Terms Beginning With 'i'

Investment Banking

What is Investment Banking?

Investment banking is a part of the banking system, catering to the needs of capital markets and market participants. Investment banking firms act as a bridge between the investors and capital seekers, including debt and equity. 

These firms provide a range of services to clients, including restructuring, capital raising, underwriting, consultancy, security broking, mergers & acquisitions, primary market services, sales and trading etc. 

Investment banking plays an important in the domestic capital markets as well as international capital markets. Investment banking firms assist market participants in complex financial transactions and provide advisory services. 

What are the key functions of investment banks?

Mergers & Acquisition: Investment bankers assist companies in finding value accretive growth opportunities through M&A. They propose clients with potential targets along with the rationale behind the transactions. 

Companies looking to grow inorganically often seek investment banks for suitable targets, and investment bankers provide the management with prospective opportunities. M&A is not limited to domestic markets and extend to international markets. 

Equity Capital Markets: Investment banks provide companies with the necessary advisory and facilitation of new equity issues by companies. They engage with the company and prospective investors to support fresh equity issues. 

Under this function, investment banks also help privately held companies making public markets debut through an Initial Public Offering. They have an extensive role in an IPO from drafting prospectus to determining an offer price.

Debt Capital Markets: Investment bankers also provide debt capital market services to organisations, including Government and companies. Debt continues to remain a favourable source of capital for businesses largely due to the relatively cheaper cost of capital and no dilution of ownership in the entity. 

Investment banks bridge the investors and capital seekers and price the debt issues of companies and Governments. Debt capital markets are a crucial source of funding of an economy and play an important role in growth. 

Leveraged Finance: It means the use of debt capital to finance the purchase of investment assets, including acquisitions, takeover and mergers. Since the cost of equity is higher than the cost of debt, corporate raiders have preferred leverage finance to buyout firms. 

Investment banks help companies to raise capital for leveraged buyouts. The key difference between debt capital markets and leveraged finance is that leveraged finance provides access to high-yield debt capital. 

Firms can use high-yield capital for leveraged buyouts, mergers & acquisitions, capital expenditure, recapitalisation, refinancing.

Restructuring: Investment banks also provide restructuring services to corporates and organisations. Under a restructuring, they attempt to remediate the bottlenecks in a business that are plaguing the performance. 

Investment banks provide full advisory to improve business performance after an in-depth study of the company. After the study, they may suggest companies to demerge a part of business, sell a part of business, restructure organisational structure etc. 

Trading & broking:  Investments banks also provides trading and broking services for securities, including equity, fixed-income, currencies, commodities, derivatives etc. In addition, they also provide research services for the asset classes covered under their offerings.

Some world-renowned investment banks

Morgan Stanley 

Morgan Stanley is a pure-play investment bank based in the USA. Its history dates back to 1935. In the first year of trading, the firm had a market share of 25% in public offerings. The firm is also present in almost all major markets in the world. 

Macquarie Group

Based in Australia, Macquarie Group also have a global footprint with a presence in almost every major market. It provides a range of services, including commodities, equity research, underwriting, IPOs, debt capital markets, investment management. 

Moelis & Company

Moelis & Company is an independent investment bank based in the USA. It provides financial advisory, M&A, recapitalisation, restructuring, capital markets, financial institutions advisory. It is present in 20 locations across the Americas, Australia, the Middle East, Asia and Europe. 

Credit Suisse

Credit Suisse is a Swiss wealth manager and investment bank with a strong presence across major markets in the world. It operates in two divisions, which include Global Markets and Investment Banking & Capital Markets. 

Deutsche Bank   

Deutsche Bank is a German global financial services and banking company based in Frankfurt, Germany. It provides corporate banking services, investment banking services, private bank service, and investment management. 

JP Morgan Chase & Co. 

JP Morgan Chase is one of the oldest financial institutions in the USA. It has a history of over 200 years. It provides a range of services, including commercial banking, financial advisory, corporate banking, institutional securities, investment management. 

In the recent past, the absolute return approach of Investing has turned out to be one of the fastest-growing investment strategies worldwide. A lot of financial advisors talk about such investments providing absolute returns. So, what exactly are the “Absolute Returns” and are they are promising? What is meant by Absolute return? Absolute return computes the increase or decrease, in an asset over a period of time, as a proportion of the original investment amount. The focus here is only on that specific asset or portfolio and not related market events. Absolute returns only consider the price movement for any specified time period. Absolute return, reckons an investment’s performance without considering the expanse of time for which investment was committed. Absolute returns can be computed for a quarter, semi- annual, annual period, 3-year duration or more. Absolute Returns are independent of Market movements and thus do not draw relative comparisons. It is one of the most commonly used investment performance metric in Hedge Funds and Mutual Funds. How to compute Absolute return? Suppose an investor Mr. Rich, invested AUD 50,000 5 years back, and the current value of his investment is AUD 75,000. The Absolute return on Mr. Rich’s investment would be 50 %, calculated using- Copyright © 2021 Kalkine Media Pty Ltd Copyright © 2021 Kalkine Media Pty Ltd So, Copyright © 2021 Kalkine Media Pty Ltd Absolute returns are just returns from point of time to other. The notion of an 'absolute return' seems very attractive to get investors’ attention as it ignores the relative market movement and promises an appreciation with zero correlation to markets. Anyhow, Absolute Return technique of computing investment yields is an apt way of calculating return on investment, predominantly in the early stages. There are numerous other types of return metrics an investor can look for later on. Major 4 types mattering most to investors being –  Absolute Return, Relative Return, Total Return & CAGR. What is the difference between Absolute Return, Relative Return, Total Return & CAGR? Absolute return refers to the gain/ loss in a single investment asset/ portfolio but to comprehend how their investments are acting relative to various market yardsticks, relative return is taken into consideration.   Relative return is the excess or deficit an asset achieves over a timeframe matched to a market index. Benchmark Return – Absolute return, gives the Relative return also called sometimes as alpha. Example, if S&P index gives a 10% return during a given period and one’s investment portfolio gives an absolute return of 12% then relative return on investment is positive/ excess 2%. Total returns take into account the effect of intermittent incomes as well as dividends. For example, in an equity investment of AUD 200 having current value AUD 240, the company also declares a dividend of AUD 10 during the year. Total returns will take into account this $10 dividend too. Thus, Total returns on the investment of AUD 200 now will be 25.00% = {(240+10-200)/200} x 100 Absolute and Total returns are easy to calculate as performance metrics, but the real challenge is when comparisons are drawn based on time period of return. Here comes in CAGR, it takes into account the term of the investment too, thus giving a more correct and comparable picture. It is computed as: CAGR (%) = Absolute Return / Investment period (equated in years) Consider for example, two investment options: One where investor earns absolute returns of 10% in 24 months and another where investor earns 5% absolute returns in 9-month duration. So, CAGR would be- For option one: CAGR = 5.00% i.e.  10%/2 (24 months/12 months is equals to 2 years) For option two: CAGR = 6.66% i.e. 5%/0.75 (9 months/12 months is equals to 0.75 years) What’s wrong with just measuring investment performance using Absolute Returns? Absolute returns will only tell an investor how much his/her investments grew by; they do not tell anything about the speed at which investments grew. When people talk about their real estate investments and say, “I bought that house for X in the year 2004. It’s worth 4X today! It has quadrupled in 17 years.” This is an application of absolute return. The drawback here is that it takes into account only the capital appreciation and doesn’t draw comparison with options having different time horizons. Investors can rely on this measure of investment performance only if they are looking for higher returns, without bothering how fast they were generated. Absolute return also doesn’t convey much about an investment compared to relative markets. Then, why do Hedge Fund/ Mutual Fund Managers choose an Investment strategy based on Absolute returns? Absolute returns should be used at times when investors are willing to shoulder some risk in exchange for a prospective to earn excess returns. This is irrespective of the timeframe and Fund administrators who measure portfolio performance in relation of an absolute return typically aim to develop a portfolio that is spread across asset categories, topography, and economic phases. They are looking for below mentioned points in their portfolios- Positive returns- An absolute returns approach of investment targets at producing positive returns at all costs, irrespective of the upside & downside market movements. Independent of yardsticks- The returns are in absolute terms and not in comparison to a benchmark yield or a market index. Diversification of portfolio- With the intention of distribution of risk, among different investment options producing positive returns in diverse ways a mixed bag of absolute return assets give a diversified investment portfolio. Less volatility- The total risk of investment is spread across the different asset held in such a portfolio. Ensuring less overall volatility in collective returns. Actively adjustable to market movements– Usually, investments look for positive returns with zero market correlation. Market shares a negative correlation with absolute return investments and vice versa. In any investment atmosphere, there are varied investment strategies and goals. Absolute return investment strategies are looking to avoid systemic risks using unconventional assets and derivatives, short selling, arbitrage and leverage. It is appropriate for investors who are prepared to bear risk for short and long-term gains.

Darvas Box system Every great trader/investor in the history of the markets had a specific method to approach the markets, which eventually led them to create a good fortune, Darvas Box system is one such method. It is a trend following strategy developed by Nicholas Darvas in the 1950s to identify stocks for good upside potential. This is one of the few methods to trade the markets which uses the combination of both the technical analysis and fundamental analysis for a much more refined decision.  The fundamentals were used to identify the stocks, and technical analysis was used to time the entry and exits. Who was Nicholas Darvas? Nicholas Darvas was arguably one of the greatest stock traders/investors during 1950s – 1960s, but surprisingly he was a ball dancer by profession and not a professional stock trader. Even while trading and building his fortune, he was on a world tour for his performances in many countries and took up trading as a part-time job. In November 1952 he was invited to a Toronto Nightclub for which he received an unusual proposition of getting paid in shares by the club owners. At that time, all he knew was there is something called stocks which moves up and down in value, that’s it. He accepted the offer and received 6k shares of a Canadian mining company Brilund at 60 cents per share, with the condition that if the stock falls below this price within six months, then the owners would make up the difference. This was the introduction of a professional ball dancer to the stock market. Nicholas Darvas couldn’t perform at the club, so he bought those shares as a gesture. Within two months, Brilund touched $1.9, and his initial investment of $3000 turned to $11400, netting in almost three times of his investment. This triggered a curiosity into the stock markets, and he started to explore trading. Origin of the Darvas Box theory Initially, he was trading on his broker’s recommendation, tips from wealthy businessmen, he even approached some advisory services or any source that he could get his hands on for the tips, but all led him to losses. After losing a lot of money, he decided to develop his own theory, and after a lot of trial and error, his observations and continuous refinements he eventually invented his theory “The Box Theory”. So what exactly is the Box Theory? Fundamentals Analysis As stated earlier, the box theory uses a judicious bend of both the technical and fundamentals. Darvas believed that in order to spot a good stock or even a multibagger, there should be something brewing up in the respective sector as a whole or some major fundamental change in that specific company. Generally, the fundamentals that Darvas used to study were on a broader sector level, and not the company-specific fundamentals. Even for the specific company Darvas used to look from a general perspective like, is the company launching a new product which could be a blockbuster hit. He completely refrained from looking at numbers and financial statements as his initial experiment with ratios and financial statements didn’t yield any good result. To know more on the three financial statements read: Income Statement (P&L) Balance Sheet Cash Flow Statement Technical Analysis Darvas was a big believer in price action and volume of the stock. He believed if some major fundamental changes were to take place in a company, this soon shows up in the stock price and its volume of trading as more people get interested in buying or selling the stock. With his observations here realized by just observing the price action, he can participate in the rally which gets triggered by some major fundamental development without actually knowing about the change. Using the box theory, Darvas used to scan stocks based on rising volume as he needed mass participation in the rally. Also, he only picked up those stocks that were already rising. His theory is all about “buy high, sell higher” instead of the conventional belief of “buy low, sell high”. After the stock satisfies both the parameters of increasing price and volume with major underlying fundamental change, Darvas looks to enter the stock. Good read on momentum trading. How and where to enter? Major part of the box theory is based on entry and exit levels. To enter a stock, Darvas looked for a consolidation phase preceded by a rally. A consolidation phase is the price action wherein the price moves up and down in a tight range, that is, a non-directional move. He would then mark the high and low of the consolidation phase with the horizontal line, essentially making it a box-like structure, hence the name “Box Theory”. The high point is called the ceiling, and low is called the floor. Whenever the stocks break above the ceiling, Darvas would look to buy one tick above the ceiling with one tick below floor as a stop-loss point. Pyramiding Darvas discovered early on, in order to become successful in the market your winning bets should yield much more profit than the loss in the losing bets. This led him to do pyramiding in his winning trade, which is clearly defined in the box theory. Pyramiding means to increase the existing position if the stock is going in the favour, which leads to a much higher profit in the winning trades. According to the box theory, the repetition of the entry criterion is the new signal for adding onto the existing position. In other words, after a position, if the stocks stage the same setup, that is, a consolidation after a rally, then the break above the ceiling of this new box would signal to increase position with the revised stop loss of 1 tick below the new floor. In any case, whenever the stock falls below the current floor, the entire position would we sold off at once. This is the only exit condition in the box theory, and there is no method of booking profit upfront as Darvas believed in holding on to a rising stock. The only way to book profit is to let the stock to take out the revised stop loss.

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 data warehousing? Data warehousing is defined as the method of gathering & handling data from different sources to get meaningful output and insights. Data warehousing is central to the BI system and is built for data analysis and reporting. Source: © nfo40555 | Megapixl.com In simple terms, a data warehouse is a large collection of data utilized by businesses to make investment decisions. What are the characteristics of data warehousing? Data warehouse has supported businesses in making informed decisions efficiently. Some of its key features are highlighted below: The data in a data warehouse is structured for easy access, and there is high-speed query performance. The end users generally look for high speed and faster response time – two features present in data warehousing. Large amount of historical data is used. Data warehouse provides a large amount of data for a particular query. The data load comprises various sources & transformations. What are the benefits of data warehousing? The Companies which used data warehousing for analytics and business intelligence found several advantages. Below are some of them: Better Data: When data sources are linked to a data warehouse, the Company can collect consistent and relevant data from the source. Also, the user would not have to worry about the consistency and accessibility of the data. Thus, it ensures data quality and integrity for sound decision making. Faster  decisions: Through data warehousing, it is possible to make quicker decisions as the data available is in a consistent format. It offers analytical power and a comprehensive dataset to base decisions on tough truths. Thus, the people involved in decision making do not have to rely on hunches, incomplete data, and poor quality data. It also reduces the risk of delivering slow and inaccurate data. How does a data warehouse work? A data warehouse is like a central repository where the data comes from various sources. The data streams into the data warehouse from the transactional system and other relational databases. These data could either be structured, semi-structured or unstructured. These data get processed, altered, and consumed in a way that the end-user can gain access to the processed data in the data warehouse via business intelligence (BI) devices, SQL clients and spreadsheets. A data warehouse merges the data that comes from various sources into a complete database. The biggest advantage of this merged data is that the Company can analyze the data more holistically. It also makes the process of data mining smooth. Copyright © 2021 Kalkine Media Pty Ltd. Component of a data warehouse A data warehouse can be divided into four components. These are: Load Manager Load Manager, also known as the front component, does operations related to the mining and loading the data into a data warehouse. Load manager transforms the data for entering into Data warehouse. Warehouse Manager The warehouse manager manages the data within the data warehouse. It analyses data to confirm that the data in the data warehouse is steady. It also conducts operations such as the creation of indexes and views, generation of denormalization and aggregations, modifying and integrating the source data. Query Manager Query Manager is a backend component that does operations concerning the supervision of user queries. End-User access tools End-User access tools comprise data reporting, query tools, application development tools, EIS tools, data mining tools, and OLAP tools. Roles of Data Warehouse Tools and Utilities The tools and utilities in a data warehouse are used for: Data extraction: The data extraction process involves gathering data from heterogeneous sources. Data cleaning: Data cleaning consists of searching for any error in the data. Data transformation: Data transformation process involves changing the data into a data warehouse setup. Data loading: This process involves data sorting, recapping, consolidating, verifying integrity. Refreshing: This process requires revising data sources to the warehouse. Application of data warehouse Data warehouse plays a considerable role across multiple sectors. Some of the sectors it caters to are highlighted below. Aviation sector In the aviation sector, a data warehouse’s role can be seen in crew assignment, route profitability analysis, any promotional activity. Banking Industry In the banking sector, the focus is on risk management, policy reversal, customer data analysis, market trends, government rules and regulations and making financial decision. Through a data warehouse, banks can manage the resources available on the deck effectively. Banks also take the help of a data warehouse to do market research, analyze the products they offer, develop marketing programs. Retail industry Retailers act as an intermediary between the producers and the customers. Hence, these retailers use a data warehouse to maintain the records of both producers as well as the customer to maintain their existence in the market. Data warehouses help track inventory, advertisement promotions, tracking customer buying trends and many more. Healthcare industry In the healthcare industry, a data warehouse is used to predict the outcome of any test and taking relevant action accordingly. Data warehouses help them to generate patient treatment report, offer medical services, track the medicine inventory. Many patients visiting hospital have health insurance. Through a data warehouse, hospitals maintain the list of insurance providers. Investment and insurance sector In the insurance and investment sector, the role of data warehouse becomes important in tracking the data pattern, customer trend and market movement.      Services sector In the services sector, a data warehouse is used for maintaining financial records, studying the revenue pattern, customer profiling, resource management and human resource management. Telecom The telecom sector uses a data warehouse in the promotion of its offerings, making sales decision, distribution decision, features to include in case they decide to launch a new product based on the customer requirement.   Hospitality The hospitality sector involves hotel and restaurant services, car rental services etc. In this sector, the companies use a data warehouse to study the customer feedback on the various services offered and accordingly design and evaluate their advertising and promotion campaigns.

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