmap of VGG16 in Pytorch Notebook [ vgg16-heatmap.ipynb ] Fine-tuning (transfer learning) of ResNet in Pytorch Notebook [ finetuning_resnet.ipynb ] Neural Style of HKUST at Starry Night in Pytorch Notebook [ neural_style_starry-hkust.ipynb ] Adversarial examples of LeNet5 with MNIST [ LeNet5_mnist_fgsm.ipynb ] [ SVM ]: To view .ipynb files below, you may try [ Jupyter NBViewer] Python Notebook for Support Vector Machines [ MAFS6010_svm.ipynb ] [ 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 ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Character-level RNN, LSTM and GRU for Name Classification [ char_rnn_classification_tutorial.ipynb ] Generating Shakespeare's Sonnet: RNN, LSTM, Bidirectional LSTM, and Momentum-LSTM [ rnn_lstm_BiLSTM_mlstm_shakespeare.ipynb ] [ rnn_lstm_biLSTM_shakespeare.ipynb ] [ shakespeare.txt ] RNN for generating Shakespeare's Sonnet [ rnn.ipynb ] [ shakespeare.txt ] LSTM for generating Shakespeare's Sonnet [ rnn_lstm_shakespeare.ipynb ] Bidirectional RNN for MNIST in pytorch [ bidirection_lstm_mnist.ipynb ] [ Youtube ] Jorgen Schmidhuber: [ Deep Learning: Our Miraculous Year 1990-1991 ] [ Critique of Honda Prize for Dr. Hinton ]	Y.Y.
06/11/2023, Mon	Lecture 08: Attention, Transformer, GPT, and BERT [ slides ] [Reference]: To view .ipynb files below, you may try [ Jupyter NBViewer] Pytorch Twitter Sentiment Analysis: RNN, LSTM, BiLSTM, Multihead Self-Attention. [ RNN (ipynb) ] [ LSTM (ipynb) ] [ BiLSTM (ipynb) ] [ BiLSTM with Multihead Attention (ipynb) ] [ BERT embedding with BiLSTM (ipynb) ] [ Sentiment140 dataset ] Pytorch Sentiment Analysis with IMDB data: RNN, (bi)-LSTM, CNN, Transformer, BERT, etc. [ GitHub ] Illustrated Transformer by Jay Alammar: [ link ] The Annotated Transformer Tutorial by Sasha Rush: [ link ] BERT generation of Shakespeare's sonnet: [ BERT_shakespeare_gen.ipynb ] BERT next sentence generation of Shakespeare's sonnet and Chinese poems: [ BERT_shakespeare_nextsen.ipynb ] Chinese BERT (Whole-Word-Masking): [ link ] [ 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. [ Project 3 ] Description: [ project3.pdf ] Kaggle Contests [ slides ] Cryptocurrency Trading Project [ slides (pdf) ] Large Language Models with Financial Analysis [ slides (pdf) ] [ 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.

by YAO, Yuan.