Financial Data Analytics with Machine Learning, Optimization and Statistics

Chen, Sam, Cheung, Ka Chun, Yam, Phillip

Financial Data Analytics with Machine Learning, Optimization and Statistics

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商品描述

An essential introduction to data analytics and Machine Learning techniques in the business sector

In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs--especially of key results--and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves.

This book can help readers become well-equipped with the following skills:

  • To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions
  • To apply effective data dimension reduction tools to enhance supervised learning
  • To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose

The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam.

Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.

商品描述(中文翻譯)

《金融數據分析：機器學習、優化與統計的應用》是一本對商業領域中數據分析和機器學習技術的基本介紹。本書由一組傑出的應用數學家和統計學家、經驗豐富的精算專業人士以及在職數據分析師組成的團隊撰寫，提供了傳統金融統計、有效的機器學習工具和數學的專業平衡組合。書中重點介紹了在金融領域和保險行業中使用的當代數據分析技術，強調數學理解和統計原則，並將其與常見的實際金融問題相連結。每一章都配有推導和證明，特別是關鍵結果的證明，並包含幾個源自常見金融背景的現實例子。書中的計算機算法使用 Python 和 R 這兩種在應用科學及學術界和工業界中最廣泛使用的程式語言實現，以便讀者能夠自行實施相關模型並使用這些程式。

本書可以幫助讀者掌握以下技能：
- 評估金融和保險數據的質量，並利用應用數據分析工具後獲得的精華知識，做出及時的金融決策
- 應用有效的數據降維工具以增強監督學習
- 根據分類或回歸預測的目的，描述和選擇適合的數據分析工具

本書涵蓋了多項專業考試所測試的能力，例如由精算學會提供的預測分析考試，以及精算學院和學會的精算統計考試。

除了是高年級本科生和研究生在金融工程、統計學、量化金融、風險管理、精算科學、數據科學和人工智慧數學課程中不可或缺的資源外，《金融數據分析：機器學習、優化與統計的應用》也應該成為有志於和正在從事商業及投資銀行的量化分析師的圖書館藏書。

作者簡介

YONGZHAO CHEN (SAM) [BSC(ACTUARSC) & PHD (HKU)] is currently an Assistant Professor at the Department of Mathematics, Statistics and Insurance, The Hang Seng University of Hong Kong. His research interests include actuarial science, especially credibility theory, and data analytics.

KA CHUN CHEUNG [BSC(ACTUARSC) & PHD (HKU), ASA (SOA)] was the Director of the Actuarial Science Programme, and is currently Head and full Professor at the Department of Statistics and Actuarial Science in School of Computing and Data Science, The University of Hong Kong. His current research interests include various topics in actuarial science, including optimal reinsurance, stochastic orders, dependence structures, and extreme value theory.

PHILLIP YAM [BSC(ACTUARSC) & MPHIL (HKU), MAST (CANTAB), DPHIL (OXON)] is currently Director of QFRM programme, and a full Professor at the Department of Statistics of The Chinese University of Hong Kong, also Assistant Dean (Education) of CUHK Faculty of Science, and a Visiting Professor in Columbia University and UTD Business School. He has more than 100 top journal articles in actuarial science, applied mathematics, data analytics, engineering, financial mathematics, operations management, and statistics. His research project CIBer won a Silver Medal in the 48th International Exhibition of Inventions Geneva in 2023.

作者簡介(中文翻譯)

YONGZHAO CHEN (SAM) [BSC(ACTUARSC) & PHD (HKU)] 目前是香港恒生大學數學、統計及保險系的助理教授。他的研究興趣包括精算科學，特別是可信度理論和數據分析。

KA CHUN CHEUNG [BSC(ACTUARSC) & PHD (HKU), ASA (SOA)] 曾擔任精算科學課程主任，目前是香港大學計算機與數據科學學院統計及精算科學系的系主任及全職教授。他目前的研究興趣包括精算科學中的各種主題，包括最佳再保險、隨機順序、依賴結構和極值理論。

PHILLIP YAM [BSC(ACTUARSC) & MPHIL (HKU), MAST (CANTAB), DPHIL (OXON)] 目前是量化金融風險管理（QFRM）課程主任，並且是香港中文大學統計系的全職教授，同時擔任中文大學科學院的助理院長（教育），以及哥倫比亞大學和德克薩斯大學達拉斯分校商學院的訪問教授。他在精算科學、應用數學、數據分析、工程、金融數學、運營管理和統計等領域發表了超過100篇頂尖期刊文章。他的研究項目CIBer在2023年第48屆日內瓦國際發明展中獲得銀獎。

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