June 23, 2024

Quantamental Investing and its near future


Blackrock’s Chief Executive Officer Larry Fink, announced to New York times during March 2017 that “The democratization of data has hugely made it too hard for an active management. They have to shift the ecosystem-that refers to as relying better on big data, artificial intelligence, factors as well as models within a quant as well as conventional investment strategic objectives.”

Mr. Fink was referring to as a noticeable shift within the firm’s investment strategy, that one could view a $30 billion assets (an 11 percent of the BlackRock’s dynamic equity funds) that is hugely elected for an essential stock-picking method also in order to incorporate quantitative approach backed by modelling as well as algorithms. As well as in doing so, the renowned investment firm was adopting an investment strategy—or, indeed, an amalgamation of dual diverse venture strategies—that has come to be known as Quantamental investing.

The term Quantamental is, in fact, a hybrid of quantitative and fundamental and thus refers to as an investment strategy that comprises uniting quantitative and fundamental approaches to investing, with the intention of improving returns. By merging these two approaches to investing—CPU power with human insight—it is hoped by those now adopting quantamental investing that such an approach can deliver superior returns. Indeed, this amalgamation is often regarded as a melding of man and machine: the investor connects the scale and control of data and blends it with the reimbursements of human insight in order to extract winning investment strategies.  

Quantitative investing, on the other hand, refers to the utilization of computer models and algorithms, as well as invariably vast measures of data, to determine trends and patterns and thus more effectively attempt to predict future security price movements. Popular quant strategies include statistical arbitrage, whereby the model seeks out arbitrage opportunities by identifying price alterations between identical securities that often tend to emerge for just a few seconds, and factor investing, in which the model identifies specific factors that impact the amount of a security such as macroeconomic, microeconomic and style factors (for example, market capitalisation).

As with as computers making the trading conclusions, quantitative investing removes the human-error component, including demonstrative or cognitive partialities, that often crops up when investment decisions are made by investment managers. This epitomizes a major gain for the quant strategy. That said, given that the algorithms themselves are designed by humans, measurable strategies are still limited to the skills of the developers behind those strategies.

Fundamental investing principally refers to the traditional bottom-up method of picking individual stocks based on the quality of their fundamentals, such as earnings and market share. By recognizing securities with prices that are not reflective of the true market value of their businesses, central investors aim to exploit this mispricing to generate alpha—that is, the excess return of an investment in relation to the return of the market (or a benchmark index). The likes of Warren Buffett and Charlie Munger of Berkshire Hathaway signify the best supporters of this approach, by means of comprehensive analysis of firms balance sheets, attached with sound judgment and knowledge, to consistently generate alpha.

Ken Perry, former chief risk officer at Och-Ziff Capital Management, once told a Bloomberg panel discussion on alternative data and stated that “Managers are now paying exorbitant amounts to obtain such data in the hope of giving them an edge over the competition. The purpose of data is to make forecasts, and the more I have, regardless of who owns it, allows me to gain a greater conviction in existing ideas.”

That said, this process currently remains in its infancy, with datasets often yielding little to no meaningful information. But data-mining techniques are also becoming more advanced all the time, with technologies such as machine learning helping programs to adapt to feedback without the need for human involvement. In turn, this is enabling investment firms to analyse more effectively big datasets and identify meaningful patterns. And with computers becoming consistently more powerful and data-storage options becoming continually cheaper, profitable opportunities for quantitative investors are growing all the time.

It should then come as little surprise that vital stock pickers have been under burden for some time. With quantitative investors having arrived the arena and swiftly gathered steam over the last 15 years or so, ultimate investors have faced rigid competition from this new breed of investor, meaning that their returns have been severely squeezed.

Indeed, pure quant firms such as Renaissance Technologies and Two Sigma have created some of the best returns in the industry. Active fund managers are also currently to lose out to passive-investment strategies that simply seek to track a particular index at a comparably low cost. Much of the preceding decade has seen passive-investment tools such as exchange-traded funds explode in popularity by effortlessly outperforming vigorous strategies. This has progressively called into query the substantial fees that active managers levy on their investors.

With research showing that wide-ranging measurable market factors such as valuation, evolution, quality and thrust have driven around 65 percent of global equity managers’ relative returns over the past 20 years, while the remaining 35 percent has been attributable to stock selection, investment managers are now ever more looking for to supplement their fundamental approach with a hearty quantitative-factor research component—and vice versa—such that quantamental investing captures a greater degree of an additional returns, at both the market-factor level and the individual-stock level. The quantamental investor also assistances by having a strategy to fall back on—should one approach fail to work sufficiently well, the investor can opt for the other method, depending on the market environment.

Often quantamental investors will have initially begun as fundamentalists before commencement to engage ideas from quantitative strategies. In practice, this would perhaps comprise hiring data scientists who can develop machine-learning algorithms to glean valuable investment insights from big datasets, which in turn can enhance the stock-picking process. BlackRock is just one of numerous high-profile vital investing firms to have made steps in this direction, with other notable names to have made a comparable transition including Point72 Asset Management and Tudor Investment. Indeed, huge financial institutions globally have in progress to dip their toes into the quant world, leveraging the insights generated from big data to boost their returns.

But there remains substantial doubt over whether fundamental investors can satisfactorily adapt to absorb such a thought-provoking discipline. Indeed, given the noteworthy modifications between the two strategies, switching to quantamental investing is likely to involve sizeable structural and organisational fluctuations within a fundamental investment firm.

For one, it will require modification in the hiring policy to employ more workforce with quantitative research and development skills, and in addition to significantly advancement of internal systems to cope with the tension on resources imposed by the big-data analytics process. Such changes, therefore, are usually associated with a substantial dollar investment. According to the Chicago-based global executive-search firm Heidrick & Struggles, asset managers can employ a three-step process in order to make the transition towards a more quantamental investing style smoothly.

  • The firm’s guidance must ensure that the organisational structure is aligned with a measured approach to its data strategy.
  • The firm should hire an active head of quantamental investing strategies to lead the effort whilst also ensuring the firm can accommodate new personnel with different skills.
  • Invest in transformation management to bridge the division between conventional approaches and the sharp reliance on analytics.

Behavioral and cultural change may seem like an arduous trial, but it would appear that it is becoming increasingly necessary. With technology playing a steadily more influential role in the asset-management process, and with traditional asset managers being consistently outperformed by submissive strategies and quant investors, it has become domineering that fund managers adapt to this new paradigm.

Obstacles do exist when it comes to taking the leap and bridging the two worlds, particularly in regards to cost. But ultimately, if they can successfully make the transition towards a Quantamental strategy, it would seem that traditional asset managers will have a better chance of surviving, evolving and ultimately thriving in this new world.



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