Expectations Wobble for Another Decade of Strong Equities

It’s a ‘particularly perilous’ time to forecast ongoing stock outperformance, says a new report from AQR.




The year in financial markets has been a wild one. Coming into 2023, few analysts expected equities to outperform, and a banking crisis in the first quarter looked like it might confirm that view. Geopolitical concerns, persistent inflation, higher interest rates and dampening consumer sentiment were also big macro themes throughout this year.

Yet, as year-end approached, equities did surprisingly well despite ongoing macroeconomic turbulence. The S&P 500 was up 8.92% in November, bringing its year-to-date return to 18.97%. Stocks in the so-called Magnificent Seven have soared. One of them—Nvidia—driven by demand for semiconductors capable of powering artificial intelligence, is up approximately 240% this year, according to Reuters data.

If equities maintain their robust performance through the end of the year, stocks will start 2024 looking expensive. Analysts are already noting this in their outlooks for 2024. While strong performance is generally good for portfolios, new research suggests that maintaining this pace of growth could be a tall order in the decade to come and that investors may have more risk in their portfolios than they expect. If equity performance is more in line with historical averages, or if markets weaken, then truly diversifying alternatives are likely to have a significant leg up over passive beta exposures.

A Rally Long in the Tooth

In a recent research note, Jordan Brooks, a principal in and co-head of the macro strategies group at AQR Capital Management, looked at equities performance over the past decade and what it would take to maintain that performance for another 10 years. And while it’s possible equities could keep the rally going for another decade, it doesn’t seem likely. To oversimplify the findings, equities would have to have a 2023 every single year for the next 10 or better.

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“We’re coming off a 10-year stretch that, despite a bear market in 2022, has been pretty sensational,” Brooks tells CIO. “So I think it’s a particularly perilous time to extrapolate current portfolio positioning based on the idea that you can repeat that outcome.”

According to Brooks’ findings, over the past decade, the excess-of-cash return on the S&P 500 averaged 11.9% per year. This puts the past decade well above the 90th percentile of rolling 10-year performance across global developed equity markets since 1950. The risk-adjusted return, or Sharpe ratio, of the market over this period, Brooks writes, was 0.82—nearly double the postwar average for global developed equity markets.

Brooks says a combination of high dividend yields, exceptional real earnings growth and high valuations drove returns over this period. The tail wind from valuations alone is in the top third of any 10-year richening period in the U.S. in more than a century, according to his findings. All three components combined contributed to a 10.2% real total return on equities. The findings also showed that low interest rates in the decade contributed an additional 1.7% excess-of-cash return simply by being invested in assets that were keeping up with inflation.

The market conditions that defined the previous 10 years have changed materially. The fed funds rate has gone from near zero to more than 5%. Inflation came roaring back after the pandemic and has only recently shown signs of meaningful decline. Geopolitical uncertainty will likely remain high over the near-to-medium term.

Optimists could look at 2023 and say all of these conditions were true and equities had a banner year. What’s more, if markets get the rate cuts they are already pricing in, that could be a tail wind on performance. Brooks is skeptical of this view.

“The focus on interest rates doesn’t take into account the ability of firms to generate cash flow in the future,” he says. “Over a longer horizon, what matters is the income you get from dividend yield, as well as how much firms grow earnings by and, ultimately, the valuation investors are willing to place on those earnings. If you expect stocks to outperform cash by 12% over the next 10 years, you would need a combination of real earnings growth on par with the best-ever decade, and you’d need to see P/E [price-earnings] ratios well above the tech bubble. Monetary policy doesn’t change that equation.”

Brooks’ math works out this way: According to his note, in the post-World War II era, the average real earnings growth is 2.6%, the 75th percentile is 4.1%, and the 90th percentile is 6.0%. There have been 10-year periods where real earnings growth exceeded 10%, and in those two cases it followed steep declines. The first was post-tech bubble, and the second was post-great financial crisis. Barring a crash next year, equities are not starting from that position. If you use roughly the postwar average and assume 2.5% earnings growth over the next decade, the cyclically adjusted price-to-earnings ratio would need to more than double from its current value of 30 to 61 to post a repeat performance. That would be nearly 40% higher than the tech bubble peak of 44.

Brooks says getting to that kind of performance would require earnings growth at levels unprecedented in a non-recession economy, and the market would have to trade at its richest level ever. It is not an impossible scenario, but it seems unlikely.

Distortion in Index Performance

Even if investors opt for the rosiest view of the future, they may want to evaluate their baseline assumptions. Brooks is not the only one raising flags about performance in equities. A recent note from AllianceBernstein highlighted how the sheer size of the Magnificent Seven companies is affecting index performance.

According to AB, the seven largest stocks in the Russell 1000 Index, which account for about 28% of the large-cap benchmark, surged by 72% this year, through December 12—eclipsing returns for the rest of the market. Similar trends were seen in global markets, such as the MSCI World Index, where the U.S. Mag seven stocks account for 19% of the benchmark. That has changed the style composition of several equity benchmarks.

AB says index providers typically target a balanced split between growth and value stocks in broad market benchmarks. However, since the largest seven stocks are growth stocks, it creates a skew across market capitalizations. Because of the size of the Magnificent Seven companies, the largest 500 companies in the Russell 1000 end up tilted toward growth stocks. And, as a result, the next 500 companies in the index end up heavily skewed toward value stocks—accounting for 73% of the weight in this segment of the market. Meaning when you look across the index, it’s hard to get an even mix of growth and value at each market capitalization. Small- and mid-cap stocks end up reading as value and mega-cap stocks end up reading as growth. The result is a more concentrated and riskier benchmark.

Passive investors could therefore get caught out if there is an abrupt change in market conditions because they would not be as diversified as they expect they are.

Both Brooks and analysts at AB suggest that now might be a good time for investors to take a close look at diversification within their portfolios. While it may be hard for institutional investors to change much if they are not already engaged in an asset-allocation study, Brooks says there are things that can be done on the margin.

“Something is definitely better than nothing,” he says, suggesting that allocations to risk-mitigating strategies, liquid alternatives or global macro could be beneficial to support performance and capital preservation, especially if volatility increases over the near to medium term.

“There’s a real chance that investors are more exposed to equity risk than they fully understand right now,” he says. “We’re looking at a lot of near-term headwinds coupled with the need for exceptional performance to maintain the same long-term performance. To me, that all adds up to a need to think through asset allocation and baseline assumptions.”

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Will AI Compromise Security for Institutional Investors?

Integrating artificial intelligence is all the rage, but organizations must consider what vulnerabilities it might expose and how they can plug them.



Institutional investors who are considering using artificial intelligence for their own marketing, investment research or back-office needs need to make sure these tools are not leaking sensitive financial data or competitive intelligence online. They must also consider if and how their external money managers, and other vendors—like investment accounting and risk management providers—are using AI to enhance their business operations, sources shared.

Fred Teufel, a director at Vigilant Compliance, a Philadelphia-based firm that focuses on the investment management industry, says large asset owners, like pension funds, endowments and foundations, “need to be asking their investment advisers about their use of technology, and more specifically AI.”

Institutional investors should ask their money managers whether they are using a third-party vendor, for instance, that leverages AI for portfolio construction. If AI is used to help pick stocks, investors also “need to understand how that portfolio is being built, where that data is coming from, and how that data is being managed,” Teufel says.

“Also, is that data accurate?” he adds. Asset owners “need to understand what is going on inside the black box. In language they can understand.”

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Proposed SEC Rules

This is especially true given the Securities and Exchange Commission’s proposed rules related to predictive analytics technology, including artificial intelligence. Proposed in July, the rules would require broker/dealers and investment advisers to take steps to address conflicts of interest that may arise from these technologies.

This has consequences for asset owners, not only due to their relationship with external money managers, but because the proposed rules apply to institutional investors as well, Teufel explains.

“There’s a lot of industry pushback on that rule, and it changes so many different things,” Teufel continues. “The SEC in the past has differentiated between institutional investors and retail investors. In its current form, they don’t differentiate between that in this rule. … Expanding the application of the rule to institutional investors, or institutional clients, is a huge scope change in terms of the way that conflicts of interest and fiduciary matters are being managed.”

The Vanguard Group is one of a group of money managers that have suggested the proposed rules would be overly broad and too restrictive for investment firms. Matthew Benchener, a managing director at Vanguard, wrote in an October comment letter to the SEC that the firm shared “the view of the Investment Company Institute and SIFMA, that applying the proposal to sophisticated institutional investors, such as registered funds and similar pooled investment vehicles, is unnecessary and could harm investors in these products.”

Benchener’s letter continued: “A ‘covered technology’ would include any analytical, technological, or computational function, algorithm model, correlation matrix, or similar method or process that optimizes for, predicts, guides, forecasts, or directs investment-related behaviors or outcomes. … This proposed definition encompasses virtually any feature or communication designed to influence investment-related behaviors from investors.”

Cybersecurity at Issue

Aside from the regulatory scrutiny of AI, sources shared specific cybersecurity concerns of which asset owners should be aware.

Mohammad Rasouli, a Stanford University AI researcher who also helps institutional investors use AI for alternative investments, says that risks introduced by the technology can include threats from hackers and other digital threats. Scammers could theoretically hack large language models like ChatGPT to obtain sensitive information published in the system, he explained.

Research has also shown that it is possible to extract sensitive information from these platforms using specific prompts, he added.

“When you train large language models with some data, it learns that data, and someone can extract that data with smart prompts,” Rasouli says.

To beef up their security, some institutional investors have begun to run large language models on their own servers and databases, “so they have full control over it,” he said.

In addition to external threats, there is also the risk of an internal data breach when using AI tools. An example could be a private equity fund using AI to scan large amounts of due diligence materials to analyze trends and, in the process, inadvertently exposing sensitive data, like nondisclosure agreements, to unintended parties, Rasouli explains.

Mark Nicholson, a principal in Deloitte & Touche LLP, also says the use of generative AI technology could result in unintended exposures—even when users have set up certain risk controls to try to prevent this.

“[Let’s say] a generative AI has been granted a control that indicates that I’m not allowed to reveal a certain individual’s name or social security number,” says Nicholson, the financial services industry leader for Deloitte’s cyber and strategic risk practice.. “But then you ask it to verify if the list is alphabetically accurate. Suddenly, you might be able to grant it the ability to circumvent that [security] control,” he said.

Furthermore, money managers have a network of providers with whom they work, which asset owners should consider, as those parties could also be using AI, impacting the integrity or security of data, Nicholson says. This could include risk management and portfolio management providers, as well as firms offering investment accounting services.

“They have a variety of third, fourth and fifth parties they engage with; it’s a hyperextended network,” Nicholson says. “The question is: Can you trust that network? … Certainly when engaging with third parties, it’s critical to know when they are using AI. Where is data held, how it is held, and where does it go? What is the architecture of the tools that access [your data]?”

Other Vulnerabilities

Joshua Pantony, co-founder and CEO of Boosted.ai, a Toronto-based AI firm that helps investment managers and institutional investors implement machine learning into their investment process, says that as large language models (advanced AI systems pre-trained on large amounts of data) become more accessible to the general public, it will also be easier for bad actors to access them.

“Now we’re going to enter a world where we see sophisticated, phishing emails that try to break into organizations” by targeting AI, Pantony says. “It hasn’t really caught on within the criminal element, but I think that’s going to change in coming years.”

He has seen that institutional investors want to be able to understand how AI models are making their predictions and if they can trust the “thinking” of the model, Pantony says.

“The No. 1 thing holding institutional investors back from using AI is the black-box nature of these things and understanding where this information comes from,” Pantony elaborates. “From our standpoint, we are very careful that it’s primarily professional investors who are using this software. You could misconstrue [the model] as overly confident,” for instance.

A key security concern for asset owners is typically making sure sensitive information, like stock price information, is secure when AI is used.

“That’s increasingly causing a lot of people to invest in private cloud platforms,” Pantony says.

Data security risks investors may be trying to prevent include unintended parties being able to access the large swaths of data that AI may scan to extract or predict market trends.

“Let’s say you are a hedge fund, and you do a lot of management calls,” and AI is used to scan this, he explains. “A lot of times, that’s perceived as being very valuable and key to investment management decisions, and you ideally don’t want other asset managers to know about that.”

The SEC recently launched a sweep of investment advisers, requesting information related to their use of AI and how the technology is overseen, The Wall Street Journal reported this month.

Vigilant’s Teufel, who is aware of the sweep, said, “There’s a lot of pressure on the SEC to do something with AI, and I think they are in the early stages of figuring out what that is.”

“They want to provide some good use cases,” for how AI is being used, Teufel adds. “These examinations, these sweeps, will either prove to the industry that they do or don’t need another rule.”

Jeff DeVerter’s title is chief technology evangelist at Rackspace Technology, a San Antonio-based cloud computing firm whose customers include financial services companies. Similarly, he says investors need to know and consider when money managers are using third-party AI firms, as the security of their systems matters too.

Additionally, for asset owners considering using open-source AI tools within their organizations, they have to consider the level of exposure free software may present.

“A lot of times companies will bring it in, customize it and make it their own,” DeVerter says. “They need to make sure it isn’t leaking data to the internet. … Then you have software-as-a-service [AI tools] that are free, or might as well be free because it’s $20 a month. The primary consideration is: You never, ever want to put your private or secure data in a place where it can be leaked.”

For that very reason, DeVerter has seen many instances of customers building private versions of public AI models, he said.

Asset owners need to consider not only if they will use public or private AI technology, but whether they will run it in their own data centers or in a private cloud environment, DeVerter says.

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Asset Managers Ponder Investments in AI as Security Risks Compound

M&A, VC, AI Activity Expected to Increase in Next 5 Years, per Coller Capital Survey

How Asset Managers Can Harness AI to Boost Profits, per EY

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