Key Points:

• The recent spike in stock market volatility has coincided with a notable correction; such behavior is common in periods of higher turbulence but is typically followed by a bounce back.
• Many factors hold up well in volatile times; others such as small size suffer when volatility spikes but tend to recover as the market calms down.
• Using diversifying factors other than market beta and considering uncorrelated absolute return strategies can benefit a portfolio, both in general and during periods of volatility.

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Recently, stock market volatility has increased dramatically. Investors are worried about a prospective increase in inflation[i] and analysts have raised the odds of a recession[ii]. Bond yields have gone up[iii] while stocks fell[iv]. Returns of stock indexes have had large swings from day to day and even intraday. The VIX, a measure of expected volatility, surged from 17 at the start of the year to over 50 in early April, a level only seen with the COVID-19 pandemic in 2020 and the Great Financial Crisis of 2008. This spike in volatility has many investors wondering what to expect. In this piece, I examine what high volatility has meant historically for the stock market as a whole and for various market segments.

Historical Volatility

Exhibit 1 shows realized volatility in the U.S. stock market back to July 1963, when data on some of the factors examined later in this piece first become available. I do not use the VIX as it has a shorter history, but results for the common period are qualitatively similar. Volatility here is measured at a monthly level, using the standard deviation of daily returns for the total market. Data through 2024 comes from the Ken French data library[v]; for 2025 I use returns of the Russell 3000 which is a close proxy.

Exhibit 1: Monthly volatility of U.S. stocks, July 1963 – April 2025.

The current spike in market volatility to 3.13% is the fourth highest since 1963. For comparison, the average over this period is 0.87% and the median is 0.72%. The three higher episodes correspond to the Black Monday crash in October 1987, the Great Financial Crisis in 2008, and the onset of the COVID-19 pandemic in early 2020. Some other observational facts stand out. Spikes in volatility can be sudden and sharp but tend to quickly revert to more typical levels. But volatility also tends to be persistent, with elevated levels around those spikes as well as in the Tech Bubble of the late 1990s, while we see other long periods of calmer markets. This pattern is borne out statistically, as the correlation of volatility from one month to the next is 64%. In contrast, the correlation of market returns from one month to the next is only about 3.5%. So if history is any guide, we may expect volatility to come down from its current peak but remain high.

Volatility and Stock Returns

The volatility we are currently experiencing has been accompanied by an overall market downturn. Major indices in the U.S. all suffered corrections and now sit well off their highs. The three other volatility peaks seen in Exhibit 1 all corresponded with bear markets. But we also know that stocks ultimately rebounded, in some cases quite swiftly such as during COVID-19 pandemic times. To gain a better understanding of how general these results are, I examine historical market returns in different volatility regimes.

For this analysis, I categorize all months from July 1963 through December 2024 into one of five buckets, depending on the market volatility that month relative to the full set of months. I will explain how to interpret these exhibits, but readers who do not get enjoyment from tables of numbers can ignore them and simply focus on the explanation in the paragraphs below.

In Exhibit 2 below, the first column of numbers is for the full period, a total of 738 months, or over 60 years. We see the well-known result that stocks have done very well historically, averaging 0.95% monthly returns. These returns have come with some volatility. The monthly standard deviation[vi] is 4.47%, with a minimum of -22.64% and a maximum of 16.61%. Such volatility is why equities command a risk premium, leading to the high and statistically significant (t-stat of 5.76) returns.

Exhibit 2: Monthly returns (in %) of U.S. stocks dependent on volatility, July 1963 – December 2024.

		Same Month Return					Next Month Return
	All	Lo20	Q2	Q3	Q4	Hi20	Lo20	Q2	Q3	Q4	Hi20
Mean	0.95	1.92	1.73	1.48	0.66	-1.05	0.82	0.87	0.68	1.16	1.23
Stdev	4.47	2.56	3.12	3.75	4.37	6.69	2.98	3.32	4.37	4.92	6.08
Count	738	148	147	148	147	148	148	147	148	146	148
T-stat	5.76	9.13	6.71	4.79	1.83	-1.90	3.34	3.17	1.88	2.84	2.46
Min	-22.64	-4.10	-6.67	-10.50	-11.23	-22.64	-7.49	-8.15	-12.19	-22.64	-17.15
Max	16.61	8.58	9.10	12.63	12.89	16.61	8.58	11.90	12.89	11.89	16.61

Source: Ken French data library, Bridgeway calculations

The next set of five columns gives monthly stock returns depending on volatility that month. The lowest quintile or 20% of months by volatility are on the left, the most volatile quintile is on the right, and the intermediate quintiles are in between. Average returns monotonically decrease as volatility increases: Stocks return 1.92% in the 148 months when volatility is lowest, declining to a loss of 1.05% in the 148 months when volatility is highest. The market declines we saw with the biggest spikes in volatility hold more generally when volatility is elevated. Not only is the average monthly return of -1.05% significant with a t-stat of -1.90, it is even more significant relative to the overall average market return of 0.95%.

While these results hold on average, they are by no means a guarantee, with plenty of variation in all volatility environments. Unsurprisingly, that variation is greatest when volatility is highest, with a standard deviation of 6.69% and the most extreme minimum and maximum returns. But even the calmest months provide a wide range of outcomes. Returns can be nicely positive or disappointingly negative in all volatility environments.

These results help explain current returns based on volatility, but what about future market returns? This question is addressed in the final five columns. Here I look at the volatility in a given month and show the next month’s returns. The picture is different. Most notably, the highest returns occur following months with the highest volatility, exactly opposite same month returns. Variations do persist, as seen by the continued higher standard deviations and return spreads in the month after high volatility. But as volatility subsides, the market tends to recover, sometimes quite strongly as seen for example after the big volatility spikes from the Great Financial Crisis and COVID-19 pandemic. Indeed, at the end of April 2025, markets partly recovered from the earlier lows of the month. This pattern is a cautionary tale for those tempted to sell when markets fall and get volatile.

Volatility and Segments of the Market

These results show how the stock market as a whole performs across volatility regimes, but what about different segments of the market? Perhaps some do better than others in periods of high volatility?

I first examine the performance of factors — groups of stocks defined by certain characteristics. For example, a common academic version of the value factor, HML, is given by the return of the highest 30% of stocks by book-to-market (B/M) minus the lowest 30%. Making use of factors in forming portfolios not only gives the potential for higher returns, but also provides a more diversified portfolio[vii]. I use common definitions from the Ken French data library, but variations are possible. For example, making use of multiple metrics (e.g. including earnings, sales and cash flow relative to price in value) or using contextual definitions can improve factor performance. I show only the average returns by volatility environment to save space, but suffice it to say that again there is plenty of variation around those averages[viii], which makes trying to time these factors difficult.

Exhibit 3: Monthly factor returns (in %) of U.S. stocks dependent on volatility, July 1963 – December 2024. Factor definitions are given in the text.

		Same Month Return					Next Month Return
	All	Lo20	Q2	Q3	Q4	Hi20	Lo20	Q2	Q3	Q4	Hi20
Mkt – Rf	0.58	1.59	1.31	1.07	0.29	-1.34	0.48	0.46	0.27	0.77	0.94
SMB	0.17	0.78	0.12	0.26	0.18	-0.52	0.18	-0.02	0.22	-0.01	0.46
HML	0.28	0.33	0.12	0.27	0.37	0.30	0.38	0.39	0.41	0.12	0.09
RMW	0.28	0.09	0.31	0.12	0.43	0.48	0.19	0.23	0.24	0.67	0.10
CMA	0.26	0.05	-0.06	0.12	0.38	0.79	0.14	0.13	0.30	0.22	0.49
UMD	0.61	0.89	0.64	1.01	0.55	-0.02	0.82	0.86	0.91	0.81	-0.34
STREV	0.44	0.56	0.54	0.37	0.35	0.39	0.25	0.23	0.10	0.48	1.14
DYLD	0.03	-0.35	-0.19	-0.34	0.28	0.75	0.04	0.08	0.11	-0.13	0.05

Source: Ken French data library, Bridgeway calculations

The top row of numbers is the market beta factor or equity risk premium, which measures the return of the stock market relative to the risk-free rate of cash, as represented by one-month Treasury bills. We see a very similar picture as in Exhibit 2: stocks do best relative to cash in low volatility months and worst in high volatility months. But in the month after high volatility, stocks outperform the most.

The next two factors, SMB and HML, represent the excess returns of small size and value. These factors form the core of many academic factor pricing models and are the basis for common equity portfolio classifications such as small value or large growth funds. When volatility is high, riskier small stocks tend to suffer and lag larger stocks. But these beaten-down small stocks typically recover much of those losses in the following month. Value stocks, on the other hand, display no notable pattern, generally doing well no matter the volatility environment. In the month following high volatility episodes, value excess returns tend to be weaker but are still positive.

RMW represents the premium of stocks with robust versus weak profitability. While always positive no matter the volatility environment, it performs best when volatility is high and investors might seek higher quality companies with better profitability. As seen with other measures, this behavior reverts in the following month to a lower albeit positive premium. CMA refers to the premium of stocks with more conservative growth in assets relative to firms that are more aggressive. Again we see a flight to quality in times of higher volatility, which while lower the following month, remains higher than other volatility environments as well as the overall average.

The following two factors are based on return patterns. UMD measures medium-term momentum and STREV gives one-month reversals. Momentum is strong overall except in months of high volatility and the subsequent month as well. This is consistent with the findings and explanation of “When and Why Does Momentum Work – and Not Work?”[ix] which noted that volatile markets can disrupt the trend following that momentum relies upon. Conversely, short-term reversals perform best in the month after high volatility. This is consistent with what we saw for the stock market overall. On average, the market falls when volatility spikes but then reverses the next month. Similarly, those individual stocks which performed worst subsequently rebound the most in the following month.

I also examined sector returns; results are not shown for brevity. For every sector, returns were by far the worst when volatility was highest, and for all but one sector those returns were negative. This one exception was Utilities, which also had its worst returns when volatility was high, but they were still positive. Utilities tend to pay high dividends, and their returns align with what we see for dividend yield in the final row of Exhibit 3. In times of turmoil, investors tend to seek out the relative safety of higher dividends.

Implications for Investors

The recent spike in market volatility is the fourth highest in the past 60 years and has coincided with a stock market drawdown. Such behavior is typical for periods of higher volatility. Higher volatility and drawdowns are features of the stock market, and lead to the equity risk premium – the long-term outperformance of stocks over less risky investments. Indeed, in the subsequent month after high volatility, stocks as a whole tend to recover, as do beaten down segments of the market such as smaller stocks.

One important implication for investors is to be wary of making big moves when volatility has soared and stocks have dropped. You could well be locking in those losses and risk missing out on the subsequent recovery. A disciplined, systematic approach can help avoid the temptation to make kneejerk reactions. Investors should build a well-diversified portfolio that can help them withstand, both financially and emotionally, the inevitable shocks that arise. Diversification can be from other asset classes; it can also come from utilizing other factors besides market beta as sources of return. Most of these factors hold up well on average both overall and during periods of high volatility. Disciplined rebalancing and appropriate risk controls can help preserve the power of diversification and reduce downside risk during volatile times. Investors may also want to consider an allocation to absolute return strategies that are uncorrelated to the market. Keeping these points in mind can help you stay calm during periods of turbulence.

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[i] See https://bridgeway.com/perspectives/factoring-in-inflation/ for a discussion of inflation and investing.

[ii] See https://bridgeway.com/perspectives/stress-test-how-factors-perform-before-during-and-after-recessions/ for a discussion of investing around recessions.

[iii] See Berkin, Andrew L. 2018. “What Happens to Stocks When Interest Rates Rise?” Journal of Investing 27 (2): 126-135.

[iv] See https://bridgeway.com/perspectives/factoring-in-bear-markets/ for a discussion of investing in a bear market.

[v] https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

[vi] Note this is the standard deviation of monthly returns over the full set of 738 months. This is different from the calculation of market volatility of Exhibit 1, which gives the standard deviation of daily returns for each month.

[vii] See “Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today” by Andrew L. Berkin and Larry E. Swedroe, BAM Alliance Press, 2016 for a discussion of factors and their use in forming a more diversified portfolio.

[viii] The full set of results is available upon request.

[ix] Berkin, Andrew L. 2021. “When and Why Does Momentum Work – and Not Work?” Journal of Investing 30 (5): 35-54.

Key Points

1. Market Neutral Strategies May Offer Protection During Trade Wars
   By balancing long and short positions, market neutral strategies aim to generate returns independent of overall market direction. This approach can help shield portfolios from the volatility and disruptions caused by tariffs and global trade tensions.
2. Diversified, Multi-Dimensional Neutrality Generally Reduces Risk Exposure
   Effective market neutral portfolios manage risk by maintaining neutrality across multiple dimensions — including sector, country, currency, market beta, and company size. This structure is designed to prevent concentrated exposure to trade-related shocks and mitigates the risk that any single event or region disproportionately affects returns.
3. Returns Driven by Stock Selection, Not Market Timing
   With broad market risks neutralized, stock selection relies on a steadfast approach using strategies like sentiment analysis, liquidity screening, monitoring industry momentum vs. stock-specific reversals, and using dynamic, real-time risk models. This is intended to enable portfolios to capitalize on inefficiencies without needing to predict geopolitical events.

As global trade tensions escalate and tariffs create significant market uncertainty, investors face increased volatility and potential portfolio declines. Tariffs disrupt established supply chains, increase costs for businesses and consumers, and often trigger retaliatory measures that further destabilize markets. These factors create an environment where traditional long-only investment approaches may struggle. At Bridgeway Capital Management, our market neutral Absolute Return strategies are designed to deliver uncorrelated returns that can thrive during such uncertain periods.

Our Absolute Return strategies employ both long and short positions designed to generate returns regardless of overall market direction. Unlike traditional approaches that only profit in rising markets, our strategies can capitalize on both overvalued stocks (through short positions) and undervalued opportunities (through long positions). This balanced approach allows us to potentially profit from the market dislocations that trade wars create while maintaining a protective posture against broader market declines. By neutralizing market exposure, we aim to provide investors with a valuable diversification tool during periods when conventional investments face heightened risk.

Portfolio Design: Keys to Managing Trade War Risks

In our market neutral portfolio, the dollar amounts invested in long and short positions are equal, in attempt to neutralize overall market exposure. This aggregate portfolio long vs short balance and other market neutral portfolio constraints allow our strategy to potentially profit from both rising and falling stock prices, resulting in a return stream uncorrelated to the broader market.

To build a market neutral portfolio resilient to trade war risks, Bridgeway employs a highly diversified approach. Our Long portfolios typically hold over 250 stocks, while our Short portfolios often exceed 300 names, selected from a universe of more than 10,000 stocks across 35+ countries and all 11 market sectors. With a vast investment universe to choose from, the strategy can be highly selective in its positions, avoiding over-concentration in any one company, sector, country, or currency that might be particularly vulnerable to trade tensions.

This breadth is combined with carefully engineered neutrality constraints across multiple systemic risk dimensions such as:

• Sector Neutrality: Sector Neutrality is a portfolio construction constraint aimed at equalizing long and short allocations within each market sector to reduce the impact of trade actions targeted at specific industries. Most of our sector exposures are less than 3%.
• Country Neutrality: Country neutrality is a key principle for managing the risks associated with international investing, particularly in the context of a global trade war. By maintaining balanced long and short positions within each country, a market neutral portfolio can effectively neutralize its exposure to country-specific risks, such as changes in trade policies, geopolitical events, or economic conditions. This approach also implies cost-effective currency neutrality, as the strategy’s net exposure to each country’s currency is minimized. When a strategy has equal long and short positions in a given country, any gains or losses from currency fluctuations on the long side will be offset by corresponding losses or gains on the short side. By keeping the majority of country exposures under 3%, our well-diversified market neutral portfolio is designed to further reduce its vulnerability to any single country’s trade war-related risks, while still maintaining the flexibility to capitalize on attractive opportunities in specific markets.
• Beta Neutrality: Beta is a measure of a stock’s or portfolio’s sensitivity to broader market movements. A beta neutral portfolio aims to achieve a beta close to zero, meaning its performance is largely independent of market swings. This is particularly valuable during trade disputes, which can trigger heightened market volatility. By balancing the beta of long and short positions, a market neutral hedge strategy can insulate its performance from the shocks and swings that trade wars can bring, providing a degree of stability in uncertain times.
• Size Neutrality: Size neutrality is an important aspect of portfolio construction in a market neutral portfolio, particularly in the context of a global trade war. By maintaining a similar distribution of market capitalizations in both the long and short portfolios, the strategy can mitigate the risk of its performance being overly influenced by the divergent fortunes of large and small companies, often impacted differently by tariffs.

By diligently pursuing neutrality across these risk dimensions, the strategy is designed to sidestep many of the risks posed by the tensions and gyrations of trade wars.

Breadth and Neutrality in Action: Navigating Trade War Scenarios

Trade wars create asymmetric impacts across sectors, countries, and company sizes, generating volatility for directly affected entities. A well-constructed market neutral portfolio with comprehensive neutrality across multiple dimensions (sector, country, currency, beta, and size) may offer effective protection against these disruptions. Let’s examine how this approach functions across various trade conflict scenarios.

When China imposes agricultural tariffs on the U.S., or when the U.S. and Europe exchange food and beverage tariffs, our sector neutrality aims to ensure that declining long positions in affected industries are offset by gains in our short positions that are also declining in value in the same sectors. This principle applies equally when automotive tariffs escalate between trading partners or when supply chain disruptions affect technology companies dependent on restricted materials like rare earth minerals.

Country and currency neutrality prevent overexposure to nation-specific risks by balancing long and short positions within each country, naturally hedging against currency fluctuations triggered by trade disputes. Beta neutrality minimizes sensitivity to broader market moves that can result from trade tensions. While large multinationals—common in industries such as technology, consumer staples, pharmaceuticals, and automobiles—often have the flexibility and resources to navigate tariffs, their global reach can also increase exposure to international trade barriers. In contrast, more locally focused industries like utilities, real estate, regional banks, healthcare providers, and domestic retailers tend to be insulated from tariffs, as their operations and supply chains are primarily domestic. However, the impact of any given tariff ultimately depends on its nature and the industry affected. This is why combining size neutrality—which balances exposures between large and small companies—with sector neutrality—which mitigates net exposure across different sectors—offers a more robust defense. Together, these portfolio construction techniques help mitigate the risk that any single country, currency, company size, or sector disproportionately affects overall returns during periods of global trade disruption.

This multi-dimensional neutrality is designed to create resilience regardless of which sectors become trade war battlegrounds. Whether agricultural products, spirits, automobiles, technology components, or industrial materials like aluminum become targets, our balanced approach maintains portfolio stability. When U.S. farmers lose access to Chinese markets, European food exporters face retaliatory measures, automakers confront higher input costs, tech companies struggle with supply chain disruptions, or manufacturers absorb increased material costs, our neutrality across all five dimensions is intended to mitigate portfolio volatility.

The key advantage of this approach is adaptability without prediction. Rather than attempting to forecast which sectors or countries will be targeted next in unpredictable trade conflicts, our market neutral strategy creates systematic resilience through its comprehensive balancing mechanisms.

Stock Selection Considerations Amidst the Tensions

With all of these neutrality constraints in place, you might wonder how we generate returns. The answer lies in our disciplined and steadfast approach to stock selection. By using portfolio construction for risk mitigation with our neutrality constraints, we remove broad market risks and focus the portfolio’s active risk on our stock picks. Our market neutral portfolio typically maintains a gross exposure of 200% and targets an annualized volatility of 10%, ensuring that the potential for returns comes from identifying individual winners and losers, rather than betting on geo-political trends or passing investment themes. We believe that idiosyncratic returns—those sourced from the breadth of specific company insights—are more consistent and repeatable than trying to profit from predicting countries, sectors, or size effects. In essence, neutrality allows our stock selection skill to shine through as the main driver of performance. 

It’s worthwhile to point to four specific aspects of our stock selection process that are particularly relevant in the current environment:

• Stock-Specific Liquidity: We believe nimble, forward-looking fundamental analysis becomes exceptionally valuable when navigating the rapidly shifting currents of trade disputes. Our liquid portfolio mandate is designed to create agility, requiring at least $250,000 of daily liquidity in every position to enable swift repositioning as conditions evolve.
• Sentiment Analysis: During periods of heightened uncertainty such as we are experiencing, our Sentiment models demonstrate that forecasts of company fundamentals consistently outperform historically reported financial statements for stock selection efficacy. We attempt to capitalize on unusual price movements supported by strong volume that quickly express market sentiment, performing daily analysis to decompose where market sentiment is already priced-in and—more crucially—where it remains unrecognized. This approach uncovers idiosyncratic opportunities by distinguishing between stock moves reflecting realistic earnings expectations versus those driven by emotional investor reactions, especially for knowledge-based, high intangible industries. (See Dugar and Pozharny, Financial Analyst Journal, 2021)
• Industry Momentum vs Stock Price Reversals: Our behavioral research on market microstructure reveals a critical pattern: industries affected by economic shocks (like tariffs) often experience strong directional momentum, while some stocks within these industries frequently demonstrate momentum reversal patterns. When trade actions target a specific industry, we observe initial broad-based reactions across all companies in that sector, regardless of their actual exposure to the tariff. This creates exploitable inefficiencies as the market eventually differentiates between genuinely vulnerable companies and those with minimal exposure or greater adaptability. By identifying industry-level momentum with carefully selected counter-momentum opportunities in specific stocks within the same industry, we can capitalize on both the initial tariff shock and the subsequent rationalization as markets reassess individual company impacts. (See Stein and Pozharny, Risks, 2022)
• Dynamic Risk Modeling: Rather than relying on conventional risk models built on long-term historical data, we deploy dynamic risk modeling calibrated to current market conditions and recent covariance patterns. Understanding recent global stock price covariances proves far more effective in capturing the systematic risks caused by trade war developments. These more dynamic recent covariance estimates adapt quickly to emerging correlation patterns as trade tensions reshape global market relationships. Commercial risk models estimated during periods of globalization fundamentally miss the mark when applied to today’s trade war environment. This adaptive methodology allows us to respond effectively as trade developments propagate through interconnected global markets with unprecedented speed and complexity.

Your Ally in Uncertain Times

In this period of elevated uncertainty, market neutral strategies warrant serious consideration for a portion of most portfolios. By engineering diversified, neutral exposure and dynamically shifting with evolving risks and opportunities, Bridgeway’s Absolute Return strategies aim to chart a steady course through the choppiest market conditions.

We invite you to contact Bridgeway to discuss how our market neutral strategies are currently positioned, where we see mispricing of market sentiment, and the role our insights can play in your portfolio. Together, we can strive to transform the challenges of a global trade war into opportunities for uncorrelated returns.

Additional Readings

• Dugar, A., & Pozharny, J. (2021). Equity investing in the age of intangibles. Financial Analyst Journal, March 2021.
• Stein, H, & Pozharny, J. (2022). Modeling Momentum and Reversals. Risks, September 2022.

The traditional rulebook for evaluating companies is becoming obsolete. Today’s most valuable corporate assets are invisible – they exist in lines of code, neural networks, and the collective knowledge of technical teams. While balance sheets excel at capturing the worth of factories and equipment, they fail to reflect the true value of artificial intelligence (“AI”) capabilities that increasingly drive corporate success.

The Art of Valuation in the Age of AI

Traditional manufacturers, with their tangible assets of plants and machinery, often trade near book value. Their worth is readily apparent – you can walk through their facilities and touch their assets. Yet many of today’s most valuable companies, those heavily invested in AI, command valuations at 15 times book value or higher. Their true worth resides in assets you cannot see:

• Sophisticated algorithms that improve with each interaction
• Vast databases that feed machine learning systems
• Research teams pushing the boundaries of AI capabilities
• Network effects that create exponential value as user bases grow

This fundamental shift demands a new valuation framework. Applying traditional metrics designed for industrial-era companies to AI-driven enterprises misses crucial value drivers.

At Bridgeway Capital Management, our Absolute Return stock selection models recognize that effective investment analysis must be contextual – adapting our process to understand how value is created differently across sectors in this new AI-driven economy.

Why Traditional Accounting Misses the Mark

Financial statements systematically understate both earnings and book value when companies invest heavily in intangibles, including, but not limited to AI:

Earnings Distortion:

• Research and  development (“R&D”) expenses are immediately written off rather than capitalized and amortized
• Training and workforce development costs reduce current earnings despite creating future value
• Marketing spending to build brand value appears as an expense despite building lasting customer relationships

Cash Flow Complications:

• High initial investment periods show negative free cash flow despite building valuable capabilities
• Traditional free cash flow metrics don’t distinguish between investment in growth versus maintenance
• Working capital metrics fail to capture investment in data and intellectual property

Book Value Understated:

• Successfully developed internal software has zero book value
• Accumulated organizational knowledge shows as zero on balance sheets
• Brand value built through marketing appears nowhere in assets

Real-World Impact Across Industries

Software/Tech:

• Google’s AI language models like BERT and LaMDA require massive R&D investment that appears as pure expense, despite creating foundational technology that powers multiple revenue streams
• TikTok’s recommendation algorithm development costs are expensed immediately, yet this AI system drives unprecedented user engagement and advertising value
• NVIDIA’s AI software development appears as current expense despite creating lasting competitive advantages in AI acceleration

Media/Entertainment:

• Disney’s investment in AI-powered content recommendation and production tools shows as cost despite enhancing streaming engagement
• Netflix’s spending on AI for personalization and content optimization reduces current earnings but builds valuable predictive capabilities
• Meta’s AI content moderation system development appears as expense despite creating platform value

Retail:

• Costco’s AI inventory management and demand forecasting investments reduce reported profits while building lasting efficiency gains
• Nike’s AI-driven design and customization platform development costs mask future revenue potential
• Amazon’s robotics and AI logistics investments appear as expenses despite creating powerful automated fulfillment capabilities

The Bridgeway Advantage

We believe a few advantages of our Absolute Return contextual stock selection based on intangible capital intensity include:

1. Emphasizes valuation metric relevancy based on intangible intensity
2. Recognizes how R&D and human capital are valued differently across industries
3. Adjusts for different sentiment and profitability patterns across sectors
4. Identifies irrational exuberance in AI-driven business models

In an era where the most valuable assets are invisible, you need an investment approach that sees the full picture. That’s the Bridgeway difference.

Additional Readings

• Dugar, A., & Pozharny, J. (2021). Equity investing in the age of intangibles. Financial Analyst Journal, March 2021.[Equity Investing in the Age of Intangibles | CFA Institute Research and Policy Center]
• Berkin, A. L., Dugar, A., & Pozharny, J. (2022). The impact of intangible capital on factor performance efficacy. Journal of Portfolio Management, July 2022. [The Impact of Intangible Capital on Factor Performance Efficacy | Portfolio Management Research]
• Berkin, A. L., Dugar, A., & Pozharny, J. (2024). Classical stock valuation in the modern era of intangibles. Journal of Investing, June 2024. [Classical Stock Valuation in the Modern Era of Intangibles | Portfolio Management Research]

Key Points:

• As the largest stocks in the S&P 500 rise to historic levels, the effective number of stocks driving this index has fallen to below 50.
• Such concentration presents risks, and previous periods have been followed by weaker returns.
• Possibilities to mitigate this risk include large cap strategies with more equal weighting, better diversified and cheaper small cap value stocks, and alternative strategies independent of market direction.

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How many stocks are effectively in the S&P 500 index of large cap stocks? Let’s start with a simpler question by removing “effectively”: how many stocks are in the S&P 500? Even this question is not straightforward, as stock indices don’t always have a count which matches the number in its name. The Wilshire 5000 is designed to represent the entire US stock market and has had notably greater or less than 5000 stocks. The Russell 1000 and 2000 indices have only roughly those numbers due to corporate actions and grandfathering rules. The S&P 500 does have 500 companies, but as of the end of 2024 had 503 holdings because three companies (Alphabet, Fox and News Corp) have multiple share classes in the index.

Now let’s return to the original question: how many stocks are effectively in the S&P 500? Because the S&P 500 is float weighted, larger stocks have a much greater weight than smaller stocks. These smaller stocks, some with weights just over 0.01%, have minimal impact on the characteristics and returns of the index. The largest stock, Apple, had a weight of 7.6% at the end of 2024, equivalent to the weight of the smallest 217 companies.

How can we measure the effective number of holdings? Statistics has an answer, based on the Herfindahl-Hirschman Index (HHI). While it sounds like a mouthful, the concept is straightforward, so bear with us for a moment. The HHI is a measure of concentration and is simply the sum of the weights squared, and can range from 0 to 1. The inverse of the HHI gives the effective number of holdings.

As an example, consider a portfolio of 10 stocks. If they have equal weight, each will be at 10% or 0.1 of the portfolio. Square 0.1 to get 0.01, then add up the 10 stocks to get an HHI of 0.1. Now invert the HHI: 1/0.1 is 10, which makes sense: each of the 10 stocks contributes equally. Next consider the case where one stock has 91% of the weight and the other nine stocks each have 1%. The HHI is now 0.83, indicating high concentration. Inverting it gives 1.2, barely over 1. The portfolio is dominated by the single large holding, with the other nine stocks having very small contributions.

The plot below shows the effective number of stocks in the S&P 500 since 1970. We have treated multiple share classes as a single stock by adding their weights, since the two share classes provide exposure to the same company. To answer the question posed by the title of this piece, the effective number of stocks was just over 46 at the end of 2024. This is the lowest number in the last 55 years. Perhaps this is not too shocking when the “Magnificent Seven” stocks (Apple, Nvidia, Microsoft, Amazon, Alphabet, Meta and Tesla) have over a third of the total weight of the index. The plot shows two other periods of high concentration: the Tech Bubble of the late 1990s and the Nifty Fifty craze of the early 1970s. See our piece “Party Like It’s 1972” for a comparison of those episodes with recent events.

Why should an investor care about concentration? One important reason is that such a high weight in a few stocks leads to a greater influence on returns and reduces diversification, called the one free lunch in investing. If the returns of those higher weight stocks begin to lag, they drag down the returns of the entire S&P 500. Indeed, this is what we observe following past periods of high concentration.

The graph below shows the HHI in blue on the left axis. Recall this measure of concentration is simply the inverse of the effective number of stocks. We see the current historic high concentration of the S&P 500, as well as peaks during the Nifty Fifty Craze and Tech Bubble. But these periods did not end well for investors, as we can see from the strongly negative returns in the early 1970s and early 2000s. And it was the stocks which had grown to be such a big weight in the index which led the way down. Note that the orange line of forward returns ends 12 months before the HHI. We don’t know what future returns will be, but the high concentration and low effective number of stocks in the S&P 500 could be viewed as a cautionary signal.

This is not a call to try to time the market. The plot does show that concentration and returns have fallen after reaching new highs, but both can keep increasing before that reversal. And other factors drive the market as well; there are jumps and falls unrelated to concentration. But history tells us that high concentration can be a warning sign. Investors may want to revisit their risk and return assumptions together with their portfolio allocations and consider appropriate actions.

What steps could investors take? One thought is to consider a large cap strategy with less concentration in the largest names. But there are caveats. An equal weighted S&P 500 offers the greatest diversification across these names, and since 1970 would have offered higher returns as well. However, giving the same weight to these 500 stocks both big and small gives the portfolio a more mid cap tilt. The weighted average market cap of the S&P 500 at the end of 2024 was over a trillion dollars[1]. For the equal weighted version, the average market cap was ten times less at about $100 billion dollars. But an equal weighted version of the largest 50 firms, roughly the number of effective holdings in the S&P 500, had an average market cap of $600 billion dollars. This is much closer to that of the index, and the weight of the Magnificent Seven is reduced to 14% from over a third.

Another suggestion is to revisit the allocation to small cap value stocks. One concern about such high concentration in the S&P 500 is that it also becomes quite expensive, with a P/E ratio at the end of 2024 of 27.4[2]. Not only is this quite high, but it is a very large spread compared to the P/E of the small cap Russell 2000 Value index at 13.8. While US stocks as a whole have gone up, the rally has been led by the mega cap growth stocks. Small value stocks still trade at quite reasonable valuations, with the smallest deep value names especially trading at a discount. Investors may want to revisit their allocation to this segment of the market. Similarly, international stocks also trade at discounted valuations to the S&P 500.

A final suggestion is to consider an allocation to alternatives which are uncorrelated to equities. Such strategies always have a potential role to play in a portfolio, with their diversification benefits. With the S&P 500 so highly concentrated and expensive, now is a particularly appropriate time for investors to reexamine their allocations within large cap stocks and to small value and international equities as well as to alternatives.

DISCLOSURES

The opinions expressed here are exclusively those of Bridgeway Capital Management (“Bridgeway”). Information provided herein is educational in nature and for informational purposes only and should not be considered investment, legal, or tax advice.

Past performance is not indicative of future results.

Investing involves risk, including possible loss of principal. In addition, market turbulence and reduced liquidity in the markets may negatively affect many issuers, which could adversely affect client accounts.

Diversification neither assures a profit nor guarantees against loss in a declining market.

The S&P 500 Index is a broad-based, unmanaged measurement of changes in stock market conditions based on the average of 500 widely held common stocks.

One cannot invest directly in an index.  Index returns do not reflect fees, expenses, or trading costs associated with an actively managed portfolio.

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[1] Source for market cap calculations: Refinitive, S&P Global Ratings, CRSP,Compustat and Bridgeway.

[2] Source for P/E ratios: FactSet and Bridgeway.