In the ongoing geopolitical crisis, investment firms are monitoring wild swings in asset prices. With uncertainty weighing on financial markets, the need to integrate with risk models and consume risk data for intraday calculations is moving upstream on the buy-side trading desk. Recently, risk monitoring has become a front-office concern with portfolio managers seeking more transparency into risk factors. This is particularly relevant to hedge funds and asset managers applying factor investing to their portfolios.
In the past, risk factor monitoring was a post-trade exercise, delegated to the middle-office. Data reports were often generated outside of the main portfolio management/trading tools and traders had to manually input data from Excel spreadsheets and other systems into their pre-trade risk analytics. Receiving insights from those risk factor exposures sooner, enables the PMs to make more timely, better decisions upfront.
Emphasis on risk is evolving as better tools become more available for investment managers to proactively monitor risk in synch with portfolio management and trading functions, noted an article in The Hedge Fund Journal. No longer viewed as a secondary process, many hedge funds have made risk factor analytics part of the portfolio management and trading function, moving it up to the front-office.
Now that there is recognition that factor analytics has moved upstream in the investment lifecycle, data is no longer just for quants, data analysts, and risk managers. Risk matters to everyone, especially to traders and portfolio managers evaluating investments and constructing portfolios as well as compliance officers.
As factor investing has grown over time, the requirements of front-office teams are changing along with the demands they are facing from investors, regulators, and compliance officers.
According to a recent report by Aite Novarica Group, buy-side risk management is moving toward multi-asset consolidation. “Risk management at buy-side firms continues to evolve with an increasing need to support the front office with ad-hoc analysis and ease the regulatory burden of greater transparency, stress testing, and liquidity risk management,” states the report’s summary.
Factor Analysis and Investing
Historically, risk factor models were mainly leveraged by leading quantitative firms who construct portfolios based on factors, such as momentum, value, quality, or low volatility, or risk management teams within traditional asset managers. More recently, however, the factor investing style has expanded to include other types of managers.
Factor analysis has gained adoption as investment firms see it as a “more precise tool” for adjusting a portfolio, reported Institutional Investor in “Sectors and Industries are So Old School. Investors now make Moves using Factors.”
According to the Invesco Global Factor Investing Study by the asset management giant, investors said they are taking a multi-factor approach and manage exposures more actively.
“Investors have sought exposure to a greater range of factors over the past three years, with value, quality, and low volatility now the most prevalent,” wrote Invesco in the study.
About three quarters of total respondents use multi-factor strategies and 30 percent make tactical adjustments in the short run, indicating their exposures are regularly updated.
In fact, 41% of investors surveyed by Invesco told the firm that they expect to be more “dynamic” in the next two years. Dynamism is about to accelerate, with 29% of investors saying their approach has become more dynamic over the past two years.
While PMs certainly look at risk exposures every day, this depends on how the model is calibrated and how sensitive the factors are to changes to the underlying constituents, which could change every day based on underlying volatility.
What has evolved over time is the technological capability to get insights from those risk factor exposures, which enables the PMs to make more timely, better decisions upfront.
Different Kinds of Factor Investors
In our view, there are several types of factor investors who will benefit from consuming risk model data in their front-office workflow.
Someone who is an “active factor investor” uses factor exposures and analysis to make factor-based investments, and thus needs the data closer to the portfolio manager and/or trader. Fundamental asset managers and long-short equity hedge funds use risk model data (i.e., such as momentum, value, or industry exposures like technology) as an input to decisions, usually on a monthly or quarterly review cycle and are considered “factor aware.” Even funds that don’t use risk factors as inputs to their investment decisions still do passive monitoring of risk factors on an ex-post basis.
The variety of flavors of risk models that investment managers can choose from is endless. They range from single asset class fundamental models, price-return based models made to be responsive to single-name risk and stress testing, to all-encompassing multi-asset factor models. Funds focused on macro trends can map granular factors to economic drivers, including credit, economic growth, rates, and inflation.
Additionally, risk factors can bolster traditional performance attribution. By decomposing positions into factors, managers gain powerful insight into traditional Brinson-style attribution through a risk factor lens. This helps answer questions like, “What underlying factors drove positive returns of my portfolio? Do I have any unintended risk exposure given my asset mix and positioning?”
Risk Models & the Integrated OEMS
With the increased demand for risk data in the front office, FlexTrade’s unified order and execution management system (OEMS) has integrated openly with different risk factor models.
FlexTrade’s OEMS, FlexONE, is already integrated with the two gold-standard providers of risk models – Axioma (Qontigo) and Barra (MSCI) – as well as with proprietary client-derived models.
Through FlexONE’s open architecture and flexible application programming interfaces (APIs), the OEMS can integrate and consume risk data from the risk models and then run calculations in real time against current exposures shown in the OMS.
Known for speed and high performance, a key differentiator of the FlexONE OEMS is its computational horsepower to run calculations in real time as market conditions change.
The ability to consolidate risk model data from external systems onto the OEMS blotter provides advantages to the buy side. By contrast, where we’ve seen a lack of functionality is when risk systems are standalone, they have positions as of yesterday, providing the PM or trader with a more static view.
A main concern in the industry is not having access to substantial data because it lives in a different technology than the OMS that PMs are mainly using. With our open architecture framework, FlexONE integrates disparate sets of data, whether its risk, ESG, or other types of data, to use throughout the system.
As orders and executions update the OEMS in real time, we allow PMs to do ‘what-if’ analysis based on proposed orders, providing PMs the ability to see what their factor exposure would be if the proposed orders were executed. Customized dashboards allow for a true “before and after” view of a portfolio, all in real time. As an added layer to the risk management framework, compliance and oversight teams may want to write rules and set limits on these factor exposures. Because factor data is fully integrated with the compliance engine, these rules are looking at the most current value of factor exposures, encompassing the most up-to-date view of positions, orders, fills, and latest prices within the OEMS. For example, a growth manager may not want to have more than 10% long exposure to a value factor.
Given the open architecture of FlexONE, we do much more than simply mirror and display data back to users. Once data is consumed into the platform, the power of the infrastructure enables us to deliver data in meaningful ways to PMs and traders. We take data access to the next level by turning it into actionable insights. This is a major differentiator of the platform since our clients’ partnerships with external data providers are what drives the roadmap of our integrations. Ultimately, these trends get embedded into FlexONE, whether it’s clients needing real-time risk data today or ESG-related data tomorrow, the applicability of this framework is endless.
Calculating Risk Exposures Intraday
In a holistic manner, the OEMS can show a firm their portfolio, positions, PnL, and orders, and then overlay a hypothetical trade. For example, let’s say the PM is thinking of buying $20 million of Apple and wants to see the impact on their portfolio exposures both at the sector level, and simultaneously at the risk factor level. The PM will get an immediate view of what that portfolio could look like before sending orders to the desk to be executed.
In the past, this process would be less real-time and was based on static numbers and positions, or it would require manual intervention to invoke the analytics engine to run. In some systems, there’s also the danger that the calculations will get stuck running. As an OEMS, FlexONE is the central hub for the latest view of positions. The secret sauce in FlexONE is the ability to run those calculations in real time throughout the day on a large quantity of positions.
A PM or trader can now adjust risk exposures on-the-fly based on real-time analytics, have the orders created and instantaneously run allocation logic, order marking, electronic locates, and compliance checks while then seamlessly routing trades electronically, all from a single platform.
Looking forward, we expect the front-office requirements for risk data to evolve further, especially as multi-factor investing gains more adoption on the buy side and extends to different asset classes. Citing “a rapid advance in the sophistication of factor investing user,” the Invesco study notes that 78% of factor users include ESG as a factor in their portfolios and that 55% of investors are using factors in fixed income, up from 40% the previous year.
As factor investing strategies become more sophisticated, we expect increased demands will be placed on the frequency of reporting and overall risk technology architecture. In the Aite Novarica risk report, nearly a quarter of respondents (23%) agreed that new requirements for supporting risk teams on the buy side include “flexible dashboards and custom reporting,” while 19% cited “demand to consolidate risk systems.” This is where the computational horsepower to calculate risks and analytics instantaneously/intraday becomes necessary and pairs well with the greater speed for large scale results which are the key differentiators for FlexONE.
As PMs and traders face regulatory pressures for more reporting on risk exposures, institutions will expect the risk systems landscape to evolve to meet their needs for stress testing and liquidity management. In the end, there is a push to accommodate multiple asset classes through the lens of factor investing, along with the need for ‘what-if’ calculations based on real time exposures, all of which points to the strengths of a high performance OEMS.
Jose Cortez, CFA is currently VP of Buy-Side OEMS Sales at FlexTrade, a global leader in multi-asset order and execution management systems.
Jose’s career combines buy-side experience with investment technology and analytics expertise. Prior to joining FlexTrade, Jose managed strategic relationships for BlackRock’s Aladdin business, providing investment and risk solutions for asset managers. Jose held roles in portfolio construction and trade implementation at The Boston Company Asset Management, a global active equity manager, now part of Newton Investment Management. Jose started his investment technology career at Eze Software Group (now SS&C Eze), leading engagements with hedge funds and asset managers.
Jose holds a BBA in Finance from the University of Massachusetts, is a CFA Charterholder and member of the CFA Society Boston. Jose is on the board of directors for The UMass Foundation which manages the university system’s endowment.