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London Stock Exchange Group
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I applied via Referral and was interviewed before Jul 2022. There were 2 interview rounds.
Data mining , data structure, computer networking
I applied via Referral and was interviewed before Jan 2020. There were 6 interview rounds.
Amortization is the process of spreading out a loan into smaller, regular payments over a period of time.
It is used to pay off a debt over time with regular payments
Each payment includes both principal and interest
The amount of interest decreases over time as the principal is paid off
Examples include mortgages, car loans, and student loans
WASO is Wake After Sleep Onset, TSO is Total Sleep Time, and EPS is Earnings Per Share.
WASO is the amount of time spent awake after initially falling asleep.
TSO is the total amount of time spent sleeping, including both REM and non-REM sleep.
EPS is a financial metric that represents the portion of a company's profit allocated to each outstanding share of common stock.
WASO and TSO are commonly used in sleep studies, whi
The financial statements (BS, IS, CF) have common elements such as assets, liabilities, equity, revenue, expenses, and cash flows.
Assets: resources owned by the company
Liabilities: obligations owed by the company
Equity: residual interest in the assets of the company
Revenue: income generated by the company
Expenses: costs incurred by the company
Cash flows: inflows and outflows of cash
A stock split increases the number of shares outstanding and decreases the price per share, but does not affect the market capitalisation.
Stock split does not affect the total value of the company
Market capitalisation remains the same after a stock split
Stock split increases the number of shares outstanding and decreases the price per share
For example, if a company has 1 million shares outstanding and the stock splits ...
Cash flow statements have three main components: operating activities, investing activities, and financing activities.
Operating activities: cash inflows and outflows from the company's core business operations.
Investing activities: cash inflows and outflows from buying or selling long-term assets.
Financing activities: cash inflows and outflows from borrowing or repaying debt, issuing or buying back stock, and paying di...
EPS is calculated by dividing the company's net income by the number of outstanding shares.
EPS = Net Income / Outstanding Shares
Net Income is the company's total earnings after expenses and taxes
Outstanding Shares are the total number of shares issued by the company
EPS is an important metric for investors to evaluate a company's profitability
Higher EPS indicates better profitability and potential for higher dividends
I applied via Campus Placement
I applied via Company Website and was interviewed in Nov 2020. There were 5 interview rounds.
Financial statements, cost sheet, debt to equity ratio, stock option, stock split, lease financing, profitability ratios.
Financial statements are reports that show the financial performance of a company.
Cost sheet of a bank includes interest expenses and income, while cost sheet of a manufacturing company includes direct and indirect costs.
Debt to equity ratio is a financial ratio that shows the proportion of debt and ...
I applied via Approached by Company and was interviewed in Jan 2023. There were 3 interview rounds.
Three Data model questions will be given to solve within 24 hours.
A case study on the number of green T-shirts sold in the US.
Identify the target audience for green T-shirts
Analyze the market demand for green T-shirts
Study the sales data of green T-shirts in the US
Identify the popular brands and styles of green T-shirts
Analyze the impact of seasonality on sales
Consider the pricing strategy of green T-shirts
Identify potential marketing opportunities to increase sales
I applied via Campus Placement and was interviewed before Apr 2023. There were 2 interview rounds.
Question:
Suppose you are trying to detect if a particular credit card transaction is fraudulent or not. The credit score of the individual to which the card belongs to had a very healthy credit score. All bills were paid in time and average transaction amount was not that high ($800). The individual had not been out of the country in the last couple of decades. Here is a list of transactions:
1) Gold jwelleries worth $5000
2) Groceries worth $35
3) Second hand car worth $8,000
4) Burgers worth $10
Which transaction looks fraudulent to you?
There is no specific answer. They just want to see how you think through the problem. One can potentially make use of data in order to deal with this problem. From that, one can estimate the probability of each of these transactions being fraudulent. Econometrically, one can develop a potential binary logit model. That would involve identifying certain features that belong to individuals like the one considered above and use these features to come up with an estimate of the probability of the transaction being a fraud.
Not just that, this also needs to include not individual specific features but external features as well. For example, the first transaction might not be as fraudulent as it looks like, because in heavily regulated markets, the risk associated with reselling the gold or exchanging it for money might be high enough to disincentivise the fraudster from buying gold. Thus regulation might also be a valid feature, and different from features describing an individuals characteristics.
Ofcourse problems of overfitting would arise ifan excessive number of features are used. Various means of finding the optimal Degrees of Freedom can be employed.
Obviously one can do better with more complicated decisioning algorihms that involve machine learning models as well.
Eventually one needs to estimate at what threshold of probability will the trasaction be declared fraudulent.
SQL query to retrieve total sales amount for each product category
Use GROUP BY clause to group the results by product category
Use SUM() function to calculate the total sales amount
Join the tables if necessary to get the required data
I have 3 years of experience working as a Data Analyst in the finance industry.
Utilized SQL to extract and analyze data from databases
Created visualizations using Tableau to present findings to stakeholders
Performed predictive modeling using Python to forecast financial trends
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Wells Fargo
HSBC Group
Cholamandalam Investment & Finance
Citicorp