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МЕЖДУНАРОДНЫЙ ИНСТИТУТ ЭКОНОМИКИ И ФИНАНСОВ

ВЫПУСКНАЯ КВАЛИФИКАЦИОННАЯ РАБОТА

на тему: Комбинирование моментум и стоимостной стратегий на российском фондовом рынке.

Студент 4 курса, 5 группы

Терентьев К. С.

Научный руководитель PhD,

к.э.н., доцент Добрынская В.В.

МОСКВА 2015 год

ABSTRACT

This paper is aimed to study the profitability of value, momentum strategies and a combination of both strategies in Russian stock market. Value investing implies buying stocks that are trading at low prices relative to their fundamental or intrinsic values, and selling those that are expensive. Momentum investing, on the other hand, implies buying stocks whose prices have been rising and selling those that have been falling. A stock that is becoming expensive would be attractive to a momentum investor but not to a value investor. Similarly, a momentum investor would sell a stock with a falling price - a stock to be purchased by a value investor. We also construct a weighted combined portfolio. Overall, 31 portfolios are constructed for a period from Jan 2001 to May 2015.

Our findings provide no evidence in favor of momentum effect in Russian stock market. The opposite is proved for value effect. Value strategy yields significant Jensen performance ratio, which is a measure of abnormal return on investment. Finally, we conclude a strategy consisting of both value and momentum can produce significant positive abnormal return, outperforming the market index, but underperforming value strategy. The research of performance measurement ratios of each portfolio indicates that value is the most profitable among studied strategies.

profitability value stock market momentum

• Introduction
• Chapter1. Literature overview
• 1.1 Evidence and Explanation of the Value Premium
• 1.2 Combining Value and momentum strategies
• Chapter 2. Data and Methodology
• 2.1 Data
• 2.2 Value and momentum measures
• 2.3 Momentum Portfolio Construction
• 2.4 Value portfolio construction
• 2.5 Combo portfolio construction
• 2.6 Hypotheses and asset pricing models
• Chapter 3. Results of empirical analysis
• 3.1 Dealing with non-normal distributions
• 3.2 Testing the median returns for significance
• 3.3 Performance measurement of constructed portfolios
• 3.4 Regression analysis (CAPM)
• 3.5 Summary
• Conclusion
• Bibliography
• Appendix

INTRODUCTION

The first published C. Asness's article showed the possibility of creation of profitable strategy through a combination of portfolios of stocks with high previous results of investment and stocks with low multiples, i.e. a choice from value stocks as we have defined them above. C. Asness built his research on the analysis of monthly data of the American stocks listed on NYSE, AMEX and NASDAQ during the period from July 1963 to December 1994. The indicator of PAST (2,12) average monthly returns for last 12 months was constructed For reflection of momentum-effect excepting the last month in order to tale into the account reversal or contrarian effect. He selected last "winners» among the value stocks screening them by two indicators: Log(BV/MV) and D/P. In order to reveal interrelation between momentum and value effects the constructed average portfolios on three variables were compared: PAST(2,12), Log (BV/MV) and D/P. Stocks were ranked on five portfolios in ascending order for every month. Then each stock quoted on one of the exchanges (NYSE, AMEX, NASDAQ) was allocated to one of five quintiles and finally portfolios were constructed with equal weights. The special attention was paid to comparison of portfolios from the fifth and first quintiles.

The testing of hypothesis for statistical significance implies the estimation of t-stat and checking the difference of returns of that opposite portfolios is not equal to 0. The test indicated the average monthly spread of returns is statistically significant and equal to 0,87%.

This work permits to draw two important inferences: investing based on past dynamics and low P/B ratio can result in additional gain; the indicators Log(BV/MV) and PAST(2,12) are positively correlated with future profitability of investment but negatively correlated with each other. Thus, an investor can predict the dynamics of stock price and build winner strategy if he knows what type of momentum/value the stock is.

Finally, he concluded, although momentum investing (taking long and short positions) demonstrates positive return for all variations of value stock selection, momentum stocks that have the highest P/B result in the highest statistically significant return of 1,47% per month. Investment based on stock selection with low P/B also brings positive return, but in order to maximize the gain it is necessary to invest in stocks with poor momentum for previous 12 months. Such strategy of portfolio construction results in statistically significant average monthly return of 0.97%. C. Asness summarized that momentum investing works better when stocks are already expensive, stocks that have high P/E or P/B ratios, stocks which are not suitable for value investors. On the other hand, value investing results in higher return if apply the strategy on stocks with poor past 12 month performance.

The relation between value and momentum effects appears to be a strong one. These results apply directly to implementing quantitative investment strategies. The interpretation of these results will be the topic for future debate in a research Value and Momentum Everywhere. The authors study the returns to value and momentum strategies jointly across eight diverse markets and asset classes: global individual stocks across four equity markets: U.S., U.K., continental Europe (excluding the U.K.), and Japan; Global Equity Indices consisting of the following 18 developed equity markets: Australia, Austria, Belgium, Canada, Denmark, France, Germany, Hong Kong, Italy, Japan, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, U.K., and U.S; the following 10 currencies: Australia, Canada, Germany (spliced with the Euro), Japan, New Zealand, Norway, Sweden, Switzerland, U.K., and U.S.; global Government Bonds (Australia, Canada, Denmark, Germany, Japan, Norway, Sweden, Switzerland, U.K., and U.S.); 27 different commodity futures.

To measure value and momentum, they use the common value signal of the ratio of the book value of equity to market value of equity, or book-to-market ratio, and for momentum, they use the common measure of the past 12 month cumulative raw return on the asset skipping the most recent month's return, MOM2-12.

The conclusion of the Asness, et al. research is that the negative correlation between value and momentum strategies in addition with their high expected returns build a simple strategy of equally weighting a portfolio with value and momentum stocks which was found out to be a powerful strategy that produces higher cumulative long term rates of return than either value or momentum alone across all studied classes of assets. The study also stresses that combining value and momentum results in a significantly higher Sharpe ratio than either a value or momentum strategy separately and makes the portfolio less volatile across markets and time periods. The so called `combo' portfolio represents a 50/50 combination of the value and momentum portfolios.

According to the study a value investor in US stocks can expect to outperform the overall stock market by 3 to 4 percent a year, while a momentum investor is expected to earn 4 to 5 percent more than the market each year. Both strategies have about the same amount of volatility, which measures the risk that the actual return investors get is different than what they expected. Combining value and momentum yields a return that is 5 percent higher per year than the market. Though this is only slightly higher than the returns from either strategy, the big difference is that volatility falls sharply by approximately 50 percent. The benefits are even larger when this mixed strategy is applied across global markets and to various asset classes resulting in an investor's expected return would be twice the return on investing in the overall US stock market given the same amount of volatility. Improving a portfolio's risk and return trade-off is important because a good investment should generate higher returns without adding more risk.

According to an article by Jim Fink there exist two ways of constructing combined portfolio. He calls the combining of value and momentum strategies as the “holy grail” of investing. The potential benefits of such a combination may arise due to negative correlation between the strategies which gives an opportunity to form a less volatile portfolio, and thus attaining smoother returns. Then he summarizes two distinct approaches available for investors to form a profitable combination. First, he proposes the method as we mentioned already used by C. Asness (2009) involved a 50% weight on a pure momentum stocks plus 50% weight on a pure value stocks. The combination yields annualized SR of 0.86 of combo strategy versus 0.45 of momentum and 0.26 of value strategy across US stocks; 1.21 versus 0.68 and 0.52 respectively for global stocks. Overall, he attained risk-adjusted return improvement across all eight studied assets.

But Fink also stresses another approach of forming a winning combined portfolio of value and momentum stocks. The method is enclosed in looking for stocks that share two characteristics, namely, stocks that have both value and momentum disposition simultaneously. He refers to a famous investor and portfolio manager James O'Shaughnessy who developed two investment strategies "Cornerstone Growth" and "Cornerstone Value." Lately, O'Shaughnessy invented a stock screener known as “Trending Value” which ranks the stocks in two steps. First step implies looking for value stocks based on criteria of low price-to-earnings, price-to-sales and price-to-free cash flow ratio. The second step deals with selection of value stocks with the highest six-month price momentum from the obtained list at step one. Further, the screener was back tested by James O'Shaughnessy himself and Patrick O' Shaughnessy. In this new research, they have found the way to boosts returns. Authors report that an updated combination of value and momentum strategies is the best performing strategy since 1963. Finally they conclude that the strategy with annualized return of 20.58% outperforms a benchmark which has a return of 10.71%. Moreover, the Trending Value strategy attains such profitability with a lower standard deviation of 42.06% versus benchmark's 42.73%.

However, Fink argues that there is no empirical evidence that O'Shaughnessy's “Trending Value” screen outperforms C. Asness' 50/50 combined portfolio of two pure sets of value and momentum companies. He also notices that Asness' approach gains from impressive negative correlation (-0.65) between the pure momentum and value stocks, while “Trending Value” strategy lacks such benefit.

Secondly, Jim Fink states that combined portfolio constructed using Asness' method may simply perform better than O'Shaughnessy's portfolio due to mixed nature of stocks included in the former. This argument can be valid since O'Shaughnessy's stock screen is based in first turn on selection of value stocks, and only after that sorts value stocks by momentum criteria of 6 month past performance. Therefore, momentum effect is treated as a secondary effect. On the other hand, C. Asness and coauthors pay equal attention to both momentum and value stocks. Finally, C. Asness rebalances combined portfolios once a month which would definitely increase transaction costs, whereas J. O'Shaughnessy rebalances his hybrid portfolio of stocks on an annual basis.

Today it does not appear that the strategy of combining good value and good momentum has attracted attention of investors. On the one hand, value investors have been conditioned to ignore technical/momentum analysis, while many chartists do not pay significant attention, if any, to valuation multiples. Until combining value and momentum becomes more widely used, it is possible that combining low valuation and upward price momentum will be a useful strategy for individual investors.

CHAPTER 2. DATA AND METHODOLOGY

The research is based on the sample of data consisting of Russian companies listed on Moscow Stock Exchange. MOEX was chosen due to it is the largest Russian stock exchange and sufficiently represents the whole market. The time period of the study covers 15 years starting from January 2000 to May 2015. Most researchers on momentum strategy, and C. Asness in the work “Value and momentum everywhere” used monthly data. Thus, we are also dealing with monthly observations. The data set includes the following series: closing price of a stock on the first day of the month, price-to-book ratio of each selected company as well on the first day of month. The data was collected from Thompson Reuters DataStream.

The process of company selection is based on the following criteria. We excluded GDR's, preferred stocks, but include delisted companies in order to increase the sample. The total number of individual common stocks extracted from DataStream was 405. Then the data has been screened to exclude missing, invalid, and non?usable observations. Some of the price and price-to-book series didn't have any values throughout the whole study period, therefore, we deleted these series. Moreover, we exclude the unbalanced series, that is, the companies that had price-to-book ratios but missed the price values. Besides, there were some price series that were copies of already existing series, which were also eliminated from the sample. Some of the series have contained valid stock price observations, but were not applicable for this empirical study, such as preferred shares and series containing Russian stocks traded outside Russia (ADRs), and series for other than stock instruments. All such series have been removed. Overall, after screening and editing the series, we got 355 companies under consideration.

From the Table 1 it can be clearly observed that the number of companies was gradually increasing from January 2001 up to 2011. The fact is that the number of companies did not decline during the global financial crises in 2008-2009. The effect was quite opposite, and the number of listed companies experienced a sharp increase, and continued to grow up to 2011, where it reached the maximum of 273 companies. The average and minimum number of companies in the sample is 147 and 28 respectively.

Also, we have retrieved the monthly values of MICEX index from moex.ru and risk-free rate as Average Moscow Interbank Offered Rates (MIBOR-90 days as recommended by Thompson Reuters) from website of the Central Bank of the Russian Federation. MIBOR is the rate which was introduced as an attempt to create a ruble analogue to LIBOR and is calculated by the CB of the Russian Federation on a daily basis. To sum up this section, we have 28202 price values, 185 MICEX values, the same figure of risk free rate and 24338 P/B ratios and. In total, the sample consists of 52910 observations.

To begin with, before the discussion of portfolio construction it would be important to recall the process of the preparation of sample data for empirical testing. We deal with the selection of calculation method of monthly total returns (simple or log returns). Since simple returns are linearly additive across portfolio of stocks, whereas log returns are not, our portfolio analysis would be based on simple returns as it would be possible to calculate total monthly return of a portfolio as a weighted sum of all stocks in it. Clearly, the same logic applied to calculate monthly market return for a studied period.

The aim of the analysis is to construct value and momentum portfolios, make a combination strategy and check whether the last has higher risk adjusted return. In order to do this procedure, it is necessary to define the value and momentum measures. To characterize value stocks we use common signal price-to-book ratio which shows to what extend a stock is fairly priced: if P/B>1, the stock is overpriced, P/B<1 the stock is underpriced, P/B=1, a stock is fairly priced, where B is book value per share, calculated as BVPS=BV/(# of shares outstanding). According to Fama-French three factor model, the use of P/B ratio is a consistent ratio to distinguish between value and growth stocks. Moreover, P/B is quick and easy to calculate. C. Asness selects past value winners on the basis of Log(BV/MV). Finally, in our previous study, we reported the significance of P/B ratio in explaining future returns in Russian stock market. Thus, P/B is a good value signal for creation of value portfolio.

For momentum we use common measure of past cumulative returns for 11 months with a one month lag, which was first discovered by J and T (1993). We skip the most recent month, which is standard in the momentum literature, to avoid the one month reversal in stock returns, which may be related to liquidity or microstructure issues. We motivate our choice with an argument that momentum effect in Russian market was already investigated by Vladimir Ioffe as we previously mentioned. He examined all 16 strategies with 12, 9, 6 and 3 month formation and holding periods. The results indicate that a winner-loser strategy based on 12/3 month formation/holding period skipping one month yields the highest return of 1.6% significant at 5% level among all other strategies. The 12/3 strategy showed the highest results eve when the portfolio was transformed to the quintiles. Therefore, we decided not to test all 16 strategies and limit out analysis on 12x3 and 12x1 strategies skipping one month, where the last (1 month holding period) has never been tested on Russian stock market.

Let us start out analysis from discussion of momentum portfolio construction. The first step of building momentum portfolio is to calculate the cumulative returns based on a formation period. In our case, we consider the performance of stocks in the sample for the previous 11 months beginning from December 2000. Cumulative returns are calculated using the formula R₁₁=, where i stands for the month. Stock return is calculated as (P_t-P_t-1)/P_{t-1 .}

The second step is to rank the stocks according to their past performance in descending order. The sorted stocks are then divided into three portfolios: the upper 1/3 is assigned to “winner” portfolio, the bottom 1/3 is assigned to “loser” portfolio. In our work we use tertiles instead of deciles in order to attain maximum diversification on lower sample size. Consequently, equally weighted “winner” portfolio, “loser” portfolio are built. Important note here is that stocks are included in portfolio with equal weights rather than using criteria of market capitalization.

Each portfolio is then held for the next 1 and 3 months. We do not consider the middle portfolio as it is not required in building the zero cost strategy. The last is formed by taking a long position in “winner” portfolio and going short in “loser” portfolio. Profitability of this strategy is measured over 1 and 3 months holding period as well as for “winner” and “loser” portfolios. All returns are represented as monthly average figures to make the results comparable.

Since momentum is an active strategy it requires frequent rebalancing of portfolio. The classical approach proposed by J and T is based on monthly rebalancing in the following way. 1/K (where K is the holding period) of the holdings is liquidated (turned into cash) and reinvested into new “winners” and “losers” based on the new past J?month cumulative returns, while the rest of holdings is carried over to the following period. . This type of rebalancing implies frequent transaction costs. First, investor pays for initial transaction, secondly he faces TC when liquidates a part of portfolio and purchases a new portfolio, and finally when he closes the position. Therefore, we decided to abandon this approach in order to reduce TC and make rebalancing simpler. Instead of 1/K liquidation, we hold the initial portfolio unchanged within a whole investing period and only adjust it every month with new winners/losers. So, the only difference is the source of funds used for rebalancing. We finance the rebalancing with free funds available to investor. It is assumed that investor has own free cash to make monthly rebalancing. Thus, we avoid the transaction costs dealt with 1/K liquidation. However, we are not dealing with direct TC in our analysis in order to make our analysis more straightforward.

For example, consider an individual investor who has available funds in amount of X. He invests 1/3 of X at t1 in “winner” momentum portfolio based on 11-month cumulative returns skipping one month and holds it for 3 months. At t2 he invests one more 1/3 of X in the new leaders keeping the same strategy. At t3 he invests the rest of X in the new leaders. Finally, at t4 he receives cumulative return for the winner portfolio constructed at t1. The procedure is repeated every month for winner/loser/zero cost portfolios. As a result, we would get aggregate portfolios formed on the monthly basis for the whole sample period.

We also consider one month holding period as the shortest one. Momentum is a short term strategy, thus we are interested in testing the short holding periods like 1 and 3. As already mentioned in literature overview, we believe momentum prevails due to investors overconfidence and confirmation bias, the public herds. This is the distinctive attribute of momentum investing as it mostly relies on people's emotions rather than company's fundamentals and performance as value investing does. So, we anticipate higher return of 1 month portfolio. If momentum effect exists then the return of winner stocks with positive past dynamics should be higher than return of loser stock portfolio.

One should mention an aspect that is not discussed in J and T research. It deals with calculation of stock returns that were not traded during the holding period, that were placed on MOEX after the analysis period, and that were delisted during folding period. If a company was not traded for some period during investing period then the return was calculated as -1, as investor cannot sell a stock and loses all funds in that position. We faced such stocks numerous times during observation period and adjusted their returns to -1. The intuition behind this argument is that investor cannot predict whether a stock would be delisted or stops being traded for some time, and this can definitely happen in reality. Thus, we did not eliminate such stocks, and took them into our analysis.

To sum up this section, we have constructed 6 momentum portfolios on the 11 month cumulative return skipping one month after formation period to take into the account reversal effect. The portfolios are the following: “Winner 12x1”, “Loser 12x1”, “Winner-Loser 12x1”, “Winner 12x3”, “Loser 12x3”, “Winner-Loser (WML) 12x3”. The results would are interpreted in Chapter 3.

Now let us proceed to value portfolio construction. As we have mentioned, price-to-book ratio was used as a determinant of selecting value stocks. First of all, all stocks were ranked according to P/B criteria in ascending order, so that the stocks with lowest P/B are at the top. In order to make the results consistent and comparable with momentum profitability, we apply the same logic for value portfolio construction as for momentum. Having ranked the stocks we divide the set on three portfolios. Middle portfolio does not fall into our analysis, only top 1/3 of stocks with lowest P/B for a previous month and bottom 1/3 of stocks with the highest P/B for a previous month are under consideration. By analogy with momentum portfolio construction we invest skipping one month after formation period. Instead of cumulative returns, we take P/B for the previous month, sort the stocks, wait a month and start investing. The thing is that the current P/B already indicates whether the stocks is under- or overvalued, so that investors don not have to take some past P/B - a one month lag P/B is sufficient to claim for the fairness of the stock price. We do not invest in stocks with negative or zero P/B ratios.

Undoubtedly, true value investors can argue that one ratio is nothing in investment analysis. As Jim Fink states that value investing “is a process based on discovering a limited number of stocks trading at prices significantly below their long-term intrinsic values and then holding them long term. Intrinsic value is usually derived from a discounted cash flow analysis, which is based on analyzing the entire life cycle of a company”. We definitely agree with this statement, but it would be problematic to test the significance of the value strategy if consider additional widely used value factors, such as P/E ratio; PEG ratio and which are used to determine a stock's value while taking the company's earnings growth into account, and is considered to provide a more complete picture than the P/E ratio; current ratio and other multiples, then we would have probably selected much fewer companies from our sample suited for this criteria. Thus, the portfolio would consist of fewer stocks which could lead to insignificant or erroneous results. A P/B ratio proved itself as good indicator of value stocks. Thus, we admit the selection criteria based on one ratio to increase the quantity of stock in portfolio and as a result attain higher diversification. Moreover, it would be more difficult to make calculations of the intrinsic value of each 355 stocks based on DCF analysis. Besides, to make the results of both momentum and value strategies comparable we stick to common holding period of 1 and 3 months.

Finally, we constructed 6 value portfolios which are monthly rebalanced as momentum portfolios: “Winner 12x1”, “Loser 12x1”, “Winner-Loser 12x1”, “Winner 12x3”, “Loser 12x3”, “Winner-Loser (WML) 12x3”.

Overall, we generated 12 momentum and value test portfolios.

As we have discussed earlier in literature review there are two distinct approaches to construct a combined portfolio. It was decided to focus on the method originally proposed by C. Asness which is based on weighting momentum and value portfolios into a single one. We do not reject validity and success of the double ranking approach, though it does not suit for our sample. Screening stocks with both characteristics requires much larger sample of companies. Nowadays, it seems us impracticable to perform such ranking under current Russian stock market size due to its relative youth. Double ranking may result in higher return but higher volatility due to lower portfolio size. Perhaps, it can be a topic of the next research, but currently we apply the construction of combo portfolios which is simply based on the averaging of WML zero cost portfolios of value and momentum strategies. In particular, we form combination of 50% WML momentum, 50% WML value stocks for one and three month holding period, and perform other variations of weights. We rebalance portfolios each month as well as C. Asness did in his study. Overall, we construct 18 combinations for every variation of weight. In total, we formed 31 portfolios.

Table 4 represents the monthly average returns and standard deviations of combination portfolios. In the next chapter we will compare portfolio risk adjusted returns, analyze descriptive statistics and perform hypotheses testing.

In order to understand whether momentum and value effects are anomalies or fair premium for risk associated with each strategy, we have to estimate expected return for each portfolio relating to risk, and then test the hypothesis whether the difference between expected and actual returns is significant. The same logic we apply for testing combined portfolios. Overall, we have to test 31 portfolios.

It is widely accepted in academia to use CAPM model for estimation of fair returns. The only source of risk is systematic risk. The model is represented by the following expression: ??_??i???_??=??_pi+??_pi(??_?????_??), where Rpi-portfolio return, R_f - risk-free rate, which is equal to MIBOR Rm - market return, proxied by MICEX index, ??_pi - portfolio alpha, a measure of excess return, ??_pi - is the measure of sensitivity between asset and market. Thus, the premium correlated with portfolio risk forms from compensation for market risk, (?????????), which depends on to what extend portfolio traces the market movements, and from compensation above market premium, ??. The significance of constant shows abnormal return of tested momentum, value and combined strategies.