The practice of quantitative analysis has moved well beyond its initial proponents in the hedge fund community and has established itself as an integral part of the overall investment process — even if the notion of quantitative analysis can differ widely from simple quantitative screening tools to completely computer-generated buy and sell signals.

With a notable increase in the total number of quantitatively driven funds, the pressure to come up with the next big quant model has grown exponentially. On average, the entire process — from idea generation to implementation — can take anywhere from 10 weeks to seven months. Given the fact that certain short-term strategies remain effective for only three to four months, rapid construction and implementation of alpha models becomes that much more urgent.

Unfortunately, today's market reality is littered with disparate homegrown and third-party applications tied together only by the creativity of the quants. This reality has fueled the need to streamline the overall workflow process and shorten the duration of the entire investment selection life cycle to compete more effectively.

As a result, the industry has seen the emergence of alpha-generation technology platforms, which are designed to create a more centralized and streamlined process and functionality to vastly improve the overall productivity of quants. As the pressure continues to mount for faster turnaround in the alpha-generation process, the market demand for a single platform to efficiently unify the entire workflow will grow rapidly.

Market Overview

At the highest level, quantitative analysis can be defined as those strategies that leverage computing power and sophisticated mathematical and statistical models to identify alpha investment opportunities. Most quantitative strategies utilize number-intensive technical analysis through various inputs — such as price, open interest, volume, volatility, and other variables that might impact overall trading and market conditions. Over the years, quant firms have created very complex quantitative strategies that employ not only technical analysis, but also fundamental variables, economic indicators and even digitized news feeds.

Aite Group's (booth #4123) estimates on the actual size of the global quant market include all traditional investment management firms and hedge funds that go well beyond simple stock screening and apply quantitatively driven monthly, daily or even intraday predictive models to drive investment decisions. In this much larger universe, 12 percent of all global assets under management (AUM) was driven by quant analysis at the end of 2007, representing US$6.65 trillion. This figure is expected to reach US$12 trillion by the end of 2010. In the hedge fund community, rapid growth of AUM in more sophisticated investment strategies (i.e., statistical arbitrage, relative value, equity market neutral, event-driven, etc.) has fueled adoption of quantitative investment analysis, accounting for close to 75 percent of all global hedge fund AUM.

From a regional perspective, North America leads the market, accounting for approximately 50 percent of global quant AUM at the end of 2007. However, Europe is not so far behind, and is expected to reach close to US$4 trillion in global quant AUM by the end of 2010.

The Quant Analysis Infrastructure

Some of the key components of the current quant analysis infrastructure ecosystem include the following:
• Data sources: Key data providers in this market vary widely depending on the data needs of the quant fund.
• Databases: Various databases can be used for the purpose of storage and testing, including some of the leading relational databases, such as SQL and Oracle. However, for quickly processing and storing massive volumes of historical and real-time data, high-performance databases are imperative.
• Analytics and statistical packages: Leading statistical packages, such as MATLAB, S+ and R (an open-source version of S+), are prerequisite tools of the trade for quants.
• Complex event processing (CEP): While to date most of the CEP engines have made their mark on the execution side of the business, a surprising number of firms are using CEP engines on the analysis side as well. CEP engines are especially useful when back-testing and going through simulation scenarios using high volumes of historical and real-time tick data.
• EMSs: While quant funds can act on system-generated signals by calling a broker or automatically creating orders to be routed to appropriate execution venues, the last leg of the quantitative investment workflow typically will end up with the orders being fed into an execution management system.

Integrating and working well with all of these different components represents a significant hurdle for most firms. Alpha-generation platforms have emerged in recent years in order to unify the different components and also to provide an efficient alpha-discovery development environment. Key characteristics of alpha-generation platforms include:
• Seamless integration with leading data sources and databases for rapid data capture, transformation and storage for later analysis;
• Ease of use in creating rigorous back-testing and simulation environments;
• Availability of prepackaged operations, routines, strategies and factors;
• Detailed documentation of the model-creation process;
• Charting, reporting and visualization tools;
• Ease of integration with leading statistical packages;
• Codeless environment for rapid strategy development;
• Full straight-through processing capabilities to enable quants to go from idea generation to order generation in a drastically reduced time frame.

This table presents a sample group of firms in the alpha-generation platform market.

Market Adoption and IT Spending

The demand for alpha-generation platforms clearly exists in the hedge fund community as funds continue to battle fiercely to capture additional alpha in an increasingly crowded marketplace. However, with the concept of quantitative investment analysis expanding into the mainstream investment arena, the need for a single platform to streamline the overall workflow will increase even more among traditional asset managers.

Aite Group estimates that at the end of 2006, firms had spent approximately US$12 million on alpha-generation platforms. However, driven by the continued adoption of electronic and algorithmic trading, the explosion in data messaging volume and type, and the growing market clout of low-latency players, the overall market demand for alpha-generation platforms will skyrocket over the next few years, reaching close to US$120 million by the end of 2011 (see chart).

Once shrouded in mystery, the quant world is gradually shedding its veil. It seems clear that the quant market is undergoing an evolutionary process, moving from the secrecy of the few to a more open and highly competitive landscape where every second counts. As competition in the quant world continues to increase, the demand for an all-encompassing platform designed to reduce development errors and time to market is destined to grow dramatically. Those firms able to implement a highly efficient process for alpha discovery will gain a significant competitive edge for years to come.