The authors are looking at the Vietnam stock exchanges and researching the factor related returns of the stocks therein; specifically, they are looking at the value, momentum, size, and liquidity factors that have been used in other studies in different markets.
In regard to the value factor, they find that portfolios of the top earnings yield companies (i.e., value companies) have returns that exceed those of the stocks with the lowest earnings yields (i.e., growth companies). Most of this excess return comes from shorting growth companies, and works best in holding periods exceeding 9 months.
In regard to the momentum factor, they only find a statistically significant different between past winner and past winners for holding periods of 1-month and 36-months. The authors conclude that momentum is effective; however, we might caution that stance due to the insignificance of the t-statistics. In looking at a chart of the excess returns, though, we see similarities to prior studies in different markets where the momentum returns revert to the mean and are ineffective after 12 month holding periods.
In regard to the liquidity factor, they find that buying liquid stocks and selling illiquid stocks is an extremely effective strategy; buying the top stocks according to liquidity seems to significantly outperform the equal weighted index over all holding periods. This might be due to the Vietnamese exchanges being new, so liquidity is very valuable until the market matures.
In regard to the size factor, they find no significant difference between the returns of small companies and big companies. In fact, they find that the smallest companies underperform the index and the big companies, which is backwards from the normal strategy related to size (i.e., that small companies outperform large companies). This may also be related to the newness of the Vietnam exchanges, wherein, larger companies are much more valuable.
Ming-Chin Chin, & Nguyen Vu Hieu. (2015). An Analysis of Investment
Strategies and Abnormal Returns in the Vietnam Stock Market. Journal of Applied Economics & Business Research, 5(4), 194–208.
The purpose of this paper is to understand the linkages between excess returns and four investment strategies - value, momentum, size, and liquidity - for the Vietnam stock market during the period 2006-2014. The empirical results suggest that a value strategy, such as the E/P and B/P ratios, and momentum
and liquidity strategies are the most successful and generate
significant excess returns, in contrast to the size strategy, which does
not workin the Vietnam stock mark. Therefore, investors who want to
make a profit when investing in the Vietnam stock market should track
published financial information and find winner stocks by referring to value, momentum, and liquidity strategies.
Our current portfolio is composed of 20
stocks, the top 5 of
which are CVS Caremark
Corporation (CVS), Total SA (TOT), General Mills Inc (GIS), Sanofi (SNY), and Intel Corporation (INTC).
The portfolio is composed 96% of large-cap equities, 51% of which are
classified as Value, 38% Core, and 7% Growth. Energy, Healthcare, and Technology sectors make up 66% of
the
portfolio. Its P/E ratio and P/B ratios are 82% and 74% of the S&P
500’s, respectively, and
its dividend yields 82% 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.
The authors are curious to know which factors in the literature are most robust; so they searched the SSRN database and found the illiquidity, low beta, value, momentum, size, and quality factors to be most studied and relevant. So they performed robustness tests for each of those factors over the 1967-2014 (in the US) and 1987-2014 (in international stock markets) period.
For the low beta factor, they found no statistically significant difference in returns between low beta and high beta stocks in the US. They did however find that the sharpe ratios of low beta portfolios were higher than the sharpe ratios of high beta portfolios. Internationally, there is no significant difference in returns between low and high beta stocks; although as was found in the US, the sharpe ratios of low beta portfolios were higher than those of high beta portfolios.
For the value factor, they found a statistically significant
difference in returns between value and growth stocks in the US. Also, they find that the sharpe ratios of value portfolios were
higher than the sharpe ratios of growth portfolios.
Internationally, there is a significant difference in returns between value and growth stocks as well as sharpe ratios (except in the UK).
For the size factor, they found a statistically significant
difference in returns between small and large cap stocks in the US.
They did however find that the sharpe ratios of small portfolios were not significantly higher than the sharpe ratios of large cap portfolios.
Internationally, there is no significant difference in returns between small and large cap stocks, nor in the sharpe ratios.
For the momentum factor, they found statistically significant
difference in returns between small cap past winner and loser stocks in the US, but not in large cap. They found the same scenario in regard to the sharpe ratio, where small cap past winners outperformed past losers, but large cap past winners did not outperform past losers.
Internationally, they found the same scenario where small cap past winners outperform past losers, but not large cap, on both a nominal and risk-adjusted basis. Japan, however, is not a good place for momentum investing.
For the illiquidity factor, they found a statistically significant
difference in returns between illiquid and liquid stocks in the US.
They also find that the sharpe ratios of illiquid portfolios were
higher than the sharpe ratios of liquid portfolios.
Internationally, there is no significant difference in returns between illiquid and liquid stocks; the same conclusion is reached on a risk-adjusted basis, where illiquid portfolios' sharpe ratios are not statistically higher than those of liquid portfolios ex-US.
For the quality factor, they found no statistically significant
difference in returns between quality and junk stocks in the US. The same conclusion is reached on a risk-adjusted basis, where quality portfolios' sharpe ratios are not statistically higher than those of junk portfolios in the US.
Internationally, there is no significant difference in returns between quality and junk stocks; although the sharpe
ratios of quality portfolios were higher than those of junk
portfolios in Europe ex-UK and Global.
Next the authors look at some more risk measures for each of the factors in long-only portfolios. For large caps, they find low beta portfolios seem to have the least volatility, and momentum portfolios have the highest; small caps are more volatile than large caps, with the value factor being the most volatile. The value factor for both large and small caps has the largest draw-down and period of underperformance (with the exception of quality, which has a 10.8 year period of underperformance).
Next the authors look at some more risk measures for each of the factors
in zero-cost portfolios. They find the low beta and quality portfolios perform the worst in regard to draw-down, volatility and underperformance; this is likely caused from the short side underperforming the market in bull market periods.
Finally, the authors look at which of the factors still provide excess returns after taking into account transaction costs. They find that only the value, small cap low beta, and small cap illiquidity factors still add value after taking into account transaction costs. All other factors erode value when taking into account transaction costs.
Beck, N., Hsu, J., Kalesnik, V., & Kostka, H. (2016). Will Your
Factor Deliver? An Examination of Factor Robustness and Implementation
Costs. Financial Analysts Journal, 72(5), 58–82.
The multifactor investing framework has become very popular in the
indexing community. Both academic and practitioner researchers have
documented hundreds of equity factors. But which of these factors are
likely to profit investors once implemented? We find that many of the
documented factors lack robustness. Size and quality, two of the more
prominent factors, show weak robustness, whereas value, momentum,
illiquidity, and low beta are more robust. Further examining
implementation characteristics, we find that liquidity-demanding
factors, such as illiquidity and momentum,
are associated with significantly higher trading costs than are other
factors. Investors may be better off accessing these factors through
active management rather than indexation.
The authors are looking to see how does liquidity effect the factor returns of stocks in the Johannesburg Stock Exchange over the 1994 - 2011 period. To do so, they setup a univariate regression for factors that are commonly used in prior studies (e.g., value, size, momentum, and volatility); then they compare the factor coefficients of those regressions to the coefficients using a population of large cap stocks only (with market capitalization being a proxy for liquidity).
They find cash-flow-to-price to be the most significant to explain the cross section returns of the population; and its effect actually increases when limiting the sample to large cap companies only. The size effect is somewhat muted when limiting the companies to large cap. Finally, the momentum effect is more pronounced with found to be more pronounced with smaller companies, in line with prior studies. Interesting to note, the market beta in the CAPM formula is statistically insignificant, but is more significant with larger companies.
Next, the authors built a bivariate regression for each of the factors, which includes the dummy variable with one of the coefficients to see what is the net effect to the regression by limiting the population to large caps only. They find that the only statistically significant change is for momentum and value factors (i.e., moving averages, dividend yield, and cash flow to price); for example, by adding liquidity to the sample, the momentum returns of the sample decrease, and by adding liquidity to the sample, the value-related returns increase.
Finally, the authors formed a univariate regression for each of the factors by ranking the stocks by the top 30% and bottom 30% of their factors, and forming a zero-cost portfolio as if buying the top-ranked 30% and shorting the bottom-ranked 30%. Again they find the cash-flow-to-price factor to be the highest coefficient (to the tune of 2% per month excess return) and with the highest t-statistic. Size is found to be negatively related to returns, in line with other studies. In addition, the momentum returns are found to be about 1% return per month, similar to other studies. As was done in the prior regressions in the paper, the authors then limited the population to large cap companies only, and found in all cases the factor-related returns are muted when limiting to large cap only; although, the cash-flow-to-price, 12-month momentum, and book-value-to-market factors still exhibit outperformance at a statistically significant level.
VAN HEERDEN, J. D., & VAN RENSBURG, P. (2016). The Impact of
Liquidity on the Cross Section of Equity Returns on the Johannesburg
Securities Exchange. Economics, Management & Financial Markets, 11(2), 59–86.
The authors are trying to find out what is important to investors when they are deciding which mutual funds to invest in. Ideally, investors should choose the mutual funds with the highest alphas; the problem is that there are many models that calculate alpha using different factors. For example, the CAPM has market return only; the 3-factor model adds size and value; the 4-factor model adds the momentum factor; and the 7 factor model adds industry factors; and the 9 factor model adds a few more).
So the authors regress the returns using each of the models onto the mutual fund flows over their period of study; and they chart the alphas for each of the models in the regression. If investors were adequately assessing the returns of mutual funds, the findings would show that the coefficients on each of the factors is insignificant and the alphas are significant. What they found is that the CAPM is the best model at explaining the flows into mutual funds; so that would mean that investors likely control for the market return when choosing mutual funds, but they don't adequately control for the other factors when assessing manager skill.
Next, the authors isolate the 7-factor model to assess how each of its factors explain the mutual fund flows. What they found is that each of the coefficients is statistically significant and positive; so that would mean that investors do not appropriately control for the factor returns when comparing different portfolios and their managers' skill. The authors also found that the market return factor had the smallest coefficient, which would be in line with the CAPM's alpha being the determinant of choice by investors.
Next the authors considered that maybe more sophisticated investors might control for the factors more adequately than unsophisticated investors. So they split investors into sophisticated and unsophisticated categories based on the distribution channel of the mutual fund purchase, sentiment of the mutual fund, and wealth level of the investors; then they reran the 7-factor regression to see which factors contributed to the fund flows for each investor category. As they expected, they found that more sophisticated investors better control for the factors than do unsophisticated investors when making mutual fund purchase decisions.
Barber, B. M., Xing Huang, & Odean, T. (2016). Which Factors Matter to Investors? Evidence from Mutual Fund Flows. Review of Financial Studies, 29(10), 2600–2642.
Our current portfolio is composed of 20
stocks, the top 5 of
which are General Mills Inc (GIS), CVS Caremark
Corporation (CVS), Total SA, Intel Corporation (INTC), and Exxon Mobil Corp (XOM).
The portfolio is composed 98% of large-cap equities, 52% of which are
classified as Value, 38% Core, and 8% Growth. Energy, Healthcare, and Technology sectors make up 65% of
the
portfolio. Its P/E ratio and P/B ratios are 84% and 86% of the S&P
500’s, respectively, and
its dividend yields 87% 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.
The authors are interested to know whether the Fama-French 3-factor model (i.e., market, size, value factors) or the 4-factor model (i.e., with momentum added) is better at explaining the 1999 - 2010 returns for stocks of the Amman Stock Exchange in Jordan.
As was done in prior studies, the authors sorted companies into thirds by market capitalization and by book-to-market ratios, and formed 9 equal-weighted portfolios by size and value/growth (e.g., large-growth, medium-growth, small-growth, and so on). They find their results to be consistent with prior studies that found smaller companies have higher returns than big companies, and value stocks have higher monthly returns than growth stocks.
Next, the authors formed a regression of the 4-factor model for all securities in their sample over the same time period. They find the High-minus-Low factor (i.e., Value vs Growth) is the only factor to be statistically significant; all of the other factors' coefficients were not statistically different from zero.
Next, the authors formed a regression of the 3-factor model over the 9 portfolios that were discussed before. They find the market factor to be significant across all portfolios, the Small-minus-Big factor to be significant across all portfolios except the medium sized, and the High-minus-Low factor to be significant across 5 of the 9 portfolios in no discernible pattern. The R-squared ranges from 18% to 83%; the regressions of the larger companies and the growth companies tend to have higher R-squared values. The intercept (i.e., alpha) is not statistically significant in 8 of the 9 portfolios. So we might say that the 3-factor model for the large-sized and growth companies tend to do a pretty good job of explaining the variation in returns.
Finally, the authors form another regression of the 4-factor model over the same 9 portfolios. The authors find that adding the momentum factor to the regression does not improve the explanatory power of the regression. The previous 3 factors' coefficients remain substantially unchanged in magnitude and significance. Only 3 of the 9 portfolios boast a significant momentum factor coefficient. Also, the R-squareds did not significantly improve. So we might conclude that the 4-factor model does not do a better job than the 3-factor model at explaining the source of variation in returns for stocks in the Amman Stock Exchange over the 1999 - 2010 period.
Al-Mwalla, M. (2012). Can Book-to-Market, Size and Momentum be Extra
Risk Factors that Explain the Stocks Rate Of Return?: Evidence from
Emerging Market. Journal of Finance, Accounting & Management, 3(2), 42–57.
The existence of momentum returns has long been established by prior studies, but there has not been a definitive reason for why momentum returns occur. The authors suggest the reason is that investors are myopic (i.e., nearsighted), and that speculators may overweight public information and underweight private information, which would result in prices taking longer to reach their true fundamental value and for more short-term information to drive prices. In addition, institutional investor myopia may result from short-term incentives, producing price drifts similar to those found in behavioral models.
The authors took many countries and ranked them according to their level of cultural myopia. Then they compared that myopia index to the level of momentum returns in each of the countries. In doing so, they find that countries that tend to be myopic usually have higher momentum returns.
Next, the authors ranked and split the countries into 3 equal groups, then simulated forming equal-weight portfolios of countries for the "Myopic" third, the "Neutral" third, and the "Long-Termist" third. They find that the myopic portfolio has much higher momentum returns than the long-termist portfolio over the 1988-2015 period. So we might argue that countries that are culturally more myopic might have higher momentum returns, so the level of myopia of investors might explain momentum returns.
Finally, the authors form regressions of momentum returns and control for other factors that have been found to be related to momentum returns in other research. The authors find that despite controlling for many other factors, the myopia factor is still significantly related to momentum returns.
Docherty, P., & Hurst, G. (2018). Investor Myopia and the Momentum Premium across International Equity Markets. Journal of Financial & Quantitative Analysis, 53(6), 2465–2490.
