Implementing Machine Learning Finance (27 results)
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Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Paperback or Softback. Condition: New. Implementing Machine Learning for Finance: A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios. Book.
- Softcover
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- Softcover
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- Softcover
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- Softcover
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- Softcover
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Condition: New. 2021. 1st ed. paperback. . . . . .
- Softcover
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- Softcover
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Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be print…ed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
- Softcover
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- Softcover
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Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Implementing Machine Learning for Finance | A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios | Tshepo Chris Nokeri | Taschenbuch | xviii | Englisch | 2021 | Apress | EAN 9781484271094 | Verantwortliche Person für die EU: APress in Springer Science + Business… Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Softcover
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Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance… analysis, including predictive analytics using data science procedures.The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios.By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems.What You Will LearnUnderstand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio managementKnow the concepts of feature engineering, data visualization, and hyperparameter optimizationDesign, build, and test supervised and unsupervised ML and DL modelsDiscover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and pricesStructure and optimize an investment portfolio with preeminent asset classes and measure the underlying riskWho This Book Is ForBeginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders) 200 pp. Englisch.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, , United KingdomMajestic Books
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- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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- Softcover
- Print on Demand
Seller: moluna, Greven, , Germanymoluna
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£ 43.00
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Beginning-Intermediate user level|Bridges the gap between finance and data science by presenting a systematic method for structuring, analyzing, and optimizing an investment portfolio and its underlying asset classesC…overs.
- Softcover
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance ana…lysis, including predictive analytics using data science procedures.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 200 pp. Englisch.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance anal…ysis, including predictive analytics using data science procedures.The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios.By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems.What You Will LearnUnderstand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio managementKnow the concepts of feature engineering, data visualization, and hyperparameter optimizationDesign, build, and test supervised and unsupervised ML and DL modelsDiscover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and pricesStructure and optimize an investment portfolio with preeminent asset classes and measure the underlying riskWho This Book Is ForBeginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders).
