Enabling Access to Aggregated Data and ... - Avere Systems

White Paper

Enabling Access to Aggregated Data and Quantitative Models for Competitive Advantage Sponsored by: Avere Systems Bill Fearnley August 2016

IN THIS WHITE PAPER Financial firms face continued pressure to control costs, manage risk, increase return on equity (ROE), and find incremental alpha to boost investment returns. As leading firms take advantage of innovations in Big Data and analytics, they are using new sources of data and advanced analytics to boost returns and financial performance. Technically, the challenge is aggregating all the data required to support sophisticated models and assembling all the computing power and computing assets needed to run these complex models. Firms need to run their proprietary quantitative models on an as-needed basis. Typically, firms prefer to maintain their code and data on-premises because of both time-to-market and competitive reasons. The aggregated data comes from a combination of on-premises data sources and external data that is stored or accessed through the cloud. An interesting approach to taking advantage of the power of the cloud and lowering operational risks is to use a file system and data caching layer that helps bridge cloud compute resources with existing network-attached storage (NAS). Avere Systems has designed its tiered file system solution to manage high-performance hybrid cloud and on-premises computing and data storage operations. In the search for investment alpha, volatile markets demand faster and better analysis. Investment firms should increase their investments in scalable and on-demand capacity larger data sets to help improve analyst productivity and provide access to new sources and combinations of data.

SITUATION OVERVIEW Since the financial crisis, financial firms face multiple headwinds including increased regulation, new line-of-business and capital restrictions, and thinner operating margins as a result of the low interest rate environment. These factors continue to put pressure on revenue and earnings growth and improvements in their ROE. Investment firms also face increasing volatility in global markets and across asset classes. Market volatility puts increased pressure on analysts and portfolio managers to reevaluate their existing positions and search for new (and actionable) investment ideas. A myriad of events can affect volatility and need to be analyzed on an increasingly frequent basis to stay ahead of (and keep up with) market moves. For example, geopolitical turmoil, currency fluctuations, commodity prices, and central bank intervention have combined to increase volatility as well as credit and market risks.

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Credit risks are affected by borrowing standards used to evaluate counterparties and borrowers, portfolio risk management strategies and events, and circumstances that can hurt the credit standing of a firm's customers and counterparties. Effectively managing credit risk is a critical component of risk management and the long-term success of financial firms. Firms must constantly assess a myriad of market data and information to ensure their credit risks are within acceptable limits and that they are maximizing their risk-adjusted rates of return across markets and asset class. Investment firms face credit risk across a variety of financial instruments such as bonds, equities, options, derivatives, futures, and foreign exchange transactions. The analysis of past performance and firm experience can provide insights into markets, assets, and counterparties. And increasingly, firms are using aggregated data (e.g., market price data, regulatory filings, and industry news) and advanced analytics to evaluate credit risks and make portfolio changes accordingly to maximize portfolio alpha. As more financial firms use aggregated data and advanced analytics, portfolio managers and analysts must discover, develop, and test new investment ideas on a constant basis. Depending on the individual investments and the construction of the portfolio, investment managers often face a combination of domestic and international market risk factors. Firms must monitor and manage a myriad of market risks (e.g., currency, geopolitical events, interest rates, inflation, liquidity, and specific regional risks). Investment managers manage market risks through diversification across products, sectors, regions, and time horizons (e.g., short- and long-term holdings). As financial firms face increased pressure to manage risk, control costs, improve returns, and increase ROE, these challenges require increasingly sophisticated analysis and modeling. Advanced analysis requires an increasing number of sources and types of data and analysis of these inputs over longer time horizons to help capture as many market scenarios as possible. In this white paper, we discuss how firms are using aggregated internal and external data as advanced analytics to perform sophisticated analytics and simulations to help maximize investment returns and performance and to help mitigate risk (see Figure 1).

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FIGURE 1 Big Data and Risk Analytics Credit Risk

Market Risk

Operational Risk

Asset and liability management

Fraud risk

Liquidity risk

Compliance risk

Portfolio risk

Enterprise risk mgmt.

Source: IDC, 2016

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New data and advanced analytics that help firms discover and develop investment alpha: Firms are always looking for incremental information to help improve returns, limit losses, and help improve risk management and reporting. The ability to capture and analyze pricing histories enables portfolio managers and analysts to look at investment ideas and portfolio strategies over longer time horizons and over multiple economic, political, and market cycles. Firms continue to add external data sources, such as regulatory and legal filings, political data, supply chain data, and sensor data (e.g., weather tracking), that provide incremental information that help identify actionable inflection points. Investors are always looking for an investment edge that provides differential upside, and firms are increasing their investments in data aggregation and advanced analytics to help uncover new and actionable ideas.

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Credit and market risk: While firms have always analyzed and managed their credit and market risks, they are under increased pressure from regulators, customers, counterparties, and investors to reduce risk and increase investment returns and ROE. Assessing and analyzing risk require sophisticated quantitative models and huge amounts of market and pricing data to enable analysts and portfolio managers to build and operate sophisticated simulations. Often, the testing data sets are hundreds of gigabytes (and sometimes terabytes) in size because the models use pricing data on multiple types of financial instruments and asset types (e.g., equities, fixed income, currencies, commodities, and derivatives). In addition, file sizes are also affected by the time horizons of the data collected and used in the models. Analysts use longer time horizons to enable them to evaluate their risk exposures over a wider variety of market conditions and multiple market volatility situations. For example, in asset and liability management risk analytics, analysts use simulations to measure the effect of liquidity and changes in interest rates on profitability, liquidity, and the profitability of the firm. Often, analysts will run simulations over a variety of time horizons in an effort to analyze the potential effects of changes in asset and liability allocation.

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Technically, the challenge is aggregating all of the data required to support sophisticated models and assembling all of the computing power and computing assets needed to run these complex models. Firms need to run their proprietary quantitative models on an as-needed basis. Typically, firms prefer to maintain their code and data on-premises because of both time-to-market and competitive reasons. The aggregated data often comes from a combination of on-premises data sources and external data that is stored or accessed through the cloud. Often, firms have grown their on-premises computing capability and, therefore, have more compute power available for the firm. Many firms have invested in large numbers of servers, called clusters, that enable more sophisticated analytics by distributing workloads over multiple servers. Cluster computing combined with access to vast amounts of data enables analysts to run more complex simulations faster, which helps improve productivity and deliver actionable ideas and insights faster. 

Portfolio risk and investment analysis: Analysts and portfolio managers are constantly seeking out new investment ideas and strategies. Portfolio risk and investment analysis simulations against historical data covering multiple market and economic cycles help analysts get an idea of how their ideas will perform "in the wild." The ability to run pricing analytics on short notice is critical for investment firms and asset managers because timely and incremental insights can provide investment alpha as well as a competitive advantage. This is especially true in volatile markets as well as active news cycles in the markets (e.g., elections, corporate actions, central bank announcements, and earnings season). Analysts need the ability to test "what-if theories" in a myriad of situations and scenarios. If the ideas perform well against the historical back test, they can be implemented quickly and give the firm a competitive advantage. If the ideas don't perform well, the analysts can make adjustments to the simulated portfolio and run the model again in the search for better performance. And if the ideas perform poorly, it enables the analysts to "fail faster" and do more work to uncover other new strategies and investment ideas. Firms need the ability to run complex models and simulations that can be assembled and run quickly. Discovering, testing, and trading on actionable ideas can help boost investment returns, which helps attract new investors and additional assets to the firm.

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Operational risk: Financial fraud is a major challenge for financial firms because they can suffer financial losses and be assessed fines and other sanctions by regulators. Negative media coverage can hurt their brand and reduce customer, employee, and counterparty trust. Financial fraud detection and prevention continues to get more difficult as bad actors and other criminal elements get more aggressive in creating and developing new fraud schemes and tactics. Increasingly, firms are aggregating and analyzing disparate sources of internal and external information in an effort to uncover new patterns of activity and relationships that may be signs of financial fraud.

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Compliance risk and regulatory reporting: Financial firms must comply with a variety of compliance programs and are required to provide detailed periodic and ad hoc reporting about their efforts to detect and prevent financial crime, and these programs are often designed to help constrict the flow of funds to an array of bad actors. Compliance systems monitor transactions and customer profiles to help uncover suspicious activities that may be signs of compliance or regulatory violations. Many firms are augmenting their existing systems with aggregated internal and external data and advanced analytics in an effort to discover and monitor relationships between people, organizations, places, and events for signs that criminals and other bad actors may be working together to commit financial crime.

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Critical infrastructure for critical middle-office analytics: Financial firms are embracing Big Data and analytics in investment analysis and risk management. As new sources of data become available (e.g., new data-as-a-service providers and Internet of Things [IoT] sensor data), firms are evaluating their technology infrastructure to determine when new investments will yield the best returns. For some firms, the decision will be to not make major changes to their technology tools and staff. Other firms will consider increases in capital spending (capex) to access and aggregate more data and provide advanced analytics tools. But firms face increasing costs for real estate and the infrastructure required to build and support tier 1 datacenters (e.g., software, hardware, web and network access, redundant electrical power, and systems monitoring). Increasingly, leading financial firms are evaluating and investing in hybrid computing models. Hybrid models are gaining momentum because they allow firms to capture the advantages of on-premises computing resources (e.g., control and customization) and the advantages of cloud computing (e.g., burst capacity and capacity-consumed pricing models). Often, investment firms want the ability to customize and tune infrastructure for specific tasks and want the added security of having proprietary data and algorithms on-premises. But many firms are facing pressure to reduce or optimize their technology budgets, and they are looking for ways to provide "on demand" resources to support investment analysis and research. Cloud computing provides scalable or "burst" capacity that can be scaled quickly as needed, especially when demand for data peaks and spikes (e.g., market volatility, news cycles, and earnings season). During periods of increased volatility, portfolio managers and analysts often need to run multiple models and stress tests to evaluate present (and future) investment ideas and portfolio strategies. But how do firms leverage the power of cloud computing without putting competitive assets at risk?

SOLUTION OVERVIEW An interesting approach to taking advantage of the power of the cloud and lowering operational risks is to use a file system and data caching layer that helps bridge cloud compute resources with existing network-attached storage. Avere Systems has designed its tiered file system solution to manage highperformance hybrid cloud and on-premises computing and data storage operations. In addition:

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Faster access and improved aggregation of data: Analysts often need access to huge data sets for financial modeling, analytics projects, and investment portfolio simulations. Avere's high-performance file systems are clustered and can scale out across dozens of nodes in a single cluster. Large-scale clusters can handle tens of thousands of client connections, which enables firms to power thousands of computing cores concurrently, speeding up processes and allowing analysts to complete large projects with faster and fewer iterations. By speeding up the completion of individual jobs, firms can expand the number and rate of jobs processed, improving efficiency and capacity utilization while providing the additional compute capacity when it's needed most (e.g., volatile markets and event-driven volatility). Another benefit is that it aggregates internal and external data while maintaining the confidentiality of internal and proprietary models as well as internal data. The ability to aggregate large data sets also provides better throughput for analytics projects because the analytics tools can be pointed at a large data set just once and the project can be completed faster. Many financial services firms are investing in cloud-based data resources so that they can access capacity on demand without incurring the capital expenditures of staging their own infrastructure.

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Larger data sets mean fewer iterations and faster actionable insights: Analysts and portfolio managers are always on the lookout for new information and running analytics and models that can help them uncover actionable ideas and strategies. In the asset management and hedge fund segments, the search and battle for new and actionable ideas continue to be intense. Avere's file system enables firms to access large amounts of market data, whether firms store their market data on-premises or at leading cloud services providers such as Amazon Web Services and Google Cloud Platform by caching active data closest to the compute engines. And aggregated data means advanced models can be completed with fewer iterations, which can result in faster insights and a competitive advantage.

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Speed is important — Avere helps lower storage latency: For large analytic projects that involve parallel analysis, lower latency means that analysts can complete jobs faster and improve their productivity. The benefits of lower latency can be magnified over concurrent projects. Avere's file system (run on its physical or virtual FXT Edge filers) reduces system latency and delivers data and makes it available when and where the data is needed with active caching. This is also important for ad hoc inquiries for analysis and information from senior management, portfolios, and regulators. Again, low latency and speed are critical and add in the search for investment alpha and competitive advantage.

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Compute on demand and burst compute capacity: Capacity on demand is important for analysts because they often don't know exactly how much compute power they will need for specific projects. The flexibility of capacity on demand from the cloud can help control costs because analysts can get the capacity they need for specific projects and the compute capacity can be reallocated to others in the firm once the project is over. Improving capacity utilization helps improve return on investment (ROI) as well as analyst and portfolio manager productivity.

CHALLENGES/OPPORTUNITIES Increasingly, investment analysts and asset managers are using Big Data, analytics, and quantitative models to help discover, develop, and test investment ideas and strategies. And the pressure to discover new and actionable ideas is more intense today as regulatory restrictions, low interest rates, and market volatility put pressure on analysts and portfolio managers to get more innovative in developing new and actionable ideas. Many financial institutions are heavily invested in Big Data and analytics and have existing relationships with a myriad of solution providers. Many have also invested in advanced analytics capabilities and keep them in-house. And firms continue to look for new ways to use technology to discover new and actionable ideas and will continue to invest heavily in research tools and technologies. As a result, financial services remains an attractive market segment for Big Data and analytics providers. Financial services firms will often remain committed to platforms they invest in heavily, especially systems that support mission-critical operations (e.g., trading, investment research, and compliance). As a result, the financial sector will remain a focus for most Big Data and analytics vendors and will continue to be a highly competitive vertical market for solution providers because of the potential opportunities available.

ESSENTIAL GUIDANCE In financial services, data is the fuel that feeds the profitability engines. Big Data and analytics combined can improve investment performance and provide alpha, reduce potential losses, and help monitor and manage risk across the firm. And firms should be developing strategies to deploy on-premise IT resources and cloud services to improve capacity utilization to boost investment returns,

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find incremental alpha, and improve the efficiency of technology infrastructure and staff. In the search for investment alpha, firms should increase their investment in the following: 

Scalable capacity — capacity on demand. Financial analysis runs on data and sophisticated models. Increasingly, analysts and portfolio managers need access to aggregated data sets to test their proprietary quantitative models and get actionable ideas and data faster. Therefore, firms need access to scalable and on-demand compute capability and data that enables them to combine internal and external data and allows proprietary data and financial models to remain on-premises. And often, the demand comes in waves. During market volatility, uncertain times present opportunities as well as new risks, and firms are using data and advanced analytics to improve investment returns and better manage risk.

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Larger data sets to help improve analyst productivity — shorter time to actionable insights. Often investment managers and hedge funds use data sets that are hundreds of gigabytes or even terabytes in size. It is critical that analysts and portfolio managers get to run sophisticated quantitative models against larger data sets because they enable them to do it in fewer iterations. The ability to run the quantitative models in shorter time allows analysts to sift through their investment ideas and "hunches" more quickly, thus enabling them to spend more time with more promising ideas and discard investment ideas and strategies that don't test well.

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New sources and combinations of data. Big Data and open source tools and technologies have enabled firms of all sizes to get access to more powerful analytics and new sources and types of data. As a result, the bar has been raised for all investment managers and hedge funds. The search for alpha and proprietary insights has gotten more challenging, which requires firms to look for new sources and combinations of data to analyze with proprietary models — for example, combining transaction data, market pricing data, and supply chain information to analyze sell-through trends and changes in revenue, margins, and earnings.

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Volatile markets demanding faster and better analysis. Market volatility has increased as a result of central bank moves on interest rates, geopolitical events, and increases in program trading and volatility as a result of market events. Volatile markets require better analysis both for regulators and for investment managers. And firms need to be able to satisfy the needs of regulators, investors, and employees for faster analysis for compliance, fraud, and other periodic regulatory reporting requirements. Increasingly, firms are under pressure to provide sophisticated analysis in response to ad hoc queries from investment managers, customers, and regulators. The ability to access data and compute power on demand is an important component to being able to do sophisticated analytics more quickly and as needed.

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About IDC International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications and consumer technology markets. IDC helps IT professionals, business executives, and the investment community make factbased decisions on technology purchases and business strategy. More than 1,100 IDC analysts provide global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries worldwide. For 50 years, IDC has provided strategic insights to help our clients achieve their key business objectives. IDC is a subsidiary of IDG, the world's leading technology media, research, and events company.

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