Better than Alpha

Better than Alpha Plot Summary

Introduction

Traditional alpha, the excess returns achieved through active management over market benchmarks, has been the holy grail of investment management for decades. Yet a profound transformation is underway in how alpha is conceptualized, measured, and pursued across financial markets. This transformation stems from changing market dynamics, technological advancements, and evolving investor expectations that have collectively eroded the sources of traditional alpha. The journey through financial markets reveals how alpha opportunities have systematically disappeared across public equities, hedge funds, and increasingly in private markets. As these traditional hunting grounds have become more efficient, the very definition of investment success needs reconsideration. Rather than chasing benchmark outperformance through increasingly complex strategies, a more sustainable approach focuses on probability-based decision making that increases the likelihood of meeting specific investment objectives. This shift from benchmark-relative thinking to outcome-oriented investment represents not just a technical adjustment but a fundamental reimagining of what constitutes value in investment management.

Chapter 1: The Evolution of Alpha: From Stock Picking to Factor Investing

The history of alpha begins with pioneering investors like Jonathan Bell Lovelace, who founded Capital Group in the 1930s after accurately predicting the 1929 market crash. These early practitioners applied fundamental research to identify undervalued companies, generating consistent outperformance for decades. Benjamin Graham's value investing principles similarly produced remarkable returns, with his partnership achieving approximately 20% annual returns from 1936 to 1956 when markets averaged around 12%. This persistent outperformance appeared to contradict efficient market theories developed by economists like Harry Markowitz and William Sharpe in the 1950s and 1960s. Their models suggested that excess returns from diversified portfolios were likely attributable to luck rather than skill. The efficient market hypothesis gained further support from researchers like Burton Malkiel and Eugene Fama, who demonstrated that stock returns were essentially random and that consistent market-beating performance was statistically improbable. The watershed moment came in 1993 when Fama and Kenneth French published groundbreaking research identifying three factors that consistently predicted stock returns: market exposure, company size, and book-to-market ratio. This research validated value investing principles while simultaneously suggesting that what appeared to be alpha was actually exposure to systematic risk factors. Further research by Mark Carhart in 1997 added momentum as a fourth factor, and later studies identified additional anomalies like the low-volatility effect. As financial theory evolved, so did investment practice. What was once considered skill-based alpha gradually transformed into replicable factor exposures or "smart beta." Investment managers who had been lauded for their stock-picking prowess were revealed to be simply harvesting risk premiums that could be systematically accessed through rules-based strategies. Morningstar's style boxes emerged to categorize funds by their factor exposures rather than their alpha generation capabilities. This evolution culminated in the rise of passive investing, championed by Jack Bogle and Vanguard. Today, assets in passively managed equity funds exceed those in active management, with indexers holding $4.27 trillion compared to active managers' $4.24 trillion. The overwhelming evidence shows that over long periods, the vast majority of active managers fail to outperform their benchmarks after fees, suggesting that traditional alpha in public markets has largely disappeared.

Chapter 2: How Market Efficiency Eroded Traditional Alpha Opportunities

Market efficiency has systematically increased across financial markets, steadily eroding alpha opportunities. Efficiency, defined as the degree to which publicly available information is reflected in security prices, varies across markets and evolves over time. Public equity markets have become particularly efficient, with vast amounts of capital chasing increasingly scarce opportunities. The structural transformation of markets has been dramatic. In 1996, approximately 9,000 publicly listed stocks in the United States were analyzed by about 6,500 mutual funds and 500 hedge funds. By 2019, the investment landscape had inverted: fewer than 4,000 public stocks were being scrutinized by approximately 9,700 mutual funds, 5,000 exchange-traded products, and nearly 10,000 hedge funds. The number of investment professionals engaged in price discovery has exploded from around 5,000 to well over 1 million over the past half-century. Transaction costs have plummeted while market depth and liquidity have increased substantially. The average bid-ask spread for S&P 500 stocks has compressed from 0.60% in 1990 to just 0.02% today. Meanwhile, daily trading volume has surged from 450 million shares to over 7 billion. These efficiency improvements mean that even if information asymmetries exist, they are quickly arbitraged away before most investors can capitalize on them. Empirical evidence confirms this efficiency trend. A study comparing returns among equity managers in the U.S. and emerging markets revealed that the contribution to average returns from top-decile managers has declined significantly over time in both markets. In U.S. large-cap equities, the top 10% of managers enhanced overall returns by just 52 basis points annually, compared to 87 basis points in emerging markets. Moreover, this effect has diminished by half over seven years, suggesting emerging markets are rapidly becoming more efficient as well. The paradox of market efficiency further complicates alpha generation. As more investors attempt to exploit inefficiencies, markets become more efficient, eliminating those very opportunities. Conversely, if investors universally believed in perfect market efficiency and abandoned active management, inefficiencies would eventually reemerge. This dynamic creates a perpetually moving target for alpha seekers, with diminishing returns to their efforts as markets mature. The collective evidence points to a systematic erosion of traditional alpha opportunities across public markets. When 85% of active managers underperform their benchmarks over ten years, and more than 90% trail over fifteen years, the prospects for generating persistent alpha through security selection appear increasingly remote.

Chapter 3: The Rise of Quantitative Strategies and Behavioral Finance

The quest for alpha has driven investment management toward increasingly sophisticated quantitative strategies and behavioral insights. As traditional stock-picking alpha disappeared, investment firms began deploying statistical models to identify and exploit market anomalies systematically. This evolution can be traced through pioneering firms like BARRA, founded by Barr Rosenberg and later joined by Richard Grinold and Ronald Kahn, which developed sophisticated factor models in the 1970s and 1980s. These early quantitative approaches evolved into "Alpha Tilts" strategies at firms like Barclays Global Investors (now part of BlackRock). Rather than attempting to pick individual winning stocks, these strategies tilted diversified portfolios toward factors like value, quality, and momentum that academic research had shown to predict higher returns. The results were impressive: the Barclays Global Alpha Tilt Large Cap Stock Fund outperformed the S&P 500 in fourteen of its first seventeen years through 2003. The success of these strategies spawned an entire industry of factor-based investing. Specialist firms like Dimensional Funds Advisors, Research Affiliates, AQR, and others developed competing offerings under labels like "fundamental indexing" or "smart beta." Research published in the Journal of Portfolio Management demonstrated that these factor exposures generated consistent excess returns over four decades. For instance, a strategy buying the cheapest quintile of stocks while shorting the most expensive quintile would have earned 11.6% annualized from 1972 to 2012. Behavioral finance emerged as the theoretical foundation explaining why these factor premiums persist despite being widely known. Daniel Kahneman and Amos Tversky's prospect theory, which earned Kahneman a Nobel Prize, demonstrated that investors make predictably irrational decisions under uncertainty. Behavioral biases like loss aversion, overconfidence, and the disposition effect create persistent market inefficiencies that sophisticated investors can exploit. Quantitative strategies have become increasingly complex with the explosion of computing power and data availability. Today's leading quantitative hedge funds employ machine learning algorithms to process petabytes of alternative data—satellite imagery, credit card transactions, web traffic—seeking signals invisible to traditional analysis. Renaissance Technologies' Medallion Fund, arguably the most successful hedge fund in history, has generated annual returns around 39% for nearly three decades by exploiting these statistical inefficiencies. The evolution from discretionary stock-picking to systematic factor investing to AI-powered quantitative strategies represents a fundamental shift in how alpha is pursued. Traditional alpha based on superior information or analysis has given way to process alpha derived from sophisticated algorithms and behavioral insights. For most investors, the challenge is no longer finding alpha but determining whether what appears to be alpha is actually a previously undiscovered beta factor or simply noise in the data.

Chapter 4: Private Markets: The Last Frontier for Traditional Alpha

Private markets have emerged as perhaps the final domain where traditional alpha-generation techniques remain viable. Unlike public markets where information is widely disseminated and analyzed by millions of participants, private markets feature significant information asymmetries, barriers to entry, and heterogeneous assets that create persistent opportunities for skilled investors. The historical performance of private equity illustrates this potential. From inception through the 1980s, top-tier firms generated returns far exceeding public market equivalents. According to Cambridge Associates data, private equity funds raised during early periods delivered IRRs averaging 33%, representing a multiple of invested capital (MOIC) of 4.7x. However, as institutional capital has flooded into the asset class, returns have compressed significantly, with more recent vintages generating IRRs closer to 15% and MOICs under 2.0x. Information advantages remain substantial in private markets. When evaluating private businesses, skilled investors can access proprietary deal flow, conduct extensive due diligence unavailable to public market investors, and negotiate directly with company management. Furthermore, private equity firms can actively create value through operational improvements, strategic repositioning, and financial engineering—techniques unavailable to passive public market participants. The dispersion of returns in private markets dwarfs that of public equities, creating meaningful opportunities for manager selection. While top-quartile public equity managers outperform bottom-quartile managers by merely 2.6% annually, this spread approaches 20% in private equity and exceeds 40% in venture capital. This dispersion reflects both the heterogeneity of private assets and the substantial impact of manager skill. Persistence of performance also remains stronger in private markets. Studies by Kaplan and Schoar demonstrated that top-performing private equity funds are significantly more likely to outperform in subsequent funds, with top-tercile managers having a 48% probability of remaining in the top tercile with their next fund. While some evidence suggests this persistence has weakened in recent years, it remains far stronger than in public markets. Nevertheless, the alpha opportunity in private markets faces similar erosion patterns. Dry powder (uncommitted capital) has reached record levels, pushing valuations higher and compressing return expectations. The average purchase price multiple for large-cap private equity transactions has risen from 7.8x EBITDA in 2000 to 12.2x in 2018. Moreover, differentiated strategies are increasingly being commoditized as successful approaches are copied by competitors. The drivers of returns in private equity have also evolved. While financial engineering through leverage dominated in the 1980s and multiple expansion through rising valuations characterized the 1990s, today's returns increasingly depend on genuine operational improvements. Firms must truly enhance revenue growth and profit margins rather than merely applying financial techniques or riding market tailwinds.

Chapter 5: Redefining Alpha: From Benchmark Outperformance to Outcome Orientation

The conventional definition of alpha as excess return over a benchmark has become increasingly problematic in modern financial markets. This narrow conception frames investment success in relative terms, where beating an index—regardless of absolute returns or achievement of financial goals—becomes the primary objective. A more practical approach redefines alpha as the increased probability of achieving specific investment outcomes. Traditional alpha carries inherent measurement challenges. Benchmark selection is highly subjective and often manipulated to present returns in the most favorable light. An equity manager who generates 10% when the market returns 10% appears to have zero alpha. However, by selecting a slightly different benchmark that returned 9.5%, the same manager can suddenly claim 50 basis points of alpha. This mathematical sleight of hand creates an illusion of skill where none may exist. More fundamentally, benchmark-relative performance fails to address investors' actual financial objectives. Pension funds need to meet future liabilities, endowments must support institutional spending, and individuals require specific returns to fund retirement or education expenses. Whether a portfolio outperforms a benchmark by 2% is irrelevant if the absolute return falls short of these requirements. In a prolonged bear market, outperforming by losing less still results in failure to meet financial goals. A more meaningful approach focuses on the probability of achieving required returns. Traditional portfolio construction using mean-variance optimization targets the highest expected return for a given level of volatility, but this approach is flawed in several ways. First, it relies heavily on point estimates for expected returns, which are notoriously inaccurate. Second, it treats volatility as the primary risk measure when the true risk is failing to meet financial objectives. Outcome-oriented investing reframes risk as the probability of shortfall below required returns. While a traditional portfolio with an expected return equal to the required rate may have a 50% probability of meeting objectives, an alternative portfolio with slightly higher volatility but higher expected returns might offer a 75% probability of success. The latter portfolio, despite appearing "riskier" by conventional measures, actually reduces the probability of failure. This conceptual shift necessitates new approaches to portfolio construction. Rather than optimizing for efficient frontiers, investors should consider scenario analysis across multiple potential future states. By stress-testing portfolios against adverse conditions and establishing reasonable ranges for returns rather than point estimates, investors can build more robust portfolios designed to achieve specific outcomes across varied market environments. Probability-based frameworks also provide a more intuitive way to communicate investment risk to stakeholders. Discussing the odds of meeting retirement needs or funding obligations resonates more clearly than abstract discussions of standard deviations or tracking errors. This clarity helps establish realistic expectations and can prevent emotionally-driven decisions during market turbulence.

Chapter 6: Smart Thinking and Process Alpha as Superior Investment Approaches

The pursuit of traditional alpha typically focuses on finding investment edges through superior information or analysis. However, cognitive biases and decision-making flaws frequently undermine even the most sophisticated investment strategies. Smart thinking and process alpha offer systematic approaches to overcome these limitations and generate superior outcomes. Behavioral biases severely impact investment results. Confirmation bias leads investors to seek information confirming existing views while discounting contradictory evidence. Loss aversion causes disproportionate pain from losses compared to pleasure from equivalent gains, often resulting in premature selling of winners and holding of losers. Overconfidence drives excessive trading and concentration in familiar assets. Research shows that individual investors who trade the most earn 5.5% less annually than less active counterparts. Smart thinking involves deliberately countering these biases through System 2 processing—slow, deliberate, and logical thinking—rather than relying on System 1's quick, intuitive reactions. However, System 2 thinking requires significant mental energy and cannot be sustained continuously. Decision fatigue inevitably sets in when investors face numerous complex choices, leading to deteriorating decision quality over time. A more sustainable approach implements process alpha—systematizing decision-making to reduce the impact of cognitive biases. This begins with prioritizing where deliberate thinking should be applied. Since asset allocation drives 90-95% of portfolio risk and return, this strategic decision warrants extensive System 2 resources. Conversely, manager selection within asset classes, while consuming most of investors' time, typically has far less impact on outcomes. For asset allocation decisions, smart thinking involves establishing realistic, data-driven capital market assumptions rather than anchoring on historical averages or yield targets. This process should consider multiple scenarios and potential outcomes rather than point estimates. For instance, rather than simply assuming 8% equity returns, investors should consider how different valuation levels historically correlate with subsequent performance and develop ranges of reasonable outcomes. Process alpha extends to portfolio management through disciplined rebalancing procedures. Simple calendar-based or range-based rebalancing can be enhanced through "smart rebalancing" that incorporates adaptive capital market assumptions. Research shows that such approaches can add significant value while maintaining consistent risk parameters. For manager selection, where decisions are more frequent and numerous, checklists and systematic screening frameworks can help ensure consistency and reduce behavioral biases. Due diligence processes should focus on identifying characteristics associated with persistent outperformance, such as alignment of interests, organizational culture, and investment philosophy, rather than merely chasing recent performance. The evidence for process alpha is compelling. Studies show that investors conducting more thorough due diligence achieve substantially better outcomes. For angel investments, those spending more than 40 hours on due diligence averaged 7.1x returns versus 0.8x for those spending less than 5 hours. Similar effects have been documented in private equity, where more rigorous due diligence correlates with higher returns. Smart thinking and process alpha shift focus from trying to outguess markets to making better decisions consistently. By acknowledging cognitive limitations and implementing systems to overcome them, investors can achieve superior outcomes even without traditional information advantages.

Chapter 7: Organizational Alpha: Creating Value Through Governance and Structure

Investment success increasingly depends on organizational design and governance structures that align authority with expertise. While traditional alpha focuses on portfolio decisions, organizational alpha emerges from creating institutional environments where the right people make the right decisions for the right reasons. Evidence suggests this governance alpha can add hundreds of basis points to long-term returns. Effective governance begins with understanding different sources of authority. Hierarchical authority derives from formal positions—board members, trustees, executives—who have decision-making power by virtue of their roles. Expert authority comes from specialized knowledge and experience in relevant domains. Charismatic authority stems from personal characteristics that inspire trust and followership. Problems arise when these authorities are misaligned, particularly when hierarchical authority is disconnected from expert authority. Research consistently shows that investment organizations with misaligned governance structures underperform. One study found that pension boards with higher percentages of politically appointed trustees generated 90 basis points lower annual returns in private equity investments. Another study revealed that pension plans with ex officio board trustees underperformed by 300 basis points compared to systems where allocation authority resided with investment professionals. The consequences of poor governance manifest through several mechanisms. Adverse selection occurs when decision-makers lack the expertise to evaluate investment opportunities effectively, often resulting in hiring based on brand names rather than capabilities. Perverse incentives arise when decision-makers' personal interests conflict with organizational objectives. Implementation shortfall emerges when bureaucratic processes delay execution of investment decisions. Effective governance structures clarify roles and responsibilities between oversight and execution. Boards and investment committees should focus on setting objectives, establishing risk parameters, and monitoring performance against goals. Day-to-day investment decisions should be delegated to qualified professionals with relevant expertise and appropriate incentives. As investment complexity increases, the importance of proper delegation becomes more critical. Organizational structure must adapt to the specific investment strategy and available resources. A large pension fund pursuing complex alternative investments requires different governance than a small endowment following a simpler approach. The fiduciary standard of care encompasses not just prudence and reasonable care but also skill, competence, and diligence—qualities that may necessitate delegation when internal expertise is limited. Performance evaluation should separately assess the contribution of policy decisions, asset allocation, and implementation. This disaggregation allows organizations to identify precisely where governance improvements would add the most value. For instance, if policy objectives are not being met primarily due to poor strategic asset allocation decisions, governance reforms should focus on that process rather than manager selection. The State of Wisconsin Investment Board exemplifies effective governance in practice. Its investment board operates independently from the broader retirement system, with clear separation between board oversight and staff implementation. Investment professionals are delegated appropriate authority and compensated based on long-term performance. The result is consistent outperformance of both policy benchmarks and required returns, culminating in a funded ratio around 102%—among the best in the country. Ultimately, organizational alpha emerges from aligning hierarchical, expert, and charismatic authority to create institutions where the best ideas win regardless of their source. By ensuring that investment decisions are made by those most qualified to make them, organizations can systematically improve outcomes over time.

Summary

Traditional alpha has undergone a profound transformation across financial markets. What was once attributed to superior skill in security selection has increasingly been recognized as exposure to systematic risk factors that can be accessed through rules-based strategies. This evolution has progressed furthest in public markets, where efficiency has rendered genuine alpha opportunities exceedingly rare, but similar patterns are now emerging in hedge funds and even beginning to appear in private markets. The future of investment management lies not in chasing the mirage of benchmark outperformance but in reorienting toward outcome-focused approaches. This paradigm shift involves embracing probability-based thinking about risk, developing systematic processes to overcome cognitive biases, and creating governance structures that align decision-making authority with expertise. Success will increasingly be measured not by information advantages that yield excess returns over arbitrary benchmarks, but by the consistent application of sound investment principles that maximize the probability of achieving specific financial objectives. Rather than pursuing alpha in its traditional form, investors should focus on building margin of safety into their investment approach through realistic assumptions, robust processes, and effective governance.

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Christopher Schelling

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