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.
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.
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.
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.
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.
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 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.
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.
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/
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?
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.
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 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?"
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?
In theory green stocks should have lower expected returns, this however, is not what we've seen. So the question is what has caused the outperformance of green stocks? And has that outperformance cost value investors their returns?
Using data on 65,000 stocks from 23 countries, they evaluated the performance of the Fama-French factors, examining the factor premia in global markets to verify their robustness across different company size categories and geographical regions. Their data sample covered the period 1987-2019.
About a year and a half ago, after one of the worst relative drawdowns the value factor has ever seen, I wrote a piece showing the value factor was cheap relative to history. Since then, value strategies are on a solid run (look at pretty much any type of value strategy and I think you'd agree). Today? The valuation spread between the cheapest 10% and the universe of stocks is cheaper. We are at levels beyond 1999 by some measures.
Deep Value Cliff Asness, John Liew, Lasse Heje Pedersen, and Ashwin ThaparJournal of Portfolio ManagementA version of this paper can be found hereWant to read our summaries of academic finance papers? Check out our Academic Research [...]