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The Value Factor and Deleveraging

How do you separate the signal from the noise? To have confidence that a factor premium, or strategy, isn’t just the result of data mining - a lucky/random outcome - we recommended that you should require evidence that the premium has been not only persistent over long periods of time and across economic regimes, but also pervasive across sectors, countries, geographic regions and even asset classes; robust to various definitions (for example, there has been both a value and a momentum premium using many different metrics); survives transactions costs; and has intuitive risk- or behavioral-based explanations for the premium to persist.

The Role of the Secular Decline in Interest Rates in Asset Pricing Anomalies

Jules van Binsbergen, Liang Ma and Michael Schwert, authors of the September 2022 study “The Factor Multiverse: The Role of Interest Rates in Factor Discovery,” posed an interesting question: Are the findings of at least some of the reported anomalies the direct result of the 40-year secular decline in global interest rates and thus not really anomalies?

The Cross Section of Stock Returns Pre-CRSP data: Value and Momentum are confirmed as robust anomalies

We study the cross-section of stock returns using a novel constructed database of U.S. stocks covering 61 years of independent data.

Momentum Everywhere, Including in Factors

Managed portfolios that exploit positive first-order autocorrelation in monthly excess returns of equity factor portfolios produce large alphas and gains in Sharpe ratios. We document this finding for factor portfolios formed on the broad market, size, value, momentum, investment, prof- itability, and volatility. The value-added induced by factor management via short-term momentum is a robust empirical phenomenon that survives transaction costs and carries over to multi-factor portfolios. The novel strategy established in this work compares favorably to well-known timing strategies that employ e.g. factor volatility or factor valuation. For the majority of factors, our strategies appear successful especially in recessions and times of crisis.

Does Intangible-Adjusted Book-to-Market Work?

The book-to-market ratio has been widely used to explain the cross-sectional variation in stock returns, but the explanatory power is weaker in recent decades than in the 1970s. I argue that the deterioration is related to the growth of intangible assets unrecorded on balance sheets. An intangible-adjusted ratio, capitalizing prior expenditures to develop intangible assets internally and excluding goodwill, outperforms the original ratio significantly. The average annual return on the intangible-adjusted highminus-low (iHML) portfolio is 5.9% from July 1976 to December 2017 and 6.2% from July 1997 to December 2017, vs. 3.9% and 3.6% for an equivalent HML portfolio

Combining Factors in Multifactor Portfolios

Reschenhofer’s findings demonstrate the important role that portfolio construction rules (such as creating efficient buy and hold ranges or imposing screens that exclude stocks with negative momentum) play in determining not only the risk and expected return of a portfolio but how efficiently the strategy can be implemented (considering the impact of turnover and trading costs)—wide (narrow) thresholds reduce (increase) portfolio turnover and transactions costs, thereby increasing after-cost returns and Sharpe ratios. His findings also provide support for multiple characteristics-based scorings to form long-only factor portfolios, encouraging the combination of slow-moving characteristics (such as value, investment and/or profitability) conditional on fast moving characteristics (such as momentum), to reduce portfolio turnover and transactions cost. Fund families such as AQR, Avantis, Bridgeway and Dimensional use such an approach, integrating multiple characteristics into their portfolios conditional on momentum signals.

Can Machine Learning Identify Future Outperforming Active Equity Funds?

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.

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.

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.

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.

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.

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.

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.

2022 Democratize Quant Conference Recap and Materials

We recently hosted our Democratize Quant Conference (sign up here for updates).

This post is a recap of what we heard and some resources we can make available to the public.

Our 5th Annual Democratize Quant 2022 is Live. Sign-up!

We will be hosting our 5th annual Democratize Quant conference later this month via Zoom. The event is 100% free but we do screen participants to enforce our "no spammers" policy. https://alphaarchitect.com/democratizequant/

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?

Is The Value Premium Smaller Than We Thought?

From 2017 through March 2020, the relative performance of value stocks in the U.S. was so poor, experiencing its largest drawdown in history, that many investors jumped to the conclusion that the value premium was dead. It is certainly possible that what economists call a “regime change” could have caused assumptions to change about why the premium should exist/persist.

Factor Investing in Sovereign Bond Markets

The reported results we covered have important implications for investors in terms of portfolio construction, risk monitoring, and manager selection. Because these common factors explain almost all the returns of bond portfolios, investors should construct their bond portfolios using low-cost, passively (systematically) managed funds with these factors in mind and then carefully monitor their exposure to these systematic risks.

Value investing: What history says about five-year periods after valuation peaks

Value stocks are historically cheap compared to the past. Given this fact, a natural question is the following, "After the last two times Value had a "peak" of the factor being cheap, how did it do the subsequent five years?"

The Relationship Between the Value Premium and Interest Rates

We've been suffering through the deepest and longest drawdown in values history. Looking for a scapegoat to explain the lackluster performance many have pointed to low interest rates as the root cause of the underperformance. The question is have interest rates impacted value in the past?

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