Information Technology in Financial Organizations - Essay Example

Information Technology in Financial Organizations Word Count: 2592 Table of Contents Introduction Asset Management Credit Services Data Management Algorithmic Trading Conclusion Introduction Business functions in financial organizations in today's world are directed to attract more customers and investors. Just a few decades back, most of the work was carried out manually thereby requiring a lot of effort, manpower and time to carry out even the most basic functions of the organization (tam-inc., 2007). However, with the advent of information technology, all the functions became a cake's walk for the employees. More time and money saved by implementing information technology in financial organizations. The business functions were by far automated. Less manpower was required to perform a single operation. Various technologies have been invented to automate certain business functions of an organization. The type of technology depends totally on the type of operation and the organization's infrastructure support. The financial institutions have adapted to various advanced technologies so as to enhance the services they provide to the customers dependent on the upgraded business processes. Many financial institutions collect the information related to individual customers such as their personal details and their financial details associated with the institutions and various businesses carried out over a period of time. This is information is then processed and then certain data can be obtained automatically by implementing certain technologies. Asset Management Asset Management serves the investment needs of institutions, governments and government agencies around the world. An asset can be defined as anything owned by an individual that has a cash value, including property, goods, savings, and investments. Asset management, therefore, refers to the management of the assets by money managing teams. Though the major emphasis is on managing the investment portfolios of a company, asset management also includes management of physical assets such as money, equipment and property, as also the non-tangible assets such as information and the workflow processes (ittoolkit, 2007). Assets, in any commercial set up, include the monetary investments, plants, infrastructure and its human resources. Asset management is, therefore, a process that aims at the optimum utilization of resources for maximum returns at the minimum investment or costs. The first priority of any asset management team is to identify the company's 'assets' or resources. Once these are identified, the team can then focus on the business process or, in other words, understand the functioning of the tangible or non-tangible assets (netsimplicity, 2005). Preparing the monetary investment portfolios is an important aspect of asset management. The investment portfolios give a clear picture of the income- expenditure ratio, as well as the financial status of a company. Based on the study, the asset management team can remove deficiencies, or modify the investment structure to maximize returns. Property, plant, and equipment are the tangible assets of the company. Asset management involves the study and analysis of the actual property on which the plant is built and all the equipment that is required to run the business. Plant and equipment need effective management. Their depreciation values needs to be studied. Their analysis helps the team to arrive at a decision whether to repair or replace machinery in order to reduce running costs. Human Resources include the non-tangible resources of the company. Managing human resources involves studying individuals, departments; divisions, planning for improvement of skills, improving comfort level and security, and, thereby evolving a policy for maximum output by the employees (Cole, 2006). Ensuring accurate tax and paying for these on time is also one of the ways which companies consider an attractive option rendered by asset management. Depreciation, amortization, and other costs are also some of the costs that asset management accounts for, also valuable in increasing productivity and returns. Asset management also aids in the proper disposal of assets in ways that comply with environmental rules and regulations. Credit Services "Credit service charge" means a finance charge composed of the sum of (1) all charges payable directly or indirectly by the buyer and imposed directly or indirectly by the seller as an incident to the extension of credit, including any of the following types of charges which are applicable: time price differential, service, carrying or other charge, however denominated, premium or other charge for any guarantee or insurance protecting the seller against the buyer's default or other credit loss; and (2) charges incurred for investigating the collateral or credit worthiness of the buyer or for commissions or brokerage for obtaining the credit, irrespective of the person to whom the charges are paid or payable, unless the seller had no notice of the charges when the credit was granted (dmsvoip, 2006). The term does not include charges as a result of default, additional charges, delinquency charges, deferral charges, sellers' points or charges of a type payable in a comparable cash transaction. A credit card allows consumers to purchase products or services without cash and to pay for them at a later date. To qualify for this type of credit, the consumer must open an account with a bank or company, which sponsors a card. They then receive a line of credit with a specified dollar amount. They can use the card to make purchases from participating merchants until they reach this credit limit. Every month the sponsor provides a bill, which tallies the card activity during the previous 30 days. Depending on the terms of the card, the customer may pay interest charges on the amount that they do not pay for on a monthly basis. Credits cards are a relatively recent development. The banks and companies that sponsor credit cards profit in three ways. Primarily they make money from the interest payments charged on the unpaid balance, but they also can make money by charging an annual fee for the use of the card. The income from this fee, which is typically only $50 or $75 per customer per year, can be substantial considering that the larger companies have tens of millions of customers. In addition, the sponsors make money by charging merchants a small percentage of income for the service of the card (siperian, 2007). This arrangement is acceptable to the merchants because they can let their customers pay by credit card instead of requiring cash. The merchant makes arrangements to participate in a credit card program with a merchant bank, which in turn works with a card-issuing bank. The merchant bank determines what percentage of the total purchase value has to be paid by the merchant to the card-issuing bank. The amount varies depending on the volume and type of business, but in general it is between 1-2%. A percentage of that amount is kept by the merchant bank as a transaction-processing fee. For companies like American Express which sponsor cards, the processing fee may be significantly higher. Furthermore, sponsors may generate income by leasing credit card verification equipment to merchants (especially if the merchants can not afford to purchase the equipment themselves.) Finally, sponsors may profit by charging service fees for late payments. Data Management Data quality is crucial to high performance, but expensive to obtain. Investment Banks are being challenged with aggressive regulatory requirements and timelines to more accurately and effectively manage credit and operational risk (Marlin, 2005). A key element to adhering to these requirements is to accurately identify counterparties within their corporate hierarchy, react to corporate actions, and manage Security and Market data. Firms such as D&B, Avox, S&P, and CounterpartyLink provide high quality information for the legal entities and their corporate hierarchy, securities and corporate actions. This information must be matched and linked to accounts and transactions within the investment bank (accenture, 2006). Certain Data Management softwares act as the central repository importing data from multiple external data sources, organizing the information into corporate hierarchies, and linking these entities to the accounts and transactions in multiple internal systems. Information is then made available to downstream systems to support Credit Risk Management, Trade Operations, Compliance, and other programs requiring a complete view of the counterparty. Algorithmic Trading In electronic financial markets, algorithmic trading, also known as algo, automated, black-box, or robo trading, is the use of computer programs for entering trading orders with the computer algorithm deciding on certain aspects of the order such as the timing, price, or even the final quantity of the order. It is widely used by hedge funds, pension funds, mutual funds, and other institutional traders to divide up a large trade into several smaller trades in order to manage market impact, opportunity cost, and risk. It is also used by hedge funds and similar traders to make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information. Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following) (streambase, 2007). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically. As markets become more volatile, the potential to either make money or avoid large losses has become a real-time problem. Algorithmic trading, also known as algo trading, automated trading, "black box" trading, or "white box" trading, has traditionally required inflexible custom-coding by skilled developers in order to build and run the sophisticated computer programs that divide large trades into smaller trades and decide on order price, execution timing, execution venue, and quantity of share to buy or sell.Now, hedge funds, mutual funds, pension funds, and other asset management firms can leverage an organization's computer processing software and rapid development environment to test, build, and deploy low latency algorithmic trading applications (streambase, 2007). These applications which beat the competition, avoid market impact costs, reduce transaction costs, and increase profits can be built as little as hours to days and run at rates up to hundreds of thousands of messages/second. With algorithmic trading technology, leading trading organizations track critical market conditions across multiple markets and instantaneously execute sophisticated strategies to capture short-lived trading opportunities. A unique graphical development environment is provided that enables analysts or application developers to quickly build algorithmic trading applications, test various models and strategies, deploy new trading applications in production, and modify trading models or rules on-the-fly in reaction to market conditions. Many different algorithms have been developed to implement different trading strategies. These algorithms or techniques are commonly given names such as "iceberging", "Guerrilla", "benchmarking", "Sniper" and "Snif-fer". These algorithms include moving averages such as VWAP or TWAP, Bollinger Bands, MACD, RSI, spread pairs, iceberging, On Balance Volume (OBV) calculations, or your own custom analytics. Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called "iceberging". The success of this strategy may be measured by the average purchase price against the VWAP for the market over that time period. One algorithm designed to find hidden orders or icebergs is called "Guerrilla". By using the next generation query languages, including algorithmic trading technology in the a development environment, you can also easily integrate real-time and historical data, as well as back-test on up to years of historical data before immediately deploying on real-time streams without having to change the application or infrastructure. Algorithmic trading applications execute on the run-time server, a high-performance, enterprise-classengine which runs queries, computations and custom analytics on fast-moving market data streams (Bates, 2007). Algorithmic trading technologies can also detect patterns of trading activity and trigger an instantaneous response-with sub-millisecond latency, intelligently routing orders to an appropriate venue to optimize price, response time, transaction fees, or other attributes -- all within milliseconds. The faster the speed of the operations, the more efficient and faster the satisfaction of customer will be. Algorithmic trades require communicating considerably more parameters than traditional market and limit orders. A trader on one end (the "buy side") must enable their trading system (often called an "Order Management System" or "Execution Management System") to understand a constantly proliferating flow of new algorithmic order types. The R&D and other costs to construct complex new algorithmic orders types, along with the execution infrastructure, and marketing costs to distribute them, are fairly substantial. What was needed was a way that marketers (the "sell side") could express algo orders electronically such that buy-side traders could just drop the new order types into their system and be ready to trade them without constant coding custom new order entry screens each time. A classical arbitrage strategy might involve three or four securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency (Bates, 2007). If the market prices are sufficiently different from those implied in the model to cover transactions cost then four transactions can be made to guarantee a risk-free profit. Algorithmic trading allows similar arbitrages using models of greater complexity involving much more than 4 securities. Market making involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price in order to benefit from the bid-ask spread. Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange. A "benchmarking" algorithm is used by traders attempting to mimic an index's return. An algorithm designed to discover which markets are most volatile or unstable is called "Sniffer". Any sort of pattern recognition or predictive model can be used to initiate algo trading. Neural networks and genetic programming have been used to create these models. Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits. More sophisticated models and intelligent programs have created the question of whether the models will break down. Other issues include the technical problem of latency or the delay in getting quotes to traders, security and front running, and the possibility of a complete system breakdown leading to a market crash. The cost of developing and maintaining algorithms is still relatively high, especially for new entrants, as the need for stability, bandwidth and speed is even higher than for regular order execution. Firms which have not developed their own algorithmic trading have had to buy competing firms. For example Citigroup paid $680 million in July 2007 for Automated Trading Desk, a 19 year old firm that trades about 200 million shares a day, accounting for about 6 percent of trading volume in U.S. markets. Though its development may have been prompted by decreasing trade sizes caused by decimalization, algorithmic trading has reduced trade sizes further. Jobs once done by human traders are being switched to computers. The speeds of computer connections, measured in milliseconds, have become very important. Conclusion Algorithmic Trading as we have seen is one of the most implemented techniques in Investment Banking. Algorithmic Trading is compatible with any banking operational architecture which proves to be a strong feature for algo trading. The use of computer programs for entering trading orders with the computer algorithm deciding on certain aspects of the order such as the timing, price, or even the final quantity of the order involves the application development of an organization. Algorithmic Trading is also used to make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information. Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation. Such specifications are involved in the infrastructure of an organization. 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