Research Insights Archives

Using Institutional Investor’s Trading Data in Factors

The authors investigate how the interaction between entries and exits of informed institutional investors and market anomaly signals affects strategy performance. The long legs of anomalies earn more positive alphas following entries, whereas the short legs earn more negative alphas following exits. The enhanced anomaly-based strategies of buying stocks in the long legs of anomalies with entries and shorting stocks in the short legs with exits outperform the original anomalies, with an increase of 19–54 bps per month in the Fama–French five-factor alpha. The entries and exits of institutional investors capture informed trading and earnings surprises, thereby enhancing the anomalies.

Does Emerging Markets Investing Make Sense?

The analysis above suggests that portfolios that include or exclude emerging allocations are roughly the same. For some readers, this may be a surprise, but for many readers, this may not be "news." That said, even if the data don't strictly justify an Emerging allocation, the first principle of "stay diversified" might be enough to make an allocation.

Of course, the assumptions always matter.

Arbitrage and the Trading Costs of ETFs

This article examines ETF creations and redemptions around price deviations and finds that the expected arbitrage trades are relatively rare in a broad sample of equity index ETFs. In the absence of these trades, price deviations persist much longer. Creation and redemption activity appears to be constrained when exchange conditions would lead to a costlier arbitrage trade, and the size of the price deviations mainly impact the likelihood rather than the amount of trading. The authors also find some evidence that creations and redemptions are less likely to trade on price deviations when they would be required to trade the underlying stocks against broad market movements. Their results suggest that several factors may discourage the built-in ETF arbitrage mechanism and that investors may receive poorer trade execution in these conditions as a result.

Relative Sentiment and Machine Learning for Tactical Asset Allocation

The application of machine learning models to Sentix relative sentiment data appears to extract more predictive information than our original, simplistic approach was capable of.

Factors Investing in Cryptocurrency

We find that three factors—cryptocurrency market, size, and momentum—capture the cross-sectional expected cryptocurrency returns. We consider a comprehensive list of price- and market-related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long-short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three-factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.

The Unintended Consequences of Single Factor Strategies

Since the 1992 publication of “The Cross-Section of Expected Stock Returns” by Eugene Fama and Kenneth French factor-based strategies and products have become an integral part of the global asset management landscape. While “top-down” allocation to factor premiums (such as size, value, momentum, quality, and low volatility) has become mainstream, questions remain about how to efficiently gain exposure to these premiums. Today, many generic factor products, often labeled as “smart beta”, completely disregard the impact of other factors when constructing portfolios with high exposures to any single factor. However, recent research, such as 2019 study “The Characteristics of Factor Investing” by David Blitz and Milan Vidojevic, has shown that single-factor portfolios, which invest in stocks with high scores on one particular factor, can be suboptimal because they ignore the possibility that these stocks may be unattractive from the perspective of other factors that have demonstrated that they also have higher expected returns.

Visualizing the Robustness of the US Equity ETF Market

Market commentators sometimes suggest that the equity ETF market is just a bunch of "index funds" that all do essentially the same thing: deliver undifferentiated stock market exposure.

How true is that statement? Fortunately, we can test the hypothesis that the ETF market is roughly a few thousand different ways to capture the same basic risk/returns. To do so, we leverage our Portfolio Architect tool to quantify the active share of all US equity ETFs against the S&P 500 index (the king of indexes).

Global Factor Performance: June 2022

The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref]

Do Connections Pay Off in the Bitcoin Market?

This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.

Short-term Momentum

We document a striking pattern in U.S. and international stock returns: double sorting on the previous month’s return and share turnover reveals significant short-term reversal among low-turnover stocks, whereas high-turnover stocks exhibit short-term momentum. Short-term momentum is as profitable and as persistent as conventional price momentum. It survives transaction costs and is strongest among the largest, most liquid, and most extensively covered stocks. Our results are difficult to reconcile with models imposing strict rationality but are suggestive of an explanation based on some traders underappreciating the information conveyed by prices.

DIY Asset Allocation Weights: June 2022

No exposure to domestic equities. No exposure to international equities. Half exposure to REITs. Full exposure to commodities. No exposure to intermediate-term bonds.

Options Hedging & Leveraged ETFs in Market Swings

Earlier this year, GameStop stock rose like crazy in only a few hours with the effects of broker-dealer options hedging spurred by retail investor buying pressure. And from February to March 2020, options trading activity was also pointed to as a contributor to stock swings in the Covid-19 selloff. The market dropped 30% and then recovered quickly over the following weeks. It has been documented that the need for market makers to hedge their positions with options (given rapid changes in stock prices) can contribute to market and stock price swings. However, might there be other factors also at play in these types of stock and market fluctuations?

Is There a Gender Gap in Kickstarter Campaigns?

This study focuses on the launch phase of the leading reward-based crowdfunding market—Kickstarter. It documents the behavior of male and female entrepreneurs in raising early stage capital. We find that women share as entrepreneurs in the platform (34.7%) does not equal to their share in the overall population, and they are concentrated in stereotyped sectors, both as entrepreneurs and as backers. We also find that women do not set lower funding goals than men, they enjoy higher rates of success than men, even after controlling for project categories and funding goals, and that backers of both genders have a tendency to fund entrepreneurs of their own gender. Our survey of Kickstarter backers finds evidence of taste-based discrimination by male backers.

S&P 500 Valuations with and without Technology

A few quick charts for our readers. As we all know, technology-related sectors and names have been crushed. But is blood in the streets? Not really.

Strategies to Mitigate Tail Risk

Investors care about more than just returns. They also care about risk. Thus, prudent investors include consideration of strategies that can provide at least some protection against adverse events that lead to left tail risk (portfolios crashing). The cost of that protection (the impact on expected returns) must play an important role in deciding whether to include them. For example, buying at-the-money puts, a strategy that eliminates downside risk, should have returns no better than the risk-free rate of return, making that a highly expensive strategy.

Life Insurance Instruments May Help Improve After-Tax Wealth

Fee-only fiduciary advisors often summarily dismiss the use of life insurance solutions as financial planning tools—perhaps due to past experiences trying to get clients out of poorly structured, high expense policies. In this post, Colva Actuarial Services and Colva Capital principal Rajiv Rebello explains how fiduciary advisors can properly structure life insurance products and utilize low-expense/no-commission products to provide better after-tax diversification and returns for the fixed income portion of their clients’ portfolios as opposed to investing in bonds directly.

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.

Value Investing: Headwinds, Tailwinds, and Variables

Investing is no different. A question we regularly get in the current environment is "How does inflation affect value stocks?" Well...it depends. I could show you some data on how value stocks did in the 70's (period of high inflation) versus how they did in the 90's (low inflation). But if WW3 broke out tomorrow, wouldn't that variable quickly top all other variables? Probably. So let's table that variable.

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.

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