Cao, L., Tay, F., and Hock, F. (2003): “Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting.” IEEE Transactions on Neural Networks, Vol. 27–33. An investment strategy that lacks a theoretical justification is likely to be false. 1, pp. 3, pp. Multi-asset analytics provider, APEX: E3 announced that it has arranged an algorithmic crypto trading competition between students of the University of Oxford and the University of Cambridge. Lo, A. Bailey, D., Borwein, J, López de Prado, M, and Zhu, J (2014): “Pseudo-mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance.” Notices of the American Mathematical Society, Vol. 70, pp. 1471–74. 72, No. 21, No. Shafer, G. (1982): “Lindley’s Paradox.” Journal of the American Statistical Association, Vol. Machine Learning for Asset Managers M. López de Prado, Marcos, The Capital Asset Pricing Model Cannot Be Rejected, Analytical, Empirical, and Behavioral Perspectives, Quadratic Programming Models: Mean–Variance Optimization, Mutual Fund Performance Evaluation and Best Clienteles, Journal of Financial and Quantitative Analysis, Positively Weighted Minimum-Variance Portfolios and the Structure of Asset Expected Returns, International Equity Portfolios and Currency Hedging: The Viewpoint of German and Hungarian Investors, Improving Mean Variance Optimization through Sparse Hedging Restrictions, It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification, Portfolio Choice and Estimation Risk. We will explore the new challenges and concomitant opportunities of new data and new methods for investments and delegated asset management. Hastie, T., Tibshirani, R, and Friedman, J (2016): The Elements of Statistical Learning: Data Mining, Inference and Prediction. Boston: Harvard Business School Press. 453–65. Bansal, N., Blum, A, and Chawla, S (2004): “Correlation Clustering.” Machine Learning, Vol. 1, pp. The company claims that Aladdin can uses machine learning to provide investment managers in financial institutions with risk analytics and portfolio management software tools. (2011): “Trend Discovery in Financial Time Series Data Using a Case-Based Fuzzy Decision Tree.” Expert Systems with Applications, Vol. (2002): “The Statistics of Sharpe Ratios.” Financial Analysts Journal, July, pp. 2, pp. 22, No. (2010): Econometric Analysis of Cross Section and Panel Data. Available at http://iopscience.iop.org/article/10.3847/0067-0049/225/2/31/meta. Wei, P., and Wang, N. (2016): “Wikipedia and Stock Return: Wikipedia Usage Pattern Helps to Predict the Individual Stock Movement.” In Proceedings of the 25th International Conference Companion on World Wide Web, Vol. 13, No. 45, No. 44, No. 8, pp. (2011): “Predicting Stock Returns by Classifier Ensembles.” Applied Soft Computing, Vol. Formed in 2017, Cambridge Machines Asset Management (CMAM) comprises a multi-disciplinary team of experienced market practitioners, academics and data scientists. 1, pp. 65–70. MIT Press. International Journal of Forecasting, Vol. 2, pp. ML tools complement rather than replace the classical statistical methods. 85–126. * Views captured on Cambridge Core between #date#. Financial problems require very distinct machine learning solutions. 626–33. Email your librarian or administrator to recommend adding this element to your organisation's collection. Hayashi, F. (2000): Econometrics. Wright, S. (1921): “Correlation and Causation.” Journal of Agricultural Research, Vol. But we are only at the beginning of what is possible—and what asset managers will have to embrace if they want to keep up. 38, No. 73, No. Human involvement will still be critical for risk management and framework selection, but increasingly the strategy innovation process will be automated. 1, pp. 1st ed. 755–60. 1915–53. Booth, A., Gerding, E., and McGroarty, F. (2014): “Automated Trading with Performance Weighted Random Forests and Seasonality.” Expert Systems with Applications, Vol. Cohen, L., and Frazzini, A (2008): “Economic Links and Predictable Returns.” Journal of Finance, Vol. 726–31. Varian, H. (2014): “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives, Vol. 89–113. Read stories and highlights from Coursera learners who completed Python and Machine Learning for Asset Management and wanted to share their experience. comment. Sorensen, E., Miller, K., and Ooi, C. (2000): “The Decision Tree Approach to Stock Selection.” Journal of Portfolio Management, Vol. ISBN 9781108792899. Successful investment strategies are specific implementations of general theories. Kraskov, A., Stoegbauer, H, and Grassberger, P (2008): “Estimating Mutual Information.” Working paper. Facsimile Transmission Pearson Education. Facsimile Transmission Potter, M., Bouchaud, J. P., and Laloux, L (2005): “Financial Applications of Random Matrix Theory: Old Laces and New Pieces.” Acta Physica Polonica B, Vol. Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic … 1, pp. AI is a broader concept than ML, because it refers to the 1, pp. Rousseeuw, P. (1987): “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Computational and Applied Mathematics, Vol. 57, pp. Clarke, Kevin A. 26–44. 2, pp. Diseño y Maquetación Dpto. Sharpe, W. (1975): “Adjusting for Risk in Portfolio Performance Measurement.” Journal of Portfolio Management, Vol. Wooldridge, J. Available at https://doi.org/10.1371/journal.pcbi.1000093. 77, No. 129–33. 6210. 1989–2001. Goutte, C., Toft, P, Rostrup, E, Nielsen, F, and Hansen, L (1999): “On Clustering fMRI Time Series.” NeuroImage, Vol. Marcos is the author of several graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, 2020). By closing this message, you are consenting to our use of cookies. Kim, K. (2003): “Financial Time Series Forecasting Using Support Vector Machines.” Neurocomputing, Vol. 9, pp. Springer. Kuan, C., and Tung, L. (1995): “Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks.” Journal of Applied Econometrics, Vol. Greene, W. (2012): Econometric Analysis. and machine learning in asset management Background Technology has become ubiquitous. 88, No. Machine learning for asset management has become a ubiquitous trend in digital analytics to measure model robustness against prevailing benchmarks. 3rd ed. 5–6. 63, No. Easley, D., López de Prado, M, and O’Hara, M (2011b): “The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading.” Journal of Portfolio Management, Vol. Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic … 7947–51. This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. 605–11. 1st ed. Wiley. 557–85. 401–20. Springer Science & Business Media, pp. 77–91. 347–64. Bontempi, G., Taieb, S., and Le Borgne, Y. 259, No. Springer. Šidàk, Z. 6, No. Tsai, C., and Wang, S. (2009): “Stock Price Forecasting by Hybrid Machine Learning Techniques.” Proceedings of the International Multi-Conference of Engineers and Computer Scientists, Vol. 3, pp. López de Prado, M. (2018a): Advances in Financial Machine Learning. Gryak, J., Haralick, R, and Kahrobaei, D (Forthcoming): “Solving the Conjugacy Decision Problem via Machine Learning.” Experimental Mathematics. Benjamini, Y., and Liu, W (1999): “A Step-Down Multiple Hypotheses Testing Procedure that Controls the False Discovery Rate under Independence.” Journal of Statistical Planning and Inference, Vol. Żbikowski, K. (2015): “Using Volume Weighted Support Vector Machines with Walk Forward Testing and Feature Selection for the Purpose of Creating Stock Trading Strategy.” Expert Systems with Applications, Vol. 2, No. 15, No. 318, pp. 346, No. Download it once and read it on your Kindle device, PC, phones or tablets. Holm, S. (1979): “A Simple Sequentially Rejective Multiple Test Procedure.” Scandinavian Journal of Statistics, Vol. 689–702. 1, pp. 259–68. Lewandowski, D., Kurowicka, D, and Joe, H (2009): “Generating Random Correlation Matrices Based on Vines and Extended Onion Method.” Journal of Multivariate Analysis, Vol. 7th ed. 184–92. 61, No. Brian, E., and Jaisson, M. (2007): “Physico-theology and Mathematics (1710–1794).” In The Descent of Human Sex Ratio at Birth. Cavallo, A., and Rigobon, R (2016): “The Billion Prices Project: Using Online Prices for Measurement and Research.” NBER Working Paper 22111, March. 1st ed. Bailey, D., and López de Prado, M (2013): “An Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization.” Algorithms, Vol. Data Acquisition, Processing and Modelling To understand why, we need to go back to its definitions. Hacine-Gharbi, A., and Ravier, P (2018): “A Binning Formula of Bi-histogram for Joint Entropy Estimation Using Mean Square Error Minimization.” Pattern Recognition Letters, Vol. 39, No. 41, No. Usage data cannot currently be displayed. 37, No. 1st ed. Applying machine learning techniques to financial markets is not easy. 65–74. Kolanovic, M., and Krishnamachari, R (2017): “Big Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing.” J.P. Morgan Quantitative and Derivative Strategy, May. 5, pp. We use cookies to improve your website experience. Successful investment strategies are specific implementations of general theories. 28–43. Available at https://ssrn.com/abstract=3177057, López de Prado, M., and Lewis, M (2018): “Confidence and Power of the Sharpe Ratio under Multiple Testing.” Working paper. 2, pp. 138, No. Here are six ways in which machine learning has transformed the … Here are six ways in which machine learning has transformed the … 378, pp. Machine learning, artificial intelligence, and other advanced analytics offer asset managers a significant information advantage over peers who rely on more-traditional techniques. Trippi, R., and DeSieno, D. (1992): “Trading Equity Index Futures with a Neural Network.” Journal of Portfolio Management, Vol. Black believes that evolving and adapting to new technology is important to keeping a competitive advantage in the asset management industry. Supervised Machine Learning methods are used in the capstone project to predict bank closures. 1, pp. We will explore the new challenges and concomitant opportunities of new data and new methods for investments and delegated asset management. Princeton University Press. 3, pp. 647–65. 1st ed. Princeton University Press. I’d rather learn 4-5 basic things from a simple book than learn many advanced and wrong concepts form a De Prado just for the chance of learning a couple sexy/complicated concepts. Smart infrastructure asset management through machine learning holds particular advantages for the infrastructure and asset owner, for whom operation and maintenance accounts for 80% of the whole life cost. 7, pp. Efron, B., and Hastie, T (2016): Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Springer. 83, No. 2, pp. CFTC (2010): “Findings Regarding the Market Events of May 6, 2010.” Report of the Staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues, September 30. for this element. According to … 35–62. 14, pp. Available at https://ssrn.com/abstract=3365271, López de Prado, M., and Lewis, M (2018): “Detection of False Investment Strategies Using Unsupervised Learning Methods.” Working paper. (2017): “Classification-Based Financial Markets Prediction Using Deep Neural Networks.” Algorithmic Finance, Vol. View all Google Scholar citations IN ASSET MANAGEMENT BARTRAM, BRANKE, AND MOTAHARI ... Investment Strategies (QIS) group, Cambridge Judge Business School, ... ligence” and “machine learning” has increased dramatically in the past five years (Figure 1). Nakamura, E. (2005): “Inflation Forecasting Using a Neural Network.” Economics Letters, Vol. (2012): “Machine Learning Strategies for Time Series Forecasting.” Lecture Notes in Business Information Processing, Vol. Available at https://ssrn.com/abstract=3365282, López de Prado, M. (2019c): “Ten Applications of Financial Machine Learning.” Working paper. 325–34. 106, No. 42, No. Tsay, R. (2013): Multivariate Time Series Analysis: With R and Financial Applications. 7, pp. McGraw-Hill. Machine Learning, una pieza clave en la transformación de los modelos de negocio MachineLearning_esp_VDEF_2_Maquetación 1 24/07/2018 15:56 Página 1. Meila, M. (2007): “Comparing Clusterings – an Information Based Distance.” Journal of Multivariate Analysis, Vol. Kolm, P., Tutuncu, R, and Fabozzi, F (2010): “60 Years of Portfolio Optimization.” European Journal of Operational Research, Vol. In 2014, we published a ViewPoint titled The Role of Technology within Asset Management, which documented how asset managers utilize technology in trading, risk management, operations and client services. 481–92. He still considers himself an engineer. (2005): “The Phantom Menace: Omitted Variable Bias in Econometric Research.” Conflict Management and Peace Science, Vol. The authors introduce a novel application of support vector machines (SVM), an important machine learning algorithm, to determine the beginning and end of recessions in real time. 99–110. 112–22. Otto, M. (2016): Chemometrics: Statistics and Computer Application in Analytical Chemistry. 82, pp. 5, pp. If you feel like citing something you can use: Snow, D (2020).Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies.The Journal of Financial Data Science, Winter 2020, 2 (1) 10-23. Qin, Q., Wang, Q., Li, J., and Shuzhi, S. (2013): “Linear and Nonlinear Trading Models with Gradient Boosted Random Forests and Application to Singapore Stock Market.” Journal of Intelligent Learning Systems and Applications, Vol. 8, No. Moreover, decisions for asset movement between branches are largely arranged between individual branch managers on an as-needed basis. and machine learning by market intermediaries and asset managers • If you attach a document, indicate the software used (e.g., WordPerfect, Microsoft WORD, ASCII text, etc) to create the attachment. 29, pp. 3, pp. 356–71. Available at https://ssrn.com/abstract=3073799, Harvey, C., and Liu, Y (2018): “Lucky Factors.” Working paper. (2011): “Predicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange.” Expert Systems with Applications, Vol. (1994): Time Series Analysis. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset … Springer. April. 231, No. 6, pp. Patel, J., Sha, S., Thakkar, P., and Kotecha, K. (2015): “Predicting Stock and Stock Price Index Movement Using Trend Deterministic Data Preparation and Machine Learning Techniques.” Expert Systems with Applications, Vol. 5, No. Hamilton, J. Robert, C. (2014): “On the Jeffreys–Lindley Paradox.” Philosophy of Science, Vol. Wiley. ML is not a black box, and it does not necessarily overfit. Štrumbelj, E., and Kononenko, I. (2002): Principal Component Analysis. Zhu, M., Philpotts, D., and Stevenson, M. (2012): “The Benefits of Tree-Based Models for Stock Selection.” Journal of Asset Management, Vol. 2, pp. Opdyke, J. Among several monographs, Marcos is the author of the several graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, 2020). The notebooks to this paper are Python based. The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound … Machine Learning for Asset Managers by Marcos M. López de Prado, Cambridge University Press (2020). 65, pp. Creamer, G., and Freund, Y. Wiley. 101, pp. 42, No. Marcenko, V., and Pastur, L (1967): “Distribution of Eigenvalues for Some Sets of Random Matrices.” Matematicheskii Sbornik, Vol. (2012): “Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques.” Engineering, Technology and Applied Science Research, Vol. 33, No. 1, pp. Starting with the basics, we will help you build practical skills to understand data science so … 6, pp. Wang, Q., Li, J., Qin, Q., and Ge, S. (2011): “Linear, Adaptive and Nonlinear Trading Models for Singapore Stock Market with Random Forests.” In Proceedings of the 9th IEEE International Conference on Control and Automation, pp. MacKay, D. (2003): Information Theory, Inference, and Learning Algorithms. 289–300. Porter, K. (2017): “Estimating Statistical Power When Using Multiple Testing Procedures.” Available at www.mdrc.org/sites/default/files/PowerMultiplicity-IssueFocus.pdf. Witten, D., Shojaie, A., and Zhang, F. (2013): “The Cluster Elastic Net for High-Dimensional Regression with Unknown Variable Grouping.” Technometrics, Vol. /doi/full/10.1080/14697688.2020.1817534?needAccess=true. López de Prado, M. (2018b): “The 10 Reasons Most Machine Learning Funds Fail.” The Journal of Portfolio Management, Vol. Hence, an asset manager should concentrate her efforts on developing a theory, rather than on back-testing potential trading rules. Available at https://ssrn.com/abstract=2249314. Blackrock’s use of machine learning. 437–48. 694–706, pp. 4, pp. Dr. López de Prado's book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance, and the first one to provide practical solutions to unique challenges faced by asset managers. 49–58. 2, pp. 105–16. 22, pp. Markowitz, H. (1952): “Portfolio Selection.” Journal of Finance, Vol. 42, No. A recent McKinsey white paper argues that artificial intelligence is broadly impacting the asset management industry, not only transforming the traditional investment process. 96–146. One of the projects that we have underway is called ‘STAR’ (System Tool for Asset Risk). 307–19. Molnar, C. (2019): “Interpretable Machine Learning: A Guide for Making Black-Box Models Explainable.” Available at https://christophm.github.io/interpretable-ml-book/. 41, No. Asset Allocation via Machine Learning and Applications to Equity Portfolio Management Qing Yang1, Zhenning Hong2, Ruyan Tian3, Tingting Ye4, Liangliang Zhang5 Abstract In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets. 5–6, pp. ML tools complement rather than replace the classical statistical methods. The purpose of this monograph is to introduce Machine Learning (ML) tools that can help asset managers … (2007): “A Boosting Approach for Automated Trading.” Journal of Trading, Vol. 1, pp. 163–70. 90, pp. Abstract. 365–411. Machine learning investment strategies aim to deliver persistent, uncorrelated alpha streams while adapting to changes in market conditions—without the human input required in other quantitative investment approaches. As more asset managers bring AI in-house, the demand for external research products will shift as internal machine learning subsumes external analyst and sales roles. Krauss, C., Do, X., and Huck, N. (2017): “Deep Neural Networks, Gradient-Boosted Trees, Random Forests: Statistical Arbitrage on the S&P 500.” European Journal of Operational Research, Vol. Wang, J., and Chan, S. (2006): “Stock Market Trading Rule Discovery Using Two-Layer Bias Decision Tree.” Expert Systems with Applications, Vol. Skip to main content. 3–44. 20, No. Easley, D., López de Prado, M, and O’Hara, M (2011a): “Flow Toxicity and Liquidity in a High-Frequency World.” Review of Financial Studies, Vol. 22, No. Kahn, R. (2018): The Future of Investment Management. 29–34. ACM. Registered in England & Wales No. Creamer, G., Ren, Y., Sakamoto, Y., and Nickerson, J. 19, No. 3651–61. Moreover, Mind Foundry has a privileged access to over 30 Oxford University Machine Learning PhDs through its spin-out status. López de Prado, M. (2018): “A Practical Solution to the Multiple-Testing Crisis in Financial Research.” Journal of Financial Data Science, Vol. 2. Schlecht, J., Kaplan, M, Barnard, K, Karafet, T, Hammer, M, and Merchant, N (2008): “Machine-Learning Approaches for Classifying Haplogroup from Y Chromosome STR Data.” PLOS Computational Biology, Vol. 25, No. The topics covered in this course are really interesting. As a result, AI and machine learning are not threatening to put wealth managers out of business just yet. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. Theofilatos, K., Likothanassis, S., and Karathanasopoulos, A. Cambridge University Press. 9, No. 1. 1–10. 20, pp. Sharpe, W. (1994): “The Sharpe Ratio.” Journal of Portfolio Management, Vol. Laborda, R., and Laborda, J. Wasserstein, R., Schirm, A., and Lazar, N. (2019): “Moving to a World beyond p<0.05.” The American Statistician, Vol. Lochner, M., McEwen, J, Peiris, H, Lahav, O, and Winter, M (2016): “Photometric Supernova Classification with Machine Learning.” The Astrophysical Journal, Vol. ML is not a black box, and it does not necessarily overfit. 1st ed. 10, No. Machine learning can help with most portfolio construction tasks like idea generation, alpha factor design, asset allocation, weight optimization, position sizing and the testing of strategies. 118–28. About Machine Learning for Asset Managers, Check if you have access via personal or institutional login. Plerou, V., Gopikrishnan, P, Rosenow, B, Nunes Amaral, L, and Stanley, H (1999): “Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series.” Physical Review Letters, Vol. 1977–2011. 594–621. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to “learn” complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects. 67–77. (2009): “Causal Inference in Statistics: An Overview.” Statistics Surveys, Vol. This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. We remind you that each one leads to a Certificate and can be taken independently.You will learn at your own pace and benefit from the expertise of global thought leaders from EDHEC Business School, Princeton University and the finance industry. 348–53. The winning team will keep their seed capital and returns. 4, pp. 2767–84. 84–96. Machine learning is making inroads into every aspect of business life and asset management is no exception. • Do not submit attachments as HTML, PDF, GIFG, TIFF, PIF, ZIP or EXE files. Bateson Asset Management ('BAM') is a boutique investment management company specialising in quantitative sustainable investing. 36, No. Rosenblatt, M. (1956): “Remarks on Some Nonparametric Estimates of a Density Function.” The Annals of Mathematical Statistics, Vol. Dunis, C., and Williams, M. (2002): “Modelling and Trading the Euro/US Dollar Exchange Rate: Do Neural Network Models Perform Better?” Journal of Derivatives and Hedge Funds, Vol. 2, pp. Andrew Baxter worked at British Aerospace as an engineer before joining the investment management world. 21–28. 3, pp. 1, pp. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. 6, pp. 10, No. 1823–28. AQR’s Reality Check About Machine Learning in Asset Management Exploring Benefits Beyond Alpha Generation At Rosenblatt, we are believers in the long-term potential of Machine Learning (ML) in financial services and are seeing first-hand proof of new and innovative ML-based FinTechs emerging, and investors keen to fund 1st ed. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. CFA Institute Research Foundation. Bailey, D., and López de Prado, M (2012): “The Sharpe Ratio Efficient Frontier.” Journal of Risk, Vol. 1, pp. Element abstract views reflect the number of visits to the element page. About the Event The goal of this conference is to bring together professional asset managers and academics to understand and discuss the role of artificial intelligence, machine learning, and data science in the finance industry. 341–52. Cambridge University Press. 29, No. 4, pp. 7–18. IDC (2014): “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things.” EMC Digital Universe with Research and Analysis. Steinbach, M., Levent, E, and Kumar, V (2004): “The Challenges of Clustering High Dimensional Data.” In Wille, L (ed. BAM is located in London and regulated by the Financial Conduct Authority (FCA). • Do not submit attachments as HTML, PDF, GIFG, TIFF, PIF, ZIP or EXE files. 5 Howick Place | London | SW1P 1WG. Harvey, C., and Liu, Y (2018): “False (and Missed) Discoveries in Financial Economics.” Working paper. 7046–56. Machine Learning for Asset Managers by Marcos M. López de Prado, Cambridge University Press (2020). Cambridge University Press. Cao, L., and Tay, F. (2001): “Financial Forecasting Using Support Vector Machines.” Neural Computing and Applications, Vol. Benjamini, Y., and Hochberg, Y (1995): “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society, Series B, Vol. This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. Machine Learning for Asset Managers (Elements in Quantitative Finance) - Kindle edition by de Prado, Marcos López . ML is not a black box, and it does not necessarily overfit.