Alpha Architect

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.

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.

A Deep Dive into the Low Beta Premium

The superior performance of low-volatility stocks was first documented in the literature in the 1970s—by Fischer Black in 1972, among others —even before the size and value premiums were “discovered.” The low-volatility anomaly has been shown to exist in equity markets around the world. Interestingly, this finding is true not only for stocks but for bonds as well. In other words, it has been pervasive...but

Are Financial Crises Predictable?

Who among us wouldn't want to be the savior that predicts a market crisis and saves our clients from losses in capital -- or even better -- profits from them? A central topic of interest for academics is whether there are more precise tools to predict financial crises. Those who believe so dedicate their efforts to finding early warning indicators.

An Introduction to Investing in Carbon Markets

Carbon markets are quickly making their way to the forefront of Environmental, Social, and Governance (ESG) investing, as well as the finance community as a whole. The Kraneshares Global Carbon ETF, (Ticker: KRBN) (whose holdings I’ll dive into shortly) was one of the top 5 performing ETFs in 2021 on a % return basis (Ferringer, Best performing ETFs of the Year - etf.com). However, it doesn’t appear that 2021 was a one-hit-wonder for Carbon Markets, but instead, the beginning of a new and very real trend.

New Accounting Standards and Factor Investing

How well do quantitative investors navigate around the changes to the accounting standards that are endemic to the financial data used in quantitative strategies? The numbers reported on financial statements are wholly governed by regulation and by each firm’s interpretation of those accounting standards. So how do quants stick to their empirical evidence on old data methods or do they react in terms of the strategy when the change in standards is material?

Factor Investing: Are Internally Generated Intangibles Worthless?

As mind-bending as it sounds, although a company’s internally generated intangible investments generate future value, they are currently not accepted as assets under US GAAP. Omission of this increasingly important class of assets reduces the usefulness and relevance of financial statement analysis that uses book value. In fact, Amitabh Dugar and Jacob Pozharny, authors of the December 2020 study “Equity Investing in the Age of Intangibles,” concluded that the relationship between financial variables and contemporaneous stock prices has weakened so much for high intangible intensity companies in both the U.S. and abroad that investors can no longer afford to ignore the changes in the economic environment created by intangibles.

Behavioral Finance: Does Culture Affect Equity Analysts?

The literature shows that where we come from affects both how we perceive other people as well as how we are perceived by others. These perceptions can also affect economic behavior. In this study, the author analyzes the role of cultural biases in analysts’ stock recommendations in Europe.

ETF Tax Efficiency isn’t Always Efficient

Compared to mutual funds or separately managed accounts, ANY benefit from redeeming in-kind is a bonus. That being said, not all ETFs and situations are created equal when it comes to tax efficiency, and the "golden rule" always applies - when given the option, the IRS wants to create scenarios where they receive tax dollars now instead of later. Here are some big-ticket items that cause inefficiencies (read as taxes…), many related to the “golden rule” above.

Does diversification always benefit investors? No.

This article examines the extent to which these assumptions hold and the extent to which investors should want them to hold. The authors deliver a clever quote from Mark Twain (or maybe it was Robert Frost) that nails the issue in simple terms: “Diversification behaves like the banker who lends you his umbrella when the sun is shining but wants it back the minute it begins to rain”. Nicely expressed!

Factor Investing: Is a Human Capital Factor on the Horizon?

Taken together, our results suggest that firms’ personnel expenditures reflect not just the cost of labor in the current period but also the investment in human capital contained within that cost, and that market participants fail to fully understand the opportunity and efficacy of human capital development embedded in the disclosure of the expense.

Trend-Following Filters – Part 5

There are two general types of Kalman filter models: steady-state and adaptive. A steady-state filter assumes that the statistics of the process under consideration are constant over time, resulting in fixed, time-invariant filter gains. The gains of an adaptive filter, on the other hand, are able to adjust to processes that have time-varying dynamics, such as financial time series which typically display volatility and non-stationarity.

Who Bears the Cost of Machine Learning in Credit Markets?

The primary idea behind this research is that a more sophisticated statistical technology (in the sense of reducing predictive mean squared error) produces predictions with greater variance than a more primitive technology. These technologies range from a simple logistic regression of default outcomes based on borrowers and default variables to random forest machine learning models. Said differently, improvements in predictive technology act as mean-preserving spreads for predicted outcomes—in this case, predicted default propensities on loans, which also means that there will always be some borrowers considered less risky by the new technology, or “winners”, while other borrowers will be deemed riskier “losers”, relative to their position under the pre-existing technology.

Machine Learning: The Recovery of Missing Firm Characteristics

Firm characteristics are often missing, which forces both researchers and practitioners to come up with workarounds when handling missing data. Previous approaches resorted to either dropping observations with missing entries or simply imputing the cross-sectional mean of a given characteristic. As both procedures accompany serious drawbacks (see below), there is a need for more advanced methods. The authors set up an attention-based machine learning model, motivated by recent advances in natural language to find some answers

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