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Portfolio Management : Delivering on Strategy
Portfolio management is becoming the ‘must have’ for organizations to prosper and survive in this decade and beyond.No longer can the organizational focus be one of following best and repeatable practices as resource limitations mean only those programs, projects, and operational work that add business value can and should be pursued.Executives are focusing on strategic ability and managing complexity, which can only be done through a disciplined portfolio process in ensuring the best mix of programs, projects, and operational work is under way.In turn, the portfolio is constantly in flux as difficult decisions are made if a project, for example, is no longer contributing to business value and providing benefits and should be terminated to reallocate resources to one of higher priority.Commitment to this difficult approach is necessary at all levels, and communication is required so everyone knows how their work contributes to the organization’s strategic goals and objectives. Portfolio Management: Delivering on Strategy, Second Edition focuses on the benefits of portfolio management to the organization.Its goal is to provide senior executives a view on how portfolio management can deliver organizational strategy.The emphasis is on the specific aspects within the portfolio management discipline and how each aspect should be managed from a business perspective and not necessarily from a portfolio management perspective.Highlights of the book include:Agile portfolio management Delivering organizational value Portfolio management and uncertainty Portfolio governance Marketing a portfolio Portfolio management success Starting with a review of the project portfolio concept and its development, this book is a reference for executives and practitioners in the field, as well as a students and researchers studying portfolio management.
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Assessment as Learning : Maximising Opportunities for Student Learning and Achievement
Based on a solid theoretical basis of assessment-as-learning and updated empirical evidences, this timely book significantly expands the existing scope of assessment-as-learning typically developed in Western contexts. This edited volume updates theoretical and empirical advances in assessment-as-learning in complex learning processes, brought together by an international panel of authors.The contributors provide a wide range of practical ways to harness the power of assessment-as-learning to make it work more effectively not only in the classroom, but also across other achievement-related situations (e.g. examinations, learning processes before and after classes). Assessment as Learning provides a deep contemporary insight into the field of formative assessment, and brings much-needed international perspectives to complement the current Western-focused research.This is a valuable contribution to the discussion, and provides useful insight for researchers in Education.
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Deep Learning in Quantitative Finance
The complete and practical guide to one of the hottest topics in quantitative finance Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts.Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance.Inside, you’ll find over ten chapters which apply deep learning to multiple use cases across quantitative finance.You’ll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text.Readers will be able to work through these examples directly.This book is a complete resource on how deep learning is used in quantitative finance applications.It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics.You’ll also learn about the most important software frameworks.The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging.The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniquesOffers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learningDemonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion websiteIntroduces the most important software frameworks for applying deep learning within finance This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.
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Applied Fundamentals in Finance : Portfolio Management and Investments
This textbook provides a comprehensive introduction to portfolio management and investments.Focusing on four core areas – portfolio management, equities, bonds, and derivatives – it is primarily intended for undergraduate and graduate students alike.However, it will also benefit practitioners working in the fields of financial analysis and portfolio management and professionals who aspire to such professional activities in the financial industry.To ensure its high practical relevance, the book includes a host of case studies and examples from real-world practice, mainly from the German and Swiss financial markets.Additionally, the book shows how to implement the models in Microsoft Excel.
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What are the learning opportunities and learning resources for beginners in Godot C?
Learning opportunities for beginners in Godot C include online tutorials, documentation, and community forums. The official Godot website offers comprehensive tutorials and documentation for beginners to learn the basics of Godot C. Additionally, there are many community forums and online communities where beginners can ask questions, seek help, and learn from more experienced developers. These resources provide a supportive environment for beginners to learn and grow their skills in Godot C.
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Do you have a good strategy for learning vocabulary?
Yes, I have a good strategy for learning vocabulary. I like to use flashcards to review and memorize new words, and I also try to use the words in sentences or conversations to help solidify their meaning in my mind. Additionally, I make an effort to read and listen to a variety of materials in the language I am learning to expose myself to new vocabulary in context. Finally, I regularly review and practice using the words I have learned to ensure that they stick in my long-term memory.
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What are good learning techniques for time management?
Good learning techniques for time management include creating a daily or weekly schedule, prioritizing tasks based on importance and deadlines, breaking down large tasks into smaller, more manageable ones, and setting specific goals and deadlines for each task. Additionally, using tools such as calendars, to-do lists, and time tracking apps can help in staying organized and on track. It's also important to regularly review and adjust your schedule as needed to ensure optimal time management.
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Is distance learning in online marketing management worthwhile?
Distance learning in online marketing management can be worthwhile for individuals who are looking to enhance their skills and knowledge in the field without the constraints of traditional classroom settings. It offers flexibility, convenience, and the ability to learn at one's own pace. Additionally, online marketing management is a rapidly evolving field, and distance learning can provide access to the latest trends and tools in the industry. However, it is important to choose a reputable and accredited program to ensure the quality of education and the value of the certification.
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Machine Learning and AI in Finance
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events.During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems.Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables.The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions.This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features.Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
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Behavioral Finance and Your Portfolio : A Navigation Guide for Building Wealth
Become a more strategic and successful investor by identifying the biases impacting your decision making. In Behavioral Finance and Your Portfolio, acclaimed investment advisor and author Michael M.Pompian delivers an insightful and thorough guide to countering the negative effect of cognitive and behavioral biases on your financial decisions.You’ll learn about the “Big Five” behavioral biases and how they’re reducing your returns and leading to unwanted and unnecessary costs in your portfolio. Designed for investors who are serious about maximizing their gains, in this book you’ll discover how to: ?Take control of your decision-making—even when challenging markets push greed and fear to intolerable levels ?Reflect on how to make investment decisions using data-backed and substantiated information instead of emotion and bias ?Counter deep-seated biases like loss aversion, hindsight and overconfidence with self-awareness and hard facts ?Identify your personal investment psychology profile, which you can use to inform your future financial decision making Behavioral Finance and Your Portfolio was created for individual investors, but will also earn a place in the libraries of financial advisors, planners and portfolio managers who are determined to counteract the less principled and data-driven aspects of their decision making.
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Flipped Learning in Physical Education : Opportunities and Applications
This is the first book to introduce flipped learning in the context of physical education.It is a timely exploration of pedagogical approaches that draw on digital technologies that can allow learning online and at a distance to support important learning time for physical activity. The book discusses the role of online and digital technology in education, and physical education more speci?cally, and examines the key features that define flipped learning, its boundaries, and its format.Drawing on modern learning theories, the book explains why educators and practitioners may choose to use flipped learning and how the approach can improve physical activity opportunities.It also considers the challenges and the guiding principles involved in implementing flipped learning in different countries, cultures, and contexts. Full of practical guidance, and drawing on cutting-edge research, this book is invaluable reading for all students, researchers, pre-service and in-service teachers, and coaches working in physical education or youth sport.
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Deep Learning for Finance : Creating Machine & Deep Learning Models for Trading in Python
Deep learning is rapidly gaining momentum in the world of finance and trading.But for many professional traders, this sophisticated field has a reputation for being complex and difficult.This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses.By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading.This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning modelsExplore the details behind reinforcement learning and see how it's used in time seriesUnderstand how to interpret performance evaluation metricsExamine technical analysis and learn how it works in financial marketsCreate technical indicators in Python and combine them with ML models for optimizationEvaluate the models' profitability and predictability to understand their limitations and potential
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What training opportunities are available for people with learning difficulties?
There are various training opportunities available for people with learning difficulties, including vocational training programs, job coaching services, and life skills training. These programs are designed to help individuals develop the necessary skills and knowledge to succeed in the workplace and in their daily lives. Additionally, there are specialized training programs offered by organizations and community centers that cater specifically to individuals with learning difficulties, providing them with the support and resources they need to reach their full potential. Overall, these training opportunities aim to empower individuals with learning difficulties to achieve their goals and lead fulfilling lives.
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Why deep learning compared to machine learning?
Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.
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Is distance learning as recognized as traditional learning?
Distance learning is becoming increasingly recognized and accepted as a legitimate form of education, especially with the advancements in technology and the widespread availability of online courses. However, traditional learning still holds a higher level of recognition and credibility in many circles, such as certain industries or academic institutions. Ultimately, the recognition of distance learning versus traditional learning can vary depending on the context and the perceptions of individuals or organizations.
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Is distance learning worse than on-campus learning?
The effectiveness of distance learning versus on-campus learning depends on individual preferences, learning styles, and the specific course or program. Distance learning can offer flexibility and convenience for those with busy schedules or other commitments, while on-campus learning may provide more opportunities for in-person interaction and hands-on experiences. Both modalities have their own set of advantages and disadvantages, and the quality of the learning experience ultimately depends on the resources, support, and engagement available to the student in either setting.
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