This is a form of inventory financing. It refers to the financing wherein a lender lends to a borrower who uses inventory as collateral.
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
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.
It is a calculation used to set aside the financial threat of a levered company from its business threat, in corporate finance. The equation correlates beta of a company that is funded by both debt as well as equity with a company having no debt, i.e., its unlevered firm.
What is a Market Index? A market index could be defined as a representation of a security market, market segment, or asset class of freely tradable market instruments. A market index is primarily made up of constituent marketable securities and is re-calculated on a daily basis. There are basically two forms or variations of the same market index, i.e., one version based upon the price return known as a price return index, and one version based upon total return know as a total return index. Why Do We Need A market Index? Ideally, a large number of market participants including investors and institutional funds gather and analyse vast amounts of information about security markets; however, doing so could be a very troublesome and tiring task as the work is both time consuming and data-intensive. Thus, a large number of market participants prefer to use a single measure that could represent and consolidate a plethora of information while reflecting the performance of an entire security market of interest. This is where market indexes play a major role as they are often a simple measure to reflect the performance of any underlying market of interest. For example, S&P500, NASDAQ, are believed to reflect the true performance and picture of the U.S. stock market in particular and U.S. economy in general. Likewise, many indexes such as S&P/ASX 200 is believed to reflect the performance of the Australian stock market and so on. Index Construction Constructing a market index is almost similar to constructing a portfolio of securities as the construction of an index requires: Target Market and Security Selection The first and the primary decision in constructing an index is to identify the target market and select financial instruments which reflect the true nature of the underlying market. The target market, which determines the investment universe and securities available for inclusion, could be based on any asset class, i.e., equities, fixed income, commodities, real estates or on any geographic region. Once the target market is identified, the next step is to select securities which represent the true nature of the target market and decide on the number of securities to be included in the index. Ideally, a market index could be of all securities in the target market or a representative sample of the target market. For example, some indexes such as FTSE 100, S&P 500, S&P/ASX 200, fix the number of stocks to be included in the index while indexes like Tokyo Stock Price Index (or TOPIX) select and represents all of the largest stocks, known as the First Selection. For such indexes, the included securities must meet some basic parameters like pre-decided market capitalisation, the number of shares outstanding, to remain in the index. Weight Allocation The weight allocation varies considerably among indexes depending upon the method of weight allocation, and it basically decides on how much weight each security in an index carry. The method of weight allocation is one of the most important parts that investors need to understand thoroughly as it has a substantial impact on the value of an index. Some of the most widely-used weight allocation methods are as below: Price Weighting This method was originally used by Charles Dow to construct the Dow Jones Industrial Average (or DJIA) and is one of the simplest methods. The price weight method determines the weight of each individual security of an index by dividing the price of the security by the sum of prices of all securities. In simple terms, each security gets the weight of its price in proportional to the total price of the index. The primary advantage of this method is its simplicity; however, the method leads to arbitrary weights for each security as the method is highly sensitive to some market actions such as stock split. Equal Weighting As the name suggests, this method assigns equal weight to all securities in an index. Just like equal weighting, the major advantage of this method is its simplicity; however, this method tends to underrepresent the value of large securities and overrepresent the value of smaller securities. Market-Capitalisation Weighting Market-Capitalisation method weight each constituent by dividing its market capitalisation with the total market capitalisation of the index, i.e., the sum of the market capitalisation of each constituent. The market capitalisation could be determined by multiplying the number of outstanding shares of the security with its market price per share. Rebalancing and Reconstitution Rebalancing of a market index could be defined as the adjustment to the weights of the constituent securities. Depending upon the method of weighting an index, the weight of each individual security tends to change due to market actions or price appreciation and deprecation, in similar fashion to a stock portfolio requires scheduled rebalancing. A majority of market indexes are rebalanced on a daily basis as price tends to often change regularly. On the other hand, reconstitution could be ideally defined as the process to change the constituent of a market index. As suggested above, many market indexes such as TOPIX require each constituent security to meet some parameters for the inclusion; however, due to market dynamics, various securities tend to get added or removed from an index time to time. Uses of Market Index Originally, market indexes were created to provide a sense to investors on how a security market performed on a given day. However, with the development of the modern finance theory and growing numbers of indexes in the market, uses of market indexes have been expanded significantly. Some of the major uses of market indexes are as below: To Gauge the market sentiment A market index is usually a collection of the opinion of market participants; thus, they reflect the attitude and behaviour of the market participants, making them one of most widely used tool to gauge the market sentiment. To measure and model the risk and return profile of a market Market indexes could serve as a proxy for systematic risk in many popular models such as the Capital Asset Pricing Model (or CAPM). The market portfolio, which represents the systematic risk of the market often uses a market index, as a proxy of the market portfolio as including the whole population or all stocks in the model could lead to wrong output, and it could be very costly and cost consuming. Serves as a Performance Benchmark Market indexes often serve as a performance benchmark for individual investors and especially large investors such as mutual funds, ETFs, pension funds, and large banks.