■ Data analytic thinking

■ Business problems and data science solutions

■ Adoption of alternative data

■ The risks and rewards of alternative data for investment decisions

■ Advanced technologies for alpha

■ Alternative data vendor profiles

■ The future of alternative data

■ Warming up to collective intelligence investing (CII)

■ Getting started with alternative data and CII

■ Balancing the risks and rewards of CII from different platform types

■ The road ahead for investment managers

■ Understanding why “Wall Street” wants data

■ Building a data product

■ Delivering your data

■ Marketing and selling your data

■ Protecting yourself and your data

■ Pricing your data

■ Case studies

Readings for Introduction to Data Science & Big Data

1. Provost, F. and T. Fawcett. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media Inc.

• Chapter 1 – Introduction : Data-Analytic Thinking

• Chapter 2 – Business Problems and Data Solutions

2. Dannemiller, D. and R. Kataria. (2017). Alternative data for investment decisions: Today’s innovation could be tomorrow’s requirement by Deloitte Center for Financial Services.

3. Gajjaria, A. (2018). Collective intelligence investing: Alpha generation via alternative data brings new risks by Deloitte Center for Financial Services.

4. Quandl (2017). Sell Your Data to Wall Street: The comprehensive guide to monetizing your data assets for professional investors.

■ Organization and resources of the book An Introduction to Statistical Learning: with applications in R

■ Statistical learning

■ Assessing Model Accuracy

■ Motivation for using neural nets to recognize handwritten digits

■ Perceptron neurons

■ Sigmoid neurons

■ The architecture of neural networks

■ A simple network to classify handwritten digits

■ Learning with gradient descent

■ Implementing a network to classify digits

■ Why deep learning matters

Readings for Machine Learning: Introduction to Algorithms

1. James, G., D. Witten, T. Hastie and R. Tibshirani. (2013). An Introduction to Statistical Learning: with applications in R. New York, NY: Springer. Chapters 1, 2.1 & 2.2

• Chapter 1 – Introduction

• Chapter 2.1 - What is Statistical Learning?

• Chapter 2.2 – Assessing Model Accuracy

2. Nielsen, M. A. (2015). Using Neural Networks to Recognize Handwritten Digits. In Neural Networks and Deep Learning, Determination Press

■ Models, induction and prediction

■ Supervised segmentation

■ Visualizing segmentations

■ Classification via mathematical functions

■ Regression via mathematical functions

■ Simple linear regression

■ Multiple linear regression

■ Considerations in the regression model

■ Concepts of time series

■ Statistical models

■ Modeling volatility

Readings for Machine Learning: Regression, Support Vector Machine, Time Series Models

1. Provost, F. and T. Fawcett. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media Inc.

• Chapter 3 - Introduction to Predictive Modeling: From Correlation to Supervised Segmentation

• Chapter 4 - Fitting a Model to Data

2. James, G., D. Witten, T. Hastie and R. Tibshirani. (2013). An Introduction to Statistical Learning: with applications in R. New York, NY: Springer

• Chapter 3 – Simple Linear Regression

3. Aas, K. and X. K. Dimakos. (2004). Statistical modeling of financial time series: An introduction. Oslo Norway: Norwegian Computing Center

■ Overfitting and its avoidance

■ Subset selection

■ Shrinkage methods

■ Tree-Based methods

Readings for Machine Learning: Regularization, Regression Trees, Random Forest & Overfitting

1. Provost, F. and T. Fawcett. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media Inc.

• Chapter 5 – Overfitting and Its Avoidance

2. James, G., D. Witten, T. Hastie and R. Tibshirani. (2013). An Introduction to Statistical Learning: with applications in R. New York, NY: Springer.

• Chapter 6.1 – Best Subset Selection

• Chapter 6.2 – Shrinkage Methods

• Chapter 8.1 – The Basics of Decision Trees

• Chapter 8.2 – Bagging, Random Forests, Boosting

3. Aas, K. and X. K. Dimakos. (2004). Statistical modeling of financial time series: An introduction. Oslo Norway: Norwegian Computing Center

■ Calculating and interpreting similarity and distance

■ Discussing technical details related to similarities and neighbors

■ Describing and evaluating classifiers

■ Describing a key analytical framework and calculating expected values

Readings for Machine Learning: Classification & Clustering

1. Provost, F. and T. Fawcett. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media Inc.

• Chapter 6 – Similarity, Neighbors, and Clusters

• Chapter 7 – Decision Analytic Thinking : What Is a Good Model?

■ Visualizing model performance

■ Backtesting protocol in the era of machine learning

■ An investigation of the false discovery rate and the misinterpretation of p-values

■ A data science solution to the multiple-testing crisis

Readings for Machine Learning : Performance Evaluation, Support Vector Machines & False Discoveries

1. Provost, F. and T. Fawcett. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media Inc.

• Chapter 8 – Similarity, Neighbors, and Clusters

2. Arnott, R., C. B. Harvey, and H. Markowitz. (2019). A Backtesting Protocol in the Era of Machine Learning. Journal of Financial Data Science, 1(1), 64-74

3. Colquhoun, D. (2014). An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society Open Science, London, U.K.

4. >López de Prado, M. (2019). A Data Science Solution to the Multiple-Testing Crisis in Financial Research. Journal of Financial Data Science, 1(1), 99-110.

■ Evidence and probabilities

■ Broad issues involved in mining text

■ Text representation

■ Additional text representation approaches beyond “bag of words.”

■ Classification

■ Math behind Naïve Bayes classifiers

■ Training the Naïve Bayes classifiers

■ Optimizing for sentiment analysis

■ Evaluation of sentiment analysis results

Readings for Data Mining & Machine Learning: Naïve Bayes & Text Mining

1. Provost, F. and T. Fawcett. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media Inc.

• Chapter 9 – Evidence and Probabilities

• Chapter10 – Representing and Mining Text

2. Jurafsky, D. and J. Martin. (2018). In Speech and Language Processing

• Chapter 4 – Naïve Bayes and Sentiment Classification

■ Big data for business

■ Ethical issues

■ The ethics test

■ The nature of and business risks of artificial intelligence (AI)

■ Values that form the cornerstone of an ethical framework for artificial intelligence in business

■ The role of business decision-makers

■ General Data Protection Regulation (GDPR)

■ Separating ethics and compliance

■ Separating ethics and compliance

■ The GDPR Embedding Wheel

■ Doing good data science

■ Oaths and checklists

■ The five C’s

■ Taking responsibility for our creations (Data’s Day of Reckoning)

Readings for Big Data & Machine Learning: Ethical & Privacy Issues

1. Institute of Business Ethics. (2016, June). Business Ethics and Big Data (IBE Issue 52). London, U.K.

2. Institute of Business Ethics. (2018, January). Business Ethics and Artificial Intelligence (IBE, Issue 58). London, U.K.

3. Institute of Business Ethics. (2018, May). Beyond Law: Ethical Culture and GDPR (IBE, Issue 62). London, U.K.

4. Loukides, M., M., H. Mason and DJ. Patil. Ethics and Data Science

■ Artificial intelligence and its techniques

■ Applications of AI and Five Technologies Trends that Leap-Frog AI

■ Machine learning and finance

■ Artificial Intelligence in Investment Management

■ Four Pillars for Transformation of Investment Management Firms

■ Adoption and Implementation Risks

■ Regulatory and supervisory issues around FinTech

■ Relationship between artificial intelligence, machine learning, and big data, and algorithms

■ Categories of machine learning algorithms

■ Drivers of the growth in the use of fintech and adoption of artificial intelligence

■ Use cases of artificial intelligence and machine learning in the financial sector

■ The micro-financial analysis of artificial intelligence and machine learning uses

■ The macro-financial analysis of uses of artificial intelligence and machine learning uses

■ Define the terms listed in the glossary

■ Alternative data and institutional investors

■ Applications of random forest regression algorithm to factor models

■ The applications of machine learning algorithms to stock selection

■ Applications of machine learning algorithms to empirical asset pricing

■ The most common errors made when machine learning techniques are applied to financial data sets

■ Using statistical techniques to evaluate trading strategies in the presence of multiple tests

■ Text mining and its applications in the insurance industry

Readings for Big Data & Machine Learning in the Financial Industry

1. Deloitte. (2018) Artificial intelligence.

2. AQR Portfolio Group Solutions (2019). Can machines “learn” finance?

3. Deloitte. (2019) Artificial intelligence: The next frontier for investment management firms.

4. Financial Stability Board. (2017). Artificial Intelligence and Machine Learning in Financial Services: Market Developments and Financial Stability Implications.

5. Monk, A., M. Prins, and D. Rook. (2019). Rethinking Alternative Data in Institutional Investment. Journal of Financial Data Science, 1(1), 14-31.

6. Simonian, J., C. Wu, D. Itano and V. Narayanan. (2019). A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model. Journal of Financial Data Science, 1(1), 32-44.

7. Rasekhschaffe, K. and R. Jones. (2019). Machine Learning for Stock Selection. Financial Analyst Journal, 13 June 2019 Volume 75, Issue 3.

8. Gu, S., B. Kelly, and D. Xiu. (2018). Empirical Asset Pricing via Machine Learning.

9. López de Prado, M. (2018). The 10 Reasons Most Machine Learning Funds Fail. The Journal of Portfolio Management, 44 (6) 120-133.

10. Harvey, C. R. and Y. Liu. (2014). Evaluating Trading Strategies. [Special 40th Anniversary Issue]. The Journal of Portfolio Management, 40(5), 108-118.

11. Zappa, D., M. Borrelli, G.P. Clemente, N. Savelli. Text Mining In Insurance.