Эффективность технического анализа на российском фондовом рынке
The article is devoted to one of the most popular approaches to forecasting market prices - technical analysis. We investigate the effectiveness of methods of technical analysis on the most liquid stocks of the Russian stock market. Derived conclusions about the overall effectiveness of technical analysis and identifies financial assets for which the use of these methods can achieve the best performance of market making operations.
Traditionally standard deviation has been considered as the main risk measure of an asset portfolio. The relevance of VaR analysis is widely recognized as an instrument for market risk quantification for investment decisions, asset allocation. Based on common practice VaR is estimated on 10-day basis and using 99% confidence interval. More accurate VaR estimation requires identifying the optimal VaR parameters. Our paper documents that historical VaR has some limitation for high volatile stock markets. We conduct empirical analysis of statistical tests of VaR estimation with frequency tests, magnitude tests, independence and autocorrelation tests for the Russian stock market. We propose an original algorithm for optimal VaR specification in terms of accuracy of VaR estimates. We used historical and semi parametric VaR (EWMA VaR and volatility adjusted VaR). For each method we consider 16 VaR specifications (which are different combinations of time horizons – 120, 250, 500 and 1000 trading days, and confidence intervals – 90%, 95%, 99%, 99,5%). We consider the unstable Russian stock market with two main Russian indexes – MICEX and RTS. Backtesting different VaR specifications show that annual 99% VaR prevails over other VaR specifications for the Russian stock indices. The significance level of confidence 1-5% are optimal on various time horizons. VaR with our method of algorithmically defined parameters is more effective than commonly used estimation procedure.
In this paper, we empirically test the dependence of the Russian stock market on the world stock market, world oil prices and Russian political and economic news during the period 2001–2010. We find that oil prices are not significant after 2006, and the Japan stock index is significant over the whole period, since it is the nearest market index in terms of closing time to the Russian stock index. We find that political news like the Yukos arrests or news on the Georgian war have a short-term impact, since there are many other shocks. These factors confirm the structural instability of the Russian financial market.
The paper presents an analysis of the stocks traded on MICEX from 2007 to 2011. In order to analyze the data, we construct a market graph model. The vertices of the graph represent stocks; the edges represent strong similarity between considered stocks returns. We suggest using the following way to calculate the similarity measure: we calculate the number of the periods when two considered stocks have the positive return simultaneously. Our results show that the market graph model with the suggested similarity measure can be used to describe the stock market dynamics in an effi- cient and concise manner.
This paper is an empirical study of the changing nature of the dependence of fundamental factors on the stock market index, which is the trend identified earlier in the Russian stock market. We empirically test the impact of daily values of fundamental factors on the MOEX Russia Index from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals that the change in the power and direction of the influence of the fundamental factors on the Russian stock market persists. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of structural breaks. We find multiple breaks probably associated with the dramatic falls of the stock market index. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time. This research is of value in macroeconomic forecasting and in the investment strategy development
The aim of this article is to prove the evidence of cross sectional momentum effect in Russian stock market within the variety of momentum strategy design elements and disclosure of the momentum effect nature.
We use a Markov chains models for the analysis of Russian stock market. First problem studied in the paper is the multiperiod portfolio optimization. We show that known approaches applied for the Russian stock market produce the phenomena of non stability and propose a new methods in order to smooth it. The second problem addressed in the paper is a structural changes on the Russian stock market after the financial crisis of 2008.We propose a hidden Markov chains model to analyse a structural changes and apply it for the Russian stock market.
Smoking is a problem, bringing signifi cant social and economic costs to Russiansociety. However, ratifi cation of the World health organization Framework conventionon tobacco control makes it possible to improve Russian legislation accordingto the international standards. So, I describe some measures that should be taken bythe Russian authorities in the nearest future, and I examine their effi ciency. By studyingthe international evidence I analyze the impact of the smoke-free areas, advertisementand sponsorship bans, tax increases, etc. on the prevalence of smoking, cigaretteconsumption and some other indicators. I also investigate the obstacles confrontingthe Russian authorities when they introduce new policy measures and the public attitudetowards these measures. I conclude that there is a number of easy-to-implementanti-smoking activities that need no fi nancial resources but only a political will.
One of the most important indicators of company's success is the increase of its value. The article investigates traditional methods of company's value assessment and the evidence that the application of these methods is incorrect in the new stage of economy. So it is necessary to create a new method of valuation based on the new main sources of company's success that is its intellectual capital.