Nitin Jaglal Untwal, Utku Kose

Data Analytics for Finance Using Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 137
• Erscheinungstermin: 15. Januar 2025
• Englisch
• ISBN-13: 9781040264003
• Artikelnr.: 72291007

Autorenporträt

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.

Inhaltsangabe

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.