determinants: case of Russia
DMITRY GLADYREV 1 , ANNA MINGALEVA 2 , VALERIYA VOLKOVA 3
Abstract: Different news affect financial markets in different ways. To study this relationship, we must measure both financial markets and news. Measuring financial markets is not totally obvious task, but usually easy, as all the data about share prices and different indices are available. For example, we can use RTS Index to measure state of Russian financial market. Measuring news is more complicated task. Some topics (e.g.
certain elections or armed conflicts) dominate in mass media over long periods, but public interest to these topics is very volatile. The research is based on hypothesis that we can determine causes of changes in financial markets by analyzing data of public interest to different news topics and financial market indices. Innovative feature of the research is using Wikipedia statistics as a measure of public interest to different news topics. This approach is much better than taking number of news (that depends on many factors including censorship) and statistics of Google searches (where each topic can be represented by many ways). It was found that number of visits of some Wikipedia articles (e.g. International sanctions during the Ukrainian crisis, Russian military intervention in the Syrian Civil War, 2016 United States presidential election and others) has a connection with state of Russian financial market. Also, the research examines these connections in different periods and use statistics of Wikipedias in different languages to distinguish public interest to the news in different regions.
Key words: market efficiency, financial markets, RTS index, news, Russia, Wikipedia JEL Classification: G14
1. Introduction
Share prices and financial market indices are very volatile. Efficient market hypothesis (EMH) claims that these fluctuations are caused by all available information (Fama, 1970). But knowledge that current market state is caused by
1 DMITRY GLADYREV, Graduate School of Economics and Management, Ural Federal University, Russia, unc-dg@mail.ru
2 ANNA MINGALEVA, Graduate School of Economics and Management, Ural Federal University, Russia, mingaleva.ann@yandex.ru
3 VALERIYA VOLKOVA, Graduate School of Economics and Management, Ural Federal University, Russia, lvolkova1997@yandex.ru
available information doesn’t help us to reveal what specific information was a reason of certain market fluctuations.
Existing researches provide some findings about relationship between different news and financial markets. Number of news, day-of-week dummy variable and news importance as proxied by large New York Times headlines are directly related with market activity (Mitchell, Mulherin, 1994; Chan, 2003). Deviations from usual level of pessimism in Wall Street Journal increase market trading volume (Tetlock, 2007). Good political news has a positive impact on financial market and decrease volatility, meanwhile bad political news has a negative influence on market and increase volatility (Suleman, 2012). Also, the choice of words and tone used by the authors of financial news articles can affect the dynamics of markets (Schumaker et al., 2012). It was shown that traders are more likely to “overreact” to unexpected and dramatic news events (De Bondt, Thaler, 1985).
Public mood in social networks caused by news and rumors is also important for financial markets (Li et al., 2014). Twitter mood has a significant influence on the Dow Jones Industrial Average (Bollen, Mao and Zeng, 2011). Before the appearance of social networks, the major communication sites for traders were different message boards. These boards also influenced decisions of traders, even after controlling for news in media (Antweiler, Frank, 2004).
Some researches were focused on certain events. It was found that the conflict between Russia and Ukraine had negative consequences for financial markets of both countries, and international news about the conflict were more influential than domestic ones (Hoffmann, Neuenkirch, 2017).
To study relationship between financial markets and news we must measure both. As all the data about share prices and different indices are available, measuring financial markets is usually easy. It’s more complicated to measure the effect of news. Existing researches provide some approaches, but these solutions are usually based on newspapers activity (that can be affected by censorship, editorial policy and other factors) or social network activity (that can be affected by category of news and crowd effect).
One more possible solution is using statistics of Google searches, because it can demonstrate public interest to the news. It is less affected by crowd effect, because unlike posts in social networks Google search requests are invisible for the society. But this approach is difficult to apply, as the same news can be described in many ways. Thus, measuring the effect of news is one of the main objectives of this research.
2. Methodology
The research offers to use Wikipedia statistics of daily page views as a tool to measure effect of different news. One of the services that provide such statistics was made by Wikimedia Toolforge supported by a dedicated group of Wikimedia Foundation staff and volunteers (URL: https://tools.wmflabs.org/pageviews).
In Wikipedia one personality, one subject and one event always have just one article, so it can be enough to track daily page views statistics of one article to measure the interest to the subject. This is a big advantage in comparison with other approaches. Unlike number of news, number of page views in Wikipedia cannot distort real interest to the subject due to censorship and editorial policy of different media.
An additional advantage of this approach is availability of Wikipedia’s statistics in different languages that can allow us to distinguish public interest to the news in different regions.
The research takes dynamics of Russian financial market (measured by RTS index based on 50 Russian stocks) since 1 January 2016 till 31 December 2018 and some events that presumably affected Russian financial market during the period.
Six events were taken into consideration. The choice of these events was based on high interest of Russian and foreign media. The following list contains the names of articles in the English Wikipedia that correspond to the selected events.
1. War in Donbass
2. International sanctions during the Ukrainian crisis 3. Annexation of Crimea by the Russian Federation 4. 2016 United States presidential election
5. Russian military intervention in the Syrian Civil War 6. 2018 FIFA World Cup
The research takes daily page views statistics of these articles in Russian and English Wikipedias and study its connection with RTS index. We presume that interest to some of these events has a connection with state of Russian financial market, especially in periods of highest interest.
The research uses two methods to determine these connections. The first method is correlation analysis over quarters. Correlation coefficients for longer periods of time are not reasonable, because RTS index depend on many factors and we should not expect linear correlation between the market and number of page views during long period. At the same time we cannot take shorter periods, as we can get too many random results due to low number of observations. The disadvantage of this approach is that market’s connection with news can be much more complicated than linear correlation. Traders often overreact for dramatic
events (De Bondt, Thaler, 1985) and market indices can move in opposite direction to fundamental values during some days after such events.
The second approach is based on analysis of days with highest interest to selected articles. Wikipedia’s statistics allows easily determine such days.
Abnormal changes in RTS index in the same period can be a strong evidence that the event which is described in the article is related with changes in the market.
This method doesn’t provide statistical accuracy, but at the same time it is very demonstrative and easy to apply.
Both approaches use first difference of RTS Index (RTS index growth), not RTS index on its own. This is since we are looking for market deviations from normal market state caused by different events, and normal market state is different in various periods (see Figure 1).
As trading activity does not take place on weekends, data excludes Saturdays and Sundays. RTS index growth for Monday is calculated as a difference with Friday value.
Figure 1. Dynamics of RTS index from 2016 till 2018 Source: Authors' own edition.
3. Results
Linear correlation coefficients between articles’ page views and RTS index growth are presented in Table 1 for each quarter from 2016 to 2018. Only significant coefficients are given in the table.
Table 1. Correlation of RTS index growth with number of views of articles (only significant coefficients)
We should not expect high values here, as financial market depends on many different factors. And we don’t have strong evidence that even selected pairs have a connection with each other, because the significance is not strong enough.
However, such analysis can be a good reason for further analysis of determined factors.
Two articles have the highest number of significant correlation coefficients both in Russian and English Wikipedias. These articles are:
International sanctions during the Ukrainian crisis;
Russian military intervention in the Syrian Civil War.
Presumably, these topics have the closest connection with the state of Russian financial market. It would be not surprising, because both topics have a significant influence on Russian economy. International sanctions create difficulties in international trade for the firms and affect the attitude to Russia in the world. But at the same time some Russian firms use opportunities created after trade restrictions and impact of international sanctions can be positive for them. Russian participation in Syrian Civil War can also have different influence on the market.
On the one hand, predominantly successful military operations could raise status of Russia in the world. On the other hand, such operations are costly for eco-nomics.
Days when each article had the highest number of page views are taken for additional consideration. We’re looking whether these days had some abnormal changes in RTS index.
Percentile of RTS index change shows how unusual was the RTS index growth in selected day. If percentile is close to 100%, it means that RTS index had unusual growth during the day, and percentile that close to 0% indicates that RTS index had unusual fall.
Two events relate to extremely high positive and negative changes in RTS index. On 27 January 2017 number of page views of the article «International sanctions during the Ukrainian crisis» in Russian Wikipedia reached the maximum, and RTS index increased by 31,99 points. Only 2% of trading days from 2016 to 2018 had higher RTS index growth. A review of the news on this day allows to find possible reason that caused high interest to the topic and RTS index change. Many newspapers made reports about Trump's willingness to cancel sanctions against Russia.
On 26 November 2018 number of page views of articles «War in Donbass»
and «Annexation of Crimea by the Russian Federation» in English Wikipedia reached the maximum, and RTS index decreased by 29,35 points. Only 2% of trading days from 2016 to 2018 had higher RTS index fall. A possible reason relates to escalation of Kerch Strait incident and introduction of martial law in Ukraine.
Table 2. Analysis of dates with highest interest to selected articles
War in Donbass 8/02/2017 –14,56 14% Assassination of DPR commander Givi International sanctions
during the Ukrainian crisis
27/1/2017 31,99 98% Rumors about Trump's plans to cancel sanctions against Russia
Annexation of Crimea by the Russian Federation
18/3/2019 16,6 88% Fifth anniversary of Russian administration in Crimea 2016 United States
presidential election
9/11/2016 17,69 90% The day after elections Russian military
intervention in the Syrian Civil War
7/10/2015 17,47 90% Russian strikes from warships in Caspian Sea 2018 FIFA World Cup 14/6/2018 –6.6% 31% First game of Russia in the
tournament English Wikipedia
War in Donbass 26/11/2018 –29,35 2% Escalation of Kerch Strait incident
26/11/2018 –29,35 2% Escalation of Kerch Strait incident
2016 United States presidential election
09/11/2016 17,69 90% The day after elections Russian military
intervention in the Syrian Civil War
08/10/2015 12,32 81% Russian strikes from warships in Caspian Sea 2018 FIFA World Cup 27/06/2018 11,24 78% Last games of group stage in
the tournament Source: Authors' own edition.
Two more events also have notable changes in RTS index during the time of highest public interest. The article «2016 United States presidential election» had maximum number of page views in the next day after Donald Trump's victory, and this day was also notable by high RTS index growth (17,69 points). The article
«Russian military intervention in the Syrian Civil War» had maximum number of page views after the news about Russian strikes from warships in Caspian Sea, and during this day RTS index increased by 17,47 points.
As well as correlation analysis, the methodology doesn’t allow to make reliable conclusions about connection and causality, but it allows to determine factors that presumably affect financial markets and certain dates when this influence could be the highest.
4. Conclusion
Wikipedia’s statistics provides daily data for page views of all articles in all language sections. It is a powerful instrument of measuring public interest to different events in different regions that has some strong advantages in com-parison with other approaches. We used this instrument to determine causes of changes in Russian financial market taking the period from 2016 to 2018.
Two different approaches are used in the research to determine influence of different events on RTS index. The first approach is a correlation analysis over quarters. It didn’t give strong evidences supporting connection of different news with the market, but allowed to determine two topics that presumably have such connection. These topics relate to international sanctions against Russia and Russian military intervention in Syria.
The second approach is based on analysis of days with highest interest to different topics. The approach allows to determine certain dates when selected topics could have the biggest influence on the market. We found days where the highest interest to different topics coincide with abnormal changes of RTS index.
It allowed us to determine certain possible causes of changes in the market.
Despite we proved that Wikipedia’s statistics can be used as a research instrument in financial market analysis, the most controversial point here is a choice of proper research algorithms that provide strong statistical significance of results. We got some interesting results with different level of credibility, but the research needs new methods which would allow getting stronger results. In the same time, methods that were applied in the research are very demonstrative and easy.
References
ANTWEILER, W., FRANK, M. Z. 2004. Is all that talk just noise? The information content of internet stock message boards. In: The Journal of finance. Vol. 59, No. 3, pp. 1259–1294.
BOLLEN, J., MAO, H., ZENG, X. 2011. Twitter mood predicts the stock market. In: Journal of computational science. Vol. 2, No. 1, pp. 1–8.
CHAN, W. S. 2003. Stock price reaction to news and no-news: drift and reversal after headlines. In:
Journal of Financial Economics. Vol. 70, No. 2, pp. 223–260.
DE BONDT, W. F., THALER, R. 1985. Does the stock market overreact?. In: The Journal of finance. Vol. 40, No. 3, pp. 793–805.
FAMA, E. F., MALKIEL, B. G. 1970. Efficient capital markets: A review of theory and empirical work. In: The journal of Finance. Vol. 25, No. 2, pp. 383–417.
HOFFMANN, M., NEUENKIRCH, M. 2017. The pro-Russian conflict and its impact on stock returns in Russia and the Ukraine. In: International Economics and Economic Policy. Vol. 14, No. 1, pp. 61–73.
LI, Q., WANG, T., LI, P., LIU, L., GONG, Q., CHEN, Y. 2014. The effect of news and public mood on stock movements. In: Information Sciences. Vol. 278, pp. 826–840.
MITCHELL, M. L., MULHERIN, J. H. 1994. The impact of public information on the stock market. In: The Journal of Finance. Vol. 49, No. 3, pp. 923–950.
SCHUMAKER, R. P., ZHANG, Y., HUANG, C. N., HEN, H. 2012. Evaluating sentiment in financial news articles. Decision Support Systems. Vol. 53, No. 3, pp. 458–464.
SULEMAN, M. T. 2012. Stock market reaction to good and bad political news. In: Asian Journal of Finance & Accounting. Vol. 4, No. 1, pp. 299–312.
TETLOCK, P. C. 2007. Giving content to investor sentiment: The role of media in the stock market. In: The Journal of finance. Vol. 62, No. 3, pp. 1139–1168.