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Da: Majestic Books, Hounslow, Regno Unito
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: Best Price, Torrance, CA, U.S.A.
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Editore: Springer, Springer Nature Switzerland, 2025
ISBN 10: 3031620631 ISBN 13: 9783031620638
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 149,79
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 172,83
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Editore: Springer-Nature New York Inc, 2024
ISBN 10: 3031620607 ISBN 13: 9783031620607
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloHardcover. Condizione: Brand New. 82 pages. 9.44x6.61x9.69 inches. In Stock.
Editore: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031620607 ISBN 13: 9783031620607
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 180,06
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the models performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the models volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the models performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Springer, Berlin, Springer Nature Switzerland, Springer, 2024
ISBN 10: 3031620607 ISBN 13: 9783031620607
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 29,95
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model's performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the model's volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility. 71 pp. Englisch.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 43,90
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Editore: Springer, Berlin|Springer Nature Switzerland|Springer, 2024
ISBN 10: 3031620607 ISBN 13: 9783031620607
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 127,40
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distin.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 118,26
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Editore: Springer, Springer Nature Switzerland Jun 2025, 2025
ISBN 10: 3031620631 ISBN 13: 9783031620638
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 149,79
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 84 pp. Englisch.
Editore: Springer, Springer Nature Switzerland Jun 2025, 2025
ISBN 10: 3031620631 ISBN 13: 9783031620638
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 149,79
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 84 pp. Englisch.