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Objective – Problem Statement – 1 • Credit Risk • Businesses or companies can fall prey to default if they are not able to keep up their debt obligations

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Objective – Problem Statement – 1

• Credit Risk

• Businesses or companies can fall prey to default if they are not able to keep up their debt obligations. Defaults will lead to a lower credit rating for the company which in turn reduces its chances of getting credit in the future and may have to pay higher interests on existing debts as well as any new obligations. From an investor's point of view, he would want to invest in a company if it is capable of handling its financial obligations, can grow quickly, and is able to manage the growth scale. A balance sheet is a financial statement of a company that provides a snapshot of what a company owns, owes, and the amount invested by the shareholders. Thus, it is an important tool that helps evaluate the performance of a business.

• Data that is available includes information from the financial statement of the companies for the previous year

(2015). Also, information about the Net worth of the company in the following year (2016) is provided which can be used to drive the labeled field.

• Explanation of data fields available in Data Dictionary, 'Credit Default Data Dictionary.xlsx'
? Data is imported and studied the initial stats about the data. ? There are in total 3586 rows and 67 columns. ? There are 63 float types, 3 int types and 1 object type datatype. ? We are replacing signs/ special characters like spaces, hyphens, brackets into underscores, and strings using string replace to avoid errors in syntax further.
• We are inserting a new column ‘DEFAULT’ as the target variable and filling the value on the condition that the company is liable to default if they have a net worth negative and zero if the net worth for the next year is positive. • We found that around 10.8 per cent are default
Statistical Summary • The figure on the right shows the statistical data about the quartile range values, median and mean of the data set
Missing Values • We found that there are missing values in the data as observed in Array Output 1 and they need to be imputed. • We have imputed the missing values using Median and we have found from Array output 2 that there are no columns with missing values.
Test Train Split • For building a model further based on Logistic regression, we need to divide the dataset into train and test data sets. • For this purpose, they are divided into 2 in the ratio of 67:33 and a random state of 42. • Target Variable is DEFAULT. • Train data has 2402 rows of values and test data has 1184 rows of values.
Building Random Forest Model – Train Set
• Accuracy of the model for train data is 0.98 • Precision is around 95% for default
Validating results – Inference
• Since the parameters for both test and train data are almost equal, so we can conclude that the Random forest model we built is reliable.
Building LDA Model – Train Set
• For Train test • Accuracy of the model for train data is 0.98 • Precision is around 93% for default
Building LDA Model – Test Se
• For Test test • Accuracy of the model for train data is 0.98 • Precision is around 95% for default
Validating results – Inference
• Since the parameters for both test and train data are almost equal, so we can conclude that the LDA model we built is reliable.
Objective – Problem Statement - 2 • Market Risk • The dataset contains 6 years of information(weekly stock information) on the stock prices of 10 different Indian Stocks. Calculate the mean and standard deviation on the stock returns and share insights. • Please find attached the files to be referred. Market Risk Dataset • You are expected to do the Market Risk Analysis using Python.
? Data is imported and studied the initial stats about the data. ? There are in total 314 rows and 11 columns. ? There are 10 int types and 1 object type datatype. ? We are replacing signs/ special characters like spaces, hyphens, brackets with underscores, and strings using string replace to avoid errors in syntax further.
Statistical Summary
• The figure on the right shows the statistical data about the quartile range values, median and mean of the data set.
Graph Trends of Stocks – INFOSYS
• From the graph, we can observe that the value of Infosys stock price with few ups and downs is considerably increasing and almost quadrupled in the span of seven years.
Graph Trends of Stocks – INDIAN HOTEL
• From the graph, we can observe that the value of the stock price is considerably increasing in the span of seven years But there is a lot of increases/ decreases in comparison to Infosys.
Graph Trends of Stocks – Axis Bank
• From the graph, we can observe that the value of Axis bank stock price with few ups and downs is considerably increasing and almost quadrupled in the span of seven years
Stock Means and Standard Deviation S T O C K M E A N S S T A N D A R D D E V I A T I O N

 • Stock means for a week on week change is calculated and it is minimum for Indian Hotel, so we can say that there is a low change in the stock price in Indian Hotel than rest of the stocks and highest change observed in Jindal Steel in increasing trend and highest decrease observed in Jet Airways

 • Highest Std found in Idea Vodafone, which infers that there is a lot of movement/ volatility in the Idea Vodafone stock than the rest of others.
Recommendations and Conclusions • For short term gains, we can choose stocks with high volatility but fewer changes in the price so we have less chance of losing our principles like Idea Vodafone and Jet Airways. • For long term benefits, select stocks with the high price changing averages but low volatile stocks like Infosys and Shree Cements to gain huge profits.