Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
Do Market Anomalies Add Up?
William J. Trainor Jr.* and Larissa Steinfeldt**
The implication from market anomaly studies suggest that trading based
on price earnings ratios, size, price book, momentum, and volatility can
produce excess returns.
This study examines these anomalies
independently and in combination to determine how often they work,
when they work, and what return can be expected. Based on the last 20
years, findings suggest excess returns are still available for some of
these anomalies with the probability of success reaching up to 90% for
any given 6-month period. Somewhat surprisingly, combining anomalies
does not lead to even greater returns. PE, size, and PB continue to be
highly correlated with returns while beta and volatility appear to be
functional risk metrics.
Finance
JEL Codes: G11 and G12
1. Introduction
In an efficient market, stock prices adjust quickly to new information that becomes available to
investors. Thus, stock prices and returns should be relatively unpredictable and are often described
as a random walk. In reality, a number of anomalies have been found that do not entirely mesh with
the Efficient Market Hypothesis as set forth by Fama (1965). Keim (2006) calls them exceptions to
the rule.
Although there is a substantial literature on a variety of supposed anomalies, the more commonly
agreed upon are the price-to-earnings (PE) ratio, size, price-to-book (PB) ratio often referred to as
market value to book value (MV/BV), momentum, and volatility. Based on the literature, the “perfect”
stock should have a low PE, low market cap, low PB, high momentum, and low volatility as all these
characteristics have been associated with excess stock returns at one time or another.
This study examines these anomalies over the last 20 years to determine the excess returns that
could have been achieved if they had been followed. Since all these anomalies were known before
this time period, the results should at least address the data mining problem to some extent. By
breaking down portfolio choices to every 6 months, this study also estimates how often and how
persistent these anomalies are over time. In an effort so see if the anomalies work during both
bullish and bear markets, the returns following the anomaly trading rules are separated by when the
market is up and down. Finally, portfolios are created based on stocks meeting multiple anomaly
criteria to see if even greater market returns may be possible.
Overall results suggest that PE, size, and PB anomalies are still going strong. There is little to no
evidence suggesting the momentum or volatility anomaly is useful for attaining excess returns, nor is
combining two or more functioning anomalies. Using PE, size, and PB values for trading work in both
*
Dr. William J. Trainor Jr,,CFA, Department of Economics and Finance, East Tennessee State University, Email:
trainor@etsu.edu
**Larissa Steinfeldt, East Tennessee State University, Email: steinfeldt@goldmail.etsu.edu
1
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
bullish and bearish markets, suggesting they are true anomalies and not proxies for some underlying
latent risk factor. The much maligned beta from the Capital Asset Pricing Model appears to be an
excellent measure of risk as it explains 60 to 85% of returns across portfolios given the direction of
the market, but similar to most all other studies, beta is statistically insignificant after combining all
market periods.
The remainder of this paper is organized as follows: Section two reviews the literature while Section
three describes the data and methodology. Section four presents the results for the individual
anomalies as well as for portfolios combining two or more of the anomalies. The paper concludes
with a short analysis along with the practical implications of this research.
2. Literature Review
One of the more easily identifiable stock statistics, the PE ratio, has likely been in use as a measure
of value since stock trading began. Thus, one might expect that any value it has in predicting excess
returns has been traded away long ago. However, Basu (1977) showed this is not the case when
finding that lower P/E stocks tend to outperform. Similarly, Banz (1981) finds that size is also an
important variable. Using data over the previous 40 years, he shows that small size stocks
outperform large stocks by 5% annually. Using yet another simple accounting type variable,
Rosenberg, Reid, and Lanstein (1985) show that low market value to book value ratios are highly
related to returns. Fama and French (1993, 1996, 2008) reconfirm these anomalies.
Stepping away from static variables, Jegadeesh and Titman (1993) found that stocks demonstrate
momentum. In essence, stocks that go up tend to keep going up while stocks that go down tend to
keep going down. Their results suggest buying stocks that go up the most over the previous 6
months will outperform over the next 6 months. Those that have negative momentum will
underperform over the following 6 months. Aby and Vaughn (1995) suggest buying strong
momentum stocks largely outperform as well. Behavioral finance explains the phenomena based on a
simple herding type mentality.
Although the volatility anomaly has been known since at least Jensen, Black, and Scholes’ (1972)
study on the CAPM, the discussion of the anomaly has seen a resurgence with Ang, Xing, and
Zhang’s (2006) study showing stocks with low volatility still tend to generate higher returns than
stocks with high volatility. Based on their research, they found these results to be relatively stable
through different holding periods and economic states.
One might expect these market anomalies to disappear as experienced investors begin exploiting
them. The January effect, first pointed out by Rozeff and Kinney (1976) and again by Tinic and West
(1984) may be a case in point as it has somewhat waned over the years. However, Haughen and
Jorian (1996) show it was still going strong at least through 1993.
List (2003) actually tests whether market experience affects the existence of market anomalies. He
reveals that it does have a remarkable influence and appears relatively robust to change and different
marketplaces. Not everyone is convinced of the value of these anomaly findings. Silver (2009)
argues no one is able to repetitively profit from investing in anomalies. He concludes that it is not
worth it and justifies this conclusion by arguing anomalies are not predictable enough and often
disappear.
2
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
However, recent studies continue to indicate that the anomalies reviewed in this research still exist.
Latif, Arshad, Fatima , & Farooq (2011) give evidence that using price-to-earnings ratio, market-tobook value, and momentum can still provide excess returns. However, they find that purchasing
winners is more risky than anything else, but then again it offers the opportunity to greater success.
Ang, Xing, and Zhang (2009) and Silva (2012) reconfirm that low volatility stocks outperform as does
Luk, Kang, and Luo (2012) using data from 2000 to 2011.
Amel-Zadeh (2008) samples the German stock market to address issues connected to the size-effect.
He finds an effect, but also finds that size is connected to strong momentum which would be
expected. Furthermore, he finds that the information flow, both positive and negative, from small
businesses to investors takes longer. This explains stronger upward and downward momentum at
certain points of time.
Finally, based on information from over 600 studies on anomalies, Len Zacks (2011) concludes that
market anomalies do stand out. He states that anomalies show a 15% growth in returns both longterm and short-term. To back up his conclusions, Zacks put together different portfolios which include
stocks that fit into different anomaly categories. His portfolios show a remarkable growth in return
after 25 years.
3. Data and Methodology
U.S. accounting and price data are drawn from Research Insight and the Center for Research in
Security Prices (CRSP). Research Insight was screened for stocks in three anomaly categories
including size, PE, and PB. Monthly return data is attained from CRSP. Momentum and volatility is
calculated based on the preceding 6 months of returns for each rolling 6 month return analyzed which
runs from Jan. 1992 to Dec. 2012. This provides 41 unique 6 month return periods. Betas for each
stock are calculated by using the previous three years of monthly data. Data from both databases
are merged resulting in a sample of 849 stocks that fit into the predetermined categories. Most
stocks were eliminated due to substantial missing data in one data set or the other which may lead to
some survivorship bias in our results. In addition, stocks with negative PEs during any 6 month
period are not included in any portfolios for the following 6 months.
For each one of the five anomalies, the values are ranked and portfolio deciles are created for every
6 month period based on values of the previous month for accounting data, and values based on the
preceding 6-months for the volatility and momentum variables. A 12 month period is also used to
measure volatility with little change in the results for this variable. Average returns in excess of the
risk free rate are calculated for all portfolios based on an equal weighting for each stock.
Additionally, the data is filtered depending on the current market direction. This determines whether
an anomaly is more observable for bull or bear markets. Regressions are run across the portfolio
deciles to estimate whether the slope is significantly different from zero.
Finally, portfolios are formed based on whether stocks meet the criteria for multiple anomalies. Using
only the anomalies that appear to have merit, several portfolios are created meeting up to 3 anomaly
criteria. These portfolios are compared to stocks on the exact opposite of the spectrum. So as to
keep at least 30 stocks in both of these extreme portfolios, quartiles instead of deciles had to be used
for 3 anomalies and quintiles for 2 anomalies. As an example, the PE and size portfolio combines
3
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
stocks of the smallest 20% of companies and the lowest 20% of price-earnings ratio. This is
compared to a portfolio that is composed of stocks in the largest 20% for size and PE ratios. Welch’s
t-test is used to determine if there is a significant difference in means.
4. Results
4.1 Overall Results
Table 1 shows the average 6-month decile return results for the five anomalies plus beta from Jan.
1992 to Dec. 2012. Deciles are from small to large. PE, size, and PB are all found to be significant
with 6-month excess returns for the smallest portfolios ranging from 13.4% to 15.6% while returns for
the largest portfolios range from 4.7% to 6.8%. Returns are almost entirely monotonically decreasing
with regressions having r-squares from 58 to 80%.
Momentum is not found to be significant in this study and in fact, those stocks that have fallen the
most over the previous 6 months perform best over the following 6 months. If one ignores the worst
and best performers, there is economically little difference in returns for the middle 8 momentum
portfolios.
Volatility actually has the highest level of significance but in the exact opposite direction of what is
expected based on the idea of an anomaly. More coherent with common sense, the highest volatility
stocks perform best. 6-month returns increase from 5.0% to 13.4% suggesting higher risk is indeed
rewarded with higher returns. This is in contrast to the Ang et al. (2006) study, but the measurement,
time period, and sample differ. In addition, no attempt was made to refine stock selection with other
variables and it is possible that a better volatility forecasting model such as GARCH may have
improved these results.
At the very least, the application of this anomaly does not seem to work in a simplified straight
forward manner as do the other variables. Finally, 6-month returns for beta sorted portfolios
reconfirms previous research in that there still does not appear to be a statistically significant
relationship between beta and returns. This result changes however if the lowest beta stock portfolio
is removed giving some hope for using beta to attain expected greater returns.
Table 1: Average Excess Returns
1
2
3
4
5
6
7
8
9
10 Slope R-sq.
PE
13.4 11.5 8.4 7.6 6.4 6.3 6.6 5.1 5.9 4.7 -0.8** 0.80
Size
15.1 10.1 8.5 8.2 6.9 7.1 6.5 6.2 6.4 5.2 -0.8* 0.72
PB
15.6 9.8 8.7 7.6 7.2 7.0 6.9 6.9 6.5 6.8 -0.7* 0.58
Momentum 12.3 8.2 7.7 6.5 7.5 7.2 7.3 7.3 7.7 9.5
-0.2
0.10
Volatility
5.0 6.1 5.6 6.9 7.0 6.9 9.4 9.4 11.8 13.4 0.9** 0.88
Beta
9.3 6.9 7.0 7.5 7.8 8.3 7.6 8.9 8.4 9.8
0.1
0.22
6-month average excess returns in percent from Jan. 1992 to Dec. 2012 based on decile portfolio
rankings from the preceding time period. **Significant at the 5% level, **Significant at the 1% level.
4
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
4.2 Anomaly Timing
In an effort to see if successful implementation of trading these anomalies depends on the market
environment, portfolio returns for each anomaly are separated into 6-month periods based of whether
the market has increased or decreased. Table 2 shows the 6 month average excess returns
separated by up and down markets. PE, size, and PB show the same relationship regardless of the
market direction suggesting they are true anomalies. Small PE, size, and PB always leads to
superior returns relative to large PE, size, and PB ratios.
Momentum is again insignificant, although both the worst and best performers over the previous 6
months do best over the next 6 months. For volatility, those that are the most volatile again perform
best in a rising market but perform poorly in a declining market. Beta also is strongly related to stocks
explaining 64% of the deviation in returns across portfolios in rising markets, and 86% of the deviation
in declining markets. This result is in line with Pettengill, Sundaram, and Mathur (1995) suggesting
beta is a good measure of risk given the direction of the market.
Summarizing, portfolios with small PE, size, and PB outperform in both bear and bull markets.
Momentum does not appear to be strong enough to profit from unless focusing on only the best and
worst performers, while volatility and beta demonstrate they are functional measures of risk as high
volatility and high beta stocks do well in up markets but poorly in down markets.
Table 2: Average Excess Returns Separated by Up and Down Markets
Up Market
1
2
3
4
5
6
7
8
9
10 Slope R-sq.
PE
17.1 12.9 10.6 10.3 9.2 9.4 10.0 8.3 10.0 8.6 -0.7** 0.59
Size 19.4 13.0 11.2 11.6 9.8 10.9 10.4 10.4 10.5 9.6
-0.7* 0.51
PB
19.9 13.0 12.3 11.1 10.0 11.0 10.6 11.0 10.9 11.2 -0.6* 0.41
Mom. 17.1 12.1 11.3 9.7 10.6 10.2 10.4 10.3 11.6 14.5 -0.2
0.06
Vol.
6.8 8.6 8.4 9.7 10.4 10.6 13.6 14.3 16.6 19.2 1.3** 0.93
Beta 11.7 9.4 9.8 10.9 11.4 11.9 11.1 13.2 12.9 15.6 0.5** 0.64
Down Market
PE
1.8 6.5 1.5 -0.7 -2.2 -3.0 -3.5 -4.5 -6.2 -6.7 -1.2** 0.85
Size
3.1 2.0 0.8 -1.5 -1.4 -3.9 -4.7 -5.5 -5.3 -7.3 -1.1** 0.97
PB
3.7 0.8 -1.4 -2.2 -0.7 -4.4 -3.7 -4.6 -6.0 -5.7 -1.0** 0.87
Mom. -1.2 -2.7 -2.7 -2.3 -1.4 -1.0 -1.4 -1.2 -3.5 -4.6
-0.2
0.15
Vol.
0.0 -1.0 -2.1 -1.1 -2.5 -3.6 -2.4 -4.3 -1.8 -3.1 -0.3* 0.51
Beta
2.6 -0.3 -0.9 -2.1 -2.3 -1.8 -2.2 -3.5 -4.5 -6.8 -0.8** 0.86
6-Month average excess returns in percent from Jan. 1992 to Dec. 2012 based on decile portfolio
rankings from the preceding time period separated by up and down markets. *Significant at the 5%
level, **Significant at the 1% level.
4.3 Probability of Success
On average, buying portfolios with low PEs, small market cap, and low PB ratios outperform. Table 3
shows the probability of success these strategies have had over the last 10 and 20 years. The
5
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
trading strategy is measured as a success if the Decile 1 portfolio return exceeds the Decile 10
portfolio return.
The most substantial finding is that the probability of success does not appear to be decreasing as
the probabilities for the last 10 and 20 years are virtually the same. The biggest change is for the PE
strategy with the probability of success actually increasing over the last 10 years to 90%. Trading
based on momentum or beta is virtually a 50-50 proposition while low volatility stocks only outperform
approximately 40% of the time. Again, not only does trading based on PE, size, or PB outperform on
average, they outperform 70% or more of the time.
Table 3: Portfolio 1 Minus Portfolio 10 Returns
Period ending
June 03
Dec. 03
June 04
Dec. 04
June 05
Dec. 05
June 06
Dec. 06
June 07
Dec. 07
June 08
Dec. 08
June 09
Dec. 09
June 10
Dec. 10
June 11
Dec. 11
June 12
Dec. 12
PE
21.0
18.4
0.4
31.1
9.1
7.7
2.5
5.2
11.2
-0.9
5.8
8.7
32.0
7.8
6.6
-13.5
9.0
1.3
20.9
16.2
Size
PB
Momentum
Volatility
31.0
21.1
15.3
-26.1
22.3
23.1
2.9
-30.2
10.5
14.6
-11.9
-4.5
16.4
5.0
-5.3
-11.3
8.7
6.3
-3.3
5.9
2.4
-4.5
-0.6
-14.6
4.0
2.5
-10.0
-1.7
3.3
4.4
4.8
5.4
7.1
1.3
5.1
-13.8
-9.4
-19.1
-14.0
3.2
1.5
-10.2
-7.6
9.7
-11.5
-1.7
7.2
22.5
55.2
48.6
76.9
-38.8
22.3
25.0
0.1
-50.4
19.3
7.0
4.5
0.0
-10.7
-7.7
5.8
-18.2
3.2
-4.6
5.0
8.5
-5.7
-9.2
-7.7
14.0
21.9
23.0
16.7
0.1
-2.4
15.3
-7.6
-5.6
Probability of Success
10 year Avg.
90.0
75.0
65.0
55.0
40.0
20 year Avg.
82.5
73.2
70.7
53.7
39.0
Return difference in percent for smallest and largest portfolios is given. Probability of
averages are given for the last 10 and 20 years, only last 10 individual years are shown.
Beta
-20.2
-5.1
10.1
-1.1
2.5
-6.0
-2.4
-2.9
-7.8
8.0
5.6
16.0
-15.6
-14.2
1.7
-1.8
-3.5
7.4
11.6
-2.5
40.0
48.8
success
4.4 Combining Anomalies
If a single anomaly works well, one might think combining anomalies to create portfolios would be
even better. Unfortunately, Table 4 shows this is not the case. Although stock portfolios that meet
multiple criteria outperform portfolios on the opposite extremes, the absolute magnitude of the excess
return falls well short of Decile 1 portfolio returns formed using only PE, size, or PB.
6
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
One of the major drawbacks in combining anomalies is being able to find stocks that have a low PE,
small size, and small price book. Even though the sample started with more than 800 companies,
combining more than three anomalies often resulted in no stocks available or so few as to make any
results meaningless. Results clearly suggest that relaxing the constraints to find enough stocks to
form an adequately diversified portfolio is associated with dramatically decreased returns relative to
forming decile portfolios on a single anomaly.
Table 4: Multiple Anomaly Excess Returns
Portfolio 1
Portfolio 5
t-stat
Mean
Std. Dev.
Mean
Std. Dev.
PE, size, PB
9.8
31.6
7.1
27.7
52.9**
PE & Size
10.4
35.1
8.8
28.8
3.28**
PE & PB
8.4
27.4
7.4
28.8
4.29**
Size & PB
9.1
33.3
7.9
25.7
4.66**
*Significant at the 5% level, **Significant at the 1% level
Average excess returns in percent for stock portfolios based on multiple anomaly criteria from Jan.
1992 to Dec. 2012. Welch’s t-stat for the difference in means is provided.
5. Conclusion
Trading based on market anomalies seemingly could lead to excess returns, assuming of course the
anomalies themselves are not traded away. A number of mutual funds and ETFs have been created
over the last several years to hopefully do just this. In their seminal paper, Fama and French (1993)
identified PE, size, and PB as significant explanatory variables of portfolio returns reconfirming what
other studies had identified even earlier. 20 years later, forming portfolios based on these variables
would have worked spectacularly.
This study finds that average 6-month returns are 8-10% higher over the following 6 months for
portfolios formed based on the lowest values of these variables compared to the highest. The
probability of success ranges from 65 to 90% over any 6-month period and the strategy works
regardless of the market environment. Momentum is not found to be significant although some
strategies based on the worst and highest momentum may have merit. Volatility and beta are related
to returns given one knows the market direction. Beta on average is not found to be significant but
volatility is, although this is in contrast to some studies suggesting low volatility stocks outperform.
From a personal investing standpoint, with PE, size, and PB variables being so readily available,
forming portfolios based on these values appears to be an easy way to attain significant excess
returns. Since these portfolios do better in both bull and bear markets, it is questionable whether they
are simple proxies for latent risk factors. Seemingly, excess returns to this type of strategy would
have been traded away by now, but that does not appear to be the case, as the probability that these
strategies work has not declined over the last 10 years.
Finally, trying to find the perfect stock or portfolio by combining anomalies does not appear to be
operationally functional. Being forced to relax the magnitude of the PE, size, or PB value to find
enough stocks that meet multiple criteria leads to sup par returns when compared to using a single
anomaly. More complicated models can be created, but the simplicity to the application of the above
7
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
strategy for common investors is going to be difficult to justify. Finally, computing power and access
to the data sets used in this study was likely limited 20 years ago. Looking back in time with the
computing power of today may lead to substantial bias and an overestimation of the returns that
actual trading can provide. Only time will tell if trading on these anomalies continues to result in
substantial excess returns.
References
Aby, C. D., & Vaughn, D. E. 1995. Asset Allocation Techniques and Financial Market Timing,
Westport, Connecticut: Quorum Books.
Amel-Zadeh, A. 2008, December 30. “The Return of the Size Anomaly: Evidence from the German
Stock Market,” Social Science Research Network. Retrieved March 2013, from:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=952472
Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. 2006. “The Cross-Section of Volatility and Expected
Returns,” The Journal of Finance, 61, pp. 259-299.
Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. 2009. “High Idiosyncratic Volatility and Low Returns:
International and Further U.S. Evidence.” Journal of Financial Economics, 91, pp. 1-23.
Banz, R. W. 1981. “The Relationship Between Return and Market Value of Common Stocks,” Journal
of Financial Economics, 9, pp. 3-18.
Basu, S. 1977. “Investment Performance of Common Stocks in Relation to Their Price-Earnings
Ratios: A Test of the Efficient Market Hypothesis,” The Journal of Finance, 32, pp. 663-682.
Fama, E. F. 1965. “Random Walks in Stock Market Prices,” Financial Analysts Journal 21(5), pp. 55–
59.
Fama, E. F., & French, K. R. 1996. “Multifactor Explanations of Asset Pricing Anomalies,” The
Journal of Finance, 51(1), pp. 55-84.
Fama, E. F. & French, K. R. 1992. "The Cross Section of Expected Stock Returns," Journal of
Finance 67, pp. 427-465.
Fama, E. F., & French, K. R. 2008. “Dissecting Anomalies,” The Journal of Finance, Vol. 63, No. 4,
pp. 1653-78.
Jegadeesh, N., & Titman, S. 1993. “Returns to Buying Winners and Selling Losers: Implications for
Stock Market Efficiency,” The Journal of Finance, 48, pp. 65-91.
Jensen, Michael C., Black, Fisher, and Scholes, M.S. 1972. “The Capital Asset Pricing Model: Some
Empirical Tests”, Studies in the Theory of Capital Markets, Praeger Publishers Inc., 1972;
Keim, D. B. 2006. “Financial Market Anomalies,” Knowledge@Wharton. Retrieved February 2013,
from
8
Proceedings of World Business and Economics Research Conference
24 - 25 February, 2014, Rendezvous Hotel, Auckland, New Zealand, ISBN: 978-1-922069-45-0
http://finance.wharton.upenn.edu/~keim/research/NewPalgraveAnomalies%28May302006%29
.pdf
Latif, M., Arshad, S., Fatima , M., & Farooq, S. 2011. “Market Efficiency, Market Anomalies, Causes,
Evidences,” Research Journal of Finance and Accounting, 2, pp. 1-14.
List, J. A. 2003. “Does Market Experience Eliminate Market Anomalies?” The Quarterly Journal of
Economics, 118(1), pp. 41-71.
Luk, P., Kang, X., & Luo, F. 2012. “Introducing GIVI: The S&P Global Intrinsic Value Index,” Social
Science
Research
Network.
Retrieved
March
2013,
from:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2062468
Pettengill, G.N, Sundaram, S. and Mathur, I. 1995. "The Conditional Relation between Beta and
Returns," Journal of Financial and Quantitative Analysis, 30, pp. 101-116.
Rozeff, M. S. and Kinney Jr. W. R. 1976. “Capital Market Seasonality: The Case of Stock Returns,”
Journal of Financial Economics, 3, pp. 379-402.
Haugen, R. and Jorion, P. 1996, The January Effect: Still There After All These Years,” Financial
Analysts Journal 52, pp. 27-31.
Silva, Harindra de. 2012. “Exploiting the Volatility Anomaly in Financial Markets,” CFA Institute
Conference Proceedings Quarterly, March, pp. 47-57.
Silver, T. 2009. “Making Sense Of Market Anomalies,” Retrieved February 2013, from:
http://www.investopedia.com/articles/stocks/08/market-anomaly-efficientmarket.asp#axzz2M46ju8ed
Tinic, S., and West, R. 1984. "Risk and Return: January vs. the Rest of the Year." Journal of
Financial Economics, 13, pp. 561-574.
Zacks, L. 2011. Handbook of Equity Market Anomalies. Wiley, Interviewer, Retrieved March 2013,
from https://www.youtube.com/watch?v=yZKvd1I0LsE
9