Part A

Context

In the realm of modern finance, businesses encounter the perpetual challenge of managing debt obligations effectively to maintain a favorable credit standing and foster sustainable growth. Investors keenly scrutinize companies capable of navigating financial complexities while ensuring stability and profitability. A pivotal instrument in this evaluation process is the balance sheet, which provides a comprehensive overview of a company's assets, liabilities, and shareholder equity, offering insights into its financial health and operational efficiency. In this context, leveraging available financial data, particularly from preceding fiscal periods, becomes imperative for informed decision-making and strategic planning.

Objective

Agroup of venture capitalists want to develop a Financial Health Assessment Tool. With the help of the tool, it endeavors to empower businesses and investors with a robust mechanism for evaluating the financial well-being and creditworthiness of companies. By harnessing machine learning techniques, they aim to analyze historical financial statements and extract pertinent insights to facilitate informed decision-making via the tool. Specifically, they foresee facilitating the following with the help of the tool:

1. Debt Management Analysis: Identify patterns and trends in debt management practices to assess the ability of businesses to fulfill financial obligations promptly and efficiently, and identify potential cases of default.
2. Credit Risk Evaluation: Evaluate credit risk exposure by analyzing liquidity ratios, debt-to-equity ratios, and other key financial indicators to ascertain the likelihood of default and inform investment decisions.

They have hired you as a data scientist andprovided you with the financial metrics of different companies. The task is to analyze the data provided and develop a predictive model leveraging machine learning techniques to identify whether a given company will be tagged as a defaulter in terms of net worth next year. The predictive model will help the organization anticipate potential challenges with the financial performance of the companies and enable proactive risk mitigation strategies.

Data Dictionary

The data consists of financial metrics from the balance sheets of different companies. The detailed data dictionary isgiven below.

• Networth Next Year: Net worth of the customer in the next year
• Total assets: Total assets of customer
• Net worth: Net worth of the customer of the present year
• Total income: Total income of the customer
• Change in stock: Difference between the current value ofthe stock and the value of stock in the last trading day
• Total expenses: Total expenses done by the customer
• Profit after tax: Profit after tax deduction
• PBDITA: Profit before depreciation, income tax, and amortization
• PBT: Profit before tax deduction
• Cash profit: Total Cash profit
• PBDITA as % of total income: PBDITA / Total income
• PBT as % of total income: PBT / Total income
• PAT as % of total income: PAT / Total income
• Cash profit as % of total income: Cash Profit / Total income
• PAT as % of net worth: PAT / Net worth
• Sales: Sales done by the customer
• Income from financial services: Income from financial services
• Other income: Income from other sources
• Total capital: Total capital of the customer
• Reserves and funds: Total reserves and funds of the customer
• Borrowings: Total amount borrowed by the customer
• Current liabilities & provisions: current liabilities of the customer
• Deferred tax liability: Future income tax customer will pay because of the current transaction
• Shareholders funds: Amount of equity in a company which belongs to shareholders
• Cumulative retained profits: Total cumulative profit retained by customer
• Capital employed: Current asset minus current liabilities
• TOL/TNW: Total liabilities of the customer divided by Total net worth
• Total term liabilities / tangible net worth: Short + long term liabilities divided by tangible net worth
• Contingent liabilities / Net worth (%): Contingent liabilities / Net worth
• Contingent liabilities: Liabilities because of uncertain events
• Net fixed assets:The purchase price of all fixed assets
• Investments: Total invested amount
• Current assets: Assets that are expected to be converted to cash within a year
• Net working capital: Difference between the current liabilities and current assets
• Quick ratio (times): Total cash divided by current liabilities
• Current ratio (times): Current assets divided by current liabilities
• Debt to equity ratio (times): Total liabilities divided by its shareholder equity
• Cash to current liabilities (times): Total liquid cash divided by current liabilities
• Cash to average cost of sales per day: Total cash divided by the average cost of the sales
• Creditors turnover: Net credit purchase divided by average trade creditors
• Debtors turnover: Net credit sales divided by average accounts receivable
• Finished goods turnover: Annual sales divided by average inventory
• WIP turnover: The cost of goods sold for a period divided by the average inventory for that period
• Raw material turnover: Cost of goods sold is divided by the average inventory for the same period
• Shares outstanding: Number of issued shares minus the number of shares held in the company
• Equity face value: cost of the equity at the time of issuing
• EPS: Net income divided by the total number of outstanding share
• Adjusted EPS: Adjusted net earnings divided by the weighted average number of common shares outstanding on a diluted basis during the plan year
• Total liabilities: Sum of all types of liabilities
• PE on BSE: Company's current stock price divided by its earnings per share

Note: A company will not be tagged as a defaulter if its net worth next year is positive, or else, it'll be tagged as a defaulter.

Part B

Context

Investors face market risk, arising from asset price fluctuations due to economic events, geopolitical developments, and investor sentiment changes. Understanding and analyzing this risk is crucial for informed decision-making and optimizing investment strategies.

Objective

The objective of this analysis is to conduct Market Risk Analysis on a portfolio of Indian stocks using Python. It uses historical stock price data to understand market volatility and riskiness. Using statistical measures like mean and standard deviation, investors gain a deeper understanding of individual stocks' performance and portfolio variability.

Through this analysis, investors can aim to achieve the following objectives:

• Risk Assessment: Analyze the historical volatility of individual stocks and the overall portfolio.
• Portfolio Optimization: Use Market Risk Analysis insights to enhance risk-adjusted returns.
• Performance Evaluation: Assess portfolio management strategies' effectiveness in mitigating market risk.
• Portfolio Performance Monitoring: Monitor portfolio performance over time and adjust as market conditions and risk preferences change.

Data Dictionary

The dataset contains weekly stock price data for 5 Indian stocks over an 8-year period. The dataset enables us to analyze the historical performance of individual stocks and the overall market dynamics.

Scoring guide (Rubric) - FRA Graded Project (Coded) Rubric

Criteria	Points
PART A: Define the problem and perform Exploratory Data Analysis - Problem definition - Check shape, Data types, and statistical summary - Univariate analysis - Multivariate analysis - Use appropriate visualizations to identify the patterns and insights - Key meaningful observations on individual variables and the relationship between variables	12
PART A: Data Pre-processing Prepare the data for modeling: - Outlier Detection (treat, if needed) - Encode the data - Data split - Scale the data - Target variable creation * The target variable is default and should take the value 1 when net worth next year is negative & 0 when net worth next year is positive	5
PART A: Model Building - Metrics of Choice (Justify the evaluation metrics) - Model Building (Logistic Regression, Random Forest) - Model performance check across different metrics	12
PART A: Model Performance Improvement - Dealing with multicollinearity using VIF - Identify optimal threshold for Logistic Regression using ROC curve - Hyperparameter Tuning for Random Forest - Model performance check across different metrics	18
PART A: Model Performance Comparison and Final Model Selection - Compare all the models built - Select the final model with the proper justification - Check the most important features in the final model and draw inferences	6
PART A: Actionable Insights & Recommendations - Actionable insights and recommendations	6
PART B: Stock Price Graph Analysis - Draw a Stock Price Graph (Stock Price vs Time) for the given stocks - Write observations	6
PART B: Stock Returns Calculation and Analysis - Calculate Returns for all stocks - Calculate the Mean and Standard Deviation for the returns of all stocks - Draw a plot of Mean vs Standard Deviation for all stock returns - Write observations and inferences	12
PART B: Actionable Insights & Recommendations - Actionable insights and recommendations	4

Please help me with the step by step Python code of above mentioned questions for the data attached the drive.

Drive link (Data): https://drive.google.com/drive/u/4/folders/1J8bnTtzqmlSbPZSdt2TUxeA6aTAwEtf9