1st Edition
Data Analytics for Finance Using Python
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 your 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 text provides a detailed summary of the book's content, highlighting its practical focus, real-world applications, and the benefits of reading the book. It's a great way to give potential readers a clear understanding of what the book has to offer.
Preface. About the Authors. Chapter 1. Stock portfolio investments management by Appling K Mean clustering. Chapter 2. Predicting stock price using the ARIMA model. Chapter 3. Stock investment strategy by using a logistic regression model. Chapter 4. Working capital management by applying the Naive Bayes model. Chapter 5. The random forest technique is a tool for stock trading decisions. Chapter 6. Stock management and decision tree technique for proper investment. Chapter 7. Descriptive statistics for stock risk analysis and its management. Chapter 8. Stock prediction using multiple regression model Chapter 9. F-test for stock risk assessment. Chapter 10 T-test for stock risk assessment Chapter 11. Z- test for stock risk assessment Chapter 12. Support vector machine learning model for stock prediction. Chapter 13 Stock risk analysis by using visualization. Chapter 14. LSTM for stock price prediction. Chapter 15 NPL for sentiments analysis for stock.
Biography
Dr. Nitin Jaglal Untwal is a distinguished scholar and educator in the field of finance, with a remarkable academic background and research expertise. Holding a doctorate in finance and master's degrees in related fields like Commerce /Management/Econometrics, he has established himself as a prominent authority in financial data analytics, technology management, and econometrics modeling. With over 11 years of experience in teaching and research, Dr. Untwal has published numerous papers in esteemed databases like Scopus and Web of Science, solidifying his reputation as a leading researcher in his field. Recognized as a postgraduate faculty member by the S.P.University of Pune since 2008, he has also achieved success in prestigious eligibility tests, including UGC-SET in Management and State Eligibility Tests Commerce. Additionally, he has completed a Faculty Development Program from the esteemed Indian Institute of Management, Kozhikode (IIM-K). Dr. Untwal's wealth of knowledge and experience make him an invaluable contributor to this literary work.
Dr. Utku Kose, a distinguished scholar in computer science and engineering, joins Dr. Untwal in this literary endeavor. With over 200 publications to his name, Dr. Kose has demonstrated his expertise in artificial intelligence, machine ethics, biomedical applications, and more. His impressive academic background and extensive research experience make him a significant addition to this authorship.
Together, Dr. Untwal and Dr. Kose bring their collective expertise and knowledge to this book, offering readers a unique and insightful perspective on their respective fields.
