The Evolving Landscape of Insurance and Private Capital
Partnerships between asset managers with insurers, reinsurers and investors can provide attractive risk-adjusted returns.
Since the first quantitative equity funds launched more than 30 years ago, the demise of systematic investing has been predicted many times over. Most recently quants faced the “Quant Winter” that started in 2018 and lasted nearly three years. Many thought that this extended period of underperformance by systematic strategies was the nail in the coffin for quant investing. But quantitative equity strategies are not only alive and well, they are also in a remarkable position to address today’s evolving investor concerns.
While the investment opportunities that arise from quants’ ability to efficiently process data are important, the precision with which quants apply that data to construct portfolios and manage risk is what sets them apart. Expertise in portfolio construction allows quants to build diversified portfolios that aim to deliver on alpha promises while avoiding uncompensated risk. This precision may serve clients in several capacities, from adding consistent alpha in the most inefficient illiquid markets, to serving as a predictable core position that provides solid beta exposure with the ability to add value in most market environments. While upside may be more modest than that of concentrated active managers, the consistent application of the investment strategy enables quants to avoid style drift, and their flexible approach to portfolio construction makes it easier to customize for modern institutional investor needs, including being adapted for tax efficiency.
As the concept of investment bias began to take hold in the 1970s and 1980s, many investors started to explore how to prevent biases such as overconfidence, loss aversion, and confirmation bias from disrupting sensible investment decisions. In response, quantitative investors built simple models that took advantage of these biases and applied their models dispassionately through bubbles and dips. As the first quant equity funds launched, simple measures of valuation, quality, momentum, and earnings trends delivered consistent alpha. The concept of “risk-adjusted returns” wasn’t yet appreciated, and investors were drawn to concentrated, high-conviction portfolios where portfolio managers could tell compelling stories about each stock held in their portfolio. In contrast, listening to a quant drone on about capturing market inefficiencies was enough to put investors to sleep.
Today, those simple models have evolved to incorporate vast amounts of newly available data, paired with new analytical techniques such as natural language processing (NLP) and machine learning. In this more data-intensive, technology-driven world, quants can more effectively deliver on their traditional strategies while also expanding into more custom solutions that cater to nuanced client preferences. Outlined below are three ways in which quants can deliver outcomes for investors.
The most inefficient markets in the world have great alpha potential and often face greater investment challenges. For example, illiquidity is both a risk and a cost investors face when investing in areas like emerging markets, small or micro caps. When dealing with liquidity challenges, a quant’s ability to measure alpha potential and model trading costs to produce “net alpha” expectations is crucial. A portfolio with many small positions is both easier and cheaper to trade than a more concentrated strategy with larger blocks of buys and sells in the market. Thus, a systematic small cap emerging markets strategy typically has a higher capacity limit than its more concentrated counterparts.
But inefficient markets also include a vast amount of data: tens of thousands of companies, multiplied by their underlying data points – often messy, inconsistent, or missing. This presents the challenge of both verifying the reliability and relevance of the data and determining whether the company or opportunity shows investment potential. Importantly, neither excessive nor dirty data poses a challenge for quants because they have a long history of leveraging tools to process data in these opaque markets. As a result, quants can bring valuable insights to these less efficient markets, allowing them to build diversified portfolios with more focused risk/return tradeoffs. As shown in the chart below, quant has delivered more consistent alpha than fundamental in the 9/2008-9/2023 time period.
EM Small Cap's Median Rolling 3 Yr Excess Return
Style drift, both intentional and unintentional, is often an issue faced by investors who may chase trends or leave portfolio drift unchecked. Divergence from the portfolio guidelines may not only impact a client’s risk-return profile, but it can also lead to unpleasant surprises when the market inevitably shifts. A systematic process consistently keeps the portfolio true to its style. Traditional strategies may also take bigger positions at the country/sector level. These bets may lead to alpha, but certainly lead to higher risk. On the other hand, quant strategies typically maintain tighter portfolio limits and, therefore, derive their alpha from stock selection as opposed to country or sector bets.
2012 - Q3 2023
Source: PGIM Quant, Factset, MSCI
2012 - Q3 2023
Source: PGIM Quant, Factset, MSCI
The ability to add value without taking large country/sector bets is an advantage in a world where geopolitical and macro risks are ever-present. With a comprehensive perspective across an inefficient market, a quant can capitalize on alpha opportunities in a cost-effective manner that also limits concentration risk.
While transaction costs are less of a headwind in developed markets, there are also fundamental benefits to a systematic, repeatable investment process that may lead to consistent outcomes in more efficient markets. Investors are under tremendous pressure to both meet their fiduciary duties and deliver strong returns in line with their investment policy statement. Investors need to balance their desire for alpha with downside risk, while keeping their costs low. Because quant strategies focus on delivering attractive risk-adjusted alpha at a lower fee – a sweet spot in public equity markets – they can provide a solution to this challenge. By allocating higher risk and fee budgets to areas that are more likely to be rewarded such as private equity or private credit, investors can focus their public equity segments to highly efficient quant strategies which can provide a diversified, customizable, lower-fee, style-pure solution. The chart below shows that additional risk beyond 2-3% tracking error – where quants typically fall in emerging markets – has not paid off over the last 10 years.
EM Large Cap Core's 10 Year TE vs Excess Return
Investor needs have become more nuanced, and their need for active solutions that are nimble, customized and diversified has grown. University endowments are facing calls from their student government associations to divest from fossil fuels. Family offices need to balance portfolio diversification with tax impact in order to manage capital gains. Institutions of all types are trying to navigate the existing and potential impact of one global macro event after another.
Take China for example. There are varying options on how to approach an investment in the world’s second largest economy. Do investors continue to invest in standard emerging markets to gain exposure to China? Do they invest in a China-only strategy? Do they eliminate an allocation to China altogether? A systematic manager can build completion portfolios around an existing China strategy, allow for custom country weights, or eliminate China exposure all together without losing alpha potential in their emerging markets allocation.
Once again, a quant’s advantage in data analysis comes into play here. With such a broad view of the universe, quants are able to partner with investors to build custom portfolios that solve for a wide array of issues without sacrificing alpha or blowing up tracking error targets.
Periods of underperformance are often used by critics to forecast a style’s impending demise, but the reality is that all active strategies struggle at some point in a market cycle. Investors should look forward; the investment landscape has become exponentially richer with information, and quantitative tools have evolved to efficiently interpret and capitalize on these new data points. This environment is ripe for quantitative strategies to not only continue to deliver favorable long-term investment results, but also to provide more targeted solutions to solve investor problems.
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