Our current portfolio is composed of 20
stocks, the top 5 of
which are Exxon Mobil Corp (XOM), CVS Caremark
Corporation (CVS), Adobe Systems (ADBE), AT&T (T), and Microsoft Corporation (MSFT).
The portfolio is composed 100% of large-cap equities, 32% of which are
classified as Value, 49% Core, and 20% Growth. Energy, Healthcare, and Technology sectors make up 67% of
the
portfolio. Its P/E ratio and P/B ratios are 77% and 79% of the S&P
500’s, respectively, and
its dividend yields 77% more than the S&P 500.
About Sawyer Investment Management Company:
SIMCO
is a Texas-registered Investment Adviser with its principal place of
business in Dallas, Texas. It was formed on January 1, 2015 and is
wholly owned by Ryan Sawyer, who is a CFA Charterholder and a Certified
Public Accountant.
SIMCO specializes in the
construction of equity portfolios, and is therefore an ideal resource
for long-term investors. The firm goes through a rigorous process for
selecting each and every holding in the portfolio. Rooted in the
empirical research of academia, the portfolios are generally
characterized as large-cap value momentum. For more information about
how the portfolios are managed, see our website.
Our current portfolio is composed of 25
stocks, the top 5 of
which are CVS Caremark Corporation (CVS), Total SA (TOT), Exxon Mobil Corp (XOM), Adobe Systems (ADBE), and Microsoft Corporation (MSFT).
The portfolio is composed 100% of large-cap equities, 23% of which are
classified as Value, 57% Core, and 21% Growth. Energy, Healthcare, and Technology sectors make up 70% of
the
portfolio. Its P/E ratio and P/B ratios are 75% and 79% of the S&P
500’s, respectively, and
its dividend yield is 68% higher than that of the S&P 500.
About Sawyer Investment Management Company:
SIMCO
is a Texas-registered Investment Adviser with its principal place of
business in Dallas, Texas. It was formed on January 1, 2015 and is
wholly owned by Ryan Sawyer, who is a CFA Charterholder and a Certified
Public Accountant.
SIMCO specializes in the
construction of equity portfolios, and is therefore an ideal resource
for long-term investors. The firm goes through a rigorous process for
selecting each and every holding in the portfolio. Rooted in the
empirical research of academia, the portfolios are generally
characterized as large-cap value momentum. For more information about
how the portfolios are managed, see our website.
The authors are using a Global Tactical Asset Allocation strategy to tilt their portfolio toward asset classes that are more favorable. Typically this is done through a "building block" model, where each of the asset classes uses a different model to build the overall allocation strategy. There are a few problems with this: securities within asset classes aren't compared to securities in other asset classes, it takes a lot of time to build all of the different models, and there has to be a good risk-management process in place to mitigate unnecessary risk. As such, the authors propose a single model, they name the Global Tactical Cross-Asset Allocation (GTCAA) strategy, that is not subject to those limitations. This allocation approach selects asset classes, rather than securities within asset classes, as has been done in previous studies. If they find this approach to work, it could be a challenge to market efficiency.
The authors then summarize the results of a few previous studies in favor of momentum strategies; for example, Jegadeesh and Titman's 6-month results, Fama and French 12-1 momentum strategy, and Rouwenhorst's international markets, and Pirron's futures market studies. Based on those previous studies, they will look at the 1-month return strategy, 12-1 momentum strategy, and value strategies across asset classes. In their study, they found these value and momentum strategies to exhibit statistically and economically significant returns of 7-8% over the 1986 - 2007 period. In addition, when they combined the value and momentum strategies, they find excess returns of 12% over the same period. These strategies also outperformed in the 1974-1985 out-of-sample period, overcame transaction costs, and overcame typical risk factors such as the Fama-French and Carhart 4-factor models. These results are significant, because it provides a single model for practitioners to use easily; also they use variables that can be used across all the asset classes.
Data and Methodology
The authors use 12 asset classes; these include 3 US equity, 3 international equity, 3 US bonds, 2 international bonds, and 1 month libor over the 1985 - 2007 period. They selected these asset classes to achieve ease of data retrieval, ease of modeling, liquidity, large capitalization, and lack of correlation with other asset classes in the study. For each of the asset classes, they found the excess returns in local currency and subtracted the local risk-free rate to simulate the return of a typical futures contract. Over the 1985 - 2007 period, emerging markets returned the highest at 10.8% and Japan equity returned the worst at 0.7%. When matched against their standard deviations, all assets seemed to have similar Sharpe ratios, excluding Japan equity which seemed to have very high volatility.
To form portfolios of these asset classes, at the beginning of each month the authors rank them according to their momentum or value scores and put them into quartiles (with 3 asset classes in each quartile). In doing so, they will calculate the average 1-month returns of each quartile as well as the return of the top quartile minus the return of the bottom quartile. They will use the 1-month momentum and 12-1 momentum strategies, and they will use yield measures for the value strategy. In addition, they will use a combination strategy which allocates 25% to the 1-month momentum strategy, 25% to the 12-1 momentum strategy, and 50% to the value strategy.
The authors realize this strategy is somewhat simplistic, so they make a few adjustments to the yields to adjust for risks; otherwise, for example, high yield bonds will most likely always get high allocations over risk-less bonds. So, they subtract 1% yield for government bonds, 2% from US investment grade bonds, 6% from US high-yield bonds, 1% from emerging market equities, and 2% from US REITS.
Main Results
Over the 1986 - 2007 period, each of the individual strategies' top quartiles outperformed the bottom quartiles by 7-8%, and the combination strategy's top quartile outperformed the bottom quartile by 12%. There was also a somewhat monotonic relationship of these returns across the quartiles. Each of these results were statistically significant. The information ratios were about 0.60 for the individual strategies and 1.19 for the combination strategies. As would be expected, the returns of the momentum strategies were positively correlated with each other; however, the momentum strategies were negatively correlated with the value strategy. And that diversification is why the combined strategy performs so well.
The authors also look at a chart of the returns for each strategy over time. Each of the 3 individual strategies perform similarly over the period; however, the combination strategy's return was stable and significantly outperformed the individual strategies over the period.
Next, the authors wanted to determine whether these returns were caused by biases to certain asset classes; so for the combined strategy, they look at the percentage of time that each asset class is allocated to a particular quartile. The US REIT seems to be the most frequent asset class in the top quartile, and UK equity in the bottom quartile; however, no asset class seems to have too big or too small of an allocation in a particular quartile. Therefore, the authors find this be evidence there is no bias toward any particular asset class.
Next, the authors analyze the loadings toward the Fama/French and Carhart 4 factors (i.e., market, size, value, and momentum factors). The returns of the 1-month momentum strategy seem to be explained by the size factor and alpha. The returns of the 12-1 momentum strategy seem to be explained by the market and momentum factors. The returns of the value strategy seem to be explained by market, size, momentum, and alpha; although interestingly, those returns have a negative relationship with the market factor and the momentum factor. Finally, the returns of the combination strategy seem to be explained by alpha, size, and value factors, but the alpha figure is a significant 11%.
Robustness Tests
The authors now analyze the transaction costs of utilizing these strategies. The 1-month strategy has the highest turnover at 1675%, while the valuation strategy has the lowest turnover at 234%; since the 1-month strategy had the lowest returns and the highest transaction costs, its net returns are the lowest. However, the high returns of the combination strategy allowed it to have the highest net returns of all the strategies, when the transaction costs are estimated to be below 0.40%. When the transaction costs exceed 0.40%, the value strategy has the highest net returns, because of its lower turnover. Even at a transaction cost of 0.50%, the combination strategy has excess returns of 4.6%.
Next, the authors replicate the prior results over the 1974 - 1985 period to see if the strategies work over an out-of-sample period. Due to lack of data, there are only 8 asset classes in this analysis. The results are quite similar to those found in the 1986 - 2007 period, with the top quartile returns exceeding those of the bottom quartiles, with high information ratios, and t-statistics across all strategies; these measures were, however, a bit lower than was found in the 1986 - 2007 period. As was found before, the combination strategy significantly outperformed the individual strategies.
Next, the authors understand that some assets are more volatile than others; so the more volatile ones may have more extreme ranks (i.e., end up in the top quartile or bottom quartile more often) than the less volatile asset classes. So, they re-perform the tests with adjusted allocations based on tilting the weights to obtain a 10% volatility for each of the asset classes. The results are similar to the returns we found in the prior sections, with the top quartile outperforming the bottom quartile, the information ratios significant, and the t-statistics significant; as we found before, the combination strategy continues to outperform the individual strategies.
Finally, the authors are concerned that the returns of the strategies are principally because of a single or few asset classes. So the authors provide a chart of the average returns of each asset class when allocated to each quartile. In the top quartile, no asset class averages a return less than 0.3%, and the average return across all asset classes is 0.8%; in the bottom quartile, no asset class averages a return greater than 0.2% (except US mid-cap equities). The authors find this to be evidence that no individual asset class causes the majority of returns in the strategies; the returns seem to be spread across all the asset classes.
Discussion
These results could certainly be due to data mining or chance; however, the authors don't expect this to be the case, and expect these results to continue going forward. The authors also caution, that it could be possible that these returns could just be compensation for a risk that was not modeled; the authors also think that explanation is likely not true, either, because the alpha was 12%, so that presents a very high hurdle to be consumed by risk measures. Also, there didn't seem to be any risk-differences between top and bottom quartile portfolios (e.g., volatility, skewness, etc.). Further, the authors look at how the strategies would have performed in different regimes (e.g., high/low interest rate environment, high/low term spread environment, high/low credit spread environment, and high/low volatility strategy). They find the returns for each strategy to be similar across all 4 strategies in different regimes; however, the value and combination strategies seem to perform differently in different credit spread and interest rate regimes.
These findings provide a challenge to the efficient market hypothesis; however, the authors note that it is challenging to use a risk-model that works across asset classes (because different asset classes have different risks). The authors propose the results of these strategies may be due to behavioral effects that make it difficult for the smart money to arbitrage away these results. For example, practitioners may find cross-asset allocation to be too challenging or the strategy and valuation measures may be too simplistic. Secondly, typical asset managers specialize in individual asset classes; so they are more concerned with selecting individual securities within the asset class, rather than how the asset class as a whole will perform. Finally, allocations by end-investors may be primarily driven by long-term considerations (e.g., pension funds' ALM), fixed allocation mechanisms (e.g., 401ks), herding behavior, or recent performance. The authors believe these constraints will continue going forward, so the results of these strategies will continue as well.
The authors note that hedge funds have the greatest ability to capture this alpha and arbitrage away the results. So to understand whether they currently are doing so, the authors regress the returns of the strategies against the returns of various hedge fund strategies. They find the returns of the 12-1 momentum strategy seem to explain the returns of global macro, long-short equity, managed futures, and multi-strategy. The returns of the combined strategy seem to be related to the managed futures and multi strategy returns. But for the most part, the individual strategies' returns don't seem to be related to hedge fund strategies' returns, so maybe these returns are not being arbitraged away by hedge funds.
Summary, implications and extensions
In summary, the individual strategies (i.e., 1-month momentum, 12-1 momentum, and value) all earned significant return premiums of 7-8% over the 1986 - 2007 period, and the combined strategy (i.e., 25% 1-month momentum, 25% 12-1 momentum, and 50% value) earned an alpha of 12%. Even after adjusting for the Fama/French (i.e., market, size, and value) and Carhart factors (i.e., momentum), there still remains a significant excess return for the strategies. The authors argue against risk-based explanations and instead suggest the market to be macro-inefficient; this is because there is not enough smart money to arbitrage away these alphas due to various constraints. Future researchers could extend this study by expanding the number of asset classes, expanding the number of predictor variables, or introducing portfolio optimization to the allocations.
Our current portfolio is composed of 20
stocks, the top 5 of
which are Schlumberger Ltd (SLB), Total SA (TOT), CVS Caremark
Corporation (CVS), Adobe Systems (ADBE), and Exxon Mobil Corp (XOM).
The portfolio is composed 100% of large-cap equities, 30% of which are
classified as Value, 55% Core, and 15% Growth. Energy, Healthcare, and Technology sectors make up 71% of
the
portfolio. Its P/E ratio and P/B ratios are 77% and 76% of the S&P
500’s, respectively, and
its dividend yields 198% of the S&P 500.
About Sawyer Investment Management Company:
SIMCO
is a Texas-registered Investment Adviser with its principal place of
business in Dallas, Texas. It was formed on January 1, 2015 and is
wholly owned by Ryan Sawyer, who is a CFA Charterholder and a Certified
Public Accountant.
SIMCO specializes in the
construction of equity portfolios, and is therefore an ideal resource
for long-term investors. The firm goes through a rigorous process for
selecting each and every holding in the portfolio. Rooted in the
empirical research of academia, the portfolios are generally
characterized as large-cap value momentum. For more information about
how the portfolios are managed, see our website.
The authors note empirical studies have found that momentum
strategies have been found to exist across 2 centuries, several asset classes,
and geographies.However, there have
also been several rebuttals claiming that momentum strategies may not work effectively
for various reasons.The authors in this
paper intend to defend 10 myths about momentum strategies.
The first myth says that momentum strategies offer returns
that are too small and sporadic.The authors
reference several prior studies that show the robustness of momentum strategies
across several countries, regimes, and asset classes.They then use Ken French’s Up-minus-Down
(Winners minus losers) strategy over the 1927 – 2013 period, 1965 – 2013 period,
and the 1991 – 2013 period.Across all periods,
they find that this momentum strategy earned about 8%, significantly outperforming
other common factors such as RMRF (market premium), SMB (size premium), and HML
(value premium) strategies on both nominal returns and Sharpe ratios. The authors also examine the percentage of
annual periods and five year periods that the momentum strategy was
profitable.They find that about 80% of
the periods were profitable, while the other factors’ positive return percentages
were less.Finally, the authors built a
value/momentum portfolio, which outperformed the other factors even further on
both a Sharpe ratio and percent positive metrics.Therefore, the authors find this to be good
evidence to refute the claim that momentum strategies are small and sporadic.
The second myth says that momentum can only be exploited on
the short side (and is not very useful to long-only investors).The authors use the same time periods as in
Myth 1 and split the alphas of the returns of the UMB strategy into the long
and short pieces.In doing so, they find
that about half of the return relates to the profit from going long the past
winners, and half of the return is due to going short the past losers.The authors also reference a paper that finds
the same results over 86 years of US equity data, 40 years of international
equity data, and 40 years of data from other asset classes.Therefore, the authors find this to be good
evidence to refute the claim that momentum strategies are only profitable on
the short side.
The third myth says that momentum is only present in small-cap stocks and not in large-cap stocks. However, several researchers have found that small-cap and large-cap stocks contribute rather equally to momentum returns. The authors use Ken French's data to analyze the momentum returns (UMB) of large-cap versus small-cap stocks; in doing so, they find that small companies tend to have a larger momentum return than large-cap companies, but the difference is fairly small. Doing the same strategy using value/growth (HML), they find that small-cap companies have significantly higher returns than large-cap returns; and in fact, large-cap value premiums are significantly zero. So, it seems the momentum strategies do not prefer small over large-cap stocks as much as value strategies do. And actually, they note that if Fama/French had not normalized for company size, there would likely have been no value premium found at all. The authors also make note that Fama and French found in 2012 that over a 1989-2011 period in international equities, small cap momentum stocks had slightly higher returns than large cap momentum stocks, but the large-cap momentum returns were significant. This was also found in the 1927 - 2013 period used in the current paper.
The fourth myth says that momentum returns do not exceed trading costs. The thought is that momentum strategies have higher turnover and therefore higher trading costs. The authors use real-world trading costs from AQR Capital's trades over the 1998-2013 period in 19 developed equity markets across different factor strategies. They find that per-dollar trading costs for momentum strategies are actually quite low compared to other strategies (e.g., value, size, etc.). They also note that trading strategies should be used to reduce trading costs (e.g., limit orders rather than market orders). They find that prior studies using estimated trading costs often inflate them due to averages that include trades of retail investors (rather than just institutional investors). Using the AQR data, they find that since momentum strategy returns significantly exceed the returns of small-cap strategies and since trading costs of momentum strategies are lower than trading costs of small-cap strategies, the net returns to momentum strategies significantly exceed those of small-cap strategies.
The fifth myth says momentum strategies do not work for taxable investors; this of course relates to the higher turnover of momentum strategies. Prior research has found, however, that the tax burden of momentum strategies are about the same as value strategies, despite their having 5-6 times the turnover. This is due to momentum strategies instructing the investor to hold winners longer (which typically results in long-term capital gains status) and to sell losers (which results in deductions). Also, value strategies typically have high dividend exposure, which is taxed at ordinary rates; however, momentum strategies do not have as high of dividend exposure. As such, since momentum strategies' returns are significantly higher than value strategies' and they typically both have about the same tax burden, then momentum strategies outperform on an after-tax basis. Another item to note is that tax optimization is a lot easier to implement when concentrating on capital gains (because you can control when to sell securities) rather than dividends (because you can't control when you receive dividends).
The sixth myth says momentum strategies are best used for a screen rather than as an actual factor. However, it seems counter intuitive to say that momentum strategies are good or useful but to denounce it at the same time. This may be due to naysayers anchoring to the concept of market efficiency. The authors suggest that if all the previously discussed myths were true, it might be useful as a screen; but since they were debunked, we might say that momentum has more of a right to be an actual factor than value or size do.
The seventh myth says that investors should be worried about momentum returns disappearing. The authors argue that this should be the case for any factor, not just momentum. They argue, however, that momentum has had a more stable record than other factors over time (as was found in the first few myths in this paper). They note that naysayers may believe this myth due to the relatively newness of momentum studies in academia, and the use of behavioral reasons rather than risk-based causes of momentum. The authors remind us, however, that momentum has been found to exist going back 200 years and across dozens of equity markets; also, they note that any factor could be due to behavioral or risk-based factors (e.g., the promotion of value stocks could increase demand/price and reduce their premium to zero). The authors reference a 2013 paper that finds the out-of-sample period did not result in reductions to momentum returns (which might be evidence of continuity of the strategy). And importantly, the authors used Ken French's data to form portfolios of stocks with both momentum and value characteristics; they find that even if the returns of the momentum factor are zero, including them in the portfolio increases the sharpe ratio of the portfolio due to the diversification benefits.
The eight myth says that momentum is too volatile to rely on. However, we remember that the sharpe ratios of momentum strategies found in the previous myths are significantly higher than those of other factor strategies; and since volatility is taken into account when calculating the sharpe ratio, we know this myth to be untrue. The authors note the myth may come about due to the recent 2009 crash in momentum returns when the market significantly increased after the recession; in that instance, the momentum strategy would have held low-beta winners and high-beta losers, meaning the market would have moved up faster than the winners in the momentum strategy did. The authors note, however, that 1999 was bad for value investors and 2008 was bad for passive investors; so, they argue that an entire strategy should not be denounced just because of a few bad periods. They also note 1932 was bad for momentum and 1930 was bad for value. But, it was found that using momentum and value together during these bad times would have significantly reduced the volatility; using Ken French's data, the authors find that the worst drawdown for value-only was 43%, the worst drawdown for momentum-only was 77%, but when using value and momentum together, the worst drawdown was 30%. Also, being long-only would have also fared ok, because most of the loss in the momentum strategy is due to being short the high-beta stocks that suddenly increase in value.
The ninth myth says different measures of momentum can give different results over different periods. This is actually true, but may be a good characteristic, rather than bad. The argument of the naysayers is that data mining could be used to find the best strategy; but the authors note that out-of-sample evidence is robustly in favor of momentum strategies. Also, other factor strategies use different measurements as well (e.g., P/B, P/E, D/P, for value strategies). In the same vein, such metrics as past returns, past earnings, and analyst revisions are used in ranking momentum stocks; it was found in other studies, however, that each of these measures are effective independently and together. As such, this should be taken as a sign of robustness, rather than a critique, of momentum strategies.
The tenth myth is that there is no theory behind momentum. The authors note this is not fair, because other factors (e.g., value and size) do not have definite supporting theories either, and are in fact still heavily debated. The two behavioral reasons for momentum are that investors typically under-react to new information or delay-overreact to new information; both of these have been found to occur. The other possibility is that momentum occurs due to compensation for risk; for example, growth companies have a risk that they will not have enough cash to support their growth. The authors argue that no matter the reason that momentum occurs, there certainly does seem to be a persistence of momentum over time and should continue to exist going forward. In addition, the naysayers might be anchoring to the efficient market hypothesis and claim that the momentum premium should be arbitraged away; however, we've seen this to not be the case in the prior myths.
In conclusion, in this paper, the authors debunked 10 myths about momentum strategies; they welcome further debate around this paper or the effectiveness of momentum strategies.
Asness, Cliff S. and Frazzini, Andrea and Israel, Ronen and
Moskowitz, Tobias J., Fact, Fiction and Momentum Investing (May 9,
2014). Journal of Portfolio Management, Fall 2014 (40th Anniversary
Issue); Fama-Miller Working Paper. Available at SSRN: https://ssrn.com/abstract=2435323 or http://dx.doi.org/10.2139/ssrn.2435323
About Sawyer Investment Management Company:
SIMCO is a Texas-registered Investment Adviser with its principal place of business in Dallas, Texas. It was formed on January 1, 2015 and is wholly owned by Ryan Sawyer, who is a CFA Charterholder and a Certified Public Accountant.
SIMCO specializes in the construction of equity portfolios, and is therefore an ideal resource for long-term investors. The firm goes through a rigorous process for selecting each and every holding in the portfolio. Rooted in the empirical research of academia, the portfolios are generally characterized as large-cap value momentum. For more information about how the portfolios are managed, see our website.
Our current portfolio is composed of 38 stocks, the top 5 of
which are Total SA (TOT), GlaxoSmithKline (GSK), Microsoft Corporation (MSFT), Exxon Mobil (XOM), and Air Products and Chemicals Inc (APD). The portfolio is composed 98% of large-cap equities, 29% of which are classified as Value, 52% Core, and
17% Growth. Energy, Healthcare, and Technology sectors make up 51.6% of the
portfolio. Its P/E ratio and P/B ratios are 81% and 86% of the S&P 500’s, respectively, and
its dividend yields 179% of the S&P 500.
About Sawyer Investment Management Company: SIMCO is a Texas-registered Investment Adviser with its principal place of business in Dallas, Texas. It was formed on January 1, 2015 and is wholly owned by Ryan Sawyer, who is a CFA Charterholder and a Certified Public Accountant.
SIMCO specializes in the construction of equity portfolios, and is therefore an ideal resource for long-term investors. The firm goes through a rigorous process for selecting each and every holding in the portfolio. Rooted in the empirical research of academia, the portfolios are generally characterized as large-cap value momentum. For more information about how the portfolios are managed, see our website.
