The changing shape of buy-side risk technology

Buy-side risk managers and FactSet’s global head of quantitative analytics gathered for a Risk.net webinar to discuss topical risk management trends for asset managers and to consider the industry challenges posed by the recent Covid‑19 pandemic

The Panel

• Boryana Racheva-Iotova, Senior Vice-President and Global Head of Quantitative Analytics and Risk, FactSet
• Racim Allouani, Head of Portfolio Construction and Risk Management, KKR
• Lisa Wang, Director of Investment Risk Management, AllianceBernstein
• Moderator: John Anderson, Contributing Editor, Risk.net

Intense competition, market volatility and a demanding regulatory environment continue to raise the stakes in investment risk management. As asset managers grapple with squeezed budgets and elusive sources of return, the unprecedented disruption caused by the Covid-19 pandemic has served as a painful reminder that future gains and innovation will rely on sound risk management principles across the full set of portfolio and compliance risks. 

Risk leaders must enable and support new investment platforms, data and analytics capabilities, operations and strategies, while ensuring their enterprises remain sound, secure and compliant. 

One discussion topic during the webinar was how specific trends that had already begun taking shape in the industry, had accelerated as a result of Covid-19. These trends include the requirement for reviewing the approaches towards building asset location mix, asset-liability management (ALM) and goals-based investing into wealth management. 

According to Boryana Racheva-Iotova, senior vice-president and global head of quantitative analytics and risk at FactSet, the socioeconomic and geopolitical uncertainty caused by the pandemic has already resulted in much more sophisticated approaches to building asset allocation mix and ALM. 

 “A lot of acceleration has been observed within the solutions and advisory groups within the asset management community, as well as the trends towards shifting assets under management that are outsourced, chief information officers, and so forth,” she said. 

She addressed FactSet’s focus on supporting firms and risk managers through ongoing change and upcoming trends: “We continue to see those trends being quite strong and, through that, the need to support our clients with data, models and solutions to execute on those activities more efficiently,” she added. 

There has also been a greater focus on building a more holistic understanding of risk, specifically liquidity and credit risks. A key question for the market right now is how the recovery from the Covid-19 pandemic will happen, and what the recovery process will look like. 

Given the market volatility during the crisis, asset managers have needed to get a better grip on understanding alpha and upside potentials, and must be able to take advantage of these opportunities. 

 “We see much more attention on more detailed analysis of alpha as well as risk, really understanding exposures extremely well and what can lead to disruption and dislocation of the exposures, dislocations in the correlations between different asset classes, as well as between securities and types of risk drivers,” said Racheva-Iotova.

New approaches, new trends

A newer approach to risk has also been a major trend. There has been increased attention on risk budgeting, particularly tail-risk budgeting, tail contribution to risk from factors, as well as a group of assets’ securities. 

 Deliberative risk management is also getting a lot of attention. This approach requires special tools and risk models that necessitate full repricing to capture all of the non-linearities that can come with particular trades, as well as robust stress-testing. 

 “In terms of stress-testing, we see hugely increased interest in the types of stress tests, as well as the complexity of the stress tests being built,” said Racheva-Iotova.

Stress-testing has also evolved for AllianceBernstein. Just as different levels of aggregation are being included more, stress tests are being incorporated into earlier stages of the portfolio construction process. For example, if a particular amount is allocated to a specific position, strategy or sector, there needs to be an analysis of the various levels of risks shown within the stress tests.  

“Those types of risks get aggregated at an earlier stage of the portfolio construction. So that is one of the things that has been evolving and has really accelerated post-Covid-19,” said Lisa Wang, director of investment risk management at AllianceBernstein. 

For Racim Allouani, who oversees portfolio construction and risk management at KKR, the main risk management focus has been ensuring companies in the private market have enough liquidity to survive the current pandemic, as well as to potentially keep going during a further shutdown should things change in the future. 

 “We might have to pick our battles, and what we are a little concerned about in the post-Covid world, is that we will probably see some attrition and higher defaults. It might be concentrated in some pockets of the market,” he said. 

In credit, the recovery – or lack of – has varied depending on the specific sector. Retail, travel, leisure and energy have been hit materially, while other sectors, such as pharma, utilities and tech, are outperforming. While orderly and expected, these dispersions have been crucial to monitor and then capitalise on from a risk management perspective for KKR. 

For other firms, there has been a gradual increase in demand for a more comprehensive risk analysis. At AllianceBernstein, the risks at the portfolio level, as well as various layers of aggregation –whether it be at country, sector or strategy level, individual stock or individual positions level – must all be factored into risk management analysis and decisions.

 That has developed into looking through “multiple lenses” of risk, said Wang. 

“The other trend we have observed is to bring in a more integrated risk view, in the sense that we care about the allocation to individual stocks, we care about the risk contributions coming from individual positions, we care about what their stress-test characteristics are like, we care about their liquidity. So, for portfolio construction, we are looking through multiple lenses of risks as well,” she said. 

The growing importance of data

To be able to manage and review this comprehensive risk analysis means there is a greater demand for larger datasets and for predesigned sets of reports into data in the most consistent way possible. This helps risk managers stay on top of volatile sectors too. 

The importance of data is a growing trend among buy-side risk managers, particularly as the use of machine learning and artificial intelligence is on the rise. More data and more diverse data is continually needed as the spectrum of risk drivers grows. 

“But that data needs to be useful data, and it should be data that helps us to isolate the risk-related signals instead of just introducing noise into the risk modelling process,” said Racheva-Iotova.

For FactSet, risk data involves two perspectives: the breadth and quality of datasets, and lookback periods that can be meaningful. For example, the relationship between equity markets and credit default swaps (CDS) 20 years ago is hard to fathom because there may be a lack of data from that period and some markets might not have existed, such as CDS. Machine learning can help in these circumstances. 

Alternative datasets are becoming increasingly important, and can constantly change the risk parameters as the market landscape becomes more complicated and new types of risk forces and risk drivers are constantly changing the profile. 

“Those are alternative new datasets that are definitely helpful, but, again, you need to have a particular purpose, you need to have a particular goal, and then certainly look for the right datasets,” said Racheva-Iotova. “In some instances, the datasets themselves will first of all have to be analysed through machine learning techniques in order to extract the relevant signals before incorporating them into the risk modelling.”

The use of big data has been particularly helpful to KKR during the Covid-19 pandemic. The firm used ‘high-frequency data’, such as the patterns of credit cards, spending, reopenings and hospital data, and a lot of big data that had not typically been tapped into to any great extent. 

“This helped analyse which parts of the economy would be reopening faster. We were doing that in China, for example, because they faced the whole crisis before the West. So we have been using much more data, including alternative data, in this episode of Covid-19, even for the private side,” said Allouani. 

Managing risk in today’s world of asset management sits alongside bringing on the right technology solution. Larger businesses have an advantage given their deep pockets, and Allouani feels it could be “difficult” for small and medium-sized players to remain relevant without combining the right technology with the necessary talent pool. 

“It needs to go hand-in-hand with the necessary talent who can understand the benefits of the technology, apply it for the purpose of the particular asset manager and for the purpose of the investment approaches and investment mandates that they have,” he said. 

Theoretical innovation

While theoretical innovation has been around for decades, especially for public markets, implementing it practically within an organisation and its governance is the harder aspect of a risk manager’s job.

“This is the type of proven innovation that is a must for everybody. This type of innovation is something that needs to be observed within the risk management process, no matter the size of the asset manager. Some asset classes can be managed with relatively accessible technology and others need more customisation and out-of-the-box thinking and implementation,” said Allouani.

For some small to medium-sized players that want a comprehensive set of risk analytics but don’t necessarily have the same budget as some of the bigger players, the key is to plan this in a more structured way. 

 Technology can also be a strength in this kind of scenario. For example, business intelligence-type analytics can help link with the risk data. The type of processing that is generated from this business intelligence software can save businesses a lot of time in terms of building their own risk presentations.

“If you want to view different layers of risk in your individual portfolios, into your individual sectors and strategies, business intelligence software is a tool you could actually use for the small to medium-sized players. So you don’t have to build everything from scratch with a large budget dedicated to a technology development effort,” said Wang. 

Technology has also played a role for AllianceBernstein from an operational risk perspective during the Covid-19 pandemic, and accelerated another trend that was already starting to take hold at the asset manager – a concerted effort to move to a virtual work environment, which has picked up pace since March. 

“Within our own firm we have been migrating to a virtual work environment even before Covid-19. So, even when we are in the office, we don’t have to log in from a particular desktop. We have virtual desktops set up so we can log in from anywhere within our building. Post-Covid-19, we are logging in through a virtual private network, or VPN, but the virtual desktop environment has already been set up so that transition itself is seamless,” concluded Wang.

 

Watch the full webinar, The changing shape of buy-side risk technology and the role of data for risk managers

The panellists were speaking in a personal capacity. The views expressed by the panel do not necessarily reflect or represent the views of their respective institutions.

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