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As the current generation of market microstructure arbitrage strategies reaches the saturation point, a new generation of quantitative strategies and methods is emerging. But the fourth generation of quant research and trading requires much more complex analytics and performance than the high-frequency era.

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  • Propeller-Head Ubiquity: The Next Generation of Quantitative Trading

    E. Paul Rowady, Jr. | V12:039 | June 2014 | www.tabbgroup.com

    Data and Analytics

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Vision TABB Group has been closely studying and commenting on the evolution of quantitative trading for many years. And though the fruits of quantitative research methods had already permeated a vast spectrum of trading strategies to varying degrees, depending on the level of workflow automation, among other factors just a few years ago, high-turnover versions of quant strategies attracted the most attention. They still do. This is primarily because of their awe-inspiring departure from anything related to human scale as well as tales and growing evidence of their staggering risk-adjusted returns (and absolute profits). Paradoxically, extreme success always defies attempts to remain hidden, providing an attractive scaffolding for pundits, media and students of the markets on which to build their narrative. In June 2011, TABB Group published Quantitative Research: The World After High-Speed Saturation. Our hypothesis at that time was that the latency arms race would result in fewer players capturing more theoretical capacity in core markets, asset classes and products the tip of the spear thereby forcing all high-speed quant players (that were committed to remaining in the game) into new or additional regions, asset classes, products, and/or slower turnover frequencies. In other words, ultrahigh-speed trading in US equities had become saturated, and players would need to diversify. Heres the excerpt:

    Without so much as a whimper, the whole game has changed right under our noses. Like a Rose Bowl parade marching band taking that hard turn onto Colorado Boulevard, the quant game is set to head in a new direction, except without the hoopla. Thats the nature of secretive sports: Theres no trophy presentation at the end of the season. And theres no memo about the lockout. The more alert players move on, never disengaging from stealth mode. Of course, if last years [2010] Securities Industry and Financial Markets Association (SIFMA) technology conference was any indication, the saturation of latency eradication systems was palpable, indicative of the changes we face today. A handful of firmswearing both sell-side and prop jerseysare successfully defending the limited capacity in high-frequency and micro-market structure arbitrage strategies in the US. With the prohibitive costs of incremental speed and dwindling volumes, punctuated by regulatory zeal, the average quant team must face up to the inevitable: change or die. Alpha is elusive, ephemeral and enigmatic. There is no guarantee of persistence. For many of the players, the required measure of change will be too much to handle. Over the past year, two prominent option-focused prop shops have yielded to the superior speed and prowess of their competitors and taken their balls home. In one of these cases, you couldnt possibly have thrown more bleeding-edge, out-of-this-world low-latency technology at the problem. The impediments were beyond hardware, but more likely related to a lack of quantitative creativity. Undoubtedly, others will follow. For those willing to brave the journey ahead, there are plenty of juicy opportunities to explore, some of which will certainly surpass the capacity and profit potential of current microstructure arbitrage trades. But hunting for new sources of alpha requires new gear. Surrounding and feeding this new gear is a requirement for new skills. Quants need to cultivate a new zest for solving even messier data challenges; they also need to develop a passion for the financial equivalent of a deep anthropological dig. The need for speed will never end. Only the disproportionate focus of the recent past will be reallocated. Cross-asset, cross-regional, multi-temporal, asymmetric versus symmetric trades, and even enhanced front-to-back automation will all benefit from this reallocation. Welcome to the age of multidimensional alpha.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    In March 2014, we conducted a survey to help us update our understanding of the pulse of the quantitative trading landscape. The results of that survey can be found in this report. Based on our understanding of quantitative history and coupled with these latest survey results TABB Group is updating its vision and hypothesis for the quant landscape: First, saturation has spread beyond US equities into other regions, products and asset classes. Today, fewer players are capturing more of the available theoretical capacity for global high-speed alpha. At the same time, that theoretical capacity has been shrinking. Second, the future for quantitative methods has never been brighter. Our multi-dimensional alpha concept is still intact. But the bridge from here to there as stated in earlier research will require new skills and the digestion of a lot more data. Lastly, quantitative methods will permeate more of the traditional investment strategy spectrum and front-to-back office workflows to the point of propeller-head ubiquity. Oddly enough, ubiquity will shroud quants in a cloak of invisibility. Automated methods will become indistinguishable from everything else, since automated methods are becoming an increasing large component of the whole. The only question now is: When?

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Table of Contents

    VISION................................................................................................................................................................2TABLEOFCONTENTS...........................................................................................................................................4INTRODUCTION..................................................................................................................................................5

    2014QUANTSURVEY.......................................................................................................................................6QUANTITATIVETHEORETICALCAPACITYFRAMEWORK..............................................................................................6

    INGREDIENTSFORSPECIALSAUCE.......................................................................................................................8LOWHANGINGFRUIT.......................................................................................................................................12

    ASSETCLASSDIVERSIFICATION..........................................................................................................................12PRODUCTCLASSDIVERSIFICATION......................................................................................................................13REGIONALDIVERSIFICATION..............................................................................................................................14

    HIGHHANGINGFRUIT.......................................................................................................................................16ARAREGLIMPSE...............................................................................................................................................19CONCLUSION....................................................................................................................................................23

    ENDOFAGENERATION....................................................................................................................................23BEGINNINGS..................................................................................................................................................24

    ABOUT..............................................................................................................................................................25TABBGROUP................................................................................................................................................25THEAUTHOR.................................................................................................................................................25

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Introduction Although the metaphor might not be entirely accurate, we cant help but think of the Roadrunner and Wile E. Coyote with the quants starring as the Roadrunner and the rest of us observers, for better or worse, as Wile E. Since mid-2011, the lather over high-frequency trading (HFT) has only grown, especially recently with the publication of Michael Lewiss Flash Boys. The chatter on TabbFORUM and its newly launched cousin, QuantFORUM, also has provided a growing archive of fact-based research, ravings of opinion leaders, and other commentary. Beyond the ebbs and flows of the media circus, however, there are many more tangible clues that have added up over the interim period that have provided rare glimpses into the revenue breakdowns of leading proprietary quant shops and which further serve to support or refute our original hypotheses: that those firms engaged in the highest-turnover strategies, and primarily those focused on US equities, would need to look elsewhere; to focus in other regions, asset classes, and products and ultimately, across all of these, including slower turnover frequencies. We have called that new era Multidimensional Alpha, and identifying where we are in that overarching narrative is the goal of this report. Since mid-2011, the regulators, too, seem to be more on their game when it comes to high-speed trading in comparison to many other topics. The January 2013 rollout of the Market Information Data and Analytics System (MIDAS) by the US Securities and Exchange Commission (SEC) is quintessentially indicative of the idea that Wile E. may finally be gaining ground on the Roadrunner (See Exhibit 1). Or, at least, that someone is paying attention to TABB Groups ongoing recommendation to show more of the data in the form of pictures! Exhibit 1 MIDAS - Order Book Reconstruction Visualization Tool

    Source: Securities and Exchange Commission Unlike in the cartoons, TABB Group believes that Wile E. does periodically catch up to the Roadrunner that the observers and analysts can eventually attain insight into how certain micro-structure arbitrage strategies work. That part is really the story of alpha decay and natural information leakage and explains why purveyors of these strategies

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    need a continuous flow of new ideas. What formerly lasted years or months, now may last only weeks or even days. Because of this dynamic, our original prediction on the growth of multidimensional alpha was right at least in part. Are we at the end of the high-speed arms race? Could we have finally reached the theoretical capacity at the tip of the spear? Could there be only one firm or a few firms capturing all of it? And if so, whats next?

    2014 Quant Survey In March 2014, TABB Group conducted a survey of market participants targeting mainly quants, but generally any informed observer of the evolution of quantitative trading strategies. Exhibit 2 is a breakdown of firm types from this survey.

    Exhibit 2 Survey Methodology Breakdown of Firm Types (N = 51)

    Source: TABB Group

    However, before we dive into the newest clues to the evolution of quantitative trading, it would make sense to showcase the prism through which we are observing this landscape. It turns out that, though broad in terms of the possibilities, the scope of those possibilities is finite. Identifying sources of theoretical capacity is where most quant strategy discovery begins.

    Quantitative Theoretical Capacity Framework At its base, this story is about automating that which formerly has been done manually. We have made this claim ad nauseam over the years, but it still bears repeating. First, tasks in a workflow become automated, and then significant components if not, the entirety of the workflow become automated. Following full workflow automation is refinement, optimization, and higher-performance automation. In some product classes, we are clearly at the stage of highest performance automation; and where microscopic shifts in market structure implementations can open and close alpha capture opportunities in an instant. Where do quants look for alpha? The optimal target exhibits a convergence of factors, but generally comes down to two: liquidity and level of automation. (A third factor complexity makes the scenario even more ideal, but we will get to that later.) Those products that are liquid and highly automated represent a breeding ground for high

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Sharpe Ratio (SR) strategies. This means that smaller players can leverage a small amount of capital for high returns with low risks (see Exhibit 3). Exhibit 3 Quantitative Strategy Framework

    Source: TABB Group The fact that quant equity strategies can have little or no exposure at the end of a day makes all the difference in the world; since holding positions overnight creates margin requirements and an exceedingly more complex level of portfolio construction, going home flat is a huge deal. But as competition has heated up, and the arms race of latency-sensitive infrastructure relative to the perceived theoretical alpha has shifted, quant firms have been seeking new sources of performance. This means migrating beyond the high-capacity/highest Sharpe Ratio products (blue zone, above) closer toward the fringes of capacity and SR (green zone, above). Translated, this means new regions, products and asset classes. This was the claim we made in mid-2011 and more specifically in mid-2013: that quant firms would need to look in new product classes, asset classes, and regions for quantitatively-harvested sources of alpha. Turns out, the Roadrunner already was a few steps ahead of us. In the next 3 sections of this report, we showcase and respond to the findings of TABBs March 2014 quant survey. In the 4th section, we highlight publicly available data from two leading high-speed trading firms that ultimately complement the survey findings. The 5th section summarizes TABBs latest views on the state and future of quantitative trading and methods.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Ingredients for Special Sauce The components of competitive advantage or special sauce, in the vernacular for quantitative trading firms are the most intensely discussed and often misunderstood topics on the global markets landscape. This is the part of the story where the survey yielded some of the most fascinating results. First, as background, our line of questioning divided the sources of competitive advantage framework into four components: human capital, software and processing, data, and hardware and infrastructure. When asked to rank these components, survey participants were clear in their opinions overall. Human capital translated by TABB as developer, analytical and management skills is the most important ingredient for special sauce in quantitative trading/methods; and, surprisingly, hardware and infrastructure the stuff that is perceived to deliver the speed was ranked last. Software and processing vs. data as competitive components were ranked almost evenly (see Exhibit 4). Exhibit 4 All Survey Participants - Sources of Competitive Advantage (n=27)

    Source: TABB Group These rankings were different than what we might have originally expected. However, looking more deeply into the results, proprietary trading firms expressed differing opinions from the averages which were more in-line with our original expectations (see Exhibit 5, next page).

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Exhibit 5 Proprietary Trading Firms - Sources of Competitive Advantage (n=14)

    Source: TABB Group Prop firms ranked hardware and infrastructure as the strongest contributors to special sauce, with data a somewhat distant last, on average. TABB Group believes that the overall variance in results is indicative of the underlying variance in strategies. We also believe that attribution of competitive advantages to hardware and infrastructure is often a misinterpretation of the value of human capital; that human capital influences, both positively and negatively, the inherent value of hardware, infrastructure, software, and processing. This is true, also of derived data / analytics. When these results are taken in conjunction with the results from the expected change (1-2 years from now) in sources of competitive advantage (see Exhibit 6, next page) and overlaid with our experience with quantitative trading strategies (both in practice and observation), we have the following reactions: Hardware: Hardware and infrastructure is a declining source of competitive

    advantage for a vast majority of quant players. TABB Group has been calling this the democratization of infrastructure for the past year or more. With nearly plug-and-play level ultralow-latency connectivity offerings from the likes of CFN Services, Verizon, BT Radianz and others representing the growing performance competiveness of third-party solutions as well as other platform offerings such as UBS NEO quant firms that compete largely on speed will find hardware, though still incredibly important, a declining source of competitive advantage. Only the largest, leading firms will be able to continue to afford to deploy and maintain mainly proprietary infrastructure and, even then, those firms could be challenged by further declines in capacity for trading strategies using core products, asset classes and regions. Going forward, quantitative firms will 1) off-load more of their infrastructure needs to managed services providers, and 2) simultaneously migrate more infrastructure to one focused more on computational engines able to execute more complex calculations at much higher speeds than today. These developments will become part of the new special sauce, and where connectivity, co-location and even cloud grow in the realm of competitive necessities.

    Human Capital: TABB Group believes that human capital should always be ranked highest relative to the other components of competitive advantage, independent of trading strategy or stage of workflow. Human capital is the one source of special sauce that is least susceptible to democratization. This is where we found the strongest shift in opinions in the survey data. Furthermore, TABB believes that certain players high ranking of hardware and infrastructure is actually a misinterpretation of the value of their human capital. With proprietary infrastructure

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    remaining a key competitive advantage by a declining list of firms, persistence of this special sauce can only happen by virtue of the skills and creativity of key employees.

    Data: Though we might have expected a different outcome from these results, it is true that data or at least, raw market data is the most democratized of the four components in our framework. For certain asset classes and products in the most developed regions, both historical and real-time market data is incredibly clean. Scrubbing unclean market data used to be a competitive advantage. Now it is not. Today, everyone has access to the same clean market data despite the fact that direct access to many sources of market data is expensive. Custom developed security masters the reconciliation of security ID taxonomies across vendors and platforms used to be a competitive advantage, too. Now it is not. Along the road ahead, new strategies will require a mix of new and different data sources. We can detect a sense of this from a slight uptick in the importance of data in the forward-looking results of the survey (refer to Exhibit 6, next page). Though they will lack the velocity in message rates of most market data, these new sources many of them from fundamental, economic and social media realms, among others will represent new complexities on the basis of their diversity. And, of course, reference data especially legal entity identifiers (LEIs) that are critical for use in fixed income and rates strategies are essential for mapping context in inherently illiquid products and markets. Harvesting more signal from more data will be among the leading themes for new strategy development in the coming era.

    Software and Processing: Like data, the ranking of software and processing as a competitive advantage for quant strategies fell somewhere in the middle. TABB Group interprets this ambiguity as a statement about the relatively limited level of processing that actually occurs in the highest-turnover strategies. TABB Group expects software and processing to become more important as the data complexity, highlighted above, explodes in the development of next-generation quant strategies.

    Coordinated Framework: TABB Group expects the ranking of competitive ingredients for next-generation quant strategies will be as follows: human capital, software/processing, data, hardware/infrastructure. Truth be told, in an optimized environment these factors should be more equivalent.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Exhibit 6 All Survey Participants Expected Change in Sources of Competitive Advantage (n=27)

    Source: TABB Group

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Low-Hanging Fruit TABB Group has been closely monitoring the diversification of high-turnover and the broader spectrum of quantitative trading strategies for many years. Our claim in 2011 that ultrahigh-speed trading in US equities was saturated is further borne out in our most recent findings. In fact, this sentiment finally may have gone mainstream with comments like dont bother joining the high-speed arms race coming out at recent industry conferences. The difference between 2011 and now, however, is this: While US equities were saturated where leading quant firms were often consuming the alpha of other slightly slower quant firms other regions, asset classes and products were not nearly as saturated. Some proverbial lower-hanging fruit includes the key dynamics of high liquidity, high levels of workflow/trade automation, and lower competition perfect targets with high theoretical capacity to extend quantitative methods from core strategies and markets. The question now is whether any of these lower-hanging opportunities still exists within the scope of ultrahigh-turnover strategies. Or has saturation proliferated into other assets, products and regions? The answer to these questions will have a strong impact on the next generation of quantitative research and the development of new automated trading strategies.

    Asset Class Diversification Survey participants rankings of current and future comparisons by asset class support TABB Group predictions from 2011 and 2013; that there continues to be a need for diversification into additional asset classes, particularly fixed income (see Exhibit 7). These results show that in the coming years, the industry expects declines in focus on equities and other asset class (which we would bundle together as alternatives or experimental assets, such as real estate or structured products). Exhibit 7 Quantitative Strategy Rankings Current and Future Comparison by Asset Class

    Source: TABB Group

    Our survey finds clear expectations for increased focus on FX and fixed income, which have the greatest potential (comparative theoretical capacity to equities notwithstanding) among all asset classes. However, in the case of fixed income, we are

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    concerned that the pace of transformation to more automated trade workflows is slower than expected or desired. The pace of adoption of electronic execution of a broad spectrum of fixed income products (cash and derivatives) may not supply enough opportunities when compared to the overall demand for new high-turnover strategies.

    Product Class Diversification Product class diversification is primarily focused on cash, futures and single-stock options, where cash represents cash equities and equivalents, such as exchange-traded funds (ETFs) and exchange-traded products (ETPs), but does not include cash bonds and is presumed not to include spot FX (even though technically it is the ultimate cash product) and where futures represent multi-asset exposures, including equity-index, fixed income, FX and commodities. Since options on futures typically have low liquidity, the options category here relates primarily, if not totally, to single-stock options (see Exhibit 8). As with expected asset class diversification (above), survey participants also expect the focus on the more mature products in quant strategies (i.e., cash equities) and experimental other products, such as swap futures, to decline, on average although for different reasons. The issues in cash equities are well noted herein. Newer products, including swap futures, are worthy of experimentation across the expanded spectrum of rates products, but their current low level of liquidity and concentrated adoption may not justify the investment right now. Cleared swaps are similar in terms of potential for experimentation, but the timing might not yet be good and the average trade sizes are still way too large for most quant shops. The one exception in core cash equity strategies has been the intense interest in and addition of ETFs and ETPs, which have highly similar characteristics to equities with the added potential for index arbitrage-like overlays. Exhibit 8 Quantitative Strategy Rankings: Current and Future Comparison by Product Class

    Source: TABB Group

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    As previously mentioned, single-stock options markets are not only fiercely competitive, with a number of seasoned incumbents; they also represent uniquely complex computational challenges. With the exception of index and ETF arbitrage strategies, most other securities, including stocks and most futures, move independently. Because of this, high-frequency quant strategies can be deployed without regard to portfolio construction or portfolio risk overlays. (The primary risk management tactic for high-turnover strategies with independent positions is a notional exposure boundary for each position that is influenced by the expected liquidity between a point in time and the end of the primary trading session for that day.) On the other hand, options represent a constantly moving matrix of puts, calls, strikes and expirations and all based on the relevant underlying movement. So in options market-making, not only do strategies have the speed imperative; they also have a computational imperative; and despite mythology about speed, the computational lift for single stocks is nowhere near that of an options matrix. As a result, it will be fascinating to see how well high-frequency quant firms born in the equities arena enter and grow in the options arena. Other than fixed income (which is transforming slowly), options may offer compelling potential, but represent markets that are very difficult in which to compete and harvest alpha. TABB Group believes that this is a space that could see some (M&A) transactions. So far, leading equity-centric HFT quants have been able to migrate well into futures products, competing well with futures-centric firms, such as Infinium Capital, Jump Trading, and Chopper, without the need to partner. It remains to be seen whether the same dynamic will play out in options. Maybe this is what Virtu is really after with its IPO a better currency for transactions. That said, the jury is still out on how well New York-Chicago mergers actually work in practice.

    Regional Diversification Regional diversification is a classic move to extend the theoretical capacity of core alpha. The original options market making powerhouses of the 1970s and 1980s, including OConnor & Associates, first took their special sauce from Chicago to New York, Philadelphia and San Francisco. From there, they sought to OConnorize derivatives markets in London, Tokyo, Sydney and elsewhere. Its a fairly well-worn path. So, too, have noted quant firms especially high-turnover quant firms marched from Chicago, New York, Amsterdam and a few other locations to set up shop outside of their home turf. Though often proving successful in highly developed markets namely, US, (Western) Europe, and Japan liquidity and, therefore, theoretical capacity falls off dramatically from there. So while there are numerous other markets where core quant strategies will work well, the level of profitability may not justify the technology investment in the current environment. Survey responses suggest that there is movement of focus from developed markets to developing and emerging markets (see Exhibit 9, next page). But these are longer-term expeditions, many of which will require patience while markets, economies and political frameworks evolve. Without strong core cash-cow strategies, some of these regional extensions may not have the funding to stay the course particularly in those scenarios where the regulatory rhetoric has turned inhospitable.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Exhibit 9 Quantitative Strategy Rankings: Current and Future Comparison by Region

    Source: TABB Group

    As we look back across developments in asset classes, products and regions, we cant help but wonder if the saturation we saw in 2011, primarily in US equities, has spread across the full spectrum of high-turnover strategies. If high-turnover strategies for cash equities, futures and options in developed markets are pretty much at the point where leading HFT firms are consuming the alpha of other HFT firms, then that is likely a signal that the pure speed game is just about played out. And, for what its worth, when highly experienced quant executives turn from discovery and capture of new alpha to lobbying the politicians like some of our friends at the Modern Markets Initiative (MMI) and KOR Trading we take it as a strong signal that these sources of trading opportunities are nearing the tip of the spear.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    High-Hanging Fruit Shifts in quantitative trading strategies can be detected by shifts in the types of data being consumed. The highest-speed strategies use only raw (and consolidated) market data and relatively simple analytics, such as if-then, VWAP (volume-weighted average price), and the occasional anti-gaming tactic. Theres no time to use anything else. Beyond the bleeding edges of speed, however, the slower the turnover, the more other types and sources of data are required. These sources can be broadly bucketed into categories such as market data, reference data, fundamental data, commentary (including traditional news, industry research, and social media), and the derived data or analytics that flow off the back of all that raw data. The ranking of data categories reflects the amalgamation of quantitative strategy needs (see Exhibit 10). In the survey taxonomy, we assume that derived data, analytics and proprietary data are largely the same, even though proprietary data might also include raw categories such as trades, quotes, client data, or infrastructure monitoring and tuning output. Finally, given the number of respondents that ticked other, we sense that there could have been some confusion with this taxonomy, and therefore we have a good excuse to do more surveying around data. Exhibit 10 Rank of Data Source Types (n=27)

    Source: TABB Group

    TABB Group has the following reactions and comments on these findings: Market Data: All strategies are fed in one way or another by market data

    (independent of latency requirements), so it is no surprise that this category comes out on top. It should always come out on top, with the possible exception of those strategies including exotic products with rarely observable prices and that are typically marked to models. Two themes that resonate the strongest today for market data are: 1) breadth of coverage across regions, asset classes and products

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    since new strategies will be more multidimensional than ever before and 2) something we are calling the long tail of hybridization, which is the long-term or perpetual management of combined in-house and managed services/cloud infrastructures for market data.

    Reference Data: As we have detailed in recent research (Fixed Income Market Data: Growth of Context, Rate of Triangulation, January 2014) reference data continues to grow in importance, particularly as the pantheon of trading strategies becomes more global and unified across traditionally siloed regions, assets and products. Reference data is what creates these linkages, and as a result, the visceral nature of context. Though security identifiers via security masters are among the most developed of the reference data categories, legal entity identifiers (LEIs), unique product identifiers (UPIs), and unique trade identifiers (UTIs) are where new developments and new capabilities need to be focused given our outlook for new quantitative strategy development.

    Fundamental, Commentary and Big Data: Few quant firms have ever had to care about fundamentals, commentary, or anything related to big data (other than the explosion in message rates through about 2011). One possible exception to this would be low-latency, event-based strategies that leverage a data category known as machine-readable news. As high-turnover, market micro-structure arbitrage and other latency-sensitive strategies became popular in the early 2000s, a massive bifurcation of the quant community resulted: those engaged in the latency arms race and everyone else. This bifurcation was actually the beginning of the 3rd generation of the application of quantitative methods in capital markets; the 1st generation being born as a result of the first market data feeds in the 1980s and the 2nd generation being born in the merger of signal generation and trade execution or, the heyday of statistical arbitrage in the 90s. As the quant community embarks upon the 4th generation of quant trading what TABB Group might suggest is the Age of Multidimensional Alpha they will care much more about all sources and categories outside of market data (and in addition to market data). And in that transition a mission for those who choose to accept it the combined infrastructure and data management complexities will reach new heights. The benefits are likely to be greater theoretical strategy capacity (via slower turnover), but with lower Sharpe Ratios.

    As for big data and social media: Dont hold your breath. Though the topic is outside the scope of this report and discussed more specifically in other TABB Group research it should be reiterated here that capital markets big data is small relative to many other industries. However, it is incumbent upon all trading firms to adopt new data management techniques and high-performance infrastructures as if it were the biggest, since trading firms need to consume big data better than most, even if they dont necessarily produce it. On social media: It is worthy of experimentation and, yes, there are occasionally strong trading signals therein but given the low signal-to-noise ratio, TABB Group does not see social media becoming a mainstream component of quantitative trading soon. Despite the mass impact it is having on human communications, social media is not yet mature enough to be a persistent and reliable source of trading intelligence across a broad range of securities and regions.

    Derived Data, Analytics: Otherwise known as trading indicators, or signal processing, what you do with the data the analytics has everything to do with winning and losing. As quantitative trading firms evolve from pursuing the highest turnover sources of alpha to slower turnover opportunities, their thinking will need

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    to migrate from more horizontal to more vertical. Or 2-dimensional to 3-dimensional. Trading signals will have less to do with the history of independent time series and more to do with comparisons across time series, similar to a massive global, multi-asset matrix. Another way to think of this might simply be independent versus relative value, except the new relative value will need to be on an unprecedented scale, such as global relative value. In any case, analytics based on correlations, liquidity, valuation, portfolio construction, and event-risk detection will become more important than ever before. Much more on this and related topics from TABB Group in future research.

    These reactions are supported, in part, by additional findings from the survey. While there are still those firms seeking more speed, the desire to slow things down is non-trivial; 19.4% of respondents claimed their average turnover frequency (TOF) was slowing. With this fact in mind, we would then expect portfolio construction to shift from more asymmetric where positions are independent to more symmetric which relies on portfolio construction and maintaining market neutralities since asymmetric strategies most often rely on speed. This is also evident in the findings, where symmetric strategies represented at least 29% of responses (see Exhibit 11). Exhibits 11 Quantitative Strategies: Changes in Turnover Frequency and Portfolio Construction Changes in Turnover Frequency Nature of Portfolio Construction

    Source: TABB Group We confess that there are some notable flaws in these findings, as well which have more to do with survey construction. For instance, faster in the changes in TOF chart above could mean several things, including slower strategies becoming faster, but not at the same scale as the microsecond crowd. We confess this to emphasize that understanding the quant landscape takes an exceedingly detailed understanding of tiny clues; clues that cannot be extracted in a single, high-level survey and why experiential overlays are critical to that level of understanding. To return to an earlier metaphor, this is precisely why Wile E. has a tough time keeping up with the Roadrunner. Bottom line: It seems odd to say, but if the latest generation of high-turnover strategies represented the equivalent of low-hanging fruit, then the next generation of quant strategies will need to pick the fruit from much higher up in the proverbial tree.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    A Rare Glimpse It is extremely rare that the public gets a glimpse into the profitability, revenue segmentation, or strategy breakdown of proprietary trading firms. In recent times, it is not since the attempted IPO of Chicago proprietary options trading firm Peak 6 in October 2001 that we have had such a look. However, in the past two years, the public has been gifted unprecedented transparency into two of the worlds foremost quantitative prop trading powerhouses: GETCO and Virtu. The first as a result of GETCOs merger with Knight Capital, and the second as a result of Virtus ongoing IPO trajectory (which has since been delayed. as of the publication of this report). Related filings in both cases give us a taste for all three of the above: profitability, revenue segmentation, and strategy breakdowns. Both the words and the numbers in these filings tell a story that TABB Group has been following for years. In the GETCO now KCG data (below), we can see how a high dependence on Americas trading (most likely focused on US equities market making) in the 2009 (and earlier) period led to regional diversification, mainly to Europe first and then Asia-Pacific (APAC), which includes Singapore, Hong Kong, Japan, and Australia (see Exhibit 12).

    Exhibit 12 GETCO Form S-4 (12 Feb 2012): Trading Revenues (2007 Sep 2012, $millions)

    Source: KCG, TABB Group

    It is further evident from this data that overall profitability was highly correlated to volatility first (2009-2011) and competition second (2012). With aggressive globally coordinated central bank interventionism strategies ramping up in the post-global-financial-crisis (GFC) period of late 2008 and early 2009, strategies that were dependent on (higher) volatility suffered as global markets became awash with volatility-crushing quantitative easing (QE). Note the positive impact on revenues in

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    2011, which TABB Group attributes to a correlation to the then looming threat of a US default and the resulting spikes in volatility. Furthermore, TABB Group believes that the nearly $300 million in revenue declines between the 9 months ending September 2011 and September 2012 were due to something more: competition. But we need not necessarily speculate on causes and changes in tactics. Notable highlights from the GETCO/KCG filing deliver the answers:

    Declines [in] revenues were impacted by lower overall market volumes, reduced levels of volatility, increased competition and a higher percentage of internalization in U.S. cash equity markets;

    Headcount increases related to early investments in the Global Arbitrage, Mid-Frequency and Options trading businesses; and

    Colocation and data line expenses increased to $81.4 million in 2011 from $55.2 million in 2010. The increase in colocation expense was primarily due to the build out of new colocations in Mahwah, New Jersey, Basildon, United Kingdom, and Tokyo, Japan, as well as higher vendor rates for data and services.

    Bottom line: TABB Group believes that the market ecosystem was no longer supporting the same level of theoretical capacity for all high-turnover participants, and the competition was becoming increasingly fierce. Finally, although the reasons may have had more to do with operational retrenchment in the wake of the GETCO-Knight merger in mid-2013 than the threat of competition, we note that the KCG annual report for 2013 shows non-US revenues declining dramatically, from 46.5% of total KCG revenues for 2011 (which is an almost exact regional split of the GETCO-only numbers) to 18.9% of total revenues for 2013. Whether for enhanced operational integrity, the threat of increasing competition or some combination of these and other factors, TABB Group believes that there was a retrenchment to the core regional strategy in 2013.

    Exhibit 13 Virtu S-1 (10 Mar 2014): Total Adjusted Net Trading Income (000s, USD)

    Source: Virtu, TABB Group

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    But then, we turn to Virtu. The Virtu data, made public just this past March 2014, continues the narrative (see Exhibit 13, previous page). This is an example of a firm with a more diversified portfolio of strategies even if they are variants of high-turnover, latency-sensitive strategies. It is because of this diversification that revenues continued to increase for Virtu in the post-GFC period, particularly in the past 3 years, when many high-frequency players were suffering or exiting. Interestingly, with declining rates of growth in its core Americas equities strategies and flat-ish growth in its global commodities futures strategies, Virtu continued to more than make up for lost ground due to success in areas like FX, non-US equities, options and fixed income (see Exhibit 14, below).

    Exhibit 14 Virtu S-1 (10 Mar 2014): Total Adjusted Net Trading Income (000s, USD)

    Source: Virtu, TABB Group

    With growing competition and strategy maturity in global equities and futures strategies, the target for further high-turnover strategy growth will need to be in fixed income, including the ongoing Dodd-Frank-led transformation in the swaps market. (But will this be enough to overcome declines in other products, assets, and regions?) Also, bear in mind that this diversification is likely to be difficult in options strategies which means single-stock options strategies. While Virtu may have discovered a unique and persistent speed advantage across products, regions, and asset classes (an incredible feat, representing the pinnacle of what TABB Group believes is its core value proposition), it will be met with formidable competition from firms such as Wolverine, Susquehanna, Ronin, IMC, Optiver and a few others. As a result of all this, TABB Group believes that the timing of Virtus filing is indicative, if not prophetic, of more persistent market shifts. Whether Virtu ever goes public (given the current delay), there is some rather stark writing on the wall: Chances are that the expected gains in high-turnover fixed income strategies will not make up for the stagnation or declines in everything else for the foreseeable future.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Virtu has taken diversification in high-turnover (market-making) quant strategies to new levels. According to its recent S-1 filing, Virtu makes markets in more than 10,000 securities and other financial instruments on more than 210 unique exchanges, markets and liquidity pools in 30 countries around the world (see Exhibit 15). Chances are that this is the most comprehensive list of venues less the specific dark pools where high-frequency trading is even possible on the face of the planet today.

    Exhibit 15 Virtu S-1 (10 Mar 2014): Liquidity Venues

    Source: Virtu, TABB Group

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    Conclusion This report has focused on high-turnover quantitative strategies. The main reason for this is to leverage newly available intelligence to build a case for where TABB Group believes the broader quant landscape is in its evolution today and why it may be in transition to a new generation of indistinguishable quantitative strategies and methods. We wish the bias was not like that, but the fact of the matter is and despite the level of opacity and complexity embedded in many of its methods that the fascination with speed has made this segment of trading strategies much more observable than our interpretation of the strategies in the next generation. Its paradoxical, but with growing propeller head ubiquity, quantitative methods will become infused into just about all strategies and workflows and therefore, may in fact disappear from distinct observation. The following is a summary of that hypothesis:

    End of a Generation Market microstructure arbitrage strategies otherwise blanketed less accurately by a term that we have tried to avoid using here high-frequency trading (HFT) have reached their full potential in the current global markets environment. The saturation effect has spread from equities in developed markets to just about everything else. Several factors could change this assessment for the better, including material upticks in volumes and/or volatility in the most developed markets. This means large markets with the most advanced high-performance market infrastructure. Smaller markets with high-performance market infrastructure add incremental opportunity, of course. But they dont tip the scales or change this assessment materially. Only the BRICs represent large untapped potential here. But the level of economic, political, technical and even cultural change necessary to make these markets into hospitable Petri dishes for growth of high-speed quant trading means that the transformations have a very long horizon. In other words, the BRICs dont represent a short-term bridge to the near-term future success of quantitative trading.

    Meanwhile, the headwinds are enormous, and growing. Believe it or not, echoes of the GFC continue to reverberate in the bowels of G8 markets. Lets start with market demographics: Diverse demographics of the pre-GPC period a requirement for a healthy market ecosystem, including high-speed market-makers have been replaced with an aggressive bacteria, otherwise known as coordinated central bank interventionism, or by the more publicity-friendly term, quantitative easing (QE). (See, for example, the trajectory of CNBC viewers to fact check on this one!) The goal of these efforts is to squash any signs of volatility or deflationary movement that would diminish (primarily) equity markets as a tool of public psychology management (aka, the driver of consumption and spending). This is the part of the story that we did not see all that clearly in early 2011. Moreover, TABB Group doesnt see a logical end to this policy. (By the way, dont be fooled by the reduction in monthly asset purchases by the Fed and its allies. They have their own quants now. And these quants know how to use market jujitsu with less and less money to influence trajectories of market indices and force the algo community to drive individual security correlations back into line.)

    One of the symptoms of low volatility has been to destroy the performance of other active trading strategies, thereby leaving primarily high-speed market makers, long-term buy and hold investors, and the central banks (led by the US Fed). For purposes of quantitative strategy analysis, the knock-on effects of these shifts in market demographics and volatility have basically resulted in an accelerated competitive frenzy known to many as the low-latency arms race where leading HFTs have essentially been eating their own. This has led to the tip-of-the-spear phenomenon we

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    hypothesized about in mid-2011. (Note: As if exactly on cue, Infinium Capital Management - of HFT and 600 West Chicago Avenue fame announced during the development of this research that it is winding down.)

    Without new and different players at the proverbial poker table, as many players crapped out, these leading firms dwindling in numbers as they may be have needed to diversify into new regions, asset classes and products. From the available data, we can now observe unequivocally that this was already happening over the past 5 years. Finally, notice that nothing has been said here yet about regulations (or the less obvious fact that some of the greatest operators in quantitative methods have turned to lobbying). Of course, this is a significant factor in the enormity of the headwinds high-speed quants face. Suffice it to say, that factor is not positive.

    Beginnings Today, TABB Group believes that the highest-speed strategies are approaching the limits of their native capacity in the current environment, particularly in cash equities and futures-based strategies in equity-index and commodities. FX market-making strategies across both futures and spot likely have room for additional growth, particularly if wholesale blocks (in the spot market) can be accessed through liquidity venues that are accommodative to low-latency market-making.

    This leaves fixed income and options (primarily single-stock options) open for interpretation. Evidence shows that leading quant market-making firms are heading in this direction, but the revenue accumulation is coming off of a small base. TABB Group believes that these market opportunities are unique in that there are pricing and valuation components. Unlike equities, as previously mentioned, risk-taking in these products and asset classes cannot be assessed independently. They are not horizontal, but vertical. Relative-value techniques are critical to success here, and the HFT community doesnt have those skills (yet).

    The good if not, great news here is that when the high-performance infrastructure of the current generation of HFTs (which we call Gen 3) is integrated with a global big data vision, some exciting things could happen. Business combinations that merge these two may make a lot of sense in the near term. Meanwhile, arguably the true quant legends, including Renaissance Capital and D E Shaw, are already there and have been there since early in the 2nd generation of the 1990s.

    This 4th generation of quantitative research and trading is not a greenfield or even a tilted battlefield. The pattern recognition and performance needs are much more complex than in the high-frequency era. Firms singularly focused on speed, with less diversification today, are either already gone, or in the process of making an impossible transition. For those firms that can make the transition which means a global HPC infrastructure capable of identifying and harvesting volatility spikes in a relative-value manner anywhere, at any time, in any automated product workflow using magnitudes more data than today may successfully become a long-standing member of the multidimensional alpha era. But please note that there are legendary quant firms that already have been playing there for, oh, about two decades. For those firms that dont want to compete on this basis, enterprise risk analytics for market, credit and operational risks; surveillance; and new software-defined infrastructure (SDI) to combat inherent technical fragmentation in our business are use cases that remain sorely in need of quantitative attention. Once that happens, quantitative methods will be sufficiently ubiquitous to be indistinguishable.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    About

    TABB Group TABB Group is a research and strategic advisory firm focused exclusively on capital markets. Founded in 2003 and based on the methodology of first-person knowledge, TABB Group analyzes and quantifies the investing value chain, from the fiduciary and investment manager, to the broker, exchange and custodian. Our goal is to help senior business leaders gain a truer understanding of financial markets issues and trends so they can grow their businesses. TABB Group members are regularly cited in the press and speak at industry conferences. In 2010, TABB launched TabbFORUM, the online capital markets community for peer-to-peer contributed opinion and analysis covering current industry issues, tracked daily by 23,000-plus professionals. In October 2013, QuantFORUM, a new online channel for the global quantitative investing community, went live. For more information about TABB Group, visit www.tabbgroup.com.

    The Author E. Paul Rowady, Jr. Paul Rowady, Principal and Director, Data and Analytics Research, joined TABB Group in 2009. He has more than 24 years of capital markets, proprietary trading and hedge fund experience, with a background in strategy research, risk analytics and technology development. Paul also has specific expertise in derivatives, highly automated trading systems, and numerous data management initiatives. He is a featured speaker at capital markets, data and technology events; regularly quoted in national, financial and industry media; and has provided live and taped commentary for CNBC, National Public Radio, client media channels and others. With TABB, Pauls research and consulting focus ranges from market data, text analytics, social media impacts and data visualization to OTC derivatives reform, clearing and collateral management; and includes authorship of reports such as Patterns in the Words: The Evolution of Machine-Readable Data, Enhanced Risk Discovery: Exploration into the Unknown, The Risk Analytics Library: Time for a Single Source of Truth, The New Global Risk Transfer Market: Transformation and the Status Quo, Real-Time Market Data: Circus of the Absurd, and Quantitative Research: The World After High-Speed Saturation. Paul earned a Master of Management from the J. L. Kellogg Graduate School of Management at Northwestern University and a B.S. in Business Administration from Valparaiso University. He was also awarded a patent related to data visualization for trading systems in 2008.

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    Propeller-Head Ubiquity: The Next Generation of Quantitative Trading | July 2014

    www.tabbgroup.com New York + 1.646.722.7800 Westborough, MA + 1.508.836.2031 London + 44 (0) 203 207 9397

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