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Titlebook: Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Sele; Manuel Moura,Rui Neves Book 2025 The

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發(fā)表于 2025-3-21 18:29:46 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Sele
編輯Manuel Moura,Rui Neves
視頻videohttp://file.papertrans.cn/945/944563/944563.mp4
概述Implementation of an ensemble of machine learning classifiers that forecasts which stocks will beat the market.Implementing a Risk-off filter that indicates high market risks.Study the precision of th
叢書名稱Synthesis Lectures on Technology Management & Entrepreneurship
圖書封面Titlebook: Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Sele;  Manuel Moura,Rui Neves Book 2025 The
描述.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 ret
出版日期Book 2025
關(guān)鍵詞Machine Learning; Computational Finance; Value Investing; Ensemble Method; Predictive Forecasting
版次1
doihttps://doi.org/10.1007/978-3-031-62061-4
isbn_softcover978-3-031-62063-8
isbn_ebook978-3-031-62061-4Series ISSN 1933-978X Series E-ISSN 1933-9798
issn_series 1933-978X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:29:23 | 只看該作者
Introduction,filter that decreases exposure under market uncertainties. This chapter outlines the objectives of the study, its unique contributions to the fields of finance, and machine learning and sets the stage for the methodology and analysis that follow.
板凳
發(fā)表于 2025-3-22 00:56:25 | 只看該作者
Conclusion,et performance, highlighting the practical applications of machine learning in investment strategy formulation. The chapter also discusses the weaknesses of the present research and mentions the directions for future work, which can be used for the improvement and the subsequent research in finance and machine learning.
地板
發(fā)表于 2025-3-22 04:57:13 | 只看該作者
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發(fā)表于 2025-3-22 11:11:51 | 只看該作者
Introduction,ensemble of machine learning algorithms. This strategy uses financial indicators derived from companies’ financial statements and includes a Risk-Off filter that decreases exposure under market uncertainties. This chapter outlines the objectives of the study, its unique contributions to the fields o
6#
發(fā)表于 2025-3-22 14:04:46 | 只看該作者
State-of-the-Art,a brief description of the structure of the stock market and its operations, followed by a comprehensive analysis on financial statements and fundamental analysis. The chapter then moves to an analysis of machine learning in finance, including the application of time series classification and highli
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發(fā)表于 2025-3-22 19:42:32 | 只看該作者
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發(fā)表于 2025-3-22 22:18:40 | 只看該作者
System Validation, classifying stocks and evaluates the results of the investment module. It examines stock classification results and the performance of the investment module using metrics such as the total return, volatility and Sharpe Ratio followed by the effectiveness of the Risk-Off Filter. Through a series of
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發(fā)表于 2025-3-23 01:36:32 | 只看該作者
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發(fā)表于 2025-3-23 08:52:26 | 只看該作者
Book 2025 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 t
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