The authors want to understand the momentum of various styles (e.g., the size or value factor) in securities' returns; for example, these styles have outperformed in some time periods and underperformed in other time periods; so is there a persistence in the returns to these styles over time, and would it be a good investment strategy to buy into the styles that have performed well in the recent past?
To do so, the authors split the stocks of the FTSE 350 into the 9 Morningstar style boxes (i.e., 3 size boxes times 3 value/growth boxes), then explore the returns to portfolios that go long the recent highest performing style and short the recent lowest performing style over various formation and holding periods. In doing so, they find the only time periods where the strategy has statistically significant returns is for the 3/3 formation/holding period and the 6/9 formation/holding period, and the excess returns were in the 0.58% to 0.78% range.
Next, the authors formed price momentum portfolios by going long the recent highest performing decile of stocks and going short the recent lowest performing decile of stocks. They find that the only formation/holding periods with statistically significant excess returns are the 3/9 and 6/9 formation/holding periods, and the excess returns were in the 0.67% to 1.60% range.
Aarts, F., & Lehnert, T. (2005). On style momentum strategies. Applied Economics Letters, 12(13), 795–799.
https://doi.org/10.1080/13504850500373602
Barberis and Shleifer (2003) suggest that US investors classify assets
into different styles based on, for example, market capitalization or
B/M ratios. They find that prices can deviate substantially from
fundamental values
as a style's popularity changes over time. In this paper, we discuss
implications of this prediction and empirically investigate the
profitability of style momentum
strategies for the UK stock market. Results suggest that a simple
trading rule can generate significant positive returns, but for our
sample of FTSE 350 stocks those strategies are less profitable and more
risky compared to regular momentum strategies.
The authors are studying price momentum across several European countries over the period 1987 - 1999. Specifically they want to analyze the prevalence of earnings surprises, which would suggest the cause of momentum returns to be the underreaction to information, as has been previously studied.
They formed their porfolios similarly to prior researchers and ranked the portfolios into deciles of past returns, for 1/3/6/12 month holding periods. They find a monotonic increase in returns in all formation periods. Also, they provide risk-adjusted returns (i.e., controlled for size, value, market, and country risk), which show a monotonic increase in alpha as the past returns increase; however, the alpha is only positive in the top 3-4 deciles.
Next, the authors explore the relationship between momentum returns, and those attributable to the value, size, and earnings growth factors. They hope to determine whether momentum returns are in fact just returns to value, size or earnings growth expectations. Regarding the value factor, they find that the momentum returns and the value returns are negatively related; the momentum returns are negatively related to the market cap; and the expected earnings growth is not related to momentum. Therefore, they find momentum is effective on a stand-alone basis and is not supplanted by other factors.
Next, the authors look at earnings surprises for each of the deciles of momentum portfolios. They find that as the past returns increase, so do the earnings surprises. The authors suggest that analysts tend to be too pessimistic in their forecasts of past winner portfolios and too optimistic in their forecasts of past loser portfolios. They also find that the higher momentum portfolios also tend to have higher earnings forecast revisions than do the lower momentum portfolios. Finally, the higher momentum portfolios tend to have a higher percentage of positive earnings forecast revisions than do lower momentum portfolios.
van Dijk, R., & Huibers, F. (2002). European Price Momentum and Analyst Behavior. Financial Analysts Journal, 58(2), 96.
https://doi.org/10.2469/faj.v58.n2.2526
Previous studies have found evidence that selecting stocks with positive price momentum is effective in the U.S., European, and emerging stock markets periods up to a year. The reasons that historical price momentum forecasts the direction and magnitude of stock returns, however, are not clear. Insight into the determinants of price momentum would allow investors to judge whether and how price momentum should play a role in their investment strategies. Studying the European stock markets, we found that positive price momentum
is caused by analyst underreaction to new earnings information. We
found earnings surprises, expected earnings growth, and earnings
revisions to be systematically related to historical price movements.
Importantly, the data show that European price momentum is distinct from the widely documented value and size effects. Our findings clarify the benefits of assessing analyst behavior to predict whether momentum investing might work in the next period.
The authors are attempting to determine how are momentum returns affected by different levels of capital investment within companies, on average. They calculate capital investment in three different ways: the capital expenditures as a percentage of fixed assets, the change in capital expenditures from year to year, and the change in accruals (i.e., working capital) from year to year.
Their population includes United States publicly trades stocks during the period 1965 - 2004, and on average, firm's capital expenditures tend to be 14.6% of total capital assets, and their expenditures on capital assets and working capital do not significantly change year to year, on average. These three measures of investment are also positively correlated with each other; but are not correlated with company size.
Next, the authors explored the momentum returns of this population to see what level of capital investment was made, on average, within different levels of momentum returns. They sorted the population into portfolio quintiles of prior returns spanning 3 - 12 months, and recorded the returns of those portfolios over holding periods of 3 - 12 months. Their results corroborate prior studies that find past winners outperform past winners in all formation and holding periods. In relation to capital investment, they find that the past winners tend to have lower capital investment than past losers, and the level of capital investment tends to have a U-shape with the level of past returns.
Next, the authors isolate the formation/holding period of 6 months to form 5x5 sorts of level of momentum and level of capital investment. They find that the best performing portfolio tends to have high momentum and high capital expenditure; and the worst performing portfolio tends to have low momentum and high capital expenditure. The momentum returns tend to increase almost monotonically as the level of capital expenditure increases within the highest ranking momentum stocks, but decreases almost monotonically as the level of capital expenditure increases in the lower momentum rankings. The momentum returns tend to exhibit a U-shape with increases in the other measures of capital investment (i.e., change in capital expenditures, and change in accruals). However, the long-only returns of all portfolios (i.e., not the zero-cost portfolios, which are discussed above) tend to decrease as capital investment increases; the reason the higher capital investment portfolio does well for the zero-cost portfolio is because the high momentum returns decrease slower than the low momentum returns at each increase in capital investment.
Next, the authors look at the returns of the zero-cost portfolio within different subperiods within the 1965 - 2004 period. Within all the subperiods, the momentum returns increase almost monotonically with each increase in capital expenditures. Within all subperiods, the momentum returns exhibit a U-Shape with each increase in change in capital expenditure and change in accruals.
Next, the authors did a 10x3 sort, with 10 levels of momentum and 3 levels of capital investment; for the momentum, they also looked at different lengths of formation and holding periods from 3 - 12 months. For the momentum returns, they calculated zero-cost portfolios as the top decile minus the bottom decile. In line with their prior results, they find the momentum returns to increase monotonically with each increase in level of capital expenditure across all formation/holding periods. In addition, they find the same U-shape of returns across changes in the change in capital expenditure and change in accruals.
Next, the authors formed a Fama French 3-factor regression (i.e., controlling the returns for market, size, and value returns) to explore the risk-adjusted returns for the portfolios. They find similar results as the prior results, where the highest alpha portfolio is the one with the highest momentum and highest capital expenditure, and the lowest alpha portfolio is the one with the lowest momentum and highest capital expenditure. The zero-cost alpha tends to increase as capital expenditures increase, and the portfolio alphas tend to decrease at each increase in capital expenditure. When looking at the change in capital expenditure and change in accrual methods, we see a decrease in portfolio alphas as capital investment increases, but the zero-cost alphas exhibit a U-shape in line with the results of prior tables.
Citation: Jiang, G., Li, D., & Li, G. (2012). Capital investment and momentum strategies. Review of Quantitative Finance & Accounting, 39(2), 165–188.
Link to paper: https://doi.org/10.1007/s11156-011-0250-3
Abstract: The main purpose of this paper is to investigate whether capital investment can affect stock price momentum. We provide empirical evidence that momentum
strategies tend to be more profitable for stocks with large capital
investment or investment changes. We present a simple explanation for
our empirical results and show that our finding is consistent with the
behavioral finance theory that characterizes investors' increased
psychological bias and the more limited arbitrage opportunity when the
estimation of firm value becomes more difficult or less accurate.
The authors want to understand how common anomalies in the literature affect portfolio returns when combined. To do so, they center in on the size, value, profitability, investment, and momentum anomalies. Historically, companies with smaller market capitalization, higher book-to-market ratios, higher return on assets, lower investments in assets, and past 6-month returns tend to outperform their respective complements.
They first look at the returns for each of these factors by putting them into quintiles and comparing returns across the 1971 - 2013 period in the US, excluding micro-cap companies which are found to be most sensitive to the factors in prior literature. They find all the factors to exhibit monotonic returns with changes in the quintiles in favor of the known factor relations.
They then split the quintiles into quintiles of the remaining factors into 5x5 portfolios to show the sensitivity of certain factors within the primary factors. They find that smaller companies tend to be more sensitive to size, profitability, and the investment factors, with momentum generally being unaffected by size (but this is consistent with prior studies that show momentum to increase only in the smallest of companies, which are excluded from this study). They then find that growth companies tend to be larger, more profitable, make higher investments in assets, and have higher momentum. Next, they find that less profitable companies tend to be smaller, categorized as value, make less investments, and to have no effect on momentum. Next, they find that companies who make smaller investments in assets seem to be smaller, be considered value, have lower profitability, and have no effect on momentum. Finally, high momentum companies tend to be characterized as growth and be irrespective of other factors.
Next the authors double-sorted the portfolios into 5x5 portfolios to see what are the returns of different levels of the factors in combinations. They also calculate a monotonic relationship to see which factors are likely to explain monotonic return increases with changes in those factors.
In relation to size, the authors find that the smallest companies are the only ones who show a monotonic increase in returns with increases to the value factor; none of the company sizes result in monotonic changes in returns with increases to the profitability factor; only the smallest companies show a monotonic increase in returns with increases to the investment factor; and all but one of the company size quintiles show a monotonic increase in returns with increases to the momentum factor.
In relation to value, the authors find that the top 3 quintiles of value show monotonic increases in returns with decreases in company size; none of the levels of book-to-price result in monotonic changes in returns with increases to the profitability factor, only companies in the top quintile of value show monotonic increases in returns with decreases in investment; and only the growth companies show monotonic increases in returns with increases in momentum.
In relation to profitability, there are monotonic relationships in several of the quintiles of the other factors; however, there does not seem to be a relationship where more or less profitable companies exhibit consistently higher or lower returns with increases or decreases in the other factors. In general, the returns of more or less profitable companies are not predictable in combination with other factors. However, companies with low profitability and high investment tend to perform the worst; while companies with low profitability and high momentum tend to perform the best. This contrast is evidence that level of profitability may not have an effect on returns when combining with other factors whose influence is more prominent.
In relation to investment, there are monotonic relationships with a few of the levels of company size and momentum; however, the changes in level of the other factors does not seem to have a monotonic relationship when controlling for level of investment. The highest return comes from high momentum, low investment companies as well as the low investment and small size companies; the smallest returns come from the high investment, low momentum companies.
In relation to momentum, the authors find the top 3 quintiles of momentum show monotonic increases in returns with decreases in company size; only the past losers tend to monotonically increase in return with increases to the value factor; and the changes in level of the other factors does not seem to have a monotonic relationship when controlling for the level of momentum. The highest returning portfolio seems to be the high momentum, with low investment, profitability, book-to-price ratios, or company size. The least profitable portfolio seems to be those with low momentum, combined with high investment or low book-to-price ratio.
Next, the authors pull the corner portfolios from the previously discussed 5x5 sort to explore the risk adjusted returns (i.e., sharpe ratios, volatility, SKAD) of long-only, short-only, and zero-cost portfolios with each factor combination. They find that the long-only portfolio outperforms the short-only portfolio in all cases except one (i.e., the ROA/BP portfolio). They find that the highest sharpe ratio of the zero-cost portfolio comes from the BP/MOM portfolio, which is in line with prior studies that find combining value companies with momentum companies creates good diversification effects and therefore good risk-adjusted returns. This portfolio also has the second-highest long-only return (i.e., after BP/ROA). Portfolios that include the investment factor also tend to be the ones with the highest sharpe ratios.
Finally, the authors form Fama-McBeth regressions with each of the factors to see how much of the portfolios' returns are explained by the factors. When considering only two factors at a time, they find that a 1 standard deviation increase in the book-to-price ratio and past 6-month return can produce a 4.1% increase in return to the portfolio; and a 1 standard deviation decrease in the past year percentage change in assets and increase in past 6-month returns can produce a 4.3% increase in return to the portfolio. When all factors are regressed at once, they find a 1 standard deviation decrease in the past year percentage change in assets and increase in past 6-month returns can produce a 4.2% increase in return to the portfolio; the value factor was not statistically significant in the all-factor regression. The authors suggest the likely reason the other factors are not prominent is because they are mostly emphasized in micro-cap companies which are excluded from this study. The investment and momentum factors are significant in all combinations with other factors.
Karell, V., & Yeomans, J. S. (2018). Anomaly Interactions and the Cross-Section of Stock Returns. Fuzzy Economic Review, 23(1), 33–61.
