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Your Essential Guide to Quantitative Hedge Fund Investing

Aerial Dance A Guide to Dance with Rope and Harness

The ADHD Guide to Career Success Harness your Strengths Manage your Challenges

From the Post Enron Accounting Scandals to the Subprime Crisis A Financial History of the United States 2004–2006

The Ecology of Hedgerows and Field Margins

The Ecology of Hedgerows and Field Margins

Hedges and field margins are important wildlife habitats and deliver a range of ecosystem services and their value is increasingly recognised by ecologists. This book reviews and assesses the current state of research on hedgerows and associated field margins. With the intensification of agriculture in the second half of the last century field sizes were increased by amalgamation and the rooting out of hedges synthetic pesticide and inorganic fertiliser use increased and traditional methods of hedge management were largely abandoned. The book is split into two main sections. The first deals with definitions current and historic management the impact of pesticides the decline in hedge stock and condition and new approaches to hedge evaluation using remote sensing techniques. The second section explores the pollination and biological pest control benefits provided by hedges and field margins and examines the ecology of some of the major groups that are found in hedgerows and field margins: butterflies and moths carabid beetles mammals and birds. A case study on birds and invertebrates from a research farm managed as a commercial enterprise but which attempts to farm with wildlife in mind brings these themes together. A final chapter introduces the neglected area of hedges in the urban environment. The book will be of great interest to advanced students researchers and professionals in ecology agriculture wildlife conservation natural history landscape environmental and land management. | The Ecology of Hedgerows and Field Margins

GBP 44.99
1

Get Organized Digitally The Educator’s Guide to Time Management

Commodity Trade and Finance

Policy and Marketing Strategies for Digital Media

Design for Motion Fundamentals and Techniques of Motion Design

GBP 44.99
1

The Language of Symmetry

Urban Change and Citizenship in Times of Crisis Volume 2: Urban Neo-liberalisation

The Ethical Kaleidoscope Values Ethics and Corporate Governance

The Brain-Based Classroom Accessing Every Child’s Potential Through Educational Neuroscience

Stock Market Volatility

Spirituality A Guide for Clinicians

Agents and Multi-Agent Systems in Construction

Managing People in the Hybrid Workplace

Helping Couples and Families Navigate Illness and Disability An Integrated Approach

The Politics of International Political Economy

Applied Differential Equations with Boundary Value Problems

Writing and Reading Connections Bridging Research and Practice

Recovery Meaning-Making and Severe Mental Illness A Comprehensive Guide to Metacognitive Reflection and Insight Therapy

The English Militia in the Eighteenth Century The Story of a Political Issue 1660-1802

Machine Learning for Factor Investing Python Version

Machine Learning for Factor Investing Python Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise. | Machine Learning for Factor Investing Python Version

GBP 66.99
1