Search results for "python for finance 2025"
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R 1,160.00
Python for Accounting and Finance
Python for Accounting and Finance: A Mind-Mapping Approach is an innovative textbook written for accounting and finance students and professionals with no prior coding experience. It introduces Python programming through a mind mapping approach that presents complex concepts in a clear and structured way to support understanding and retention.The book is organised into four parts: Python Basics, Data Analysis and Visualisation, Automation, and Machine Learning. It places programming within practical accounting and finance contexts so that learners can see the direct application of coding in their field. The textbook uses mind maps as a core instructional method. These visual diagrams show programming concepts and their connections, helping learners to understand, organise and recall information, particularly those who learn through visual representation.Enhanced with Google Colab notebooks, this book creates a highly supportive learning experience, equipping students of accounting and finance with essential programming skills for their speciality.
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Python for Accounting and Finance
This book is a comprehensive guide to the application of Python in accounting, finance, and other business disciplines. This book is more than a Python tutorial; it is an integrative approach to using Python for practical research in these fields. The book begins with an introduction to Python and its key libraries. It then covers real-world applications of Python, covering data acquisition, cleaning, exploratory data analysis, visualization, and advanced topics like natural language processing, machine learning, predictive analytics, and deep learning. What sets this book apart is its unique blend of theoretical knowledge and real-world examples, supplemented with ready-to-use code. It doesn't stop at the syntax; it shows how to apply Python to tackle actual analytical problems. The book uses case studies to illustrate how Python can enhance traditional research methods in accounting and finance, not only allowing the reader to gain a firm understanding of Python programming but also equipping them with the skills to apply Python to accounting, finance, and broader business research. Whether you are a PhD student, a professor, an industry professional, or a financial researcher, this book provides the key to unlocking the full potential of Python in research.
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Tidy Finance with Python
This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with Python, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using pandas, numpy, and plotnine. Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques.Key Features:Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance.Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copying and pasting the code we provide.A full-fledged introduction to machine learning with scikit-learn based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods.We show how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat, including detailed explanations of the most relevant data characteristics.Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises.
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R 581.00
Introduction To Python For Quantitative Finance: From Scratch To Productivity
Written by a data scientist with decades of practical experience on some of the most challenging datasets, this book caters to both novices and professionals working in the fields of data science and machine learning. It enables novices to enter these fields with confidence and ease. There are no formal prerequisites. An introduction to Python is included. The book then proceeds to cover some of the language's most advanced features. The mathematical foundations that follow are meticulously explained. The discussion then proceeds to a practical consideration of data science and machine learning. The material is delivered through case studies developed on real datasets. The reader is invited to follow these case studies, and so develop experience in working with real data. Numerous solved exercises make this work suitable for self-study. The contents of this book have been taught by the author to students of different age groups and levels of experience at Imperial College London and at the Thalesians Intensive Summer School in Artificial Intelligence over the years.
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Stochastic Finance with Python
Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You’ll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You’ll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.What You Will LearnUnderstand applied probability and statistics with financeDesign forecasting models of the stock price with the stochastic process, Monte-Carlo simulation.Option price estimation with both risk-neutral probabilistic and PDE-driven approach.Use Object-oriented Python to design financial models with reusability.Who This Book Is For Data scientists, quantitative researchers and practitioners, software engineers and AI architects interested in quantitative finance
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AI FINANCE: FINANCIAL MODELING - AI DRIVER® SERIES
This book focuses on bridging the gap between the widespread use of artificial intelligence tools and the absence of systematic training in finance education. It introduces the DRIVER® methodology, a structured six stage framework — Define&Discover, Represent, Implement, Validate, Evolve, and Reflect — designed to guide readers in professional AI collaboration while maintaining rigorous foundations in financial theory.Validated in classroom settings at Purdue University, where students described it as their most useful course, the DRIVER® approach equips learners with both conceptual depth and practical skills. Readers progressively build a portfolio of AI powered financial models, ranging from portfolio optimization to derivatives pricing, while cultivating the systematic thinking that distinguishes professionals from casual experimenters.The text is designed for a broad audience: finance students seeking to strengthen their analytical toolkit, professionals aiming to advance their modeling expertise, and institutions implementing AI transformation strategies. Importantly, no prior programming experience is required. The curriculum introduces Python fundamentals before advancing to sophisticated financial applications, ensuring accessibility while delivering immediate real world value.By combining financial theory with structured AI methodology, this book provides a transformative resource for modern finance education and practice. It reshapes how readers approach modeling, risk analysis, and decision making in an AI driven era, offering a comprehensive foundation for the next generation of financial professionals.
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Quantitative Research Methods in Corporate Finance
Focused on empirical methods and their applications to corporate finance, this innovative text equips students with the knowledge to analyse and critically evaluate quantitative research methods in corporate finance, and conduct computer-aided statistical analyses on various types of datasets. Chapters demonstrate the application of basic econometric models in corporate finance (as opposed to derivations or theorems), backed up by relevant research. Alongside practical examples and mini case studies, computer lab exercises enable students to apply the theories of corporate finance and make stronger connections between theory and practice, while developing their programming skills. All of the Stata code is provided (with corresponding Python and R code available online), so students of all programming abilities can focus on understanding and interpreting the analyses.
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Python in Excel
Seasoned Experts Liam Bastick and Kathryn Newitt unveil a groundbreaking guide to mastering data analysis and automation with Python in Excel Python in Excel: Unlocking powerful data analysis and automation solutions is a hands-on, practical guide that leverages the authors’ decades of expertise to teach readers how to harness the newly integrated Python capabilities within Microsoft Excel, mapping familiar Excel concepts to Python foundations. Covering essential topics such as Python syntax, data structures, data analysis with pandas, visualization, and the use of AI tools like Copilot, the book empowers professionals to elevate their data analysis and automation skills. Readers will gain the ability to perform advanced forecasting, build predictive models, and streamline workflows, making them more efficient and competitive in today’s data-driven workplace. By reading this book, you will: Understand the fundamentals of Python programming directly within ExcelMap your existing Excel skills to Python concepts for easier learningAutomate repetitive tasks and workflows Analyze and visualize data with powerfulPython libraries like pandas Build advanced forecasting and predictive models without leaving ExcelExplore and manipulate data efficiently using Python’s concise syntaxIntegrate AI tools such as Copilot to assist with coding and analysisConnect Excel with external data sources using Python and Power QueryDevelop custom solutions for business modeling and data scienceCreate a roadmap for further study and mastery of Python in Excel
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