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The Case For Quantitative Equities

Improve Diversification and Add Protection to Your Portfolio


Above average equity market returns since the last recession, coupled with the historic length of the current expansion, have led many investors to consider ways to de-risk their equity exposures in recent years. Naturally, without having perfect knowledge to time the next correction, investors are often hesitant to trade out of equities for fear of missing out on further gains. These concerns are particularly germane as stock markets are currently trading back at all-time highs following bouts of volatility in early and late 2018. One solution to this conundrum is a diversified quantitative equity strategy. A robust quantitative equity portfolio strategy can offer down-side protection in a correction as well as the potential to generate strong positive returns should markets continue to perform. Below, we explain what this emerging style of equity investing is and outline a case for how its unique characteristics can help investors meet their goals irrespective of market environment.

What is Quantitative Investing?​

Quantitative (or “quant”) strategies can be described as systematic and process-driven investment approaches that rely heavily on automation and the integration of ever-growing data sets, computational methods, and processing capabilities. Most quant managers incorporate the same fundamental and technical data points into their investment analysis as discretionary managers, but a quant manager may do so in a systematic and automated manner in which research, security selection, portfolio construction, and risk management parameters are precisely defined up-front. Quant managers may also utilize larger, non-traditional data sets to capture additional investment insights. Through a systematic approach, quant managers will seek to capture trading advantages associated with their breadth of data coverage, agility in processing information, and objectivity in avoiding behavioral biases. They may also retain and integrate institutional knowledge and intellectual property into their investment process.

Buzz words like “artificial intelligence” (AI) and “machine learning” are often referenced when describing quant investing but do little to demystify the strategy. In fact, the mathematical models and theories underpinning AI and machine learning methods are often not new to academics or investment professionals. However, the ability of portfolio managers to apply these methods on a wide array of new data sets has been a major development over the last 5-10 years. Specifically, the availability of “big” and “alternative” data sets, the reduction of barriers-to-entry for computational horsepower and storage (i.e. cheaper hardware and cloud computing), and the innovative ways in which insights have been applied to investment management have all contributed to meaningful advances in quant investing.

What is Big Data and Alternative Data?

Big data refers to a field of study in which large, complex, and diverse sets of data are analyzed to extract valuable information. Big data is unique in that the variety, velocity, and volume of data is growing at an accelerating rate due to improvements in technology and the increasing connectivity of our world. Alternative data is simply the name given to big data sets that are used to expand insights into the investment process beyond those available from traditional market and financial data sets such as price, volume, earnings metrics, etc. Alternative data can be collected from social networks, websites, personal electronics and apps, questionnaires, product purchases, and electronic check-ins. The presence of sensors and other inputs in smart devices and across commercial/public information systems allows for data to be gathered across a broad spectrum of situations and circumstances.

Categories of Alternative Data Sets

Impact of Quantitative Investing

The potential insights available to investors by accessing growing alternative data sets are substantial. It is possible that we are in the midst of a leadership change whereby fundamental analysts begin to lose a previously held edge over quantitative methods due the broader application of analytical tools on more comprehensive sets of data.

Figure 2: Growth in Data Being Created

Growth in Data Being Created

Source: Lombard Odier, IDC’s Data Age 2025 Study, April 2017

This makes sense as key advantages for quantitative investing include scale and speed. On an almost real-time basis, fundamental and technical metrics can be assessed, investor sentiment can be measured, behavioral errors can be identified, and broader macro market or sector flows can be incorporated into intrinsic and relative value price targets for securities.

For instance, a quantitative system may compile and evaluate vast amounts of information that a traditional equity analyst could not possibly keep up with. As opposed to a traditional equity analyst who visits retail stores, channel checks suppliers, listens to earning calls, updates models, and interviews management teams, a quantitative equity system can incorporate satellite imagery that assesses consumer traffic or inventory, use natural language processing to parse out useful insights from earnings calls, and can continuously monitor news feeds (i.e. Reuters, Bloomberg) and social media (i.e. Twitter, Facebook) for sentiment and catalysts that potentially affect the price of a stock. With quant investing, it’s not that the informational insights from the research necessarily get applied in a new or novel way, it’s that the insights can be incorporated into the investment process across a wider universe of securities with greater speed and with higher relative conviction which previously was not possible by singular analysts or portfolio managers. In other words, traditional equity analysts are likely becoming more and more informationally disadvantaged, all while the speed in which information is being created is accelerating.

Strategy Characteristics

Quant equity strategies can target various geographies, exposures, and risk profiles. While there is no standard portfolio approach, the following items are considerations that are typical to quant equity strategies:

  • Often offered with better liquidity and/or lower fees than traditional hedged-equity strategies

  • Due to strategy sophistication and investment minimums, early investors have been institutions and sovereign wealth funds

  • Highly diversified portfolios targeting overall markets (thousands of securities)

  • Portfolio turnover can range from 3 months - 2 years (not high-frequency trading)

  • Can have either high or low market sensitivity (“beta”)

  • May be long-only or long/short (active extension, low-moderate net, or market neutral)

  • May utilize significant leverage or use no leverage at all

  • Primarily traded in developed, liquid markets

What Makes a Good Quant Fund?

The research process to evaluate a quant hedge fund strategy is quite different than the process used to assess a discretionary hedge fund portfolio. While it is important for investors to look at historical exposures and risk measures such as drawdown characteristics and risk/return profiles, it is also important to consider a wide array of qualitative factors to better understand the quality and competitive advantages of a quant manager’s strategy. To identify such portfolios, investors need to review items such as the breadth and depth of the team and firm resources, the data acquisition and processing strategy, the research, model development, integration process, the portfolio management, trading/execution infrastructure, and the manager’s approach to internal performance measurement. In addition, to avoid errors with data-mining and alleviate concerns over sustainability, an investor would want to be sure each strategy is supported by a clear economic rationale and solid investment thesis.

Historical Results

Due to the more recent rise of big data and its applications in investment management, the quant equity strategy does not have a long-tenured benchmark to represent its historical results over multiple market cycles. Since 1999, however, the HFRI Equity Hedge Quantitative Directional Index, has tracked at least five or more underlying fund constituents and currently tracks over 60 funds. For the 20-year period ending May 2019, the HFR Quant Directional index has generated roughly similar compounded returns as the S&P 500 Index and the more-fundamentally driven Equity Hedge (Total) Index with substantially less volatility than the broad market. Chart 1, below, highlights these results. Quant equity strategies also generated similar results to fundamentally traded, hedged equity strategies but significantly outpaced long-only strategies during periods of market correction. Chart 2 highlights three periods of equity market losses in 2008-09, 2010, and 2018 where quantitative strategies performed well.

Cumulative Returns and Comparative Loss Analysis

Key Benefits​

While it is clear that quantitative strategies can deliver positive returns and downside protection, akin to traditional hedged equity strategies, they may also offer distinctive qualities that include:​​

  • Added diversification in the investment selection process combining fundamental insights with quantitative methods

  • Added diversification across trade signals and instruments

  • Ability to add uncorrelated sources of alpha from both top-down (country and currency selection) and bottom-up (security selection) strategies

  • Use of highly-technical risk management systems and procedures

  • Careful consideration of transaction costs

  • Ability to fine-tune portfolio targets via directed tactical bets (i.e. factors such as value, momentum, quality, small cap)

  • Ability to fine-tune portfolio targets via specific levels of alpha, beta, or volatility


The proliferation of big data and the infiltration of technology into almost every aspect of our lives has led to the emergence of a new style of investing where participants are likely on the cusp of a significant informational advantage over traditional peers. To take advantage of these changes, quantitative managers are developing new insights that may improve both investor returns and the robustness of their portfolio approaches. Investors that can evaluate and access this unique manager universe have an opportunity to diversify away from risks associated with long-only and potentially crowded fundamental hedged equity strategies. Whether equity markets continue a bull run, or experience a more pronounced correction, quantitative equity strategies offer a unique path to improving diversification and adding protection while retaining exposure to the broad equity markets.

Special Thanks to Our Contributor

BlueArc Capital Logo

Investor Relations Contact:

+1 (404) 419 6130

Ashley Thacker Thomas

BlueArc Capital Management is a division of BlueArc Capital LLC, a specialty alternative investment firm that offers niche alternative asset strategies and funds.

Table of Contents

  • Introduction

    • Overview​

  • What is Quantitative Investing?

  • What is Big Data and Alternative Data?

  • Impact of Quantitative Investing

  • Strategy Characteristics

  • What Makes a Good Quant Fund?

  • Historical Results

  • Key Benefits​

  • Conclusion


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This article (the “Article”) is for informational purposes only and does not constitute an offer to sell or a solicitation of an offer to purchase any investment or any securities. This Article does not constitute investment advice and is not intended to be relied upon as the basis for an investment decision, and is not, and should not be assumed to be, complete. Readers should make their own investigations and evaluations of the information contained herein. The information contained herein does not take into account the particular investment objectives or financial circumstances of any specific person or entity who may receive it. Each reader should consult its own attorney, business adviser and tax adviser as to legal, business, tax and related matters concerning the information contained herein.  Except where otherwise indicated herein, the information provided herein is based on matters as they exist as of the date of preparation and not as of any future date and will not be updated or otherwise revised to reflect information that subsequently becomes available, or circumstances existing or changes occurring after the date of preparation. Certain information contained in this Article constitutes “forward-looking statements,” which can be identified by the use of forward-looking terminology such as “may,” “will,” “should,” “expect,” “anticipate,”  “target,” “project,” “estimate,” “intend,” “continue” or “believe,” or the negatives thereof or other variations thereon or comparable terminology. Due to various risks and uncertainties, actual events or results may differ materially from those reflected or contemplated in such forward-looking statements. Readers should not rely on these forward-looking statements.  Certain information reflects subjective determinations which may prove to be incorrect. There can be no assurance that the estimates or projections will be accurate or that historical trends will continue. In considering the prior performance information contained herein, readers should bear in mind past performance is not necessarily indicative of future results. All rights reserved. The material may not be reproduced or distributed, in whole or in part, without the prior written permission of PrimeAlpha LLC. 

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