Reference data is an important asset in financial firm. Due to recent crisis in global market, regularity changes and explosion of derivative and structured products, the need for valuable market & reference data has become central focus for financial institutions. For any financial transaction accurate information/data is the key element and faulty data is the major component of the operation risk.
Reference data used in financial transactions can be classified as static and dynamic
Static Data: Data elements which have unalterable characteristics such as financial instrument data, indexes, legal entity/ counterparty, markets and exchanges.
Dynamic Data: Variable data such as closing and historical prices, corporate actions.
Reference data is stored and used across front office, middle office and back office systems of the financial institutions. In a transaction life cycle, reference data is used to interact with various systems and application internally and externally. Problems related to faulty reference data continue to exist and this leads to increased operations risks and cost.
To reduce data related risk & issues and contain cost, financial institutions are looking at innovative solutions to improve data management efficiency. Centralization, standardization and automations of data management process is key to achieve this goal.
Poor data quality; lack of global standards; presence of data silos; multiple data sources leading to inefficiency in the whole data governance process.
Data duplication and redundancy across various business functions.
Lack of data governing policies.
Lack of standardized data definition.
Time consuming data source onboarding process.
Inconsistent data leading to poor reporting and management.
High manual intervention in data capturing and validation process.
Poor data quality is leading to
Deploy centralized reference data management system and create data management framework.
Create golden copy of the reference data received from the various sources within an organization that can be accessed by all business functions.
Update the data daily/real time at this single point.
Validate data at single place before distributing to relevant business functions.
Resolves data exception centrally to avoid issues at downstream systems.
Improve process efficiency by centralization of data management.
Reduced operational and data management cost.
More control over data quality and change management.
Reduced turnaround time for new business needs and meeting new regulatory requirement.
Early detection and resolution of potential data issues.
Reference data is the data used to classify other data in any enterprise. Reference data is used within every enterprise application, across back-end systems through front-end applications. Reference data is commonly stored in the form of code tables or lookup tables, such as country codes, state codes, and gender codes.
Reference data in the capital market is the backbone of all financial institutions, banks and investment management companies. Reference data is stored and used in the front office, middle office, and back-office systems. A financial transaction uses the reference data when interacting with other associated systems and applications. Reference data is also used in price discovery for the financials instruments.
Reference data is primarily classified into two types –
Static Data– Financial instruments & their attributes, specifications, identifiers (CUSIP, ISIN, SEDOL, RIC), Symbol of exchange, Exchange or market traded on(MIC), regulatory conditions, Tax Jurisdiction, trade counterparties, various entities involved in a various financial transaction.
Dynamic Data– Corporate actions and event-driven changes, closing prices, business calendar data, credit rating, etc.
Market data is price and trade-related data for a financial instrument reported by the stock exchange. Market data allows traders and investors to know the latest price and see historical trends for instruments such as equities, fixed-income products, derivatives, and currencies.
Legal Entity data
The 2008 market crisis exposed severe gaps in measuring market credit and market risk. Financial institutions are facing a hard challenge to identify the complex corporate structure of the security issuer and other counterparties & entities involved in their business. Institutions must have the ability to roll up, assess, and disclose the aggregate exposure to all the entities across all asset classes and transactions. Legal Entity is the key block of this data which will help the Financial institution to know all the parties with whom they are dealing with and help to manage the risk.
The Regulation rules like The Foreign Account Tax Compliance Act (FATCA), MiFID II will require absolute clear identification of all the entities associated with the security. LEI plays a vital role to perform such due diligence.
Data Acquisition – Data is acquired from leading data providers like Bloomberg, Reuters, IDC, Standards & Poors, etc.
Data Processing –Data normalization & transformation rules are applied & validation processes clean the data.
Golden Copy creation – Cleaned & validated data is transformed into more trusted Golden Copy data through further processing.
Data Maintenance – Manual intervention if necessary to handle the exceptions that cannot be handled automatically.
Distribution/Publishing – Golden Copy data is published to the consumer application like Asset Management, Portfolio Management, Wealth Management, Compliance, Risk & Regulatory applications, other Business Intelligence platform for Analytics.
Importance of efficient EDM system
The fast-changing regulatory & business requirements of the financial industry, poor quality of data, competition demand a high-quality centralized data management system across the firm.
In current market situation, companies must be able to quickly process customer requests, execute trading requests quickly, identify holdings and positions, assess and adjust risk levels, maximize operational efficiency and control, and optimize cost all while implementing regulatory and compliance needs in a timely fashion.
An efficient EDM system enables the business to –
Establish a Centralized database management system
Reduced manual work
Decreased operational risk
Lower data sourcing costs
Having a better view of data
Governance & auditing needs
Better overview of risk management
Tailor-made user rights
Analytics & data-driven decision
Challenges need to overcome
Data quality & data accuracy.
Siloed data and disparate data across firms making it difficult to have a consolidated view of the risk exposure.
Keeping the cost lower in such a fast-changing financial market.
Ability to quickly process customer requests, accurately price holdings, assess and adjust risk levels accordingly.
The complexity of the latest national and international regulations.
Corporate actions industry is making great strides towards automation. However, despite all the technology advancements a significant portion of the process of managing corporate actions data requires manual processing mainly due to the increasing complexity of corporate actions thanks to cross border trading made easier and local market nuances.
Another big reason why the Corporate Action industry has not achieved such a significant degree of automation lies in Corporate Actions as a back-office process which is normally seen as cost management not as revenue generator which hinders the securities firm to invest too much.
Corporate Actions processing could be divided into 3 parts:
Capture of Corporate Action data
Processing of Corporate Action data
Dissemination of tailored Corporate Action data
Each of the 3 parts has its own challenge in its way. Capturing the data is the first step in the process where we are actually working for a “Golden Copy”. A golden copy of data is selecting the best possible value from variety of source. Generating a golden copy provides the first headache to securities firm. The data from issuers are normally transmitted in the form of press releases, prospectuses, and other free text format files e.g. PDF, HTML etc. The challenges for the securities firm lies in the translating these unstructured data into information and transmitting them to various stakeholders using the standards. These various stakeholders are none other than financial industry participants – custodians, sub custodians, brokers, prime brokers etc. Their primary aim is to capture the data from various sources and produce a golden copy for the investors. This golden copy is disseminated to various investors/intermediaries depending on the need e.g. an asset/investment manager could need the information as soon as possible to enable him to decide the investment strategy whereas a portfolio manager would require it to adjust the NAV end of day.
The information that is sent to various investors does not only include golden copy data or event data, it also includes data of their holdings and entitlements from the corporate actions. This information brings in an interpretation risk where the various stakeholders does not only depend on the custodian feeds but they rely on the local feeds which are more efficient in way of presenting the data which could not be standardized in global standards e.g. tax data. Failure to interpret corporate action information correctly may lead to suboptimal trading decisions by brokerage and fund management firms for clients or for proprietary positions.
The first and foremost challenge as explained above in Corporate Action processing lies in the capture of event announcement and creation of golden copy. However, it is only the first step in a lifecycle of a Corporate Action. The more complex events which include various voluntary events e.g. tender offer, merger, rights offer, exchange offers etc. requires a lot of instructions/elections to be delivered for the event. This upward chain of communication is very complex where the elections are delivered in non-standard format via emails, phone and brings in a lot of risks. The more intermediaries in chain, the tighter would be deadline to respond back as each intermediary would set up its own deadline to process the election. The effect of corporate actions on share prices and trading activity is generally seen on important dates e.g. announcement date, ex-date, record date etc. Hence, the decision from an investor could change several times and the securities firm could receive multiple elections on the same positions. The other critical factor in election processing is the current holdings of the investor which needs to be up to date as the time of election or the processing could of election on wrong holdings could have adverse effects. The wrong holdings could be result of trading or lending activities which have not been updated in the books. Frequent reconciliation of holdings is a significant step to reduce this risk.
Capturing the data, creation of golden copy, distributing the data to different intermediaries and investors, processing of instructions for complex event does provide a lot of challenge however, the final frontier is still to be conquered where the payments of the corporate actions to be made and accounting has to be done.
Mandatory corporate actions such as dividend and interest payments, are straightforward, in that they only require a transfer of money from the bank account of the issuer to the bank account of the intermediaries and then to investor. For income from cross-border security holdings, the payment may operate less smoothly, and a delay may occur between the payment date and the time at which the cash reaches the beneficiary’s account.
For complex events which involve processing of shares, the process becomes more complex with fractions coming into picture. Sometimes, these fractions are paid as cash in lieu other times they need to be ignored. Addition/Ignoring of these fractions at the intermediary level could ultimately lead to different consolidated entitlement at its agent level. E.g. at an intermediary level, the consolidated holding is 300 shares with 3 investors each having 100 shares. In case the distribution ration of share is 1:3 where one share will be provided for every 3 shares, the consolidated position of intermediary entitled it for the benefit of 100 shares ((100+100+100)/3). However for each investor it resulted in 33.33 shares. The handling of fractions in such a case could have different implications all together
Providing cash in lieu ⇒ Intermediary does not get any cash because of rounded holdings hence it has to sell the extra share and distribute cash to each investor.
Rounding down/up ⇒ Intermediary in this example gets an extra share/less share depending on the holdings.
Other important aspect in payments / entitlements of Corporate Actions is taxation. An intermediary normally depends on local sources for tax information. Globalization of financial industry has provided an exponential rise in cross border trading activities. This means the more investors are impacted by the corporate action on a security. Taxation for an investor depends on its residency status and thus have the impact on the entitlements / payment of corporate actions.
Taxation on corporate actions is normally seen as a value added service and not all custodian are the tax agents for their investors. Taxation on corporate actions brings in lot of complexity in terms of:
Types of taxation e.g. withholding tax etc.
Part of entitlements on which tax needs to paid. Sometimes it could be cases that investor does not need to pay tax on complete or full entitlements e.g. Church tax in Germany, unfranked dividends in Australia etc.
Residency of investors. Local investors are sometimes exempt from taxes but not the foreign investors.
Tax credits where a part of tax is given back to investor.
Double taxation treaty where the reclaims are made by investor as a part of double taxation treaty between the two countries.
Apart from calculation of tax, notification of these tax details in standard form is still a frontier unexplored for the organizations. Each intermediary tries to collate this information in their own and then send to the investors which have their own methods to interpret these messages.
By automating the various corporate actions functions, organizations can ensure long-term operational efficiency and effectiveness.
Corporate Actions and Client Servicing:
Each financial organization is looking for a new way to lure clients by providing various personalized services. These now include the range from corporate actions which the organization process. Timely, high-quality corporate actions information in the front office enables better-informed trading and investment analysis and decision-making; it helps support global investment strategies, reduces interpretation errors and benefits the monitoring of accounts and positions.
Finally, in a world where FinTech and automation are at the realm of every organization, in the near future we may witness a significant change in the way Corporate Actions are processed.
A “trading system” creates a set of trading strategies which are applied to the given input data to generate entry and exit signals (buy/sell) in a trading platform.The traders/professionals who create the trading strategies to maximize the profit are called “Quants”. They use exhaustive quantitative research & analysis to build such efficient strategies by applying advanced statistical and mathematical models.
Algorithmic trading – Algorithmic trading uses various algorithms to create a trading strategy from trading ideas. The algorithms are backtested with historical data and then used with real market data to give the best return. The execution can be done manually or automated.
Quantitative trading – Advanced mathematical and statistical models are used in Quantitative trading creation and execution of trading strategies.
Automated trading – Automated trading involves automated order generation, submission, and the order execution process. However, they are not fully automated. Manual interventions are also required
HFT (high-frequency) trading – Trading strategies can be classified into low-frequency, medium-frequency and high-frequency strategies as per the holding time of the trades. High-Frequency Trading strategy holds the trading position for a fraction of a second time and executes the trading strategy automatically. Millions of trades are an executed per day in this model.
The most of the algo-trading is high-frequency trading (HFT), which attempts to capitalize on placing a large number of orders at very fast speeds across multiple markets and multiple decision parameters, based on pre-programmed instructions.
The other name of Algo Trading is black box trading.
The profit opportunities are higher in algo trading and it makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities via sentiment analysis.
Algorithmic Trading Strategies
Momentum/Trend Following: Calculate 50 days SMA (Simple Moving Average) Calculate 200 days SMA Take a long position when the 50 days SMA is larger than or equal to 200 days SMA Take a short position when the 50 days SMA is smaller than 200 day SMA. This is one of the most common algorithmic trading strategies. This follows trends in moving averages, channel breakouts, price level movements and related technical indicators. Algo Trader assumes there is a trend in the market and use the statistics to determine if the trend will continue. It does not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends. The above-mentioned example of 50 and a 200-day moving average is a popular trend following strategy.
Arbitrage Opportunities: Buying a dual listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks versus futures instruments, as price differentials do exists from time to time. Implementing an algorithm to identify such price differentials and placing the orders allows profitable opportunities in an efficient manner. Also, trading can be triggered by the acquisition of the issuer company. This is called corporate event. Such event driven strategy is applied when the trader is planning to invest based on the pricing inefficiencies that may happen during a corporate event (before or after). Bankruptcy, acquisition, merger, spin-offs etc could be the event that drives such kind of an investment strategy. These strategies can be market neutral and used by hedge fund and proprietary traders widely. Index Fund Rebalancing: Index fund has defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20-80 basis points profits depending upon the number of stocks in the index fund, just prior to index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and best prices.
Machine Learning based The major aspect of ML is learning from past data and predict the outcome of an unseen or new situation. The human learns in the same fashion however machine can process a huge volume of data much faster than human and predict the outcome. This is the way trading system works. Traders handle a large volume of historical data, analyze them and predict the stock price to establish a various trading strategy. Hence machine learning has become one of the key elements in Algo Trading system. There are many types of ML techniques depending on the nature of target prediction: Regression, Classification, Clustering, Association. The other set of categorization is Supervised (Target prediction is known to the model) vs Un-Supervised (Target prediction is unknown to the model) techniques. Python is a powerful language which supports statistical computations and can work with ML algorithms easily. R is another powerful language for statistical analysis.
Mathematical Model Based Strategies:( source Investopedia) A lot of proven mathematical models, like the delta-neutral trading strategy, which allows trading on a combination of options and its underlying security, where trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero.
Trading Range (Mean Reversion): Mean reversion strategy is based on the idea that the high and low prices of an asset are a temporary phenomenon that reverts to their mean value periodically. Identifying and defining a price range and implementing an algorithm based on that allows trades to be placed automatically when the price of asset breaks in and out of its defined range.
Volume-Weighted Average Price (VWAP): The volume weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock specific historical volume profiles. The aim is to execute the order close to the Volume Weighted Average Price (VWAP), thereby benefiting on average price.
Time Weighted Average Price (TWAP): Time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times, thereby minimizing market impact.
Percentage of Volume (POV): Until the trade order is fully filled, this algorithm continues sending partial orders, according to the defined participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels.
Implementation Shortfall: The implementation shortfall strategy aims at minimizing the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when the stock price moves adversely.
Benefits of Algorithmic Trading
Trades are executed timely and instantly to get benefit from best possible price change
Reduced risk of manual errors in placing the trades order and achieved higher performance
Reduced transaction costs
Take the benefit of multiple market conditions
Backtest the algorithm, based on available historical and real-time data
Reduced possibility of human error based on emotional and psychological factors of traders
Algo-trading is used in many forms of trading and investment activities, including:
Mid to long term investors or buy side firms (pension funds, mutual funds, insurance companies) who purchase in stocks in large quantities but do not want to influence stocks prices with discrete, large-volume investments.
Short term traders and sell side participants (market makers, speculators, and arbitrageurs) benefit from automated trade execution; in addition, algo-trading aids in creating sufficient liquidity for sellers in the market.
Systematic traders (trend followers, pairs traders, hedge funds, etc.) find it much more efficient to program their trading rules and let the program trade automatically.
Algorithmic trading provides a more systematic approach to active trading than methods based on a human trader’s intuition or instinct.