J-SCAD Asset Management
Project Summary
- Objective
- Method
- Hypothesis
- Results
- Further Research
Objective
To determine if momentum is the best strategy for obtaining positive alpha in mid-cap equities in U.S, can it be improved by using it in combination with other factors, or perhaps it can be replaced by other factors that produce a better alpha than it can. Several studies have documented the alpha of momentum strategies, such as several done by Jegadeesh and Titman. Momentum strategists look at a strong price chart, rapid earnings growth and recent positive changes in earnings growth forecast to make investment decisionsbased on the philosophy that stocks that have already outperformed the market over the past 12 months are likely to continue their winning ways, at least in the short term. Limiting the stock picks to strong 12-month performers is crucial to momentum strategies due to elimination of noise in the sample, and relative strength is the tool of choice for gauging performance.Although momentum strategies can entail momentum on periods as short as 3 months and longer, we chose a 12 month time period for our project in order to focus on one momentum strategy and dig deeper into the analysis of the results of this strategy. If we had more time we would have looked at momentum over a 3 month and 6 month period to compare and contrast results of factors versus different momentum time periods.
Method
Our objective is to find the best factors, through the best time frame, to fit each of the following momentum categories. The factors that we initially estimated to be the best were:
Relative Strength: Measures how a stock has performed compared to the overall market over a specified time period.
Earnings Momentum: Starts with fast earnings growth and positive earnings surprises. It measures the increase in the per share growth rate from one reporting period to the next.
Revenue and Rev Growth: A fundamental category that looks for a high minimum sales growth over a specific time period
Return on Investments and Equity: Momentum based on a company’s profitability
Trading and Volume: This momentum category rules out the most risky stocks that are out of favor with the trading community
In our project, we mainly focused on mid-cap equities in the U.S. market (market cap = $1B to $10B). Our total universe was narrowed to 2,322 stocks. We wanted to use the mid-cap equity screen because we felt that 1) mid-cap stocks would have less likelihood to have momentum caused by inclusion or exclusion from index funds (ex: S&P 500 inclusion can cause heavy buying causing artificial momentum) and 2) mid-cap stocks are likely to have more pure momentum from strong fundamental factors compared to large-cap stocks that can have momentum caused from the latest news which may or may not really affect the fundamental or economic factors driving the results of the company. One setback in using mid-cap equities could be that mid-cap stocks could be more susceptible to artificial momentum caused by investors fraudulently bidding up stocks causing small investors to chase the returns and produce artificial momentum.
In regards to time frame of our data, we used data from January 1985 to December 2005.
In regards to weighting, we looked at both equally weighted and value weighted portfolios. For value-weighted, we scaled down our market-capped fractiles by 7% every year we go back in time to account for inflation.
We then wanted to look at the momentum for mid-cap stocks and see if a momentum strategy could be improved by adding additional factors (both momentum based and non-momentum based) in the scoring of the stock. We chose to run three different scoring scenarios to see what strategy would maximize alpha. Our first scenario was to score our universe purely on one factor, momentum, and to score Quintile 1 as 5 and Quintile 5 as -5. Our second scenario was to run a hybrid momentum strategy giving the momentum factor a very high weight (5)and a score of 1 to the other factors; we then ranked them as 5for quintile 1 and a very low factor (-5) for quintile 5. We then included other factors such as price to book, revision ratio, 3-year EPS growth, and SUE (surprise unexpected earnings), which we found were good test factors when used individually, and give these low weights of 1 and -1 for quintile 1 and quintile 5, respectively. Our third scenario was to run the scoring test with weights based upon our subjective judgment of the factors.
In addition to these scenarios, we wanted to run univariate, bivariate, and multivariate scoring screens to determine if there were factors that scored a better alpha than momentum did.
Hypothesis
We hypothesized that alpha would be greater in scenario 2 when momentum was given the highest weight and other factors were given relatively lower weights. We thought that this would be an improvement over a pure momentum strategy because stocks with highest momentum (quintile 1) would not purely win out in a screening, but it would consider other factors attributing to momentum to make sure stocks with possible artificial momentum were weeded out of the top quintile and stocks that should have better results were removed from the lowest quintile. We also hypothesized that the subjective judgment of factors would cause alpha to be slightly better in scenario 3 than the hybrid momentum strategy of scenario 2 because we were able to weight factors we thought were best more accurately into our scoring system. However, we were interested to see if a more objective scoring system (scenario 2) was better than a subjective scoring system (scenario 3).
Results
The main conclusion of our results is that momentum is a major contributing factor to attaining high alpha in and beating the market if combined in a hybrid portfolio. Using momentum as a sole factor will achieve high returns, but not as high as when combined with other univariate factors.
In determining the factors we would use, we began by looking through the factors we initially expected would be effective in determining momentum. We found through univariate screening that there were three factors that best captured momentum, which were 12 month total return, 3 year growth EPS, and 1 year price change, that had a good alpha rating. We also found 3 other factors that also showed positive alpha results and we could use later in our testing. Overall, the other factors that we used were 12-month total return, 3-year EPS growth, 1-year price change, revision ratio, SUE and book value to price.
Scenario 1: Momentum 5 v 0
We then began running three scenarios to determine which strategy would work best with momentum. We started by running our pure-momentum strategy. Our test showed an alpha of 0.87% per month for the pure momentum strategy with an equally weighted platform. This was slightly higher than the 0.82% alpha for a value weighted portfolio. Although beta was lower for quintile 1 than quintile 5, as was expected, the overall beta for quintile 1 was higher than expected, at 1.07. However, we understood this by the fact that a momentum strategy would have an expected higher return due to the fact that they will normally have risks higher than a market portfolio.
Scenario 2 – Momentum 5 v 1
We then tested what we called a hybrid momentum strategy, which entailed using 12 month total return and scoring this with a +5 and -5 for quintile 1 and 5, respectively. We then included five other factors that we found were good factors for alpha (two being momentum based, but different from our main momentum factor, and 3 other fundamental variables) and gave these factors a score of +1 and -1 for quintile 1 and 5, respectively. This was done to see if we could make a modification to momentum that could enhance the results that momentum brings.
Our results show that alpha is actually lower for the hybrid portfolio than for the original momentum strategy and a lower overall return. It also has a smaller beta differential (beta difference between quintile 1 and 5). However, there are other factors, like information ratio, or the ratio of expected return to risk, as measured by standard deviation, for the pure momentum strategies which is lower than the hybrid portfolio. This ratio, which is a measure of portfolio performance, may prove that having momentum may not be our sole determining factor is a good way of beating the market.
Scenario 3 – Momentum 5 v 5
We then wanted to test the pure momentum strategy and the hybrid momentum strategy against a scoring strategy that was more subjective to our judgment of the factors to test the idea that momentum strategies are one of the best strategies for obtaining positive alpha and can be improved only a small amount (as shown in our earlier test of momentum versus hybrid momentum). We realized an alpha slightly higher than the pure momentum portfolio but had more risk (based on beta). However, this portfolio performed much better against the benchmark (about 24% in the hybrid portfolio versus 17% in the pure momentum portfolio).
Our scoring entailed the following factors:
Q1Q5
12 month total return: +5-3
3 yr EPS growth+5-5
1 yr price momentum+1-1
Revision ratio+5-3
SUE+5-3
BV to P+5-3
Univariate Factors
Finally, we ran our model on the univariate factors we had chosen. Our results showed that none of the factors, on its own, was able to capture the 0.93 alpha we got by the hybrid, equally weighted portfolio. Although the 12-month total return factor was the best univariate factor on its own, none of the factors was able to beat the benchmark at 24%, reach a 0.16 Sharpe ratio, and a 1.07 information ratio. See table below.
Factor Comparison(Quintile 1 less Quintile 5) => for instance, using Momentum 5 v 5, Q1 has an alpha that is 0.93 greater than Q5)
Table legend:
Momentum 5 v 5 (scenario 3, momentum with subjective factors) = Momentum given score of +5 or -5 (for quintile 1 or 5, respectively), and other factors were scored subjectively with a score up to +5 or -5 based on quintile 1 or 5, respectively(see explanation above for subjective scoring weights)
Momentum 5 v 1 (scenario 2, hybrid momentum strategy) = Momentum given score of +5 or -5 (for quintile 1 and 5, respectively), and other factors given a score of +1 or – 1 based on quintile 1 or 5, respectively
Momentum 5 v 0 (scenario 1, pure momentum strategy = Momentum given a score of +5 or -5 for quintile 1 or 5, respectively with no other factor weightings
Heat map analysis
By running the heat map model, we find that in scenario 3 (Momentum 5 v 5), the momentum strategy sends us a consistent signal: long quintile 1 and short quintile 5:
One interesting finding in the momentum scenario is that while the portfolio of going longquintile 1 and shorting quintile 5 in the equal-weighted portfoliogave a Sharpe ratio higher than that of the benchmark SP50, the value-weighted portfolio resulted in a Sharpe ratio lower than that of the benchmark.
Another interesting finding is that in a bubble year like 1999, the strategy of longing quintile 1 and shorting quintile 5 could not help us to beat the market. However, when the bubble burst in the following years like 2001, such a strategy could help us suffer a less loss than the market. Our understanding is: in a bubble year, almost all stock prices increase, and shorting would reduce your returns while reducing your risks. But in a bearish year like 2001, such a strategy could help save your money.
Further Research
Areas that we would like to research further are how differing time period momentum strategies compare to other alpha strategies in a scoring system. We would also like to study if momentum results are affected by company size.