Course Materials - Text Books, Articles and Videos

Books (Across all Courses, Listed by Author)


Cheng, Peng et al. Massive Data Analytics for Macroeconomic Nowcasting edited by Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana, https://link.springer.com/chapter/10.1007/978-3-030-66891-4_7.
    Coqueret Guillaume and Tony Guida. Machine Learning for Factor Investing. CRC Press LLC 2020. https://www.mlfactor.com/bayes.html

    Coscia, Michele. The Atlas for the Aspiring Network Scientist. IT University of  Copenhagen, 2021. https://www.networkatlas.eu/files/sna_book.pdf

    Diez, David M, Christopher D. Barr, and Mine Cetinkaya-Rundel. Openintro Statistics. 2019. Print. https://www.openintro.org/book/os/


    Efron, Bradley; Hastie, Trevor: Computer Age Statistical Inference: Algorithms, Evidence and Data Science

    Fotia, Alessio, and Ulrich Bindseil. Introduction to Central Banking. Springer, 2021. https://link.springer.com/book/10.1007/978-3-030-70884-9

    G ́eron, Aur ́elien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 2nd ed., O’Reilly Media, Inc., 2019.

    Ghirelli, Corinna et al. New Data Sources for Central Banks edited by Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana, Springer, 2021, pp. 169–194, https://link.springer.com/chapter/10.1007/978-3-030-66891-4_8.

    Hilpisch, Yves. Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging. John Wiley & Sons, 2015.

    James    , Gareth, et al. An Introduction to Statistical Learning with Applications in R. 2nd ed., Springer, 2021 https://www.statlearning.com/resources-python

    López     de Prado, Marcos. Advances in Financial Machine Learning

    Murphy, Kevin Patrick. Probabilistic Machine Learning: An Introduction https://probml.github.io/pml-book/book1.html

    Ozenbas, Deniz et al. "Liquidity and the Impact of Information Shocks." Liquidity Markets and Trading in Action: An Interdisciplinary Perspective, https://link.springer.com/book/10.1007/978-3-030-74817-3

    Ranjan, Chitta. Understanding Deep Learning: Application in Rare Event Prediction. https://www.understandingdeeplearning.com/.

    Robison, Lindon et al. Financial Management for Small Businesses. 2nd OER ed., Michigan State University, 2021. https://openbooks.lib.msu.edu/financialmanagement/

    Schumacher, J. M. Introduction to Financial Derivatives. Open Press TiU. https://research.tilburguniversity.edu/en/publications/introduction-to-financial-derivatives

    Stellinga, Bart et al. Money and Debt: The Public Role of Banks. Springer, 2021. https://link.springer.com/book/10.1007/978-3-030-70250-2

    Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 2018. http://incompleteideas.net/sutton/book/the-book.html

    Zhang, Ashton, et al.     Dive into Deep Learning. https://d2l.ai/d2l-en.pdf

    Articles by Course 

    Course 1: Financial Markets

      1. Schenk, Catherine R. "Summer in the City: Banking Failures of 1974 and the Development of International Banking Supervision." The English Historical Review, vol. 129, no. 540, 2014, 1129–1156, https://doi.org/10.1093/ehr/ceu261
      2. Kjærland, Frode et al. "An Analysis of Bitcoin's Price Dynamics." Journal of Risk and Financial Management, vol. 11, no. 4, 2018, 1–18. https://www.mdpi.com/1911-8074/11/4/63/htm
      3. Levenkron, Hannah. There’s More than Meets the Eye: Looking at Art as an Alternative Investment. 2011. Emory University, Honors Thesis. https://etd.library.emory.edu/concern/etds/z316q178t?locale=en
      4. Amirtahmasebi, Rana. "Can Cities Be Publicly Traded?" World Bank Blogs, WorldBank.org, https://blogs.worldbank.org/psd/can-cities-be-publicly-traded. Accessed 2 March 2022.
      5. "Exchange traded funds (ETFs)." Khan Academy, https://www.khanacademy.org/economics-finance-domain/core-finance/investment-vehicles-tutorial/mutual-funds/v/exchange-traded-funds-etfs
      6. Loretan, Mico, and William B. English. "Special Feature: Evaluating Changes in Correlations during Periods of High Market Volatility." BIS Quarterly Review, 2000, pp. 29–36.https://www.bis.org/publ/r_qt0006e.pdf
      7. “Collateralized Debt Obligation Overview.”Khan Academy, https://www.khanacademy.org/economics-finance-domain/core-finance/derivative-securities/cdo-tutorial/v/collateralized-debt-obligation-overview.
      8. “Collateralized Debt Obligation.”Khan Academy, https://www.khanacademy.org/economics-finance-domain/core-finance/derivative-securities/cdo-tutorial/v/collateralized-debt-obligation-cdo.
      9. “Mortgage-Backed Security Overview.”Khan Academy, https://www.khanacademy.org/economics-finance-domain/core-finance/derivative-securities/mort-backed-secs-tut/v/mortgage-back-security-overview.
      10. “Mortgage-Backed Securities III.”Khan Academy, https://www.khanacademy.org/economics-finance-domain/core-finance/derivative-securities/mort-backed-secs-tut/v/mortgage-backed-securities-iii.
      11. Open Risk Manual. "Five Cs Of Credit Analysis."https://www.openriskmanual.org/wiki/Five_Cs_Of_Credit_Analysis
      12. Romero-Torres, Jennifer et al.Securitization in India: Managing Capital Constraints and Creating Liquidity to Fund Infrastructure Assets. Asian Development Bank, 2017. https://www.adb.org/sites/default/files/publication/379076/securitization-india-infrastructure.pdf
      13. Ashcraft, Adam B., and Til Schuermann. Understanding the Securitization of Subprime Mortgage Credit. Federal Reserve Bank of New York, 2008. https://www.newyorkfed.org/research/staff_reports/sr318.html
      14. Eisinger, Jesse, and Jake Bernstein. "From Dodd-Frank to Dud: How Financial Reform May Be Going Wrong." ProPublica, 3 June 2011, https://www.propublica.org/article/from-dodd-frank-to-dud. Accessed 2 March 2022.
      15. Konczal, Mike et al. Doomed to Repeat: Debunking the Conservative Story about the Financial Crisis and Dodd-Frank. Roosevelt Institute, 2017. https://rooseveltinstitute.org/wp-content/uploads/2020/07/RI-Doomed-to-Repeat-Dodd-Frank-201706.pdf
      16. Misankova, Maria et al. "Determination of Default Probability by Loss Given Default." Procedia Economics and Finance, vol. 26, 2015, pp. 411–417, https://www.sciencedirect.com/science/article/pii/S2212567115008151

    Course 2. Financial Data

      1. Sanger, William, and Thierry Warin. "High Frequency and Unstructured Data in Finance: An Exploratory Study of Twitter. Journal of Global Research in Computer Science, vol. 7, no. 4, April 2016, pp. 6–16, https://www.rroij.com/open-access/high-frequency-and-unstructured-data-in-finance-an-exploratory-study-oftwitter-.pdf

    Course 3. Financial Econometrics

      1. Panchenko, Dmitry. "Simple Linear Regression." Statistics for Applications, Fall 2006, Massachusetts Institute of Technology. Lecture. https://ocw.mit.edu/courses/18-443-statistics-for-applications-fall-2006/c83bb7d4e501e4b1af1240fbeb407aaa_section14.pdf
      2. Panchenko, Dmitry. "Multiple Linear Regression." Statistics for Applications, Fall 2006, Massachusetts Institute of Technology. Lecture. https://ocw.mit.edu/courses/18-443-statistics-for-applications-fall-2006/dbc17cbfdac61daaff75056992d0af9a_section15.pdf_
      3. Orloff, Jeremy, and Jonathan Bloom. "7b: Covariance and Correlation." Introduction to Probability and Statistics, Spring 2014, Massachusetts Institute of Technology. Lecture. https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/resources/mit18_05s14_reading7b/ 
      4. Fee, Michale, and Daniel Zysman. "Principal Components Analysis." Introduction to Neural Computation, Spring 2018, Massachusetts Institute of Technology. Lecture. https://ocw.mit.edu/courses/9-40-introduction-to-neural-computation-spring-2018/resources/17/
      5. Osborne, Jason. "Improving Your Data Transformations: Applying the Box-Cox Transformation." Practical Assessment, Research, and Evaluation, vol. 15, no. 12, 2010, pp. 1–9. https://scholarworks.umass.edu/pare/vol15/iss1/12/ 
      6. Olken, Benjamin et al. "Nonparametric Regression and Measurement Error. " Development Economics: Microeconomic Issues and Policy Models, Fall 2008, Massachusetts Institute of Technology. Lecture: https://drive.google.com/file/d/19nVUBWNkPzbFXFaNB13Y9bc-tz1xDkzR/view?usp=sharing 
      7. Hartmann, K., Krois, J., and Waske, B. E-Learning Project SOGA: Statistics and Geospatial Data Analysis. Department of Earth Sciences, Freie Universitaet Berlin, 2018, https://www.geo.fu-berlin.de/en/v/soga-py/index.html

      8. Factor Analysis: https://www.geo.fu-berlin.de/en/v/soga-r/Advances-statistics/Multivariate-approaches/Factor-Analysis/index.html

      9. The Exploratory Factor Model (EFM): https://www.geo.fu-berlin.de/en/v/soga-r/Advances-statistics/Multivariate-approaches/Factor-Analysis/The-Exploratory-Factor-Model/index.html

      10. Explained and Unexplained Variability: https://www.geo.fu-berlin.de/en/v/soga-r/Advances-statistics/Multivariate-approaches/Factor-Analysis/The-Exploratory-Factor-Model/Explained-and-Unexplained-Variability/index.html
    1. PennState, Eberly College of Science. "13.1: Weighted Least Squares." STAT 501: Regression Methods. Lecture. https://online.stat.psu.edu/stat501/lesson/13/13.1

    2. PennState, Eberly College of Science. "13.2: Weighted Least Squares Examples." STAT 501: Regression Methods. Lecture. https://online.stat.psu.edu/stat501/lesson/13/13.1/13.1.1


    Course 4. Derivative Pricing

    1. Harvard University. “Lecture 31: Markov Chains | Statistics 110.” YouTube, 29 April 2013, https://www.youtube.com/watch?v=8AJPs3gvNlY.

    2. Computer Science Mentors. “[CS 70] Markov Chains — Finding Stationary Distributions.” YouTube, 30 April 2018, https://www.youtube.com/watch?v=YIHSJR2iJrw.

    Course 5. Stochastic Modelling

    1. Dr Chris Tisdell. "Intro to Fourier Transforms: How to Calculate Them." YouTube, 27 Sep. 2013. https://www.youtube.com/watch?v=WcNPUXfxCXA.

    2. Ben Lambert. "Characteristic Functions Introduction." YouTube, 18 June 2013, https://www.youtube.com/watch?v=mYhca1p26n4.

    3. Ben Lambert. "The Characteristic Function of a Normal Random Variable - Part 1 (advanced)." YouTube, 22 July 2013, https://www.youtube.com/watch?v=-glT8cCczfw..

    4. Ben Lambert. "The Characteristic Function of a Normal Random Variable - Part 2 (advanced)." YouTube, 22 July 2013, https://www.youtube.com/watch?v=105aWG54AL8

    5.  Ben Lambert. "The Characteristic Function of a Normal Random Variable - Part 3 (advanced)." YouTube, 22 July 2013, https://www.youtube.com/watch?v=6E-lhduaXhE.

    6. Lewis, Alan L. "A Simple Option Formula for General Jump-Diffusion and Other Exponential Levy Processes." 6 Sep. 2001, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=282110.

    7. Bates, David S. “Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options.” Review of Financial Studies, vol. 9, no. 1, Oxford UP (OUP), Jan. 1996, pp. 69–107.

    Course 6. Machine Learning in Finance

    1. Askitas, Nikos, and Klaus. F. Zimmermann. "Detecting Mortgage Delinquencies." IZA Discussion Paper No. 5895, 2011, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1906198

    2. Ali, S. E. Azhar, et al. "Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques." International Journal of Industrial Engineering and Management, vol. 12, no. 1, 2021, http://www.ijiemjournal.uns.ac.rs/images/journal/volume12/IJIEM_272.pdf.

    3.  Avery, Robert B., et al. "Credit Risk, Credit Scoring, and the Performance of Home Mortgages." Federal Reserve Bulletin, July 1996, https://www.federalreserve.gov/pubS/bulletin/1996/796lead.pdf.

    4. Shekhar, Shashank et al. "A Comparative Study of Hyper-Parameter Optimization Tools." arXiv, 2022, https://arxiv.org/abs/2201.06433.

    5.  Ho, Winky K.O, et al. "Predicting Property Prices with Machine Learning Algorithms." Journal of Property Research, vol. 38, no. 1, 2021, https://www.tandfonline.com/doi/full/10.1080/09599916.2020.1832558.

    6. Yu, Tong, and Hong Zhu. "Hyper-Parameter Optimization: A Review of Algorithms and Applications." arXiv, 2020, https://arxiv.org/abs/2003.05689.

    Course 7. Deep Learning in Finance

      1. Brownlee, Jason. "A Gentle Introduction to Ensemble Learning Algorithms." Machine Learning Mastery, 19 April 2021, https://machinelearningmastery.com/tour-of-ensemble-learning-algorithms/.

      2. Gray, Chad. "Stock Prediction with ML: Walk-forward Modeling." The Alpha Scientist, 18 July 2018, https://alphascientist.com/walk_forward_model_building.html.

      3. Gort, Berend, and Bruce Yang. "The Combinatorial Purged Cross-Validation Method." Towards AI, 31 March 2022, https://towardsai.net/p/l/the-combinatorial-purged-cross-validation-method.

      Course 8. Portfolio Management 

      1. van Hoecke, Christopher, and Max Margenot. "Estimation of Covariance Matrices." QuantRocket. https://www.quantrocket.com/codeload/quant-finance-lectures/quant_finance_lectures/Lecture26-Estimating-Covariance-Matrices.ipynb.html.

      2. Pedregosa, Fabian et al. "2.6. Covariance estimation." Scikit-learn. https://scikit-learn.org/stable/modules/covariance.html#shrunk-covariance.

      3. Pedregosa, Fabian et al. "Shrinkage Covariance Estimation: LedoitWolf vs OAS and Max-Likelihood." Scikit-learn. https://scikit-learn.org/stable/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py.

      4. Pedregosa, Fabian et al. "Ledoit-Wolf vs OAS Estimation." Scikit-learn. https://scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py.

      5. Chen, Yilun et al. "Shrinkage Algorithms for MMSE Covariance Estimation." Arxiv, 27 July 2009. https://arxiv.org/pdf/0907.4698.pdf.

      6. Pedersen, Lasse Heje, Abhilash Babu, and Ari Levine. "Enhanced Portfolio Optimization." Financial Analysts Journal, vol. 77, no. 2, 2021, pp. 1–49. https://doi.org/10.1080/0015198X.2020.1854543.

      7. Moen, Endre. "Machine_Learning_for_Asset_Managers." GitHub, https://github.com/emoen/Machine-Learning-for-Asset-Managers/tree/master/Machine_Learning_for_Asset_Managers.

      ch2_fitKDE_find_best_bandwidth.py
      ch2_marcenko_pastur_pdf.py
      ch2_monte_carlo_experiment.py

      8. Akinshin, Andrey. "The Importance of Kernel Density Estimation Bandwidth." Aakinshin, 13 Oct. 2020, https://aakinshin.net/posts/kde-bw/

      9. Scikit-learn developers. "2.8.2. Kernel Density Estimation." Scikit-learn, 2011, https://scikit-learn.org/stable/modules/density.html#kernel-density.

      10. Vanderplas, Jake. “05.13-Kernel-Density-Estimation.ipynb.” GitHub, https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.13-Kernel-Density-Estimation.ipynb.

      11. Timm, Bjarne. "Utilizing Machine Learning to Address Noise in Covariance and Correlation Matrices: An Application and Modification of Enhanced Portfolio Optimisation." 2021, Copenhagen Business School, Master's thesis, https://research.cbs.dk/en/studentProjects/utilizing-machine-learning-to-address-noise-in-covariance-and-cor.

      12. Guo, Xue et al. "Development of Stock Correlation Networks using Mutual Information and Financial Big Data." PLOS One, 18 Apr 2018, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195941.

      13. Zhou, Rongxi. "Applications of Entropy in Finance: A Review." Entropy, vol. 15, no. 11, 2013, pp. 4909–4931., https://www.mdpi.com/1099-4300/15/11/4909.

      14. Libman, Daniel et al. "Mutual Information between Order Book Layers." Entropy, vol. 24, no. 3, 2022, https://www.mdpi.com/1099-4300/24/3/343.

      15. Marti, Gautier et al. “A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets.” Progress in Information Geometry, _2017, pp. 245–274, https://arxiv.org/abs/1703.00485.

      16. Jain, Prayut, and Shashi Jain. "Can Machine Learning Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification." 26 May 2019, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3394427.

      17. Nanakorn, Natasha, and Elin Palmgren. Hierarchical Clustering in Risk-Based Portfolio Construction. 2021. KTH Royal Institute of Technology School of Engineering Sciences, Independent thesis, Advanced level. http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1609991&dswid=3735

      Course 9. Risk Management 

      Course 10. Capstone 

      YouTube

      2. StatQuest with Josh Starmer. "Gaussian Naive Bayes, Clearly Explained!!!" YouTube, 3 June 2020, https://www.youtube.com/watch?v=H3EjCKtlVog&list=PLSV3ndw2r1qUojFqv5Vt6KNIRmVN7Zic6&index=10.

      OCLPhase2. "Kruskal's ex 1." YouTube, https://www.youtube.com/watch?v=gaXM0HNErc4.
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