Замечание о предсказуемости российского фондового рынка | Вестн. Том. гос. ун-та. Экономика. 2020. № 49. DOI: 10.17223/19988648/49/12

Замечание о предсказуемости российского фондового рынка

На протяжении последних 20 лет проблемой низкой инвестиционной активности частных инвесторов стала российская фондовая биржа. Для этого есть различные причины, в том числе финансовые кризисы, ограниченный доступ к информации, высокая субъективность и отсутствие развитых и простых методов принятия инвестиционных решений. Поэтому данное исследование направлено на изучение предсказуемости российского фондового рынка в условиях нестабильности из-за кризисов, а также ограниченного доступа частных инвесторов к информации и низкой инвестиционной грамотности в целом. В данном исследовании рассматривается предсказуемость эмиссии акций на российском фондовом рынке с 31 января 2008 г. до 31 января 2017 года. Это период двух экономических кризисов для российской экономики: с 2008 по 2013 год и с 2014 по 2017 год. Авторы исследуют, могут ли доходы отраслевых портфелей предсказать будущие доходы фондового рынка. Определен конкретный набор традиционных макроэкономических переменных, выступающих в качестве предикторов доходности акций и экономики в целом. Таким образом, выбор подходов, методов и индикаторов для анализа и прогнозирования российского фондового рынка осуществлялся по трем критериям: периоды нестабильности (кризисов), информация, доступная для частного инвестора (общепринятые показатели), и четкость и обыденность методов анализа и прогнозирования. Для этих целей чаще используется подход, основанный на макроэкономических показателях или отраслевой подход. Учитывая нестабильность, вызванную экономическим кризисом в России, авторы объединили два подхода. Используя традиционное линейное регрессионное моделирование, три из девяти отраслей и пять из восьми агроэкономических предикторов были признаны статистически значимыми. Однако все модели, основанные на этих предикторах, имеют отрицательные значения псевдо-R в квадрате; следовательно, они не соответствуют исторической средней модели вне выборки. Также было обнаружено, что две из девяти прогнозных моделей, основанных на значимых предикторах, обеспечивают увеличение полезности для инвестора со средней дисперсией.

A Note on the Predictability of the Russian Stock Market.pdf Introduction Over the past decade, the Russian stock market has been developing under the conditions of globalization causing an increase in the internationalization of securities markets and competition in international financial markets. However, the Russian financial market is still not competitive on the global market. The problem of low investment activity of private investors has been featuring the Russian stock market. There are various reasons for that, among them the financial crises, limited access to information, a high subjectivity and lack of developed and simple methods for making investment decisions [1, р. 8]. There- A NoIe on Ihe PredicIabiliIy 161 fore, this research aims to study the predictability of the Russian stock market in conditions of instability due to crises, as well as the limited access of private investors to information and the low investment literacy in general. In order to maintain and stimulate economic growth in Russia, it is necessary to provide a well-developed financial center. Today, the Russian stock market is not sufficiently developed. The national stock market has limited capacity, insufficient to ensure investment needs of Russian companies. It lags behind the world's largest and most developed equity markets. Further development of the Russian stock market will ensure a balanced, innovation-based, and stable economic growth in Russia in the long run. According to analysts, the Russian stock market is expected to further decline. The almost complete absence of the collective investment schemes, as well as the low investment attractiveness as a whole, is among the factors of the weakness of the Russian equity capital market. Successful prediction of the future equity premium could lead to obtaining considerable returns. For the purpose of forecasting future market changes and making an investment decision, investors tend to take into account the historical price performance. The stock returns predictability represents a widely studied subject in the economic literature. There are various points of view on forecasting the performance of the stock market. For instance, an efficient market hypothesis assumes that stock prices reflect the currently available information, and all changes in prices are not dependent on the recently obtained information. Hence, movements of market prices cannot be predicted in general. According to an opposing view, there are different methods that allow generating information about future market prices. The equity premium predictability problem and forecasting methods for stock market movements still remain open and controversial. The main objective of this paper is to study various approaches to predicting stock market returns and to create relevant forecast models reflecting movement of the Russian stock market for the private investor. The study consists of two parts: theoretical and empirical. Part I of our study focuses on the theoretical and empirical review of publications on our research issues. Besides, a brief overview of the Russian stock market, the study database, and methodology are presented. An empirical analysis is performed using econometric methods. Finally, results are outlined and compared to those available from previous studies; conclusions are drawn. In Part II, the empirical model is mainly based on the linear regression analysis. For the purpose of this study, the initial database is analyzed over two periods. First, we apply in-sample (full sample) performance evaluation. We use traditional predictive regression-modeling, which accounts for industry and other macroeconomic indicators and market returns. Second, we conduct out-sample performance evaluation. We divide the total sample into the following two periods: from t to t1, and from t1 to t2. In the beginning, we estimate the model, using data from the t-to-t1 period, and then we reiterate this procedure in relation to the most predictable industries and indicators, using the last three ob- 162 E.S. Lavrenova, T.G. Ilina served years as the out-of-sample period. In the end, we compute forecast errors as a discrepancy between the real values of the out-of-sample period and the forecasting measures. We determine whether the derived model is a better performance predictor than the model based on historical returns. At the final stage of our empirical calculations, we estimate the meanvariance investor's utility gains and decide whether it will be profitable for him to use the equity premium predictions derived from the models to make investment decisions. We calculate the difference between the average utility for the investor, whose investment decisions are based on the predictive model, and the average utility for the investor, who formed portfolio using only information about the historical mean returns for the out-of-sample period (net average benefit). Literature review The selection of approaches, methods, and indicators for analyzing and forecasting of the Russian stock market was carried out according to three criteria: the instability (crises) periods, the information available for the private investor (generally accepted indicators), and the clarity and ordinariness of analysis and forecasting methods. Predicting stock returns is extremely important for the solution of many fundamental economic and financial issues. Therefore, it is logical that researchers spend time and resources trying to find economic indicators capable of predicting stock returns. For the purpose of this paper, we have studied numerous articles related to equity premium prediction. There are various methods for performing this analysis. The most widespread approach is predictive linear regression, which reveals dependence between stock market returns and some market indicators, such as inflation, dividend yield, or default spread. Most publications on predicting stock returns confirm a linear relation between market indices and stock returns, i.e., it is possible to predict future stock market movements applying econometric approaches. Several authors showed that, despite a number of econometric problems, it is possible to obtain a considerable predictive component from in-sample studies [3, 4]. Other authors suppose that the future stock price is unpredictable [5, 6, 7]. Regarding the Russian stock market, most of the authors conclude that the impact of oil prices on the Russian stock market performance is weak and not regular and not significant after 2006 [8]. Many authors also prove the dependence of the Russian stock market on foreign exchanges, such as the U.S. or Germany [9]. The empirical model is mainly based on the analysis of Hong et al. [10]. Using GLS estimator, they test whether the returns of the 34 U.S. industry indices forecast stock market movements. Thus, the selection of approaches, methods, and indicators for analyzing and forecasting of the Russian stock market was carried out according to three A Note on the Predictability 163 criteria: the instability (crises) periods, the information available for the private investor (generally accepted indicators), and the clarity and ordinariness of analysis and forecasting methods. A macroeconomic indicator-based approach or an industry-based approach is more often used for these purposes. Taking into account the instability in Russia caused by the economic crises, we combined two approaches to estimate models based on the industry portfolios and a particular set of traditional macroeconomic variables. A brief overview of the Russian stock market Today, the Russian stock market is emerging and has a lot of problems that prevent further progress of the market. The history of the Russian stock market began in 1993 when the main regulatory authority (the Commission on Securities and Stock Exchanges) was established. However, in reality, stock trading began only in 1996 on regional exchanges. First, the trading volume of the largest stock exchange (MICEX) grew rapidly; however, from 1998 onwards-due to negative trends in the economy- it began to decline. The Russian Financial Crisis of 1998 significantly struck the stock price of the biggest companies, and investors suffered heavy losses. In 1999, the domestic stock market began to recover; Russian and foreign investors tended to buy cheap Russian stocks. Figure 1 characterizes the dimension of the Russian stock market by market capitalization and trading volume. By 2007, the capitalization of the Russian stock market and trading volume had grown significantly, but, in 2008, these indicators decreased by 66% and 18% respectively. To compare, the price of Brent crude oil fell by 58% in 2008. 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ■ Market capitalization ■ Trading volume Figure 1. Market capitalization and volume of trade in the Russian stock market, in trillion rubles1 Since 2011, the capitalization of the Russian stock market has almost not changed. In 2012-2014, the trading volume even decreased, compared to 2009- 1 Authors' calculations. Source(s) of data: https://www.investing.com/analysis/stock-markets and http://cbr.ru/Eng/statistics 164 E.S. Lavrenova, T.G. Ilina 2011. In 2015-2016, the capitalization of Russian companies grew; however, the trading volume decreased, which proves that the activity on the Russian stock market declined (Fig. 2). The capitalization-to-GDP ratio reached 100% in 2006-2007 under the conditions of the rapid growth of both the GDP and market capitalization, which corresponds to the level of developed countries. However, after the financial crisis of 2008, this ratio decreased from 62% (in 2009) to 32% (in 2014), due to both the GDP growth and the absence of capitalization growth. Therefore, in recent years, the national securities market capitalization-to-GDP ratio diminished. This fact indicates the existence of significant gaps between the capitalization of the stock market and GDP, which also reduces the role of the Russian stock market in the world economy, and makes domestic market unattractive for investors. In 2015-2016, the capitalization-to-GDP ratio increased, partly due to the slowdown in the GDP growth rate. Figure 2. Capitalization-to-GDP ratio and trading volume-to-capitalization ratio, in %1 The interest in the Russian securities has gradually recovered since June 2012. At the end of 2016, the main Russian stock market index (MICEX) grew by 3.1%. In January 2013, MICEX grew by 6.18%; however, at the end of the year, it fell by 4.97%. Due to the events in Ukraine and economic sanctions against Russia introduced in 2014, the ruble depreciated considerably and oil prices decreased considerably. These factors contributed to the downfall of the Russian stock market index by 45% at the end of 2014. Figure 3 shows a further decline in MICEX in 2015. Here, it is also important to note that, in 2014-2015, inverse trends of the two main Russian indices MICEX and RTS were observed. This fact was stipulated by the instability and weakness of the Russian currency compared to the U.S. dollar during the period in question and the weakness of the Russian economy in general. 1 Authors' calculations. Source(s) of data: https://www.investing.com/analysis/stock-markets and http://cbr.ru/Eng/statistics National companies' liquidity (trading volume-to-capitalization ratio) has always been close to its average value of 45% (with the exception of the period of the financial crisis of 2008). However, in 2015-2016, the liquidity of the Russian stock market dropped to 30% and 18.5%, respectively2. This also demonstrates the overall negative trend of the Russian stock market. Today, about 80% of the trading volume of the Russian stock market is generated by ten largest issuers. The capitalization of the ten largest national companies has remained stable over the past five years (around 56% of total market capitalization (Table 1). In 2015, almost half of all transactions in securities was generated by the following three issuers: Sberbank, PJSC; Gazprom, PJSC; and LUKOIL, PJSC. The number of the listed companies decreased by 7.1% in the period after the sanctions, viz. from 266 companies at the end of 2015 to 247 at the beginning of 2017. Figures 4 and 5 present the results of a comparative analysis of relative indicators of development related to Russia and some developed countries. In developed markets, the turnover-to-capitalization ratio remained on average at 100% or above during the period under study (excluding 2008), whereas, in the Russian market, it fluctuated around 45% (Fig. 4). The capitalization-to-GDP ratio in developed countries was on average 150%; whereas in Russia, the maximum value of 100% was achieved only once in 2008 (Fig. 5). One of the major disadvantages of the Russian securities market is the commodity nature of economy. Hence, there is a strong dependence of economic activity on movements of the price of commodities (Fig. 6). The Russian stock 1 Authors' calculations. Source(s) of data: http://moex.com/en/indices 2 http://moex.com/en/indices 166 E.S. Lavrenova, T.G. Ilina market is also considered to be highly volatile and unstable. With this in mind, we calculated the standard deviation of the monthly returns of MICEX and three foreign indices (viz. FTSE 100, S&P 500 and Nikkei 225) over the period from December 2008 to January 2017. The obtained values of the indicator were 8.39%, 4.3%, 4.71%, and 6.55% respectively. 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ----Russia Trading vol∕cap----USA Trading vol cap ----UK Trading vol∕cap ----Japan Trading vol∕cap Table 1. Capitalization of the ten largest Russian public companies in 2015-20161 Company Capitalization, in bln. rub The share in total capitalization, in % 2015 2016 2015 2016 Gazprom, PJSC 2,957.91 3,589.69 10.2 9.2 NK Rosneft, OJSC 2,489.49 4,187.16 8.6 10.7 Sberbank, PJSC 2,002.96 3,663.19 6.9 9.4 LUKOIL, PJSC 1,835.02 2,879.56 6.3 7.4 NOVATEK, OJSC 1,657.83 2,349.13 5.7 6.0 Norilsk Nickel, PJSC 1,331.16 1,569.34 4.6 4.0 Surgutneftegas, OJSC 1,119.15 1,091.13 3.9 2.8 Magnit, PJSC 964.80 1,018.53 3.3 2.6 VTB Bank, PJSC 941.32 947.98 3.2 2.4 Gazprom Neft, PJSC 668.06 1,011.53 2.3 2.6 The sum total 15,967.70 22,307.22 55.0 57.3 Total capitalization of MICEX 29,032.88 38 953,42 100.00 100.00 Figure 4. The trading volume-to-capitalization ratio in the Russian stock market compared to the ratio in developed countries' markets, in %2 1 Authors' calculations. Source(s) of data: https://www.investing.com/analysis/stock-markets and http://moex.com/en/indices 2 Authors' calculations. Source(s) of data: https://data.oecd.org and http://www.imf.org/en/data Figure 5. Capitalization-to-GDP ratio in the Russian stock market compared to the ratio in developed countries' markets, in %1 Figure 6. Dynamics of statistical differences for the GDP, the price of Brent, and MICEX in 2008-20162 Given this, during the period under analysis, the volatility of the Russian stock market was almost twice higher compared to the market volatility of the U.K. and the U.S., and 1.3 times higher than the volatility of the Japanese market. Besides, the Russian stock market is also characterized by low investment activity of companies and private investors. Figure 7 presents data of a comparative analysis of the investment-to-GDP ratio in some countries. According to 1 Authors' calculations. Source(s) of data: http://www.imf.org/en/data and https://www.world-exchanges.org 2 Authors' calculations. Source(s) of data: http://www.gks.ru and http://www.imf.org/en/data 168 E.S. Lavrenova, T.G. Ilina this relative indicator, Russia is in a satisfactory situation. On average, in the analyzed period, the share of investment in the GDP in Russia was 20.78%; whereas it was 20.33% in the U.S., 17.29% in the U.K., 21.53% in Japan, 44% in China, 19.09% in Portugal, 20.80% in the European Union. The world's average was 22.57%. However, given that, in 2016, Russia's and China's GDP per capita at current prices had very close values (viz. $ 8,838.2 and $ 8,260.9, respectively, according to the OECD data), we can conclude that Russia is characterized by low investment activity compared to any other country at a similar stage of development. The remaining countries are characterized by the following values of GDP per capita: $19,758.7 in Portugal, $ 37,304.1 USD in Japan, $ 40,411.7 in the U.K., and $ 57,293.7 in the U.S. 50.00% ■ Russia a US ■ UK Japan ≡ China ■ Poitugal и European Union ≡ World average Figure 7. Share of investments in Russia's GDP, in %1 Another weak feature of the Russian stock market is the insufficient development of regional equity markets. Today, there are only 7 operating stock exchanges officially registered by the Central Bank of the Russian Federation, with MOEX being the largest. The other 5 exchanges are located in Moscow (the capital of the Russian Federation) or Saint Petersburg (the second significant city of the Russian Federation) and specialize predominantly in trading commodities and raw materials, or currencies. The only regional stock exchange is the Crimean stock exchange, located in Simferopol (the third city of federal significance). Table 2 presents the share of the capitalization of companies, traded on the central Russian stock exchanges (MICEX or MOEX after reorganization), in the total market capitalization in the 2011-2016 period. The indicator amounts to 93.3% on average. With this in mind, we shall note that, almost entire stock trading in Russia is conducted on the basis of one central exchange platform. 1 Calculations based on data source https://data.oecd.org A NoIe on Ihe PredicIabiliIy 169 Table 2. The capitalization structure of the Russian stock market, in %1 Year Total market capitalization, in bln. rub Capitalization of MOEX, in bln. rub Share of MOEX capitalization, in % 2011 25,533.9 19,883.9 77.9 2012 25,676.8 24,657.0 96.0 2013 26,247.0 25,255.6 96.2 2014 24,275.6 22,838.2 94.1 2015 29,032.9 28,733.2 99.0 2016 38,953.4 37,748.0 96.9 Many researchers note a dose correlation and dependence between the Russian stock market and international equity markets. We calculated correlation coefficients between the monthly rates of the returns of MICEX and FTSE 100, S&P 500 and Nikkei 225 over the period from January 2008 to June 2012 and obtained the following values 0.72, 0.72, and 0.69, respectively. In the period from October 2011 to April 2017, the correlation coefficients decreased to 0.41, 0.34, and 0.29, respectively. Hence, we can conclude that there is a positive correlation between MICEX and foreign indices. The higher correlations during the 2008-2012 period can be justified by the 2008 crisis, which strongly affected all economies. The Russian stock market is very young compared to international stock markets. It is characterized by high volatility, instability, and other features: (1) low investment activity of companies and private investors; (2) insufficient development of regional equity markets; (3) close positive relationship between the Russian and foreign markets; (4) high dependence on commodity prices. Furthermore, since 2011, the development of the Russian stock market has almost stopped. The absolute indicators characterizing the market scale have remained at the same level, never reaching the value of 2007. The relative indicators characterizing the level of the market development and its role in the economy have demonstrated stagnation or a negative trend since 2011. Developed markets continue their upward movement compared to the Russian stock market, which reached the level of developed countries only once, viz. in 20062007. Methodology In-sample The empirical part of our study started with in-sample performance evaluation of the entire observed sample for the period from January 2008 to January 2017. We used the traditional predictive regression approach, which enabled us to check if there is a linear relationship between the equity premium and the predictors (1), as described by Hong et al. [10]. 1 Calculations based on data sources http://moex.com/en/indices and https://www.investing.com/analysis/stock-markets 170 E.S. Lavrenova, T.G. Ilina RMt = + βf't Predι,t-1+ βi'2RMt-r+ Gi't, (1) where RMf- is market excess returns over the risk-free rate in month t; Predi,t-1 stands for predictor i with a one-month lag; RMf-_] represents a variable that controls the existence of autocorrelation in the equity premium; and e, ^ is the error term. We are interested in the coefficient β,,f. which indicates the ability of each predictor to facilitate prediction of the stock market profitability. The analysis of the predictability of the stock market was performed with the traditional linear predictive regression (ordinary least squared estimation). In the framework of this approach, we used robust standard errors, i.e., corrected for heteroskedasticity and autocorrelation. As software support, Microsoft Excel and Gretl were employed. In the beginning, the capability of industry returns to predict the movement of the Russian stock market was analyzed. In order to estimate the predictive ability of industries to lead the future stock price of companies, we analyzed 9 portfolios using (1). Eq. 1 was calculated separately for each of the 9 industries, viz. oil and gas, electric utilities, telecoms, metals and mining, manufacturing, finance, consumer goods and services, chemicals, and transport. Then, we expanded our approach, over other variables and estimated the predictive ability of the following macroeconomic variables: inflation rate, bond yield spread, excess returns of the MICEX corporate bond index, oil price, USD/RUB exchange rate, market volatility index, and dividend yield. Finally, after estimating 16 predictive regressions using (1), we identified significant predictors of the Russian stock market. In order to determine the in-sample significance of the predictors, the standard t-statistic test was performed (2). t = (2) t A' where is an estimated coefficient and ^ is its standard deviation. Out-of-sample For predictors identified as significant in the framework of in-sample analysis, out-sample performance evaluation was implemented. The total sample was split into the following two periods: (1) from to t, which comprised the January 2008-December 2013 period, and (2) from t to , which covered the January 2013-January 2017 period. First, Eq. (1) was estimated for each predictor using data from the -to-t period. Then, we calculated the estimated parameters of the regression for the constant, the MICEX index and predictors for the period t (December 2013). Hence, at moment t+1 (January 2014), applying (3), it was possible to predict the MICEX returns, with estimated coefficients for the previous month. (3) ^^i'∕αn2014 =

Ключевые слова

equity premium, Russian stock market, stock prediction, macroeconomic variables, industry indices, equity premium, Russian stock market, stock prediction, macroeconomic variables, industry indices, эмиссионный доход, российский фондовый рынок, прогноз акций, макроэкономические переменные, отраслевые индексы

Авторы

ФИООрганизацияДополнительноE-mail
Лавренова Екатерина СергеевнаТомский государственный университетаспирант Института экономики и менеджментаlavcathrine@yandex.ru
Ильина Татьяна ГеннадьевнаТомский государственный университетканд. экон. наук, доцент, зав. кафедрой финансов и учета Института экономики и менеджментаilinatg@mail.ru
Всего: 2

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 Замечание о предсказуемости российского фондового рынка | Вестн. Том. гос. ун-та. Экономика. 2020. № 49. DOI: 10.17223/19988648/49/12

Замечание о предсказуемости российского фондового рынка | Вестн. Том. гос. ун-та. Экономика. 2020. № 49. DOI: 10.17223/19988648/49/12