Fair Labor Standards Act protects workers against unfair pay and work regulation. It establishes minimum wage, proper recordkeeping of activity, requirements for overtime pay, and standards for youth employment in the US.
What is Data Mining? Data mining is a process that facilitates the extraction of relevant information from a vast dataset. The process helps to discover a new, accurate and useful pattern in the data to derive helpful pattern in data and relevant information from the dataset for organization or individual who requires it. Key Features of data mining include: Based on the trend and behaviour analysis, data mining helps to predict pattern automatically. Predicts the possible outcome. Helps to create decision-oriented information. Focuses on large datasets and databases for analysis. Clustering based on findings and a visually documented group of facts that were earlier hidden. How does data mining work? The first step of the data mining process includes the collection of data and loading it into the data warehouse. In the next step, the data is stored and managed on cloud or in-house servers. Business analyst, data miners, IT professionals or the management team then extracts these data from the sources and accordingly access and determine the way they want to organize the data. The application software performs data sorting based on user’s result. In the last step, the user presents the data in the presentable format, which could be in the form of a graph or table. Image Source: © Kalkine Group 2020 What is the process of data mining? Multiple processes are involved in the implementation of data mining before mining happens. These processes include: Business Research: Before we begin the process of data mining, we must have a complete understanding of the business problem, business objectives, the resources available plus the existing scenario to meet these requirements. Having a fair knowledge of these topics would help to create a detailed data mining plan that meets the goals set up by the business. Data Quality Checks: Once we have all the data collected, we must check the data so that there are no blockages in the data integration process. The quality assurance helps to detect any core irregularities in the data like missing data interpolation. Data Cleaning: A vital process, data cleaning costumes a considerable amount of time in the selection, formatting, and anonymization of data. Data Transformation: Once data cleaning completes, the next process involves data transformation. It comprises of five stages comprising, data smoothing, data summary, data generalization, data normalization and data attribute construction. Data Modelling: In this process, several mathematical models are implemented in the dataset. What are the techniques of data mining? Association: Association (or the relation technique) is the most used data mining technique. In this technique, the transaction and the relationship between the items are used to discover a pattern. Association is used for market basket analysis which is done to identify all those products which customer buy together. An example of this is a department store, where we find those goods close to each other, which the customers generally buy together, like bread, butter, jam, eggs. Clustering: Clustering technique involves the creation of a meaningful object with common characteristics. An example of this is the placement of books in the library in a way that a similar category of books is there on the same shelf. Classification: As the name suggests, the classification technique helps the user to classify and variable in the dataset into pre-defined groups and classes. It uses linear programming, statistics, decision tree and artificial neural networks. Through the classification technique, we can develop software that can be modelled so that data can be classified into different classes. Prediction: Prediction techniques help to identify the dependent and the independent variables. Based on the past sales data, a business can use this technique to identify how the business would do in the future. It can help the user to determine whether the business would make a profit or not. Sequential Pattern: In this technique, the transaction data is used and though this data, the user identifies similar trends, pattern, and events over a period. An example is the historical sales data which a department store pulls out to identify the items in the store which customer purchases together at different times of the year. Applications of data mining Data mining techniques find their applications across a broad range of industries. Some of the applications are listed below: Healthcare Education Customer Relationship Management Manufacturing Market Basket Analysis Finance and Banking Insurance Fraud Detection Monitoring Pattern Classification Data Mining Tools Data mining aims to find out the hidden, valid and all possible patterns in a large dataset. In this process, there are several tools available in the market that helps in data mining. Below is a list of ten of the most widely used data mining tools: SAS Data mining Teradata R-Programing Board Dundas Inetsoft H3O Qlik RapidMiner Oracle BI
David Ricardo, a renowned economist, is majorly recognised for his theory on wages and profits, theory of international trade, theory of rents and labor theory of value. His economic thinking dominated throughout majority of the 19th century.
What is earnest money? Earnest money refers to a sum of money that is paid by the buyer to the seller as a form of reassurance of future payments during the sale of a house. Paying earnest money is also beneficial to the buyer because it gives him leverage to arrange the remaining funds. Earnest money can be deposited via a direct home deposit, an escrow account or in the form of good faith money. How does earnest money work? Earnest money is paid before closing on a house sale. When the seller and buyer come to an agreement on the house sale, the seller must take the house off the market. Earnest money serves the purpose of assuring the seller that the deal would not fall through. The amount paid as earnest money is usually 1-3% of the total sale value of the house. Most sellers prefer to hold earnest money in an escrow account. In case the deal does not materialize, the money can be given back to the buyer directly from the escrow account. This removes the concerns any buyer may have about whether the money would be returned by the seller or not. In case the buyer and seller go ahead with the sale, the earnest money becomes a part of the down payment. Thus, the buyer would only pay the remaining amount of the down payment. However, in case the agreement does not materialize between the buyer and the seller, the earnest money is returned to the buyer after deducting the escrow fees from it. With money locked in on one house, buyers are less likely to close a deal with any other house seller. How is the amount of earnest money decided upon? The percentage of the total amount that can be taken as earnest money varies from state to state as policies are different. Additionally, the market scenario is also a major factor affecting the amount of earnest money to be paid. Under normal conditions, 1-2% of the total sale value can be taken as earnest money. However, if the market does not have a high demand for houses, then the percentage charged as earnest money could be lower around 1%. In markets with high demand, this percentage could be as high as 3%, or even 5%. To outbid other buyers, one can pay a larger sum of money as earnest money. This would increase the buyer’s chances of securing the property. Why is earnest money important? Earnest money may not always be mandated by the seller, but in a highly competitive market earnest money may be necessarily required. Paying the earnest money makes the agreement official. Without earnest money, the deal may not be considered official in many regions. It is one of the four stages of payment while making a deal on a house. However, in certain instances, even after the payment of the earnest money, the deal may not materialize. Typically, a buying agent should be able to assist the buyer in such a case. What conditions must be met for earnest money to be refundable? Earnest money has certain contingencies attached to it for the protection of both the seller and the buyer. Even after the seller has accepted the earnest money deposit, there are certain contingencies that must be met before the deal can be finalized. These include the following: Home inspection contingency: This contingency is placed so that buyers can back out of the agreement in case the there are some faults in the property, and it is in need of repair. However, it is not necessary for the buyer to call off the deal in such a case. He can simply work with the seller to reach a mutual decision rather than scraping away the deal completely. Financing Contingency: It might be the case that a buyer had not been approved for a mortgage before making the earnest deposit. Here the financing contingency would protect the buyer. If the mortgage does not get approved even though the earnest money had been paid, then the financing contingency allows the buyer to walk away from the deal along with the refunded earnest money. Appraisal Contingency: This protects the buyer in case the property has been overvalued. Here the lender can hire a third-party investigator who can examine whether the property has been priced at a fair value or not. If the value of the house comes out to be higher than the fair value, then the buyer can walk away with a refund. Additionally, this contingency can be used to bring down the price of the sale too. Contingency for Selling the Existing home: It is quite possible that contracts are made based on whether the buyer can sell an existing home or not. If the buyer is unable to sell the existing home, then he can walk away with a refund. These contingencies can be waived by the buyer in case he is sure that the deal would close and there would be no backing off. However, it is important to note that contingencies can provide an extra cushion against adverse circumstances and they might come in handy in certain cases. What is the difference between earnest money and good faith deposit? Both terms can be used interchangeably. However, all good faith deposits are not the same as earnest money. A good faith deposit can be made directly to the mortgage lender, while earnest money is usually held in an escrow account. Both serve the purpose if providing a sense of security about the buyer sticking to the same deal and not going elsewhere. The good faith deposit eventually forms a part of the lending process. However, in case the deal does not materialize, it is possible that the borrower would not get his good faith deposit back.
What is EBITDA? Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) is a widely used financial metric in evaluating cash flows and profitability of a business. Market participants closely track EBITDA and apply it in decision making extensively. Although conventional investors like Charlie Munger had raised concerns over the use of EBITDA, it is very popular in markets, and M&A transactions are mostly priced on EBITDA-based valuation like EV/EBITDA (x). EBITDA is not recognised by IFRS and GAAP but is used extensively in the Corporate Finance world. It is now a mainstream financial metric that companies look to target. EBITDA depicts operational cash generation capacity of a firm in a given period. It acts as an alternative to financial metrics like revenue, profit or earnings per share. EBITDA allows to evaluate a business operationally and outcomes of operating decisions. Non-operating items are excluded to arrive at EBITDA. EBITDA excludes the impact of capital structure or debt/equity, and non-cash expenses like depreciation and amortisation. A particular criticism of EBITDA has been the inappropriate outlook of capital intensive businesses, which incur large depreciation expenses. Business with large assets incurs substantial costs related to repair and maintenance, which are not captured in EBITDA because depreciation expenses are accounted to calculate EBITDA. Meanwhile, EBITDA can paint an appropriate picture for asset-light business with lower capital intensity. While revenue, profit and earning per share remain sought-after headline generators for corporates, EBITDA has also found its growing application in the corporate finance world and is now a mainstream metric to evaluate a business financially. Perhaps the growth of asset-light business models has also added to the use of EBITDA. Its debt-agnostic approach to evaluate businesses has given reasons to investors, especially for high growth firms during capital expenditure cycles. But EBITDA has been present for close to four decades now. In the 1980s, the growth in corporate takeovers through leverage buyout transaction was on a boom. EBITDA grew popular to value heavy industries like broadcasting, telecommunication, utilities. John Malone is credited for coining this term. He was working at TCI- a cable TV provider. Since EBITDA has remained an important metric to determine purchase price multiples and is highly used in M&A transactions. EBITDA’s application in large businesses with capital intensive assets that are written down over a long period has been a source of concern for many investors. Although EBITDA is an effective metric to evaluate the profitability of a firm, it does not reflect actual cash flow picture of a firm during a period. Also, it does not account for capital expenditures of the firm, which are crucial in successfully running a business. EBITDA does not give a fair cash flow position because it leaves out crucial items like working capital, debt and interest repayments, fixed expenses, capital expenditure. At the outset, there can be times when EBITDA may overstate performance, value and ability to repay debt. How to calculate EBITDA? NPAT: Net Profit after tax is the amount reported by a firm in the given period. It is present on the income statement of the firm and is used in the calculation of earnings per share of an entity. To calculate EBITDA, interest expense, tax, depreciation and amortisation are added to NPAT. Interest Expense: Firms can employ debt in their capital structure, and interest expense is funds paid to lenders as interest costs on principal debt. Most companies have different financing structure, and excluding interest payments enable comparing firms on operating grounds through EBITDA. Tax: Firms also pay income tax on profits. Excluding taxes gives a fair picture of the operating performance of the business since tax vary across jurisdictions, and sometimes according to size of business as well. Depreciation: Depreciation is the non-cash expense to account for the steady reduction in value of tangible assets. Firms can incur depreciation expense on machinery, vehicles, office assets, equipment etc. Amortisation: Amortisation is the non-cash expense to account for the reduction in the value of intangible assets like patents, copyrights, export license, import license etc. Operating Profit: Operating profit is the core profit of a firm generated out of operations. It includes cash and non-cash expenses of a firm, excluding income tax and interest expenses. Operating Profit is also called Earnings Before Interest and Tax (EBIT). Read: EBIT vs EBITDA What is TTM EBITDA and NTM EBITDA? Trailing Twelve Months (TTM) or Last Twelve Months (LTM) EBITDA represents the EBITDA of the past twelve months of the firm. It allows to review the last operation performance of the business. Whereas NTM EBITDA represents 12-month forward forecast EBITDA of the firm. NTM EBITDA is also one-year forward EBITDA. Market participants are provided with consensus analysts’ estimates for a firm, which also include NTM EBITDA, NTM EPS, NTM Net Income or NPAT. What is EBITDA margin? EBITDA margin is the percentage proportion of a firm EBITDA against total revenue. It indicates the operational profitability of the firm and cash flows to some extent. If a firm has a higher margin, it means the level of EBITDA against revenue is higher. It is widely used in comparing similar companies and enable to evaluate businesses relatively. If a firm has a total revenue of $1 million and EBITDA is $800k, the EBITDA margin is 80%. What is adjusted EBITDA? Adjusted EBITDA is calculated to provide a fair view business after adding back non-cash items, one-time expenses, unrealised gains and losses, share-based payments, goodwill impairments, asset write-downs etc.