Data Analytics That Drive Real-Time Profit and Loss Management

ActiveViam |
October 19, 2022

Data Analytics That Drive Real-Time Profit and Loss Management  

Profit and, more often than not, losses drive a firm’s reputation. As time is money, making the wrong decision can cost a hedge fund dearly on both. To avoid blind spots in decision making, now more than ever in lean economic times, the ability to calculate, investigate and analyze large and complex sets of data is paramount.

Easier said than done for many firms. Oftentimes, a fund’s risk systems are unable to aggregate massive volumes of complex, dynamic data in real time. A lack of speed coupled with the necessity to perform complex calculations on fast-moving data leaves many funds at a steep disadvantage. All too often funds rely on stale data and, as a result, they underestimate the risks they face as well as the validity of their numbers. This leaves a wide-open field of risk that can come from almost any direction, especially in volatile markets. 

Navigating the Daily PnL Quandary

When trading complex financial instruments, attributing profit and loss is not a “2+2=4” task as the financial result can not be automatically pre-determined by a simple price move. Some of our Customers use Greeks to explain PnL, and it’s particularly handy for intraday PnL approximation and attribution. 

Monitoring beta, or how a financial instrument performs compared to the broader market, is one thing but tracking risk exposures on non-linear derivative instruments requires more intensive calculations and, therefore, a tool suited for this use. Reconciling end-of-day profit and loss can be a time-consuming function, but trading is generally not. 

Let’s say a fund has a portfolio of tech stocks and a trader bought call options on a particular tech name. A few days later news reports say a deal that was expected to close at this same tech firm, and, therefore raise the value of the share price, was stymied by regulatory red tape. When this occurs and the price per share is dropping, each sensitivity level is breached and a trader needs to see immediately how to hedge or get out of a trade before losses pile up. Only a technology flexible enough to update risk metrics in real time would demonstrate a full picture of risk and therefore allow a trader to make an informed business decision.   

Some action may need to be taken to curb risk. For a trader, this means the ability to access actionable information in real time that can support quick and refined decision making as markets move. For a portfolio manager, it means the ability to look at the totality of the full risk/reward scenario in front of him or her and react with confidence.

Shattering Siloes 

Very often top management, portfolio management, traders and data scientists all use disparate and different applications. Everyone works in silos and therefore data are fragmented across the various systems. This creates a foggy view of risk across the entire fund and leads to miscommunication and misunderstanding of the data. It generates inefficient management of risk, as well as ultimately, losses.

Systems that are not adequate to perform train-of-thought analysis of large complex datasets at speed are the culprit here. Loss is a non-negotiable factor in a fund’s history. If a trader’s PnL begins to mirror a loss, with the right systems in place, there is time to salvage it. 

ActiveViam is a global software provider specialized in decision support for business professionals in the financial industry. Since 2005 our mission has been to accelerate the confident decision-making process by providing a data analytics and aggregation platform fit for purpose. It is our aim to empower the financial services industry to be ready for the unexpected and to eliminate any blind spots when making a decision. We empower hedge fund top management, portfolio managers and risk managers to be confident in their decision making. Our data analytics platform has been built from the ground up to meet the needs of complex trading operations. 

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