Alpha Architect

How Race Influences Asset Allocation Decisions

Of the $69.1 trillion global financial assets under management across mutual funds, hedge funds, real estate, and private equity, fewer than 1.3% are managed by women and people of color. Why is this powerful, elite industry so racially homogenous? We conducted an online experiment with actual asset allocators to determine whether there are biases in their evaluations of funds led by people of color, and, if so, how these biases manifest. We asked asset allocators to rate venture capital funds based on their evaluation of a 1-page summary of the fund’s performance history, in which we manipulated the race of the managing partner (White or Black) and the strength of the fund’s credentials (stronger or weaker). Asset allocators favored the White-led, racially homogenous team when credentials were stronger, but the Black-led, racially diverse team when credentials were weaker. Moreover, asset allocators’ judgments of the team’s competence were more strongly correlated with predictions about future performance (e.g., money raised) for racially homogenous teams than for racially diverse teams. Despite the apparent preference for racially diverse teams at weaker performance levels, asset allocators did not express a high likelihood of investing in these teams. These results suggest first that underrepresentation of people of color in the realm of investing is not only a pipeline problem, and second, that funds led by people of color might paradoxically face the most barriers to advancement after they have established themselves as strong performers.

Trend Following: Timing Fast and Slow Trends

A large body of evidence demonstrates that momentum, including time-series momentum (trend following), has improved portfolio efficiency. Research has found that there are a few ways to improve on simple trend-following strategies. Techniques that have been found to improve Sharpe ratios and reduce tail risk include volatility scaling and combining fast and slow signals as well as combining long-term reversals. These have been incorporated by many fund managers into investment strategies. Cheng, Kostyuchyk, Lee, Liu and Ma provided evidence that machine learning could be used to further improve results. With that said, a word of caution on the use of machine learning is warranted. The powerful tools and the easy access to data now available to researchers create the risk that machine learning studies will find correlations that have no causation and thus the findings could be nothing more than a result of torturing the data. To minimize that risk, it is important that findings not only have rational risk- or behavioral-based explanations for believing the patterns identified will persist in the future, but they also should be robust to many tests. In this case, investors could feel more confident in the results if their findings were robust to international equities and other asset classes (such as bonds, commodities and currencies).

Form 3 and Form 4 Alpha: Focus on What Insiders Don’t Trade

Some individuals, e.g., those holding multiple directorships, are insiders at multiple firms. When they execute an insider trade at one firm, they may reveal information about the value of all—both the traded insider position and not-traded insider position(s)—the securities held in their “insider portfolio.” We find that insider “not-sold” stocks outperform “not-bought” stocks. Implementable trading strategies that buy not-sold stocks following the disclosure of a sale earn alphas up to 4.8% per year after trading costs. The results suggest that even insider sales that are motivated by liquidity and diversification needs can provide value-relevant information about insider holdings.

Institutions Trading Against Anomalies: Are Their Trades Informed?

An interesting question is do the trades of the more sophisticated institutional investors against anomalies provide information on returns? To answer that question, Yangru Wu and Weike Xu, authors of the study “Changes in Ownership Breadth and Capital Market Anomalies,” published in the February 2022 issue of The Journal of Portfolio Management, examined whether the entries and exits of informed institutional investors (or ownership breadth changes) interact with the aforementioned 11 anomaly signals studied by Stambaugh and Yuan can be used to improve the performance of anomaly-based strategies. They explained that they emphasized institutions’ new entries and exits because they could be triggered by private information and correlated with future earnings news, thereby capturing useful information regarding future stock returns. To determine if the trades of the institutional investors were informed, they sorted all stocks into 10 decile portfolios based on quarterly changes in ownership breadth. Their data sample covered all NYSE/AMEX/Nasdaq common stocks from May 1981 to May 2018.

Benefits of Having a Female CFO

We examine gender differences in the language of CFOs who participate in quarterly earnings calls. Female executives are more concise and less optimistic, are clearer, use fewer idioms or clichés, and provide more numbers in their speech. These differences are particularly strong in the more spontaneous Questions and Answers (QA) section of the calls and are reflected in stronger market and analyst reactions. Gender differences seem to be associated with CFO overconfidence.

Using Momentum to Find Value

Value and momentum are two of the most powerful explanatory factors in finance. Research on both has been published for about 30 years. However, it was not until recently that the two had been studied in combination and across markets. Bijon Pani and Frank Fabozzi contribute to the literature with their study “Finding Value Using Momentum,” published in The Journal of Portfolio Management Quantitative Special Issue 2022, in which they examined whether using six value metrics that have an established academic background combined with the trend in relative valuations provide better risk-adjusted returns than Fama-French’s traditional HML (high minus low book-to-market ratio) factor. The value metrics chosen were book value-to-market value; cash flow-to-price; earnings before interest, taxes, depreciation, and amortization (EBITDA)-to-market value; earnings-to-price; profit margin-to-price; and sales-to-price. Using six different measures provides tests of robustness, minimizing the risk of data mining. However with so many dials to turn there is a risk of achieving positive returns that aren't material or achieving postive results with the potential for overfitting.

The Future of Factor Investing

In this article, the author discusses current structural research and investment trends that are shaping the future of factor investing. Specifically, the author focuses on three emerging trends: the ongoing evolution of traditional factor models and strategies, recent innovation in data sources and modeling techniques, and the potential disruption from integrating factor strategies into the asset allocation process.

Betting Against Beta: New Insights

The intuition behind betting against beta is that leverage-constrained investors, instead of applying leverage, obtain an expected return higher than the market’s expected return through overweighting high-beta stocks and underweighting low-beta stocks in their portfolios. Their actions lower future risk-adjusted returns on high-beta stocks and increase future risk-adjusted returns on low-beta stocks. We take a deeper look into this idea.

Did Covid-19 Change how We Shop?

We study e-commerce across 47 economies and 26 industries during the COVID-19 pandemic using aggregated and anonymized transaction-level data from Mastercard, scaled to represent total consumer spending. The share of online transactions in total consumption increased more in economies with higher pre-pandemic e-commerce shares, exacerbating the digital divide across economies. Overall, the latest data suggest that these spikes in online spending shares are dissipating at the aggregate level, though there is variation across industries. In particular, the share of online spending in professional services and recreation has fallen below its pre-pandemic trend, but we observe a longer-lasting shift to digital in retail and restaurants.

Can Market Maker Capital Constraints Result in Mispricing of ETFs?

Capital constraints of financial intermediaries can affect liquidity provision. We investigate whether these constraints spillover and consequently cause contagion in the degree of market efficiency across assets managed by a common intermediary. Specifically, we provide evidence of strong comovement in pricing gaps between ETFs and their constituents for ETFs served by the same lead market maker (LMM). The effects are stronger for ETFs that are more illiquid and volatile, when the underlying constituents of the ETFs are more costly to arbitrage, and for LMMs with more constrained capital. Using extreme disruptions in debt markets during COVID-19 as an experiment, we show that non-fixed income ETFs serviced by LMMs managing a larger fraction of fixed income ETFs experience greater pricing gaps. Overall, our results indicate that intermediaries’ constraints indeed influence comovements in pricing efficiencies.

Bond Investing in Inflationary Times

As the chief research officer of Buckingham Strategic Partners, the issue I am being asked to address most often is about fixed income strategies when yields are at historically low levels and inflation risk is heightened due to the unprecedented increase in money creation (through quantitative easing), the extraordinary expansionary fiscal spending around the globe, and the war in Ukraine driving prices higher (especially for food and energy). As always, to answer the question we turn first to the academic evidence on which investments in general provide the best hedges against inflation.

Shorting ETFs: A look into the ETF Loan Market

We find that exchange-traded fund (ETF) lending fees are significantly higher than stock lending fees. Two institutional features unique to ETFs play significant roles in explaining the high fees. First, regulations restrict investment companies, such as mutual funds and ETFs, from owning ETFs. As these institutions are key lenders, their absence reduces the lendable supply in the ETF loan market. Second, while the create-to-lend (CTL) mechanism alleviates supply constraints when borrowing demand increases, its efficacy is limited by the associated costs and frictions. Our results speak to the limits to arbitrage in the ETF markets.

Is Sector-neutrality in Factor Investing a Mistake?

Long-only factor performance is more likely to degrade from sector neutralizing—keeping the sector component produced better long-only factors in 78 percent of the trials. The largest negative from sector neutralizing occurred for the value-weighted long-only factors that trade large stocks, arguably the most investable portfolio.

Gaining an Edge via Textual Analysis of FOMC Meetings

How investors understand and use central bank communications, aka FEDSPEAK, is oftentimes cryptic and difficult to analyze. This study attempts to provide some clarity to this issue by applying textual analysis to both high-frequency price and communication data, to focus on episodes whereby stock price movements are identifiable and on investors’ reactions to specific sentences communicated by the Fed.

Are Stock Market Bubbles Identifiable?

Robin Greenwood, Andrei Shleifer, and Yang You authors of the study “Bubbles for Fama”, published in the January 2019 issue of the Journal of Financial Economics evaluated Fama's claim that stock prices do not exhibit price bubbles. Based on a fixed threshold for the industry price increases (e.g., a 100 percent price run-up during two consecutive years) to filter their events and to analyze what happens afterward, they examined U.S. industry returns over the period 1926‒2014 (covering 40 episodes) and international sector returns (1985‒2014).

Which Articles Should You Read on SeekingAlpha.com?

The authors hypothesize that impression management consideration can also significantly determine investors’ conversations. This, in turn, can cause investors to inadvertently propagate noise with wide-ranging implications for the quality of investors’ investment decisions and asset prices.

Can Investment Flows Affect Prices? Yep.

Traditional finance theory suggests that stocks prices always reflect their fair market values based on publicly available information. Or in academic parlance, the "semi-strong" form efficient markets hypothesis serves as the null. What are the implications of this hypothesis? Well, the hypothesis suggests that the only reason a stock price will move is due to a shift in fundamentals (either through a change in expected cash flows or via the discount rate). But what about supply and demand shifts?

Employee Satisfaction and Stock Returns

“Employees are our greatest asset” is a phrase often heard from companies. However, due to accounting rules requiring that most expenditures related to employees be treated as costs and expensed as incurred, the value of employees is an intangible asset that does not appear on any balance sheet. That leaves the interesting question of whether employee satisfaction provides information on future returns.

What Percentage of Women Serve in Senior Investment Roles?

There is a “Pink” elephant in the room. The paucity of women in the key investment and decisión-making roles in finance is that “pink” elephant. While women are represented at 33%, 37%, and 63% in the law, medical, and accounting professions, respectively (Morningstar 2016), the percentage of female investment decision-makers in investment pales in comparison at less than 10%. And it gets worse if we look at sub-sectors. Take private equity, it’s 6% (Lietz, 2011), hedge funds at 3% (Soloway, 2011), or investment banking documented in this scorecard, at a global median of 0%.

Are Quant Approaches Best for Sustainable (ESG) Investing?

After 40 years or so, quantitative investing has evolved into a thriving practice. A major feature of the quantitative approach involves developing underlying numerical models and testing them on a historical (data) record and then forecasting where alpha may be embedded into the prices of a set of stocks. Whether you agree or disagree with this approach, it is difficult to deny that with the advanced state of data access and computational skill, “quants will win the day in ESG investing”. Such is the premise of this article and happily, it is accompanied by a compelling argument.

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