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Machine Learning: Challenges for Financial Market Predictive Analytics Suggest a Bayesian Solution

In: Economics Gone Astray

Author

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  • Blu Putnam

Abstract

In the age of Hollywood’s Moneyball, big data, machine learning and artificial intelligence (AI), it seems only natural to apply our new and evolving statistical tools to predicting outcomes in financial markets. Given the difficulties in predicting market behavior, the bar seems low, and open to dreams of making significant improvements in predictive analytics as applied to financial markets…

Suggested Citation

  • Blu Putnam, 2019. "Machine Learning: Challenges for Financial Market Predictive Analytics Suggest a Bayesian Solution," World Scientific Book Chapters, in: Economics Gone Astray, chapter 10, pages 121-130, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781944659592_0010
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    More about this item

    Keywords

    Economics; Macroeconomics; Monetary Policy; Fiscal Policy; Inflation; Risk Management; Federal Reserve; Quantitative Easing; Taylor Rule;
    All these keywords.

    JEL classification:

    • E02 - Macroeconomics and Monetary Economics - - General - - - Institutions and the Macroeconomy
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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