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Big data and machine learning in quantitative investment / Tony Guida.

By: Guida, Tony, 1979- [author.].
Material type: materialTypeLabelBookSeries: Wiley finance series: Publisher: Chichester : Wiley, [2019]Copyright date: �2019Description: 1 online resource (299 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9781119522089 (e-book); 9781119522218 (e-book).Subject(s): Investments -- Study and teaching | Machine learning | Big data | BUSINESS & ECONOMICS / FinanceGenre/Form: Electronic books.DDC classification: 332.60285/631 Online resources: Click to View
Contents:
Machine generated contents note: Chapter 1: Do algorithms dream about artificial alphas? Chapter 2: Taming Big data Chapter 3: State of machine learning applications in investment management Chapter 4: Implementing alternative data in an investment Process Chapter 5: Using alternative and Big Data to trade macro assets Chapter 6: Big is beautiful: How email receipt data can help predict company sales Chapter 7: Ensemble learning applied to quant equity: gradient boosting in a multi-factor framework Chapter 8: A social media analysis of corporate culture Chapter 9: Machine Learning & Event Detection for Trading Energy Futures Chapter 10: Natural language processing of financial news Chapter 11: Support-Vector-Machine Based Global Tactical Asset Allocation Chapter 12: Reinforcement learning in finance Chapter 13: Deep learning in Finance: Prediction of stock returns with long short term memory networks Biography of contributors.
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Item type Current location Collection Call number URL Copy number Status Date due Item holds
E-book E-book IUKL Library
Subscripti https://ebookcentral.proquest.com/lib/kliuc-ebooks/detail.action?docID=5614243 1 Available
Total holds: 0

Includes bibliographical references and index.

Machine generated contents note: Chapter 1: Do algorithms dream about artificial alphas? Chapter 2: Taming Big data Chapter 3: State of machine learning applications in investment management Chapter 4: Implementing alternative data in an investment Process Chapter 5: Using alternative and Big Data to trade macro assets Chapter 6: Big is beautiful: How email receipt data can help predict company sales Chapter 7: Ensemble learning applied to quant equity: gradient boosting in a multi-factor framework Chapter 8: A social media analysis of corporate culture Chapter 9: Machine Learning & Event Detection for Trading Energy Futures Chapter 10: Natural language processing of financial news Chapter 11: Support-Vector-Machine Based Global Tactical Asset Allocation Chapter 12: Reinforcement learning in finance Chapter 13: Deep learning in Finance: Prediction of stock returns with long short term memory networks Biography of contributors.

Description based on print version record.

Electronic reproduction. Ann Arbor, MI : ProQuest, 2018. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.

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