With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
The introduction of elections to district advisory bodies during the early 1980s was expected to improve the public delivery of services. However, as time passed, electoral politics led to party politics, elite fragmentation and political struggles. Politicization and hyper-politicization in the Hong Kong Special Administrative Region has brought about a fluctuating pattern between administrative recentralization, the Tsang administration’s attempts at decentralization, and the post-2019 administrative recentralization. The purpose of this book is to study the intertwining relationship between district administration and electoral politics. It also examines the political transformation of District Councils after the promulgation of the National Security Law in late June 2020. Written by experts in the field, this book is a good reference source for readers interested in district elections, politics, and administration in Hong Kong.
Education reform has become a highly political issue in the Hong Kong Special Administrative Region (HKSAR) since the transfer of sovereignty to the People’s Republic of China (PRC). Lo and Hung focus on the political struggles among stakeholders, including the government of Hong Kong, the Catholic Church, parents, students, teachers, the central authorities of Beijing, and even the bureaucratic politics between Beijing, the Hong Kong government and the Examination Authority. They examine the key elements of education reform in the HKSAR, including language and curriculum reform, national security education, civic and patriotic education, the rise of the pro-Beijing education elites and interest groups, and the revamp of examination questions and examination authority. The entire education reform in the HKSAR has pushed the Hong Kong education system toward a process of mainlandization, making Hong Kong’s education system more similar to the mainland system with emphasis on political "correctness" in the understanding of Chinese national security, history and culture. Highlighting the political struggles among the various stakeholders, this book is essential for scholars of Hong Kong and China, especially those with an interest in the relationship between education and politics.
This book explores the dynamics of China’s new united front work in Hong Kong. Mainland Chinese penetrative politics can be seen in the activities of local pro-Beijing political parties, clans and neighborhood associations, labor unions, women and media organizations, district federations, and some religious groups. However, united front work in the educational and youth sectors of civil society has encountered strong resistance because many Hong Kong people are post-materialistic and uphold their core values of human rights, the rule of law and transparency. China’s new united front work in Hong Kong has been influenced by its domestic turn toward “hard” authoritarianism, making Beijing see Hong Kong’s democratic activists and radicals as political enemies. Hong Kong’s “one country, two systems” is drifting toward “one country, two mixed systems” with some degree of convergence. Yet, Taiwan and some foreign countries have seen China’s united front work as politically destabilizing and penetrative. This book will be of use to scholars, journalists, and observers in other countries seeking to reckon with Chinese influence.
This Brief is the first comprehensive coverage of law and policy intended to protect built heritage in Hong Kong. Although characterized as a city of skyscrapers and modernity, Hong Kong has a rich cultural heritage and a surprisingly rich built heritage. The text considers what “built heritage” means in Hong Kong and what built heritage there is in Hong Kong. It introduces general readers, practitioners and students to the issues facing built heritage protection and how such protection usually develops in a modern city. In particular, it considers the problems and disputes that provided the focus for development of law and policy in Hong Kong, especially the legacy of 150 years as a British colony and the consequent identification as a “borrowed” and “temporary” place. The Brief considers how effective law and policy has been in protecting built heritage under the colonial and post-colonial administrations- their successes and failures. These include the Kowloon-Canton Railway Station, the Antiquities and Monuments Ordinance, reclamation of Victoria Harbour, violent protests at Queen’s Pier, and the introduction of mandatory heritage impact assessments for government projects. The text concludes noting recent successes, which may indicate a brighter future for the protection of Hong Kong’s built heritage.
With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
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