Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Select Outperforming Companies

Moura, Manuel, Neves, Rui

Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Select Outperforming Companies
  • 出版商: Springer
  • 出版日期: 2024-06-19
  • 售價: $1,440
  • 貴賓價: 9.5 折 $1,368
  • 語言: 英文
  • 頁數: 71
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031620607
  • ISBN-13: 9783031620607

    • 海外代購書籍(需單獨結帳)

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商品描述

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 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.

商品描述(中文翻譯)

本書發展了一種量化股票市場投資方法，利用財務指標超越標準普爾500指數（S&P500）的基準。為了達成這個目標，精心構建了一個機器學習模型的集成，包含四種不同的演算法：支持向量機（support vector machine）、k最近鄰（k-nearest neighbors）、隨機森林（random forest）和邏輯回歸（logistic regression）。這些模型均利用從公司財務報表中提取的財務比率進行預測預測。集成分類器經過嚴格的精確度測試，並將其與各個組成模型的表現進行比較。這項評估使用了滾動窗口和交叉驗證測試，以提供全面的評估框架。為了在不確定的市場期間限制風險，開發了一個風險避險過濾器，從而改善模型的夏普比率（Sharpe ratio）。最終模型的風險調整後表現，在風險避險過濾器的支持下，達到了1.63的夏普比率，超越了未使用過濾器的模型（夏普比率為1.41）和標準普爾500指數（夏普比率為0.80）。通過將模型的年標準差從15.75%降低至11.22%，實現了風險調整後回報的顯著增長，這代表著波動性幾乎減少了30%。

作者簡介

Manuel Moura is currently doing a MBA at London Business School. Prior to that he worked at LFO since 2019. He received a Master's Degree in Electrical Engineering and Computer Science with a specialization in Control Systems from Instituto Superior Técnico. At LFO, he worked as a quantitative researcher developing models to invest in the stock market and manage risk but also as a portfolio manager. He did internships in consulting at Bain & Company in Brussels and in private equity at Advent International in London.

Rui Ferreira Neves is a professor at Instituto Superior Técnico since 2005. He received the Eng -Diploma and the PhD degrees in Electrical and Computer Engineering from the Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 1993 and 2001, respectively. In 2006 he joined Instituto de Telecomunicações (IT) as a research Associate. His research activity deals with evolutionary computation and pattern matching applied to the financial markets, sensor networks, embedded systems and mixed signal integrated circuits. He uses both fundamental, technical and pattern matching indicators to find the evolution of the financial markets. During his research activities he has collaborated/coordinated several EU and National projects. He supervised 50 MSc Theses. He published more than 60 works, respectively, 7 books, 4 book chapters, 20 journal papers and 30 conference papers.

作者簡介(中文翻譯)

Manuel Moura 目前正在倫敦商學院攻讀 MBA。在此之前，他自 2019 年以來在 LFO 工作。他獲得了電機工程和計算機科學碩士學位，專攻控制系統，畢業於 Instituto Superior Técnico。在 LFO，他擔任量化研究員，開發股票市場投資和風險管理模型，同時也擔任投資組合經理。他曾在布魯塞爾的 Bain & Company 進行諮詢實習，以及在倫敦的 Advent International 進行私募股權實習。

Rui Ferreira Neves 自 2005 年以來擔任 Instituto Superior Técnico 的教授。他於 1993 年和 2001 年分別獲得葡萄牙里斯本技術大學 Instituto Superior Técnico 的工程學文憑和電機與計算機工程博士學位。2006 年，他加入電信研究所 (IT) 擔任研究助理。他的研究活動涉及進化計算和模式匹配，應用於金融市場、傳感器網絡、嵌入式系統和混合信號集成電路。他使用基本面、技術面和模式匹配指標來尋找金融市場的演變。在他的研究活動中，他協作或協調了多個歐盟和國家項目。他指導了 50 篇碩士論文，並發表了超過 60 篇作品，包括 7 本書、4 章書籍、20 篇期刊論文和 30 篇會議論文。

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