Find The Portable MBA in Economics by Philip K. y. Young in Paperback and other formats in Business & Economics > Economics - Theory.

Looking to pursue an MBA? The journey can be daunting, but fear not - this book is here to guide you every step of the way. With so many business schools and programs to choose from, it's important to know what you're looking for. That's where this book comes in. The authors help you understand the differences between full-time, modular, online, and executive MBAs, and provide expert advice on selecting the right program for your unique needs. But that's not all – the book also shows you how to make the most of your MBA experience, helping you to develop knowledge, skills, and personal growth. It explores the benefits of alumni associations and shows you how an MBA can be a game-changer for your career. And here's the best part - you don't need to spend a fortune on a Harvard or Stanford MBA to achieve your goals. The book introduces you to a range of high-quality MBA programs across the globe, from Europe to China, India to Latin America, that offer top-notch alternatives that won't break the bank. Whether you're a senior manager, entrepreneur, or fresh out of college, this book is the ultimate resource for anyone seeking to unlock their full potential with an MBA.

This book aims to provide first-hand information for readers concerned with green finance and sustainable development by summarizing the unique features, policies and practical innovations of green finance development in China and hoping to provide China's experience in the development of green finance for more regions. The global economy is currently facing historic opportunities that brings by a new round of technology, energy and industrial revolution. The new growth path of "green and low-carbon" development has become the main direction of the transformation of global economic development. What's China's green finance strategy? How does China deliver on the goal? This book builds an overview of China's green finance and sheds light on sustainable development topics from finance perspective.

This book presents an ethical theory for financial transactions that underpins the stability of modern economies. It combines elements from history, ethics, economics and mathematics to show how these combined can be used to develop a pragmatic theory of financial markets. Written in three sections; section one examines the co-evolution of finance and mathematics in an ethical context by focusing on three periods: pre-Socratic Greece, Western Europe in the thirteenth century and North-western Europe in the seventeenth century to demonstrate how the historical development of markets and finance were critical in the development of European ideas of science and democracy. Section two interprets the evidence presented in section one to provide examples of the norms reciprocity, sincerity and charity and introduce the pragmatic theory. Section three uses the pragmatic theory to interpret recent financial crises, address emergent phenomena and relate the theory to alternative contemporary theories of markets. Presenting a unique synthesis of mathematical and behavioural approaches to finance this book provides explicit ethical guidance that will be of interest to academics and practitioners alike.

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesianand frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likelyto emerge as important methodologies for machine learning in finance.