Interested in The Trade Life Cycle?
From the point a securities order is placed in a digital channel, there are several operations applied on the order and these operations depend on the asset you are trading...
This paper will walk through the operations after the securities are placed in the digital channels.
On the completion of settlement and clearing, the buy side or the sell side should have the securities or cash received. But this is not the end of the trade life cycle, especially to the buy side as they are now holding the securities, their corresponding broker and custodian need to manage the ongoing economic, risk, and compliance of the securities. This is the Post-Settlement stage and the following are the key activities in this stage.
In this article, we will be covering the Booking, Credit Risk Management, Corporate Actions Handling, and Day-End Process.
Booking
Once the securities are received, the broker needs to book the securities under the securities account of the client according to the instruction from the client. Depending on the portfolio arrangement of the client, he may have more than one securities account for an identical asset class. For example, the client may have two securities account for Hong Kong stocks from HKEX, each of the accounts is under a different portfolio.
For a client who receives cash, the broker transfers the fund to the cash account of the client according to his instruction. In addition to multiple cash accounts, these accounts could be under different financial markets or jurisdictions. For example, after selling Hong Kong stocks, the client may instruct the broker to transfer his money to a cash account in Singapore.
Today’s booking process is handled by a rule engine. Rules could be specified for the booking operation. Then rule engine will base on the transaction details, client instructions as well as the standard operating procedure of the bank to identify the booking account. COMPASS Booking Rule Engine from Axisoft is one of the solutions for this purpose.
Credit Risk Management
In the event of margin trading, there is a requirement of the pledge of collateral which could be in cash or other assets. The broker must evaluate the pledged collateral to make sure it is sufficient to cover the risk imposed by the market fluctuation. Depending on the market volatility, the evaluation could be on a regular or real-time basis. In case of the fact that the collateral is insufficient to cover the trade exposure (collateral shortfall), the broker has to ask the client to top up the collateral before a deadline, or he has to sell the securities to eliminate the risk.
Along the process, the most critical one is the calculation of credit exposure. Given the fluctuation of the market today, such calculation is required to be in real-time. In addition, the calculation is complex and required to be flexibly changed. COMPASS Credit Modelling Engine provides a means to solve the problem. Users can build and validate their credit models in Excel, the engine can execute the logic of the models.
Corporate Actions Handling
Corporate actions are business decisions, activities, or events that affect the value, status, and economics of the securities. These corporate actions are triggered by securities issuers who will then distribute the corresponding events to securities holders directly or through different agents.
There are many corporate action events, the common events for stocks are merges or splits, dividends, and rights issues. The common events for the bond are coupon payments, final redemptions, etc. These events are either mandatory (stocks split) or voluntary (rights issues).
Corporate action events can be captured manually and input into the investment systems by back-office operators. To improve the efficiency of the corporate actions processing, events can also be captured through STP, in which real-time messages or batch files of the events could be fed into the investment systems.
In most cases, the investment systems cannot take the corporate action events information directly and update the account of investors. For an instance, banks have nominal accounts in the stock exchange or other agents, the events information is regarding the holding of all the clients of the banks. Banks need to process the information to obtain the actual economic impact on each client.
In other cases, the actual economics are not stated in the corporate action information, and the banks need to calculate the actual economics of the securities for each client. For an instance, the beneficiary entitlement of bond coupon payment.
Once the economics of the corporate action events are obtained, the corresponding transactions will be created in the accounts of the investors.
Day-End Processes
After the closing of the trading day (window) and before the opening of the next trading day (window), many backend processes are running to calculate the snapshot of trading data, for example, the daily portfolio valuation, credit exposure, etc. Such processes are referred to as Day-End Processes. Such processes run after the trading day because they need trading data, such as the transactions and price, to be frozen.
The snapshot of the trading data will be sent to different systems for further processing, such as:
The Portfolio Management System will take the portfolio valuation to calculate the portfolio performance.
The Risk Management System will take the credit exposure to determine if a collateral top-up alert is required.
The Accounting System will split and book the transactions in General Ledger.
The Reporting System will generate various reports for management and market regulators.
The Investment Analysis System will produce trend and market analysis for clients and RM.
The Statement System will generate bank statements for clients.
Given the complexity of the day-end process, the demand for the continuity of the business, and the high cost of the process failure, Robotic Process Automation (RPA) is getting into the picture of managing the day-end process.
RPA is a system agent (bot) with machine learning capability. It works like a data center operator that runs applications and makes intelligence responses based on the output of the application. You can train the bot by showing it how does an operator carries out the work and handle various exceptions. Once the bot learnt the operation, it can run it on its own.
For instance, the bot can open a user interface of report generation, input the report generate parameters, run the report according, wait for the completion of the report, then distribute the report according to report recipients. In addition, it could intelligence handle various situations based on the output shown on the screen.
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