PGDM Core Subject
Financial Analytics
Course Objective
Primary PO Mapping: PO2 (Critical Thinking) & PO4 (Communication).
Strategic Focus: Utilizing quantitative tools and AI-driven insights to synthesize complex information and minimize cognitive bias in business advisory.
Mandatory Textbook: Financial Analytics with R by Mark J. Bennett and Dirk L. Hugen.
Internal Assessment Scheme (70 Marks)
|
Component |
Marks |
Description |
Mapped CO |
|
Simulation |
20 |
The Prediction Engine: Building an AI-driven stock price or churn predictor. +1 |
CO3, CO5 |
|
Case Study |
10 |
"The Moneyball Moment": Analyzing data-driven turnarounds in finance. |
CO4 |
|
Presentation |
10 |
"Data Storytelling": Presenting financial insights to a non-technical board. |
CO5 |
|
Mid Term |
10 |
Internal written exam covering Descriptive Analytics & Probability. |
CO1, CO2 |
|
Project |
10 |
"The Kaggle Challenge": Predicting financial outcomes using real datasets. |
CO5 |
|
Class Participation |
10 |
Participation in coding labs and data discussions. |
All |
20-Session Plan
Each session assumes 90 minutes of class time plus 60 minutes of pre-class preparation.
|
Session |
Topic |
Pre-Reading (Bennett & Hugen) |
Daily Assignment |
|
1 |
Introduction to Financial Analytics |
Ch 1: Data & Statistics |
|
|
2 |
Financial Data Types & Sources |
Ch 2: Financial Data |
|
|
3 |
Visualizing Financial Data |
Ch 3: Visualization |
A1: Technical Charts +1 |
|
4 |
Descriptive Stats for Returns |
Ch 4: Returns Analysis |
|
|
5 |
Probability Distributions in Finance |
Ch 5: Probability |
A2: Normal Dist. Lab |
|
6 |
Exploratory Data Analysis (EDA) |
Ch 6: Data Wrangling |
|
|
7 |
Hypothesis Testing - I (A/B Testing) |
Ch 7: Hypothesis Testing |
A3: P-value Analysis +1 |
|
8 |
Hypothesis Testing - II (ANOVA) |
Ch 8: Multi-group Analysis |
|
|
9 |
Correlation vs. Causation in Markets |
Ch 9: Correlations |
A4: Spurious Correl. |
|
10 |
Mid-Term Internal Exam |
Review Sessions 1–9 |
Mid-Term (10M) |
|
11 |
Simple Linear Regression |
Ch 10: Linear Models |
|
|
12 |
Multiple Regression for Forecasting |
Ch 11: Multi-Reg |
A5: Market Forecast |
|
13 |
Logistic Regression for Credit Risk |
Ch 12: Binary Outcomes |
|
|
14 |
The Prediction Engine (Sim 1) |
Manual: Building Predictors |
Simulation (10M) |
|
15 |
Interactive BI Dashboards (Sim 2) |
Manual: Executive Reports |
Simulation (10M) +1 |
|
16 |
Time Series Analysis & Trends |
Ch 13: Time Series |
|
|
17 |
Ethics in AI & Algorithmic Bias |
Technical Note: Data Privacy |
A6: Ethics Audit +1 |
|
18 |
Optimization & Simulation (Monte Carlo) |
Ch 14: Portfolio Ops |
|
|
19 |
Presentation: Data Storytelling |
Manual: Persuasive Defense |
Presentation (10M) |
|
20 |
Course Synthesis & Final Portfolio |
Review of Strategic Insight |