Data Analytics for Finance Using Python

Häftad, Engelska, 2024

Av Nitin Jaglal Untwal, Utku Kose, Turkey) Kose, Utku (Suleyman Demirel U.

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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 dataBuild predictive models that drive investment decisionsOptimize portfolio performance using data-driven insightsStay ahead of the competition with cutting-edge data analytics techniquesWhether 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.

Produktinformation

Utgivningsdatum2024-12-30
Mått156 x 234 x 10 mm
Vikt453 g
FormatHäftad
SpråkEngelska
SerieAdvances in Digital Technologies for Smart Applications
Antal sidor121
FörlagTaylor & Francis Ltd
ISBN9781032618234

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Nitin Jaglal Untwal, PhD, 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, and 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 Test Commerce. Additionally, he has completed a Faculty Development Program from the Indian Institute of Management, Kozhikode (IIM-K). Dr. Untwal’s wealth of knowledge and experience make him an invaluable contributor to this book.Utku Kose, PhD, 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 contributor to this book.

1. Stock Investments Portfolio Management by Applying K-Means Clustering. 2. Predicting Stock Price Using the ARIMA Model. 3. Stock Investment Strategy Using a Logistic Regression Model. 4. Predicting Stock Buying and Selling Decisions by Applying the Gaussian Naïve Bayes Model Using Python Programming. 5. The Random Forest Technique Is a Tool for Stock Trading Decisions. 6. Applying Decision Tree Classifier for Buying and Selling Strategy with Special Reference to MRF Stock. 7. Descriptive Statistics for Stock Risk Assessment. 8. Stock Investment Strategy Using a Regression Model. 9. Comparing Stock Risk Using F-Test. 10. Stock Risk Analysis Using t-Test. 11. Stock Investment Strategy Using a Z-Score. 12. Applying a Support Vector Machine Model Using Python Programming. 13. Data Visualization for Stock Risk Comparison and Analysis. 14. Applying Natural Language Processing for Stock Investors Sentiment Analysis. 15. Stock Prediction Applying LSTM.