Terms Beginning With 'w'

Warehouse Lending

  • January 07, 2020
  • Team Kalkine

The credit that is offered to a (mortgage) creditor to fund the mortgages while waiting for the lender to sell them in the secondary marketplace eventually is known as warehouse lending. Funds are used to pay for a mortgage that has been used by the borrower to buy a property.

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 Day Trading? Day trading is popular among a section of market participants. It is a type of speculation wherein trades are squared-off before the market close in the same day. An individual or a group is engaged in buying and selling of securities for a short period for profits, the trades could be active for seconds, minutes or hours.  One can engage in day trading of many securities in the market. Anyone who has sufficient capital to fund the purchase can engage in day trading. For a class of people, day trading is a full-time job.  Day traders are agnostic to the long-term implications of the security and motive is to benefit from the price changes on either side and make profit out of the asset price fluctuations within a day. They bet on price movements of the security and are not averse to take short positions to benefit from the fall in price.  Day trading is not only popular among individuals or retail traders but institutional traders as well, therefore the price movements are large sometimes depending on the magnitude of information flow and accessibility.  Everyone wants to make money faster, and many are inclined to speculate in markets, but it comes with considerable risk and potential loss of capital. People engaged in day trading also incur losses, and oftentimes outcomes are disheartening.  Day trading is a risky activity, similar to sports betting and gambling, and it could become addictive just like gambling and sports betting. Since the motive is to earn profits, the profits realised from day trading also tempt people to continue speculating.  People spend considerable time and efforts to make the most out of day trading. They have to continuously absorb and incorporate information flow, which has become increasingly accessible driven by new-age communications systems like Twitter, Facebook, forums etc. But not only information flows have been favourable, day traders are now equipped with best in class infrastructure to execute trades even on compact devices like mobile phones. The accessibility to markets is at a paramount level and gone are days of phone call trading and lack of information flows.  What are the essentials for Day Trading? Basic knowledge of markets With lack of basic knowledge of markets, day trading may yield unacceptable outcomes. It becomes imperative for people to know what’s on the stake. Prospective day traders should know about capital markets, and the securities traded in capital markets like bonds, equity and derivatives.  Buying shares and expecting a return from the price movements are on the to-do list for many. However, it is important to know about and risks and potential returns from speculating in capital markets.  After getting some basic knowledge about markets and securities, aspiring day traders should know how to analyse market prices of securities through fundamental analysis and technical analysis. Although day traders don’t practice fundamental analysis extensively, they spend considerable time to apply technical analysis, to formulate a entry and exit strategy.   Device and internet connection Trading is now possible on mobile applications as well as computer applications or websites. An aspiring day trader will likely begin with mobile phone given the accessibility, and laptops/computers are useful as scale grows larger and complex.  Internet connection is prerequisite to practising day trading, and it is favourable to have a fast internet connection to avoid glitches and potential problems. These perquisites are now available with large sections of societies.  Broker and trading platform A broker will facilitate a market for potential trades. The security brokerage industry has also seen a profound shift as technology has driven cost lower while competition is ramping up across jurisdictions. Large retail brokerages have moved towards zero commission trading in the U.S., and the same is seen being the trend across other geographies as well.  The entry of discount and online brokerages has perhaps given wings to the retail market participants as well as the retail market for security brokers. Robinhood has grown immensely popular in the United States, but there are many firms like Robinhood in other jurisdictions. Each country has some firms with business model on same lines as Robinhood.  Brokers now offer high-quality mobile applications and web services to clients, and trading security has never been so accessible. They also provide access to the global market along with a range of securities, including commodity derivatives, currency derivatives, CFDs, options, futures, bond futures etc.  Real-time market information flow   On public sources, market price information is at times not live due technical shortcomings, which will not work appropriately, especially for day traders. Brokers not only provide platform and market but several other services, including margin lending, real-time data, research.  Day traders closely track prices of securities and overall information flow to incorporate developments in bidding, and real-time data provides accurate prices throughout market hours.  Information flow largely relates to the news around the company, industry or economy. Day traders now have far better sources of information than the conventional sources, and sometimes these sources could be exclusive to a group.  What are the risks of day trading? Most of the aspiring day traders end up losing money, given the lack of experience and knowledge. They should rather only bet on capital that they are comfortable to loose, in short, they should avoid risk of ruin. Day trading is sort of pure-play speculation and application of knowledge, information flow, laced with good trading system is paramount. The only concern of day traders is movement in price, which contradicts from investments. Day traders try to time and ride the momentum in the price and exit the trade before momentum turns otherwise, which can happen frequently.  It consumes considerable time and induces stress on the individuals given the nature of security prices, which can move north and south abruptly throughout the day, hours, minutes and seconds. Day traders should have enough capital to trade in cash instead of margin.  Day trading on margin or borrowed money is extremely risky and has the potential to make a person insolvent, especially in cases of extreme risk-taking. The leverage associated with borrowed money magnifies profits as well as losses.  Aspiring day traders should equip themselves with adequate knowledge, competency and sound risk management process. Although fast money is dear to most, it is better to know what is at stake before jumping into markets with excitement.   

It is the rate of interest that is charged by the Central Bank while lending funds to other banks.

This is a form of inventory financing. It refers to the financing wherein a lender lends to a borrower who uses inventory as collateral.

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