Huang, W., Nakamori, Y., and Wang, S. (2005): “Forecasting Stock Market Movement Direction with Support Vector Machine.” Computers and Operations Research, Vol. Kim, K. (2003): “Financial Time Series Forecasting Using Support Vector Machines.” Neurocomputing, Vol. 14, pp. 1, pp.

Feuerriegel, S., and Prendinger, H. (2016): “News-Based Trading Strategies.” Decision Support Systems, Vol. Creamer, G., Ren, Y., Sakamoto, Y., and Nickerson, J. Available at https://ssrn.com/abstract=3177057, American Statistical Association (2016): “Statement on Statistical Significance and P-Values.” Available at www.amstat.org/asa/files/pdfs/P-ValueStatement.pdf. Hamilton, J. 55, No. View all Google Scholar citations 873–95.

318, pp. 26–44. Clarke, Kevin A. Available at https://ssrn.com/abstract=2249314.

5, pp. Lo, A. 77–91. 231, No. Trippi, R., and DeSieno, D. (1992): “Trading Equity Index Futures with a Neural Network.” Journal of Portfolio Management, Vol. 9, No. Šidàk, Z. Harvey, C., and Liu, Y (2018): “Lucky Factors.” Working paper. 28, No. 1, pp. 6. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 378, pp. Add Paper to My Library. 85–126. Available at www.sciencedaily.com/releases/2013/05/130522085217.htm.

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. 6, No. Holm, S. (1979): “A Simple Sequentially Rejective Multiple Test Procedure.” Scandinavian Journal of Statistics, Vol. 45 Pages 65–70.

112–22. Einav, L., and Levin, J (2014): “Economics in the Age of Big Data.” Science, Vol. Wooldridge, J. Booth, A., Gerding, E., and McGroarty, F. (2014): “Automated Trading with Performance Weighted Random Forests and Seasonality.” Expert Systems with Applications, Vol.

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. 77, No. 129–33. 83, No. 4, pp. 431–39. : Machine Learning for Asset Managers. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. ML tools complement rather than replace the classical statistical methods. Cambridge University Press. Mertens, E. (2002): “Variance of the IID estimator in Lo (2002).” Working paper, University of Basel. 2, pp. 14, No. 22, No. Sharpe, W. (1975): “Adjusting for Risk in Portfolio Performance Measurement.” Journal of Portfolio Management, Vol. (2004): “A Comparative Study on Feature Selection Methods for Drug Discovery.” Journal of Chemical Information and Modeling, Vol. 163–70. 3, pp. Gryak, J., Haralick, R, and Kahrobaei, D (Forthcoming): “Solving the Conjugacy Decision Problem via Machine Learning.” Experimental Mathematics. 1, pp.

20, pp. Zhu, M., Philpotts, D., and Stevenson, M. (2012): “The Benefits of Tree-Based Models for Stock Selection.” Journal of Asset Management, Vol. 13, No. 605–11. 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. 27, No.

1st ed.

Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. 56, No. Michaud, R. (1998): Efficient Asset Allocation: A Practical Guide to Stock Portfolio Optimization and Asset Allocation. 6, pp. Embrechts, P., Klueppelberg, C, and Mikosch, T (2003): Modelling Extremal Events.

755–60. We use cookies to distinguish you from other users and to provide you with a better experience on our websites.

López de Prado, M., and Lewis, M (2018): “Confidence and Power of the Sharpe Ratio under Multiple Testing.” Working paper. 7–18. Apley, D. (2016): “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.” Available at https://arxiv.org/abs/1612.08468. 27–33. 481–92. “ Machine learning (ML) is changing virtually every aspect of our lives. 401–20.

Cambridge University Press. 5, pp. Price includes VAT for USA. (1994): Time Series Analysis. Porter, K. (2017): “Estimating Statistical Power When Using Multiple Testing Procedures.” Available at www.mdrc.org/sites/default/files/PowerMultiplicity-IssueFocus.pdf. Kahn, R. (2018): The Future of Investment Management. 48–66. (2012): “Machine Learning Strategies for Time Series Forecasting.” Lecture Notes in Business Information Processing, Vol. 6, pp.

Benjamini, Y., and Yekutieli, D (2001): “The Control of the False Discovery Rate in Multiple Testing under Dependency.” Annals of Statistics, Vol. 234, No. López de Prado, Marcos, Machine Learning Asset Allocation (Presentation Slides) (October 15, 2019). (2016): “A Textual Analysis Algorithm for the Equity Market: The European Case.” Journal of Investing, Vol. Available at https://ssrn.com/abstract=3365282.

Bailey, D., and López de Prado, M (2013): “An Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization.” Algorithms, Vol. 5–68. Robert, C. (2014): “On the Jeffreys–Lindley Paradox.” Philosophy of Science, Vol. 591–94. Goutte, C., Toft, P, Rostrup, E, Nielsen, F, and Hansen, L (1999): “On Clustering fMRI Time Series.” NeuroImage, Vol. 6, No.

1, No. ML is not a black box, and it does not necessarily overfit. 87–106. 44, No. 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. Shafer, G. (1982): “Lindley’s Paradox.” Journal of the American Statistical Association, Vol. Copy URL. This is a preview of subscription content, log in to check access. 119–38. Kraskov, A., Stoegbauer, H, and Grassberger, P (2008): “Estimating Mutual Information.” Working paper. Access options Buy single article. 5, pp. Mullainathan, S., and Spiess, J (2017): “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives, Vol. Ballings, M., van den Poel, D., Hespeels, N., and Gryp, R. (2015): “Evaluating Multiple Classifiers for Stock Price Direction Prediction.” Expert Systems with Applications, Vol. 7, pp. 22, No.

Parzen, E. (1962): “On Estimation of a Probability Density Function and Mode.” The Annals of Mathematical Statistics, Vol. Wasserstein, R., and Lazar, N. (2016): “The ASA’s Statement on p-Values: Context, Process, and Purpose.” The American Statistician, Vol. Metrics details. 1, pp. SINTEF (2013): “Big Data, for Better or Worse: 90% of World’s Data Generated over Last Two Years.” Science Daily, May 22. 694–706, pp. 6210. 3, pp.

Concepts are presented with clarity & relevant code is provided for the audiences’ purposes. Varian, H. (2014): “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives, Vol.

Grinold, R., and Kahn, R (1999): Active Portfolio Management. Available at https://ssrn.com/abstract=3073799. Copy URL. López de Prado, M. (2019a): “A Data Science Solution to the Multiple-Testing Crisis in Financial Research.” Journal of Financial Data Science, Vol. 1, pp. Wiley. Downloads. López de Prado, M. (2018a): Advances in Financial Machine Learning. 45, No. 1st ed. Opdyke, J. 216–32.

1471–74.

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. López de Prado, M. (2019b): “Beyond Econometrics: A Roadmap towards Financial Machine Learning.” Working paper.

PlumX Metrics. Machine Learning for Asset Managers M. López de Prado, Marcos. (2010): “Automated Trading with Boosting and Expert Weighting.” Quantitative Finance, Vol. 29, pp. 4, pp.

5, pp. Financ Mark Portf Manag 34, 507–509 (2020). Ahmed, N., Atiya, A., Gayar, N., and El-Shishiny, H. (2010): “An Empirical Comparison of Machine Learning Models for Time Series Forecasting.” Econometric Reviews, Vol. 3, pp. 38, No. This article focuses on portfolio weighting using machine learning. See all articles by Marcos Lopez de Prado Marcos Lopez de Prado. 2nd ed. 10, No. 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. 5, pp. 259, No. 557–85. ML tools complement rather than replace the classical statistical methods. In this concise Element, De Prado succinctly distinguishes the practical uses of ML within Portfolio Management from the hype. 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. 6, No. 5, No.

5–6, pp. (2017): “Can Tree-Structured Classifiers Add Value to the Investor?” Finance Research Letters, Vol. Cohen, L., and Frazzini, A (2008): “Economic Links and Predictable Returns.” Journal of Finance, Vol. 3, pp. 2, pp. https://doi.org/10.1007/s11408-020-00368-y, DOI: https://doi.org/10.1007/s11408-020-00368-y, Over 10 million scientific documents at your fingertips, Not logged in 3, pp. 1, No. 118–28. 7,550. rank. 138, No. 3rd ed. Available at https://ssrn.com/abstract=3365271. Taxes to be calculated in checkout. 1, pp. 1457–93. 59–69.

Huang, W., Nakamori, Y., and Wang, S. (2005): “Forecasting Stock Market Movement Direction with Support Vector Machine.” Computers and Operations Research, Vol. Kim, K. (2003): “Financial Time Series Forecasting Using Support Vector Machines.” Neurocomputing, Vol. 14, pp. 1, pp.

Feuerriegel, S., and Prendinger, H. (2016): “News-Based Trading Strategies.” Decision Support Systems, Vol. Creamer, G., Ren, Y., Sakamoto, Y., and Nickerson, J. Available at https://ssrn.com/abstract=3177057, American Statistical Association (2016): “Statement on Statistical Significance and P-Values.” Available at www.amstat.org/asa/files/pdfs/P-ValueStatement.pdf. Hamilton, J. 55, No. View all Google Scholar citations 873–95.

318, pp. 26–44. Clarke, Kevin A. Available at https://ssrn.com/abstract=2249314.

5, pp. Lo, A. 77–91. 231, No. Trippi, R., and DeSieno, D. (1992): “Trading Equity Index Futures with a Neural Network.” Journal of Portfolio Management, Vol. 9, No. Šidàk, Z. Harvey, C., and Liu, Y (2018): “Lucky Factors.” Working paper. 28, No. 1, pp. 6. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 378, pp. Add Paper to My Library. 85–126. Available at www.sciencedaily.com/releases/2013/05/130522085217.htm.

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. 6, No. Holm, S. (1979): “A Simple Sequentially Rejective Multiple Test Procedure.” Scandinavian Journal of Statistics, Vol. 45 Pages 65–70.

112–22. Einav, L., and Levin, J (2014): “Economics in the Age of Big Data.” Science, Vol. Wooldridge, J. Booth, A., Gerding, E., and McGroarty, F. (2014): “Automated Trading with Performance Weighted Random Forests and Seasonality.” Expert Systems with Applications, Vol.

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. 77, No. 129–33. 83, No. 4, pp. 431–39. : Machine Learning for Asset Managers. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. ML tools complement rather than replace the classical statistical methods. Cambridge University Press. Mertens, E. (2002): “Variance of the IID estimator in Lo (2002).” Working paper, University of Basel. 2, pp. 14, No. 22, No. Sharpe, W. (1975): “Adjusting for Risk in Portfolio Performance Measurement.” Journal of Portfolio Management, Vol. (2004): “A Comparative Study on Feature Selection Methods for Drug Discovery.” Journal of Chemical Information and Modeling, Vol. 163–70. 3, pp. Gryak, J., Haralick, R, and Kahrobaei, D (Forthcoming): “Solving the Conjugacy Decision Problem via Machine Learning.” Experimental Mathematics. 1, pp.

20, pp. Zhu, M., Philpotts, D., and Stevenson, M. (2012): “The Benefits of Tree-Based Models for Stock Selection.” Journal of Asset Management, Vol. 13, No. 605–11. 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. 27, No.

1st ed.

Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. 56, No. Michaud, R. (1998): Efficient Asset Allocation: A Practical Guide to Stock Portfolio Optimization and Asset Allocation. 6, pp. Embrechts, P., Klueppelberg, C, and Mikosch, T (2003): Modelling Extremal Events.

755–60. We use cookies to distinguish you from other users and to provide you with a better experience on our websites.

López de Prado, M., and Lewis, M (2018): “Confidence and Power of the Sharpe Ratio under Multiple Testing.” Working paper. 7–18. Apley, D. (2016): “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.” Available at https://arxiv.org/abs/1612.08468. 27–33. 481–92. “ Machine learning (ML) is changing virtually every aspect of our lives. 401–20.

Cambridge University Press. 5, pp. Price includes VAT for USA. (1994): Time Series Analysis. Porter, K. (2017): “Estimating Statistical Power When Using Multiple Testing Procedures.” Available at www.mdrc.org/sites/default/files/PowerMultiplicity-IssueFocus.pdf. Kahn, R. (2018): The Future of Investment Management. 48–66. (2012): “Machine Learning Strategies for Time Series Forecasting.” Lecture Notes in Business Information Processing, Vol. 6, pp.

Benjamini, Y., and Yekutieli, D (2001): “The Control of the False Discovery Rate in Multiple Testing under Dependency.” Annals of Statistics, Vol. 234, No. López de Prado, Marcos, Machine Learning Asset Allocation (Presentation Slides) (October 15, 2019). (2016): “A Textual Analysis Algorithm for the Equity Market: The European Case.” Journal of Investing, Vol. Available at https://ssrn.com/abstract=3365282.

Bailey, D., and López de Prado, M (2013): “An Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization.” Algorithms, Vol. 5–68. Robert, C. (2014): “On the Jeffreys–Lindley Paradox.” Philosophy of Science, Vol. 591–94. Goutte, C., Toft, P, Rostrup, E, Nielsen, F, and Hansen, L (1999): “On Clustering fMRI Time Series.” NeuroImage, Vol. 6, No.

1, No. ML is not a black box, and it does not necessarily overfit. 87–106. 44, No. 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. Shafer, G. (1982): “Lindley’s Paradox.” Journal of the American Statistical Association, Vol. Copy URL. This is a preview of subscription content, log in to check access. 119–38. Kraskov, A., Stoegbauer, H, and Grassberger, P (2008): “Estimating Mutual Information.” Working paper. Access options Buy single article. 5, pp. Mullainathan, S., and Spiess, J (2017): “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives, Vol. Ballings, M., van den Poel, D., Hespeels, N., and Gryp, R. (2015): “Evaluating Multiple Classifiers for Stock Price Direction Prediction.” Expert Systems with Applications, Vol. 7, pp. 22, No.

Parzen, E. (1962): “On Estimation of a Probability Density Function and Mode.” The Annals of Mathematical Statistics, Vol. Wasserstein, R., and Lazar, N. (2016): “The ASA’s Statement on p-Values: Context, Process, and Purpose.” The American Statistician, Vol. Metrics details. 1, pp. SINTEF (2013): “Big Data, for Better or Worse: 90% of World’s Data Generated over Last Two Years.” Science Daily, May 22. 694–706, pp. 6210. 3, pp.

Concepts are presented with clarity & relevant code is provided for the audiences’ purposes. Varian, H. (2014): “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives, Vol.

Grinold, R., and Kahn, R (1999): Active Portfolio Management. Available at https://ssrn.com/abstract=3073799. Copy URL. López de Prado, M. (2019a): “A Data Science Solution to the Multiple-Testing Crisis in Financial Research.” Journal of Financial Data Science, Vol. 1, pp. Wiley. Downloads. López de Prado, M. (2018a): Advances in Financial Machine Learning. 45, No. 1st ed. Opdyke, J. 216–32.

1471–74.

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. López de Prado, M. (2019b): “Beyond Econometrics: A Roadmap towards Financial Machine Learning.” Working paper.

PlumX Metrics. Machine Learning for Asset Managers M. López de Prado, Marcos. (2010): “Automated Trading with Boosting and Expert Weighting.” Quantitative Finance, Vol. 29, pp. 4, pp.

5, pp. Financ Mark Portf Manag 34, 507–509 (2020). Ahmed, N., Atiya, A., Gayar, N., and El-Shishiny, H. (2010): “An Empirical Comparison of Machine Learning Models for Time Series Forecasting.” Econometric Reviews, Vol. 3, pp. 38, No. This article focuses on portfolio weighting using machine learning. See all articles by Marcos Lopez de Prado Marcos Lopez de Prado. 2nd ed. 10, No. 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. 5, pp. 259, No. 557–85. ML tools complement rather than replace the classical statistical methods. In this concise Element, De Prado succinctly distinguishes the practical uses of ML within Portfolio Management from the hype. 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. 6, No. 5, No.

5–6, pp. (2017): “Can Tree-Structured Classifiers Add Value to the Investor?” Finance Research Letters, Vol. Cohen, L., and Frazzini, A (2008): “Economic Links and Predictable Returns.” Journal of Finance, Vol. 3, pp. 2, pp. https://doi.org/10.1007/s11408-020-00368-y, DOI: https://doi.org/10.1007/s11408-020-00368-y, Over 10 million scientific documents at your fingertips, Not logged in 3, pp. 1, No. 118–28. 7,550. rank. 138, No. 3rd ed. Available at https://ssrn.com/abstract=3365271. Taxes to be calculated in checkout. 1, pp. 1457–93. 59–69.

Huang, W., Nakamori, Y., and Wang, S. (2005): “Forecasting Stock Market Movement Direction with Support Vector Machine.” Computers and Operations Research, Vol. Kim, K. (2003): “Financial Time Series Forecasting Using Support Vector Machines.” Neurocomputing, Vol. 14, pp. 1, pp.

Feuerriegel, S., and Prendinger, H. (2016): “News-Based Trading Strategies.” Decision Support Systems, Vol. Creamer, G., Ren, Y., Sakamoto, Y., and Nickerson, J. Available at https://ssrn.com/abstract=3177057, American Statistical Association (2016): “Statement on Statistical Significance and P-Values.” Available at www.amstat.org/asa/files/pdfs/P-ValueStatement.pdf. Hamilton, J. 55, No. View all Google Scholar citations 873–95.

318, pp. 26–44. Clarke, Kevin A. Available at https://ssrn.com/abstract=2249314.

5, pp. Lo, A. 77–91. 231, No. Trippi, R., and DeSieno, D. (1992): “Trading Equity Index Futures with a Neural Network.” Journal of Portfolio Management, Vol. 9, No. Šidàk, Z. Harvey, C., and Liu, Y (2018): “Lucky Factors.” Working paper. 28, No. 1, pp. 6. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 378, pp. Add Paper to My Library. 85–126. Available at www.sciencedaily.com/releases/2013/05/130522085217.htm.

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. 6, No. Holm, S. (1979): “A Simple Sequentially Rejective Multiple Test Procedure.” Scandinavian Journal of Statistics, Vol. 45 Pages 65–70.

112–22. Einav, L., and Levin, J (2014): “Economics in the Age of Big Data.” Science, Vol. Wooldridge, J. Booth, A., Gerding, E., and McGroarty, F. (2014): “Automated Trading with Performance Weighted Random Forests and Seasonality.” Expert Systems with Applications, Vol.

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. 77, No. 129–33. 83, No. 4, pp. 431–39. : Machine Learning for Asset Managers. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. ML tools complement rather than replace the classical statistical methods. Cambridge University Press. Mertens, E. (2002): “Variance of the IID estimator in Lo (2002).” Working paper, University of Basel. 2, pp. 14, No. 22, No. Sharpe, W. (1975): “Adjusting for Risk in Portfolio Performance Measurement.” Journal of Portfolio Management, Vol. (2004): “A Comparative Study on Feature Selection Methods for Drug Discovery.” Journal of Chemical Information and Modeling, Vol. 163–70. 3, pp. Gryak, J., Haralick, R, and Kahrobaei, D (Forthcoming): “Solving the Conjugacy Decision Problem via Machine Learning.” Experimental Mathematics. 1, pp.

20, pp. Zhu, M., Philpotts, D., and Stevenson, M. (2012): “The Benefits of Tree-Based Models for Stock Selection.” Journal of Asset Management, Vol. 13, No. 605–11. 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. 27, No.

1st ed.

Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. 56, No. Michaud, R. (1998): Efficient Asset Allocation: A Practical Guide to Stock Portfolio Optimization and Asset Allocation. 6, pp. Embrechts, P., Klueppelberg, C, and Mikosch, T (2003): Modelling Extremal Events.

755–60. We use cookies to distinguish you from other users and to provide you with a better experience on our websites.

López de Prado, M., and Lewis, M (2018): “Confidence and Power of the Sharpe Ratio under Multiple Testing.” Working paper. 7–18. Apley, D. (2016): “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.” Available at https://arxiv.org/abs/1612.08468. 27–33. 481–92. “ Machine learning (ML) is changing virtually every aspect of our lives. 401–20.

Cambridge University Press. 5, pp. Price includes VAT for USA. (1994): Time Series Analysis. Porter, K. (2017): “Estimating Statistical Power When Using Multiple Testing Procedures.” Available at www.mdrc.org/sites/default/files/PowerMultiplicity-IssueFocus.pdf. Kahn, R. (2018): The Future of Investment Management. 48–66. (2012): “Machine Learning Strategies for Time Series Forecasting.” Lecture Notes in Business Information Processing, Vol. 6, pp.

Benjamini, Y., and Yekutieli, D (2001): “The Control of the False Discovery Rate in Multiple Testing under Dependency.” Annals of Statistics, Vol. 234, No. López de Prado, Marcos, Machine Learning Asset Allocation (Presentation Slides) (October 15, 2019). (2016): “A Textual Analysis Algorithm for the Equity Market: The European Case.” Journal of Investing, Vol. Available at https://ssrn.com/abstract=3365282.

Bailey, D., and López de Prado, M (2013): “An Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization.” Algorithms, Vol. 5–68. Robert, C. (2014): “On the Jeffreys–Lindley Paradox.” Philosophy of Science, Vol. 591–94. Goutte, C., Toft, P, Rostrup, E, Nielsen, F, and Hansen, L (1999): “On Clustering fMRI Time Series.” NeuroImage, Vol. 6, No.

1, No. ML is not a black box, and it does not necessarily overfit. 87–106. 44, No. 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. Shafer, G. (1982): “Lindley’s Paradox.” Journal of the American Statistical Association, Vol. Copy URL. This is a preview of subscription content, log in to check access. 119–38. Kraskov, A., Stoegbauer, H, and Grassberger, P (2008): “Estimating Mutual Information.” Working paper. Access options Buy single article. 5, pp. Mullainathan, S., and Spiess, J (2017): “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives, Vol. Ballings, M., van den Poel, D., Hespeels, N., and Gryp, R. (2015): “Evaluating Multiple Classifiers for Stock Price Direction Prediction.” Expert Systems with Applications, Vol. 7, pp. 22, No.

Parzen, E. (1962): “On Estimation of a Probability Density Function and Mode.” The Annals of Mathematical Statistics, Vol. Wasserstein, R., and Lazar, N. (2016): “The ASA’s Statement on p-Values: Context, Process, and Purpose.” The American Statistician, Vol. Metrics details. 1, pp. SINTEF (2013): “Big Data, for Better or Worse: 90% of World’s Data Generated over Last Two Years.” Science Daily, May 22. 694–706, pp. 6210. 3, pp.

Concepts are presented with clarity & relevant code is provided for the audiences’ purposes. Varian, H. (2014): “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives, Vol.

Grinold, R., and Kahn, R (1999): Active Portfolio Management. Available at https://ssrn.com/abstract=3073799. Copy URL. López de Prado, M. (2019a): “A Data Science Solution to the Multiple-Testing Crisis in Financial Research.” Journal of Financial Data Science, Vol. 1, pp. Wiley. Downloads. López de Prado, M. (2018a): Advances in Financial Machine Learning. 45, No. 1st ed. Opdyke, J. 216–32.

1471–74.

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. López de Prado, M. (2019b): “Beyond Econometrics: A Roadmap towards Financial Machine Learning.” Working paper.

PlumX Metrics. Machine Learning for Asset Managers M. López de Prado, Marcos. (2010): “Automated Trading with Boosting and Expert Weighting.” Quantitative Finance, Vol. 29, pp. 4, pp.

5, pp. Financ Mark Portf Manag 34, 507–509 (2020). Ahmed, N., Atiya, A., Gayar, N., and El-Shishiny, H. (2010): “An Empirical Comparison of Machine Learning Models for Time Series Forecasting.” Econometric Reviews, Vol. 3, pp. 38, No. This article focuses on portfolio weighting using machine learning. See all articles by Marcos Lopez de Prado Marcos Lopez de Prado. 2nd ed. 10, No. 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. 5, pp. 259, No. 557–85. ML tools complement rather than replace the classical statistical methods. In this concise Element, De Prado succinctly distinguishes the practical uses of ML within Portfolio Management from the hype. 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. 6, No. 5, No.

5–6, pp. (2017): “Can Tree-Structured Classifiers Add Value to the Investor?” Finance Research Letters, Vol. Cohen, L., and Frazzini, A (2008): “Economic Links and Predictable Returns.” Journal of Finance, Vol. 3, pp. 2, pp. https://doi.org/10.1007/s11408-020-00368-y, DOI: https://doi.org/10.1007/s11408-020-00368-y, Over 10 million scientific documents at your fingertips, Not logged in 3, pp. 1, No. 118–28. 7,550. rank. 138, No. 3rd ed. Available at https://ssrn.com/abstract=3365271. Taxes to be calculated in checkout. 1, pp. 1457–93. 59–69.

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