Finance and the finance industry Books

Book SynopsisA guide to advances in machine learning for financial professionals, with working Python codeKey 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 DescriptionMachine 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 forThis 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.Table of ContentsTable of Contents Neural Networks and Gradient-Based Optimization Applying Machine Learning to Structured Data Utilizing Computer Vision Understanding Time Series Parsing Textual Data with Natural Language Processing Using Generative Models Reinforcement Learning for Financial Markets Privacy, Debugging, and Launching Your Products Fighting Bias Bayesian Inference and Probabilistic Programming

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Book SynopsisUse modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey Features Explore unique recipes for financial data processing and analysis with Python Apply classical and machine learning approaches to financial time series analysis Calculate various technical analysis indicators and backtest trading strategies Book DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learn Preprocess, analyze, and visualize financial data Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models Uncover advanced time series forecasting algorithms such as Meta's Prophet Use Monte Carlo simulations for derivatives valuation and risk assessment Explore volatility modeling using univariate and multivariate GARCH models Investigate various approaches to asset allocation Learn how to approach ML-projects using an example of default prediction Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet Who this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.Table of ContentsTable of Contents Acquiring Financial Data Data Preprocessing Visualizing Financial Time Series Exploring Financial Time Series Data Technical Analysis and Building Interactive Dashboards Time Series Analysis and Forecasting Machine Learning-Based Approaches to Time Series Forecasting Multi-Factor Models Modelling Volatility with GARCH Class Models Monte Carlo Simulations in Finance Asset Allocation Backtesting Trading Strategies Applied Machine Learning: Identifying Credit Default Advanced Concepts for Machine Learning Projects Deep Learning in Finance

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Book SynopsisResearch is an ever-increasing vital feature of academic accounting and finance, but few researchers are ever offered guidance on the research process. Research Method & Methodology in Finance & Accounting is the only book of its kind as it focuses on academic rather than student research. The text provides a clear, well-written guide to research in these subjects. This essential book, for both students and lecturers, has now been fully revised and updated, to include all of the advances made on the subject in the last 10 years.Trade ReviewPreface to the Second Edition Introduction PART 1 - TRADITIONS OF RESEARCH IN FINANCE AND ACCOUNTING 1. The Philosophy of Financial of Financial Research 2. Alternative Philosophies of Accounting Research 3. Traditions of Research in Finance 4. Traditions of Research in Management Accounting 5. Traditions of Research in Financial Accounting PART 2 - METHODS OF RESEARCH 6. Methods of Empirical Research 7. Interpretation of Positive Research 8. Methods of Case Study Research 9. The Research Output PART 3 - TECHNIQUES OF CRITICAL ANALYSIS 10. The Analysis of Literature 11. The Critical Analysis of Reasoning 12. Formal Methods of Analysis Conclusion Bibliography IndexTable of ContentsIntroduction. PART 1: TRADITIONS OF RESEARCH IN FINANCE AND ACCOUNTING. 1. The Philosophy of Financial of Financial Research. 2. Alternative Philosophies of Accounting Research. 3. Traditions of Research in Finance. 4. Traditions of Research in Management Accounting. 5. Traditions of Research in Financial Accounting. PART 2: METHODS OF RESEARCH. 6. Methods of Empirical Research. 7. Interpretation of Positive Research. 8. Methods of Case Study Research. 9. The Research Output. PART 3: TECHNIQUES OF CRITICAL ANALYSIS. 10. The Analysis of Literature. 11. The Critical Analysis of Reasoning. 12. Formal Methods of Analysis. Conclusion. Bibliography. Index.

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Book Synopsis

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Book Synopsis

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