Home eBooks Download › data analytics for finance using python

Data Analytics For Finance Using Python

Download Data Analytics For Finance Using Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Analytics For Finance Using Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Data Analytics For Finance Using Python

DOWNLOAD
Author : Nitin Jaglal Untwal
language : en
Publisher: CRC Press
Release Date : 2025-01-15

Data Analytics For Finance Using Python written by Nitin Jaglal Untwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-15 with Computers categories.

Unlock the power of data analytics in finance with this comprehensive guide. Data Analytics for Finance Using Python is your key to unlocking the secrets of the financial markets. In this book, you’ll discover how to harness the latest data analytics techniques, including machine learning and inferential statistics, to make informed investment decisions and drive business success. With a focus on practical application, this book takes you on a journey from the basics of data preprocessing and visualization to advanced modeling techniques for stock price prediction. Through real-world case studies and examples, you’ll learn how to: Uncover hidden patterns and trends in financial data Build predictive models that drive investment decisions Optimize portfolio performance using data-driven insights Stay ahead of the competition with cutting-edge data analytics techniques Whether you’re a finance professional seeking to enhance your data analytics skills or a researcher looking to advance the field of finance through data-driven insights, this book is an essential resource. Dive into the world of data analytics in finance and discover the power to make informed decisions, drive business success, and stay ahead of the curve. This book will be helpful for students, researchers, and users of machine learning and financial tools in the disciplines of commerce, management, and economics.

Financial Data Analysis Using Python

DOWNLOAD
Author : Dmytro Zherlitsyn
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2025-05-20

Financial Data Analysis Using Python written by Dmytro Zherlitsyn and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-20 with Computers categories.

This book will introduce essential concepts in financial analysis methods & models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will also help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using the Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. FEATURES • Illustrates financial data analysis using Python data science libraries & techniques • Uses Python visualization tools to justify investment and trading strategies • Covers asset pricing & portfolio management methods with Python

Data Analytics In Finance

DOWNLOAD
Author : Huijian Dong
language : en
Publisher: CRC Press
Release Date : 2025-04-30

Data Analytics In Finance written by Huijian Dong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-30 with Computers categories.

Data Analytics in Finance covers the methods and application of data analytics in all major areas of finance, including buy-side investments, sell-side investment banking, corporate finance, consumer finance, financial services, real estate, insurance, and commercial banking. It explains statistical inference of big data, financial modeling, machine learning, database querying, data engineering, data visualization, and risk analysis. Emphasizing financial data analytics practices with a solution- oriented purpose, it is a “one-stop-shop” of all the major data analytics aspects for each major finance area. The book paints a comprehensive picture of the data analytics process including: Statistical inference of big data Financial modeling Machine learning and AI Database querying Data engineering Data visualization Risk analysis Each chapter is crafted to provide complete guidance for many subject areas including investments, fraud detection, and consumption finance. Avoiding data analytics methods widely available elsewhere, the book focuses on providing data analytics methods specifically applied to key areas of finance. Written as a roadmap for researchers, practitioners, and students to master data analytics instruments in finance, the book also provides a collection of indispensable resources for the readers’ reference. Offering the knowledge and tools necessary to thrive in a data-driven financial landscape, this book enables readers to deepen their understanding of investments, develop new approaches to risk management, and apply data analytics to finance.

Python For Finance

DOWNLOAD
Author : Yves Hilpisch
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-12-11

Python For Finance written by Yves Hilpisch and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-11 with Computers categories.

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

Data Analytics Using Python

DOWNLOAD
Author : Mahmoud Ahmad Al-Khasawneh
language : en
Publisher: Xoffencer international book publication house
Release Date : 2024-08-07

Data Analytics Using Python written by Mahmoud Ahmad Al-Khasawneh and has been published by Xoffencer international book publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-07 with Computers categories.

Descriptive statistics are used to identify the fundamental characteristics of data in a research study. Simply summarized information about the sample and measurements is provided. Descriptive statistics provide information about the components and dissemination of values in single or multiple data set concisely. The classical illustrative statistics allow experts to get a quick sense of the central inclination and degree of diffusion of values in a dataset with a single glance. They are useful in gaining an understanding of data distribution as well as in comparing different data distributions, It is frequently necessary for human geographers to take into consideration the locational citations of the data they are working with. Using spatial descriptive statistics, analysts can determine the central propensity and variation of data in a given geographic area or region. The two types of illustrative analysis are mutually supportive of one another. Experts can research the geographic phenomena with which they are involved by combining both statistics and mathematics. Even though descriptive statistics are straightforward concepts in statistical assessment, they are essential and beneficial in today's world of massive amounts of data. The performance and efficacy of descriptive analysis should not be overshadowed in the face of ever-increasing huge quantities of data being generated continuously and distributed via the Internet. Descriptive statistics are characterized by inferential analysis in most cases. When you use descriptive statistics, you are merely explaining what is or what the information reveals about something. When using inferential analysis, you are attempting to draw conclusions that are not based solely on the available data. For example, we use inferential analysis to try to infer what the general public might think based on a sample of data. Alternatively, we use inferential analysis to make decisions about the likelihood that a difference between groups observed in this study is a dependable difference or one that could have occurred by chance. As a result, we use inferential analysis to conclude more general conditions from our data, whereas we use descriptive analysis to simply describe what is happening in our data. Descriptive statistics are used to present quantitative explanations in a manageable format. In a research study, we may have a large number of measures. Alternatively, we can quantify a huge number of participants using any measure.

Data Analytics For Management Banking And Finance

DOWNLOAD
Author : Foued Saâdaoui
language : en
Publisher: Springer Nature
Release Date : 2023-09-19

Data Analytics For Management Banking And Finance written by Foued Saâdaoui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-19 with Business & Economics categories.

This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance. It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies. The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks

Python For Finance

DOWNLOAD
Author : Dmytro Zherlitsyn
language : en
Publisher: BPB Publications
Release Date : 2024-07-30

Python For Finance written by Dmytro Zherlitsyn and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with Computers categories.

DESCRIPTION Python's intuitive syntax and beginner-friendly nature makes it an ideal programming language for financial professionals. It acts as a bridge between the world of finance and data analysis. This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. KEY FEATURES ● Comprehensive guide to Python for financial data analysis and modeling. ● Practical examples and real-world applications for immediate implementation. ● Covers advanced topics like regression, Machine Learning and time series forecasting. WHAT YOU WILL LEARN ● Learn financial data analysis using Python data science libraries and techniques. ● Learn Python visualization tools to justify investment and trading strategies. ● Learn asset pricing and portfolio management methods with Python. ● Learn advanced regression and time series models for financial forecasting. ● Learn risk assessment and volatility modeling methods with Python. WHO THIS BOOK IS FOR This book is designed for financial analysts and other professionals interested in the financial industry with a basic understanding of Python programming and statistical analysis. It is also suitable for students in finance and data science who wish to apply Python tools to financial data analysis and decision-making. TABLE OF CONTENTS 1. Getting Started with Python for Finance 2. Python Tools for Data Analysis: Primer to Pandas and NumPy 3. Financial Data Manipulation with Python 4. Exploratory Data Analysis for Finance 5. Investment and Trading Strategies 6. Asset Pricing and Portfolio Management 7. Time Series Analysis and Financial Data Forecasting 8. Risk Assessment and Volatility Modelling 9. Machine Learning and Deep Learning in Finance 10. Time Series Analysis and Forecasting with FB Prophet Library Appendix A: Python Code Examples for Finance Appendix B: Glossary Appendix C: Valuable Resources

Data Science And Risk Analytics In Finance And Insurance

DOWNLOAD
Author : Tze Leung Lai
language : en
Publisher: CRC Press
Release Date : 2024-10-02

Data Science And Risk Analytics In Finance And Insurance written by Tze Leung Lai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Business & Economics categories.

This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.

Python For Finance

DOWNLOAD
Author : Yves Hilpisch
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-12-11

Financial Data Analytics With Machine Learning Optimization And Statistics

DOWNLOAD
Author : Sam Chen
language : en
Publisher: John Wiley & Sons
Release Date : 2024-10-21

Financial Data Analytics With Machine Learning Optimization And Statistics written by Sam Chen and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-21 with Business & Economics categories.

An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.

Data Analytics For Finance Using Python

Recent Posts