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<\/figure><\/div>\n\n\n\n\u0423\u0414\u041a 336, 339<\/strong><\/p>\n\n\n\n DOI 10.24411\/2413-046\u0425-2019-19034<\/strong><\/p>\n\n\n\n \u041f\u0420\u041e\u0412\u0415\u0420\u041a\u0410 \u0413\u0418\u041f\u041e\u0422\u0415\u0417\u042b \u042d\u0424\u0424\u0415\u041a\u0422\u0418\u0412\u041d\u042b\u0425\n\u0420\u042b\u041d\u041a\u041e\u0412 \u0421 \u0418\u0421\u041f\u041e\u041b\u042c\u0417\u041e\u0412\u0410\u041d\u0418\u0415\u041c \u0422\u041e\u0420\u0413\u041e\u0412\u041e\u0419 \u0421\u0422\u0420\u0410\u0422\u0415\u0413\u0418\u0418 \u0413\u0420\u0410\u0424\u0418\u041a\u041e\u0412 \u042f\u041f\u041e\u041d\u0421\u041a\u0418\u0425 \u0421\u0412\u0415\u0427\u0415\u0419: \u0414\u0410\u041d\u041d\u042b\u0415 \u0421\n\u0420\u041e\u0421\u0421\u0418\u0419\u0421\u041a\u041e\u0413\u041e \u0420\u042b\u041d\u041a\u0410 \u0410\u041a\u0426\u0418\u0419<\/strong><\/p>\n\n\n\n CHALLENGING EFFICIENT MARKET THEORY BY USING JAPANESE CANDLESTICK STRATEGY: EVIDENCE FROM THE RUSSIAN STOCK MARKET<\/strong><\/p>\n\n\n\n \u0411\u0435\u0440\u0435\u0437\u044e\u043a \u0414\u0435\u043d\u0438\u0441 \u042d\u0434\u0443\u0430\u0440\u0434\u043e\u0432\u0438\u0447, <\/strong>\u041d\u0430\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0439 \u0418\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u0441\u043a\u0438\u0439 \u0423\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 \u00ab\u0412\u044b\u0441\u0448\u0430\u044f \u0428\u043a\u043e\u043b\u0430 \u042d\u043a\u043e\u043d\u043e\u043c\u0438\u043a\u0438\u00bb, \u0421\u0430\u043d\u043a\u0442-\u041f\u0435\u0442\u0435\u0440\u0431\u0443\u0440\u0433, \u0420\u043e\u0441\u0441\u0438\u044f<\/p>\n\n\n\n Denis E.<\/strong> Berezyuk, <\/strong>of National Research University \u201cHigher School \u200e<\/strong>of Economics\u201d, St.\u00a0Petersburg Campus, \u200e<\/strong>Russia<\/p>\n\n\n\n \u0410\u043d\u043d\u043e\u0442\u0430\u0446\u0438\u044f: <\/strong>\u0412 \u0441\u0442\u0430\u0442\u044c\u0435 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\u0434\u043b\u044f\n\u0438\u043d\u0432\u0435\u0441\u0442\u043e\u0440\u0430 \u043d\u0430 \u0420\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u043e\u043c \u0420\u044b\u043d\u043a\u0435 \u0410\u043a\u0446\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u0430 \u043f\u0440\u0438\u043d\u0435\u0441\u0442\u0438 \u0432\u044b\u0441\u043e\u043a\u0443\u044e \u043f\u0440\u0438\u0431\u044b\u043b\u044c\n\u0432 \u0442\u0435\u0447\u0435\u043d\u0438\u0435 \u043d\u0435\u0441\u0442\u0430\u0431\u0438\u043b\u044c\u043d\u044b\u0445 \u044d\u0442\u0430\u043f\u043e\u0432 \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u044f \u0440\u044b\u043d\u043a\u0430 \u0441 \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u043c \u0442\u0440\u0435\u043d\u0434\u043e\u043c.<\/p>\n\n\n\n Summary: <\/strong>This\npaper investigates possibilities of technical trading strategy using Japanese\nCandlestick patterns approach in the Russian equity market. The other Technical\ntrading rules and techniques such as stop-loss positions, trend identification\nperspectives, volume factor and the dividend factor were collaborated to form a\nportfolio of Russian \u201cblue caps\u201d stocks for an investor and create methods of\nits optimal management in the reality of Russian market. Using methods of\nMonte-Carlo and bootstrapping for random datasets generation the fact of\nstatistically significant return using Japanese Candlestick strategy was not\nfound on test dataset, but robustness check showed strong value for investor on\nthe high-volatile periods of Russian market. Results show that Japanese\nCandlestick patterns, which were firstly discovered and used in the beginning\nof 18th<\/sup> century in Japan, have high predicting ability in \u201cbearish\u201d\nEquity Market of Russian Stocks during 2008 and 2014, when economic and\npolitical situation in Russian Federation was not stable. However, not only\n\u201cbearish\u201d but also \u201cbullish\u201d Candlestick patterns were effective in the market\nwith negative trend and high standard deviation from the daily mean return.\nThese results were used to form an effective trading strategy of Japanese\nCandlesticks for an investor in the Russian Equity Market during high-volatile\nperiods of time.<\/p>\n\n\n\n \u041a\u043b\u044e\u0447\u0435\u0432\u044b\u0435 \u0441\u043b\u043e\u0432\u0430: <\/strong>\u0413\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0435\n\u043c\u043e\u0434\u0435\u043b\u0438 \u042f\u043f\u043e\u043d\u0441\u043a\u0438\u0445 \u0421\u0432\u0435\u0447\u0435\u0439, \u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437, \u0422\u0435\u043e\u0440\u0438\u044f \u042d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u044b\u0445 \u0420\u044b\u043d\u043a\u043e\u0432, \u0442\u043e\u0440\u0433\u043e\u0432\u0430\u044f\n\u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u044f, \u043d\u0435\u0441\u0442\u0430\u0431\u0438\u043b\u044c\u043d\u044b\u0439 \u0440\u044b\u043d\u043e\u043a.<\/p>\n\n\n\n Keywords:\n<\/strong>Japanese\nCandlesticks, Technical analysis, Efficient Market Theory, trading strategy,\nhigh-volatile market.<\/p>\n\n\n\n Introduction<\/strong><\/p>\n\n\n\n Technical\nanalysis is a method of forecasting the future price dynamics of financial\nassets and is based on such market information as trading volumes of financial\ninstruments, historical prices and other market statistics. Proponents of\ntechnical analysis point out the following positive features of this market\nforecasting methodology: broad market review which arises through the analysis\nof the large number of indicators; convenience of using this analysis with all\nfinancial instruments and assets; the ability to detect\npatterns in historical data that can occur again in the future [17]. All the\nstrengths of technical analysis are generally based on an assumption that the\nvarious indicators and indexes of this method contain determinative for future\ntrends formation in the market information [12]. This statement contradicts the\ngenerally accepted in the 20th<\/sup> century Theory of Market Efficiency\n(EMH) which claims that all market information is contained in the prices of\nfinancial assets in the markets [5]. This follows the unpredictability of\nmarket price changes for all its participants, i.e. pieces constantly change\naround their \u201cfair market value\u201d, and therefore the return above the average\nmarket\u2019s is impossible [16].<\/p>\n\n\n\n The evidence of various technical trading rules\nprofitability was shown in a number of articles. For example, \u201cfilter\u201d technical\ntrading rule with transaction costs showed statistically significant return for\nDJIA index stocks. The long and short moving averages in the MA crossing\ntrading system brought profit higher than market average in the New-York Stock\nexchange [6]. Modern machine learning methods such as neural network improved\ninvestment decisions and create an effective trading strategy for stocks from\nS&P 500 and BSE indices [1]. However, the opposite results were shown by\nusing these and other techniques of technical analysis in the articles [5] and [11].\nContradictory results of scientific works on given subject, researches of the\nabsence of constant rationality in investors\u2019 and other market participants\u2019\nactions and also increasing popularity of technical analysis using among many\ninternational markets and financial assets allowed academicians to look at this\ntopic from different point of view [9].<\/p>\n\n\n\n This article explores the method of technical\nanalysis, Japanese Candlestick patterns and its applicability for the effective\ntrading strategy formation in the Russian Equity market. The Japanese\nCandlestick patterns, which send short-term trading signals for investor, were\nfirst mentioned in the early 18th<\/sup> century by Japanese rice business\nowner [12].<\/p>\n\n\n\n This\npaper is one of the first which explores the Japanese Candlestick technique of\ntechnical analysis in the realities of Russian Stock market. The analyzed in\nthis article data were stocks\u2019 daily historical prices of the Moscow Stock\nExchange (MICEX) from 1999 to 2018. The Russian financial market choice\nexplained by both the lack of scientific researches of possibilities of\ntechnical analysis in the market and the assumption that the Russian Equity\nmarket is too \u201cyoung\u201d for Market Efficiency formation. Also, the world\u2019s\nlargest and most well-known sources of market data, such as Reuters, have begun\nto provide information of daily open, close, maximum and minimum prices of\nfinancial assets since 1990 which is ideally correlated with the choice of\nmarket and research time [11].<\/p>\n\n\n\n Daily Japanese Candlestick pattern is a figure consisting of the candle \u201cbody\u201d and \u201cshadows\u201d which represent daily price of opening and closing, maximum and minimum respectively (Figure 1). In fact, candlestick patterns are not different from the price charts, accustomed to the western investors, but the information presented in the Japanese Candles is much easier to interpret: candlesticks show the interaction of sellers and buyers in the market and give the opportunity to penetrate \u201cinside\u201d the financial market, to see the relationships within the investigated data [12]. Sequences of Japanese Candles form candlestick patterns, each carries its own meaning and market psychological implication [14]. The using of all 4 daily prices of a certain financial asset makes technical analysis a distinct difference from the other methods of technical analysis, as one candle contains much more information than in the usual closing prices time series [11].<\/p>\n\n\n\n This\npaper tests 3 modifications of the basic Japanese Candlestick trading strategy:\nstop-loss, trailing stop-loss and take profit positions separately and together\n\u2013 these technical-trading indicators are included to trading systems to reduce\nlosses from unprofitable trades. Following optimized trading techniques can\nsignificantly improve the profitability of the trading strategy for the\ninvestor [12]. <\/p>\n\n\n\n The\nprofitability of Japanese candlestick trading strategy is tested by t-test,\nMonte-Carlo method and bootstrapping for random data samples with historical\ndata patterns formation [11,15]. Profits of trading strategy in test datasets\nwill be compared with those by \u201cBuy&Hold\u201d passive strategy of Monte-Carlo\nand bootstrapping data to determine statistical significance of results.<\/p>\n\n\n\n The\nstructure of this article is organized as follows: in section \u201cJapanese\nCandlestick Patterns\u201d description of some patterns and their trading signals\nare presented. Section \u201cMeasures of trading strategy profitability\u201d describes\nmethodology for evaluating the profitability of optimized trading strategies.\nThen, section \u201cEmpirical results\u201d shows returns of Japanese Candlestick trading\nstrategy and its modifications and also displays statistical significance of\nfollowing results. Finally, last section concludes this paper.<\/p>\n\n\n\n Japanese Candlestick Patterns<\/em><\/strong><\/p>\n\n\n\n The\nmethod of technical analysis based on the Japanese Candlestick charts was mentioned\nin the beginning of 18th<\/sup> century in Osaka, Japan, where rice trader\nMunnelusa Homma sketched opening, closing, maximum and minimum daily prices of\nthe rice. One day, he noticed Candlestick sequences followed by certain market\ntrends in the future. These algorithms were Japanese Candlestick patterns,\nwhich the Western world learned only in the end of 20th<\/sup> century with\nthe release of the book \u201cJapanese Candlestick Charting techniques: A Contemporary\nGuide to the Ancient Investment Techniques of the Far East\u201d [14].<\/p>\n\n\n\n All the 106 Japanese Candlestick patterns are\ndivided into types by the number of pattern formation (from 1 to 5 days), by\nmarket nature which is predicted by pattern (bullish\/bearish) and by character\nof market trend (continuation\/reversal patterns) [12]. The convenience of\nJapanese Candlesticks and their patterns is the simplicity of their\ninterpretation. For example, the \u201cMorning Star\u201d pattern, consisting of 3 daily\ncandles, is reduced to only 1 \u201creversal\u201d candle called \u201cPaper Umbrella\u201d (Figure\n2).<\/p>\n\n\n\n The\nmathematical interpretation of Japanese Candlestick patterns is simple to\nunderstand. For example, bullish reversal pattern \u201cMorning Star\u201d, consisting of\na long black candle (day closing price is below the opening) on the first day,\nfollowed by a small candle (uncertainty of market participants occurs), and\nthen white candle smaller than first evolves on the third day, closing price is\nslightly higher than the middle of the first black candle (Figure 2),\nmathematically described in this way:<\/p>\n\n\n\n where O(t) \u2013 the opening price of an asset on a\ntrading day t, C(t) \u2013 closing price on day t. It is necessary to note that the\nterms describing daily candles (long, short, etc.) are subjective at visual\ngraphs but mathematical algorithms are used in trading systems [12].<\/p>\n\n\n\n The combination of stop-loss indicators is one\nof the methods of improving the trading strategy [7]. The main point of all\nstop-loss positions is automatic exit from trade in case of violation of the\ninitial conditions specified by the investor [14]. Trailing stop-loss indexes\nare the modification of the stop-loss position [13]:<\/p>\n\n\n\n where ATR \u2013 trailing stop-loss position; \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \u2013 opening price on day t; \n \n \u2013 trailing stop period parameter; \n \n \u2013 optimizing on the training dataset factor. <\/p>\n\n\n\n Take profit is designed to exit trades when the\ninvestor receives necessary amount of profit (set before the creation of\ntrading strategy) and in this article it equals 8%.<\/p>\n\n\n\n In this paper 16 most popular in academic\nliterature Japanese Candlestick patterns were investigated in the realities of\nRussian Equity market. The list of patterns and their descriptive statistics\ncan be seen in the Table 1 in the next section. Also, two approaches of trend\ndefining were compered (Moving Average and Exponential Moving average) in order\nto find the most effective trend approach in the Russian market [11,12]. The\npattern searching and effectiveness calculation in the historical data were\nanalyzed using Candle Scanner Software[1]<\/a>.<\/p>\n\n\n\n Measures\nof trading strategy profitability <\/em><\/strong><\/p>\n\n\n\n The\nJapanese Candlestick trading strategies\u2019 returns are tested for statistical\nsignificance using t-test (reject r=o, where r \u2013 mean return), Monte-Carlo\nmethod and bootstrapping (to compare profits to the hypothetical returns of the\n\u201cBuy&Hold\u201d strategy) [11,15]. This article will also verify the hypothesis\nof candlestick patterns\u2019 only short-term effectiveness of predictive power\n(5\/10\/15 day holding periods) [12,14].<\/p>\n\n\n\n The\nprocess of Japanese Candlestick trading strategy optimizing for the investor in\nthe Russian Equity market is divided into 4 stages, which represents movement\nfrom \u201csimplest\u201d to the most \u201ccomplex\u201d trading strategy modification:<\/p>\n\n\n\n The\nbootstrapping ang Monte-Carlo methodology has several advantages in relation to\nStudent t-test to check the statistical significance of financial returns:\nskewness and leptokurtosis (thick tails) of returns\u2019 distribution of financial\nassets accounting; dealing with time autocorrelations and conditional\nheteroscedasticity of returns. Also, the number of null models for data\nformation by Monte-Carlo method or bootstrapping make it possible to use unique\nhistorical data patterns to construct random datasets [11].<\/p>\n\n\n\n Choosing\na null model for bootstrapping or Monte-Carlo method to form a data samples of\nassets is the first step in return statistical significance testing. In paper\nof technical analysis autoregressive process of the first stage (AR(1)), model\nGARCH in-Mean (GARCH-M), exponential GARCH model (EGARCH) as well as\nRandom-Walk (RW) model were used, but regression coefficients and general data\nstatistics (in particular 0.05 quantiles of returns) were close to each other\namong all null models [11]. In this article GARCH-M and RW models were used to\nfit data by bootstrapping and Monte-Carlo methods respectively.<\/p>\n\n\n\n The RW model represents the weak form of market efficiency (WEMH) of price changes independence in the market, so increments of financial asset price is fully random:<\/p>\n\n\n\n Where pt,t-1<\/sub> \u2013 logarithms of\nfinancial asset prices in the time; \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \u2013 logarithmic return of an asset on t period\nof time, normally distributed with historical mean and variance [16].<\/p>\n\n\n\n The Generalized Autoregressive Conditional\nHeteroscedasticity in-mean model was introduced by Engle in 1987 [4] and in\ngeneral form is represented in (1)-(3):<\/p>\n\n\n\n An error in this model, <\/p>\n\n\n\n , is conventionally normally distributed and its value at time t does not correlate with previous values in . <\/p>\n\n\n\n Conditional variance \u00a0<\/p>\n\n\n\n is a linear function of the square of error and variance at previous moment of time t-1, which implies a positive consistent correlation in the second moment of return to the average. Conditional mean of return in this model rt<\/sub> is a linear function of conditional variance and noise at the previous time moment, <\/p>\n\n\n\n This process implies that volatility can change over time and expected return is a function of volatility as well as previous returns [11]. The parameters and standardized residuals are evaluated for each investigated in this paper stock in the Russian Equity market using Maximum Likelihood method.<\/p>\n\n\n\n GARCH-M model residuals are standardized using\ncalculated standard deviations for the error process, then a series of\nresiduals is formed. After that, integrated with valuation parameters,\nresiduals series form a new representative time series of a financial asset closing\nprice returns. Further, this data is exponentiated for the close price\nformation for specified period. The RW process of time series of asset closing\nprice formation by Monte-Carlo method is \u201ceasier\u201d because historical (in the\ntraining samples) mean of returns and their variance are used to form normally\ndistributed return series which are to be exponentiated [11].<\/p>\n\n\n\n The process of bootstrapping and Monte-Carlo\nmethodology is repeated 1000 times \u2013 the sufficient number of iterations for\napproximation non-biased coefficients in the null models [3]. Then 1000 new\nsamples generated for each \u201cBlue Cap\u201d in the market are ranked in decreasing\norder of the last closing price on 31.05.2018 to determine the 0.95 quantile of\n\u201cB&H\u201d strategy \u2013 this profit represents the market average return. After\nthis step, profits of trading strategies are compared with \u201cB&H\u201d 0.95 quantile,\nand in case of exceeding 0.95 quantile by trading strategy profit, concludes\nthat there is 95% probability of statistically significant profit of trading\nstrategy with following asset in the Russian Equity market in the future.<\/p>\n\n\n\n The\nrobustness check of Japanese Candlestick strategy considers the application of\noptimized trading systems to the crisis periods of 2008 and 2014 of Russian Market\nto show the effectiveness of Candlestick trading strategy during volatile time\nintervals [8]. The Monte-Carlo method and bootstrapping with RW and GARCH-M\nnull models will be applied to generate random data samples with the 2008 and\n2014 patterns to find 0.95 quantiles of \u201cBuy and Hold\u201d strategy profit.<\/p>\n\n\n\n Results<\/strong><\/p>\n\n\n\n This section represents the returns of all the\ninvestigated Japanese Candlestick trading strategy modifications. Log returns\nrepresent the first differences between the stock price logarithms [11]. Total\nprofits of trading systems are represented by weighted annual average returns.<\/p>\n\n\n\n Data <\/em><\/strong><\/p>\n\n\n\n Daily open, close, maximum and minimum prices of 23 Russian \u201cBlue Caps\u201d stocks were downloaded from the finam[2]<\/a> service. The same dividend adjusted prices were calculated using historical data of dividend payments from the Yahoo!Finance[3]<\/a> source. Dividend adjusted time series of stock prices helps to avoid an artificial overstatement of trading strategies profitability for investor [2]. Optimized on training data trading systems are to be tested on both types of historical data: dividend adjusted prices and not.<\/p>\n\n\n\n Data\nsamples includes the most liquid shares of Russian Stock Market, but due to the\nemergence of \u201cnew\u201d stocks in this market, the number of Russian \u201cBlue Caps\u201d\nwere brought up to 23 stocks. The choice of this types of stocks can be\nexplained by the fact that technical analysis is most effective in actively\ntraded financial instruments [12]. Thus, the data includes the Russian \u201cBlue\nCaps\u201d stock prices from 01.01.1999 to 31.05.2018, but the time intervals are\nunique for all the shares, and, therefore, the training and test samples of\ndata were conducted independently for all assets. It means that trading\nstrategies were optimized for each stock separately. <\/p>\n\n\n\n Japanese Candlestick trading strategy\nmodifications<\/em><\/strong><\/p>\n\n\n\n The graphic patterns following strategy <\/em><\/p>\n\n\n\n The results presented in this section show that Candlestick pattern following strategy returns are not statistically significant for 22 of 23 examined stocks (Table\u00a01). However, following strategy brings statistically significant on 0.05 level (computed using t-test) annual average profit of 0.42 and 0.47 (5-day and 10-day holding period) for investor from 1999 to 2018. Thus, with 95% probability the basic Japanese Candlestick strategy applied to VTBR stocks will bring positive return in the future [12]. The average annual profit of the strategy for VTBR stocks is higher than that of the \u201cBuy&Hold\u201d strategy (-0.09). The binomial test results show that the 50% probability of correct trade prediction null hypothesis is rejected at 0.05 significance level. The descriptive statistics of most frequent Candlestick patterns from 1999 to 2018 can be seen in Table 2.<\/p>\n\n\n\n Japanese Candlestick trading strategy modified\nby stop-loss positions<\/em><\/strong><\/p>\n\n\n\n Trading\nsystems\u2019 modifications and \u201ceffective\u201d financial assets\u2019 selection were\nconducted using training samples of each stock separately. Financial asset was\ncalled \u201ceffective\u201d for further analysis if the return of trading strategy on\ntraining data met following conditions:<\/p>\n\n\n\n where r \u2013 trading strategy\u2019s profit on training dataset for stock;\nI \u2013 specific stock name.<\/p>\n\n\n\n Where xi<\/sub> \u2013 optimized parameters for Candlestick trading\nsystem. After this step. the optimized trading strategy was to be tested on the\ntest data for each asset that was \u201ceffective\u201d on the training set.<\/p>\n\n\n\n Table\n3 shows the average annual profit of Candlestick trading strategy with\nstop-loss optimized parameters for each Candlestick pattern (1st<\/sup>\ncolumn) and 11 stocks (the number of \u201ceffective\u201d stocks were declined from 23\nto 11 \u2013 1st<\/sup> row) on test data. The returns of this modification did\nnot exceed annual average returns by \u201cB&H\u201d strategy (last row of Table 3) during\nthe same period. So. bootstrapping and Monte-Carlo methods with Random-Walk and\nGARCH-M null models are meaningless for further analysis. The annual returns\nfollowing this trading strategy a likely to be statistically significant at 0.1\nand 0.05 levels for GMKN and MGNT stock relating to other tickers (Table 3).<\/p>\n\n\n\n The\neffectiveness of following trading strategy was also tested on the dividend\nadjusted data. The results are worse than in previous step (Figure 3). The use\nof Monte-Carlo method and bootstrapping for identifying 0.95 \u201cB&H\u201d return\nquantiles took place only when profitability of trading strategy exceeds the\n\u201cB&H\u201d strategy on the same data for several stocks [11].<\/p>\n\n\n\n Japanese Candlestick trading strategy modified by stop-loss.\ntrailing stop-loss and take-profit positions<\/em><\/strong><\/p>\n\n\n\n The\nnumber of optimizing parameters in trading systems increased from 1 (stop-loss\nindicator) to 3 (Average True Range and Factor for trailing stop were added).\nTake-profit position equals 8% [12].<\/p>\n\n\n\n The returns\nof modified trading system on test datasets exceeds total returns of two\nprevious Candlestick strategies (Table 4). Almost all patterns showed\nstatistically significant at 0.05 level profits for 17 \u201ceffective\u201d stocks in\ntest dataset. both the annual average profitability of patterns and the total\nweighted sum increased.<\/p>\n\n\n\n However. following profits are lower than those\nof \u201cB&H\u201d strategy at the same periods. except for the tickers of MGNT and\nMTSS \u2013 modified trading system of Japanese Candlestick gave profit that exceeds the \u201cB&H\u201d return. But Monte-Carlo\nmethodology with RW null model shows that exceeding profits for these stocks at\ntest datasets are not statistically significant (Table 5).<\/p>\n\n\n\n Japanese Candlestick trading strategy modified by stop-loss and\ntrailing stop-loss positions<\/em><\/strong><\/p>\n\n\n\n The main\ndifference of the following trading strategy modification is absence of\ntake-profit parameter which could reduce the profit of Japanese Candlestick\npatterns during sharp market changes [14]. The parameters optimized on training\ndata are equal to those in the previous modifications.<\/p>\n\n\n\n The\npositive trend of the annual average profits increasing of Japanese Candlestick\npattern strategy occurs with the \u201ccomplication\u201d of trading system. Table 6\npresents a comparison of the average annual log returns of the trading strategy.\nB&H strategy and 0.975 quantile of Monte-Carlo datasets with mean return\nand standard deviation from the historical data. Total annual profits by\npatterns and stocks are presented in Table 7.<\/p>\n\n\n\n Thus. the trading strategy of the Japanese\nCandlestick patterns and its 3 modifications are not able to bring a\nstatistically significant profit for the investor in the Russian Equity Market\nfor 22 of 23 stocks. One exception is the VTBR stock. the annual average\nlogarithmic return of this stock by following candlestick patterns is 0.41 (50%)\nthat is more than annual return of \u201cB&H\u201d strategy. The statistical\nsignificance at 0.05 level of this profit was proved by Student t-test.<\/p>\n\n\n\n With the\ncomplication of Japanese Candlestick patterns trading strategy by stop-loss.\ntrailing stop-loss and take-profit indicators the profits are increasing\n(Figure 4). Hence. the search for the new indicators. mathematical oscillators\nof technical analysis. which would improve Japanese Candlestick trading\nstrategy in the Russian Equity Market remains relevant.<\/p>\n\n\n\n Robustness\ncheck<\/em><\/strong><\/p>\n\n\n\n In previous section it was proved that the\nJapanese Candlestick trading strategy and its 3 modifications are not\nprofitable and do not exceed \u201cB&H\u201d strategy during the relatively steady\neconomic situation in Russia. Sensitivity analysis of trading system in this\narticle means to check the optimized modification of Japanese Candlestick\nstrategy (stop-loss) during the world economic crisis time period (2008) and\ntough 2014 (due to international situation around Russia this year) [10].<\/p>\n\n\n\n Table 8 presents the average annual logarithmic\nreturns for all investigated stocks and Candlestick patterns on the sample of\n2008 and 2014 and comparison of the weighted average annual profits of trading\nsystem and \u201cB&H\u201d strategy. Modification of Candlestick strategy is more\nprofitable than \u201cB&H\u201d for all stocks during crisis periods. These results\nare very different with those from the test data for each \u201cBlue Cap\u201d. so\nMonte-Carlo check is needed. A Monte-Carlo iteration with RW null model shows\nthat Candlestick trading strategy modified by stop-loss positions for GAZP and\nAFLT stocks with 95% probability would bring positive returns higher than\naverage market (Figure 5). Optimized trading strategy\u2019s annual average profits\nfor GAZP and AFLT stocks are 0.26 and 0.28 respectively.<\/p>\n\n\n\n The robust sample was narrowed to 2008 data\nonly to check the effectiveness of trading strategy in the \u201cbearish\u201d market\nwith trend fluctuations. During 2008 all world market indexes. including the\nMoscow Stock Exchange (MICEX). had a downward trend. Table 9 represents annual\nweighted sum of log returns of Candlestick patterns which equals 0.52 (68%)\nduring 2008 \u2013 that is much higher than the profit of -1.14 (-114%) of the\npassive \u201cB&H\u201d strategy during the same period. However. effective and\nprofitable at downtrend 2008 period were both \u201cbearish\u201d and \u201cbullish\u201d patterns\nof Japanese Candlestick which can be explained by high predictive power of\n\u201cbullish\u201d patterns during short upper trend fluctuations of financial assets\n(Figure 6).<\/p>\n\n\n\n To\nverify the statistical significance of the results obtained in 2008 sample. the\nbootstrapping and Monte-Carlo methods with null RW and GARCH-M models\nrespectively by using 2008 data patterns generated 1000 random one-year samples\nfor each stock. Figure 7 shows 0.95 quantiles of \u201cBuy&Hold\u201d strategy\nreturns of random datasets. which were formed using RW (orange line in the\ngraph) and GARCH-M (grey line) null-models and comparison to Japanese\nCandlestick trading strategy profits. The statistical significance of trading\nsystem returns for 8 stocks at 0.05 level is proved by both RW and GARCH-M. It\nmeans 95% probability of higher average-market returns of Japanese Candlestick\ntrading strategy modified by stop-loss and trailing stop-loss positions during\nthe crisis periods of Russian Market for 8 Stocks.<\/p>\n\n\n\n Thus.\nrobustness check showed that Candlestick patterns are more effective during the\ndownward market trend when the mean stock returns are negative and standard\ndeviation is highly fluctuated like in the 2008 sample (Figure 8).<\/p>\n\n\n\n Conclusion<\/strong><\/p>\n\n\n\n The\nresults show that the application of Japanese candlestick patterns has no\nstatistically significant profits for 22 out of 23 ‘Blue Caps’ of MICEX market\nindex in Russia during 1999-2018 period (these shares were preselected as those\nwith high potential for applying the trading strategy). Nevertheless. the\nrobustness check of Japanese candlestick trading strategy modified by stop-loss\npositions showed the intriguing results on the 2008 and 2014 data. The\nprofitability of all investigated stocks and candlestick patterns are much\nhigher than the Buy&Hold strategy profit during the high fluctuated\ndownward trend in the Russian market. It should be noted. that this phenomenon\ndoes not fit the definition of the Efficient Market [5].<\/p>\n\n\n\n On the\none hand. the results can be interpreted as an evidence of inefficiency of\nrelatively young Russian stock market because it was outplayed by the Japanese\nCandlestick strategy. On the other hand. it was outplayed only in the crisis\nperiods. but in the rest. i.e. in normal time. application of the\nJapanese candlestick strategy showed absence of statistically significant\nprofits for more than 95% of analyzed shares that leads to an outcome that the\nRussian stock market is efficient during normal time in terms of the efficient\nmarket hypothesis. Thus. from theoretical point of view. the results shed light\non efficiency of the Russian stock market according to the efficient market\nhypothesis. From practical point of view. investors can apply the Japanese\ncandlestick strategy as a safety ring. i.e. in normal time it does nothing. but\nduring crisis periods it can save from high losses or even lead to gaining high\nprofits. Moreover. analysts forecast that there will be a financial crisis in\n2020. It means that Japanese candlestick trading strategy can be very useful in\nthe near future.<\/p>\n\n\n\n References<\/p>\n\n\n\n [1]<\/a> Candle Scanner is a technical analysis software based on Japanese Candlestick patterns and other technical trading rules integration.<\/p>\n\n\n\n![\"\"](\"https:\/\/qje.su\/wp-content\/uploads\/2019\/07\/Bezymyannyj-222.png\")
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