Financial Data Analysis (Theory)

Paper Code: 
24DFSG813
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

This course will enable students to utilize data science techniques for financial decision-making, including data processing, visualization, analysis, and modeling, enhancing their ability to evaluate investments effectively and ethically.

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

 

24DFSG 813

Financial Data Analysis

(Theory)

CO295: Examine the tools of financial data and classify the data in finance and costing

CO296: Examine Processing, organising, cleaning and validation of financial Data

CO297: Compare different Graphical and Visualization tools to present financial and non-financial data

CO298: Analyse time series visualization and trend analysis techniques

CO299: Examine Financial Modelling, forecasting and Investment Analysis techniques

CO300: Contribute effectively in course-specific interaction

 

Approach in teaching: Interactive Lectures, Discussion, Tutorials, Practical cases, Power point presentation.    

 Learning activities for the students:                              Self learning assignments,  Seminar presentation.

CA test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentations.

 

12.00
Unit I: 
Data Science for Financial Decision-Making

• Meaning, Nature, Properties and Scope of Data
• Types of Data in Finance and Costing
• Digitalization of data and Information
• Transformation of data to decision relevant information
• Communication of Information for quality decision Making
• Professional Scepticism regarding data
• Ethical use of data and information
• Data Processing, organisation, cleaning and validation :
• Development of data processing
• Functions of data processing
• Data organisation and Distribution
• Data cleaning and validation

12.00
Unit II: 
Data presentation: Visualization and Graphical Presentation

• Data Visualization of Financial and Non-Financial Data
• Objective and Function of data presentation
• Data Presentation Architecture
• Dashboard, graphs, Diagrams, Tables Report Design
• Tools and Techniques of Visualization and Graphical Presentation

12.00
Unit III: 
Data Analysis and Modelling

• Process Benefits and Types of Data Analysis
• Data mining and Implementation of Data Mining
• Analytics and Modelling
• Standards of data tagging and Reporting
• Cloud Computing, Business Intelligence, Artificial Intelligence, Robotic Process Automation and Machine Learning
• Model vs Data-Driven Decision Making

12.00
Unit IV: 
Time Series Analysis

• Understanding time series data and its characteristics
• Time series visualization and trend analysis
• Seasonality and cyclical patterns
• Introduction to forecasting techniques

12.00
Unit V: 
Investment Analysis and Evaluation

• Introduction to portfolio theory
• Measuring Risk and return
• Technical analysis and indicators
• Sentiment analysis techniques for investor sentiment measurement

Essential Readings: 

• Albright, S. C., Winston, W. L., & Zappe, C. (2019). Data Analysis and Decision Making (6th ed.). Cengage Learning.
• Chen, C., & Zhang, L. (2020). Financial Data Analysis with Python: Analyzing, Visualizing and Modeling Financial Data with Python. Apress.
• Montgomery, D. C., Peck, E. A., & Vining, G. G. (2020). Introduction to Linear Regression Analysis (6th ed.). John Wiley & Sons.
• Ruey S. T., & Tsay, R. S. (2012). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons.
• Sharma, J. (2020). Financial Analytics with R: Building a Laptop Laboratory for Data Science. Springer.

References: 

Suggested Readings:
• De Jong, E., & Wilmott, P. (2017). Financial Modeling and Valuation: A Practical Guide to Investment Banking and Private Equity. John Wiley & Sons.
• Greene, W. H. (2017). Econometric Analysis (8th ed.). Pearson.
• Johnson, R. A., & Wichern, D. W. (2020). Applied Multivariate Statistical Analysis (7th ed.). Pearson.
• Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
• McDonald, R. L. (2014). Derivatives Markets (3rd ed.). Pearson.

E-Contents:
• http://ww1.shodhganga.com/
• https://shodhgangotri.inflibnet.ac.in/
• https://www.scopus.com/home.uri
• http://www.e-book.com.au/freebooks.htm
• https://www.doaj.org/

Reference Journals:
• Journal of Financial Econometrics, Oxford University Press
• Journal of Financial Data Science, Global Association of Risk Professionals

Academic Year: 
2024-2025
  • Printer-friendly version
  • PDF version