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. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. 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.
The Strategies and Skills Learning and Development (SSLD) system is an action-oriented model for enabling clients in social work, health, mental health, and human services settings to address their needs and life goals. In Learning to Change Lives, author A. Ka Tat Tsang introduces SSLD's powerful framework and practice, which has been developed based on three decades of experience in psychotherapy, counselling, education, training, consultation, and community service. Learning to Change Lives provides detailed, step-by-step guidelines for SSLD intervention starting with engagement with the client, assessment, translating problems into intervention plans, systematic learning and development of appropriate strategies and skills. Key practice procedures are described clearly and illustrated by case examples, specific instructions, and sample worksheets. Aimed at clinical practitioners, mental health professionals, social workers, and other human service professionals, this book can be used as a manual by practitioners and as a textbook for courses and training programs.
The final destination of the Long March and center of the Chinese Communist Party's red bases, Yan'an acquired mythical status during the Maoist era. Though the city's significance as an emblem of revolutionary heroism has faded, today's Chinese still glorify Yan'an as a sanctuary for ancient cultural traditions. Ka-ming Wu's ethnographic account of contemporary Yan'an documents how people have reworked the revival of three rural practices--paper-cutting, folk storytelling, and spirit cults--within (and beyond) the socialist legacy. Moving beyond dominant views of Yan'an folk culture as a tool of revolution or object of market reform, Wu reveals how cultural traditions become battlegrounds where conflicts among the state, market forces, and intellectuals in search of an authentic China play out. At the same time, she shows these emerging new dynamics in the light of the ways rural residents make sense of rapid social change. Alive with details, Reinventing Chinese Tradition is an in-depth, eye-opening study of an evolving culture and society within contemporary China.
Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries. Describing the wide r
Systemic Functional Political Discourse Analysis: A Text-based Study is the first book which takes a comprehensive systemic functional perspective on political discourse to provide a complete, integrated, exhaustive, systemic and functional description and analysis. Based on the political discourses of the Umbrella Movement – the largest public protest in the history of Hong Kong, which occupies a unique political situation in the world: a post-colonial society like many other Asian societies and yet unlike the others, it is a Special Administrative Region of China. Though it enjoys a high degree of autonomy under the principle of ‘One Country, Two Systems’, it is still confined to being part of the ‘One Country’. The book demonstrates how a systemic functional approach can provide a comprehensive, thorough, and insightful analysis of the political discourse from four co-related and complementary approaches: contextual, discourse semantic, lexicogrammatical and historical. Apart from a thorough discussion of various systemic functional conceptions, it provides examples of various analyses from a SF perspective, including contextual parameters, registerial analysis, semantic discourse analysis, appraisal analysis, and discusses important issues in political discourse, including negotiation of self-identity, association of language, power and institutional role, and expression of ‘evidentiality’ and ‘subjectivity’. It is written not only for those who are interested in Hong Kong politics in general and political discourse in Hong Kong in particular, but also for those who work on political discourse analysis, and those who apply SFL to various other discourses such as mass media discourse, medical discourse, teaching discourse, etc. Last but not least, this book is also intended to provide a theoretical framework in discourse analysis from the systemic functional perspective for those who work in Cantonese and in other languages.
Hong Kong's Watershed: The 1967 Riots is the first English book that provides an account and critical analysis of the disturbances based on declassified files from the British government and recollection by key players during the events. The interviews with the participants, including Jack Cater, Liang Shangyuan, George Walden, Tsang Tak-sing, Tsang Yok-sing, and Hong Kong government officials, left irreplaceable records of oral history on the political upheaval. --The book analyses the causes and repercussions of the 1967 riots which are widely seen as a watershed of postwar history of Hong Kong. It depicts the prelude to the 1967 riots, including the Star Ferry riots in 1966, the leftist-instigated riots in Macau in 1966, and the major events leading to the disturbances, including the labour dispute at a plastic flower factory, the border conflict in Sha Tau Kok, bomb attacks and arson attacks on the office of British charge d'affaires in Beijing. --Gary Ka-wai Cheung has been a journalist since 1991. He worked as a reporter at Sing Tao Daily, Overseas Chinese Daily, Yazhou Zhoukan and South China Morning Post, covering fields ranging from politics, education and integration between Hong Kong and the mainland. He is currently an associate news editor at the South China Morning Post. --
This book examines the development of women in the Hong Kong Police Force (HKP) over the past 68 years, beginning from the early colonial years when calls to include women in law enforcement first emerged, to the recruitment of the first female sub-inspector in 1949, and through to the current situation where policewomen constitute 15% of the total HKP establishment. What accounts for these developments and what do they tell us about organisational culture, gender and colonial policing? This interdisciplinary work is relevant to fields including women’s studies, gender studies, policing studies, criminology, colonial history, sociology, and organisational studies, and will appeal to academics, students and lay readers interested in the development of women in policing.
Provides an exemplary model of community-based research on sexual and erotic attitudes and practices of gay men and middle-aged women in Hong Kong over a span of over fifteen years.
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