The Investment Handbook by
The all-you-need-to-know guide to Investment. The yearbook is packed with practical guidance on who to contact and how to get investment. The Investors Handbook is a comprehensive directory of venture capital firms, start-up investors and angel networks. Essential for any individual or business looking for investment, it will help entrepreneurs and business owners navigate the often complex world of sourcing finance. One of the main reasons start-ups fail is a lack of access to capital or accessing capital at the wrong time. Whatever stage a business is at, this book will help entrepreneurs and business owners understand and source in areas such as: Directory of investors When to fundraise How to meet investors Best people to connect and network with Pitching your ideas After and beyond investment A must-read book with contributions from investment experts David Bateman, Eileen Modral and Jonathan Reuvid. David Bateman, is a successful entrepreneur and has founded several businesses. He is an active investor and has spoken at many leading events and at university business schools including Oxford, Cambridge, Harvard, MIT, Wharton and Columbia. Eileen Modral, is an Investment Network Manager at Oxford Investment Opportunity Network (OION), one of the UK's most well-known and established angel networks. Jonathan Reuvid was formerly an economist for French oil company Total, and later an entrepreneur. He is a published author of a range of business titles, and was writer and editor for of 'Managing Business Risk', and 'The Investors Guide to the United Kingdom'.
Machine Learning for Finance by
A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine learning and how to put them to work in financial industries Clear explanation and expert discussion of how machine learning works, with an emphasis on financial applications Deep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learning Book Description Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas' experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What you will learn Apply machine learning to structured data, natural language, photographs, and written text How machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and more Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Dig deep into neural networks, examine uses of GANs and reinforcement learning Debug machine learning applications and prepare them for launch Address bias and privacy concerns in machine learning Who this book is for This book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.
The New Stock Market by
The U.S. stock market has been transformed over the last twenty-five years. Once a market in which human beings traded at human speeds, it is now an electronic market pervaded by algorithmic trading, conducted at speeds nearing that of light. High-frequency traders participate in a large portion of all transactions, and a significant minority of all trade occurs on alternative trading systems known as "dark pools." These developments have been widely criticized, but there is no consensus on the best regulatory response to these dramatic changes. The New Stock Market offers a comprehensive new look at how these markets work, how they fail, and how they should be regulated. Merritt B. Fox, Lawrence R. Glosten, and Gabriel V. Rauterberg describe stock markets' institutions and regulatory architecture. They draw on the informational paradigm of microstructure economics to highlight the crucial role of information asymmetries and adverse selection in explaining market behavior, while examining a wide variety of developments in market practices and participants. The result is a compelling account of the stock market's regulatory framework, fundamental institutions, and economic dynamics, combined with an assessment of its various controversies. The New Stock Market covers a wide range of issues including the practices of high-frequency traders, insider trading, manipulation, short selling, broker-dealer practices, and trading venue fees and rebates. The book illuminates both the existing regulatory structure of our equity trading markets and how we can improve it.
Undiversified : The Big Gender Short in Investment Management by
Diversification is a core principle of investing. Yet money managers have not applied it to their own ranks. Only around 10 percent of portfolio managers—the people most directly responsible for investing your money—are female, and the numbers are even worse at the ownership level. What are the causes of this underrepresentation, and what are its consequences—including for firms'and clients'bottom lines?In Undiversified, experienced practitioners Ellen Carr and Katrina Dudley examine the lack of women in investment management and propose solutions to improve the imbalance. They explore the barriers that subtly but effectively discourage women from entering and staying in the industry at each point in the pipeline. At the entry level, the lack of visible role models discourages students from considering the field, and those who do embark on an investment management career face many obstacles to retention and promotion. Carr and Dudley highlight the importance of informal knowledge about how to navigate career tracks, without which women are left at a disadvantage in an industry that lionizes confidence. They showcase a diverse constellation of successful female portfolio managers to demystify the profession.Drawing on wide-ranging research, interviews with prospective, current, and former industry practitioners, and the authors'own experiences, Undiversified makes a compelling case that increasing the number of women could help transform active investment management at a time when it is under threat from passive strategies and technological innovation.
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