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Research Article AVERAGE TRUE RANGE: HIGH VOLATILITY AS A Available Online at http://www.recentscientific.com International Journal of CODEN: IJRSFP (USA) Recent Scientific International Journal of Recent Scientific Research Research Vol. 11, Issue, 01(B), pp. 36805-36812, January, 2020 ISSN: 0976-3031 DOI: 10.24327/IJRSR Research Article AVERAGE TRUE RANGE: HIGH VOLATILITY AS A SUCCESS FACTOR FOR TRADING Dr.Ulrich R. Deinwallner PhD Management and Finance, Walden University, USA DOI: http://dx.doi.org/10.24327/ijrsr.2020.1101.4999 ARTICLE INFO ABSTRACT Article History: High volatility can be an indication to achieve excess returns with an investment strategy, according to the Efficient Market Hypothesis (EMH), since the underlying markets might exhibit less Received 6th October, 2019 th efficiency. In connection to this it was relevant to understand, if trading with low or high Average Received in revised form 15 True Range (ATR) values can improve the return results of a Moving Average (MA) trading November, 2019 strategy. The purpose of this quantitative research was to compare different MA strategies in Accepted 12th December, 2019 th different U.S. stock markets and to find an optimal ATR setting, to determine if excess returns can Published online 28 January, 2020 be achieved. The research question (RQ) was: what ATR setting can improve the return results of a MA trading strategy for U.S stock market indices? The following computations occurred: (a) simple Key Words: moving average; (b) ATR; and (c) t-Tests. I find in this study that a ATR(5) with high values Average True Range, Volatility Trading, (threshold = 25.92) is the most profitable setting to improve a Simple MA (SMA) trading strategy Moving Average, Efficient Market for the S&P500 index with (i.e., rSMA (20)_High_ATR (5)_S&P500 = 21.84 % per month), hypothesis, Portfolio Management. although the Russel 2000 provided the most trading opportunities, with (n = 967-1,099 trading days) during the time period 1999-2018. An ATR with high settings can improve the profitability when applying a SMA trading strategy for an investment. For investors who are interested in considering a volatility measure for their trading, this study can introduce empirical results. Copyright © Dr.Ulrich R. Deinwallner, 2020, this is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. INTRODUCTION the current price candle falls (Yamanaka, 2002). The ATR is an average value and can also be seen as a moving average The Average True Range (ATR) was developed by Welles (MA), since generally 14 True Range values are selected and Wilder, J. in 1970 and measures the price volatility of for divided by 14 time units (Hays, 2019; Yamanaka, 2002). In example a security (Hays, 2019, Yamanaka, 2002). A praxis the ATR is commonly used as an indicator, to determine connection was seen between the price range of a high and a a price for a stop-loss order or to determine market turnaround low price of a security for a given time period and the points through a sudden increase of the ATR (see Vervoort, underlying volatility (Yamanaka, 2002). The ATR was named 2009). Essentially, the computation of the ATR represents an True Range, since the price that occurred during the previous average value of the True Range and is commonly used in time units closing was included, instead of only considering the praxis as an indicator. stock current price range (Yamanaka, 2002). The advantage was that after-hour announcements that impacted the market Volatility Trading price at its opening, were also accounted in the next day’s price Zhang, Shu, and Brenner (2010) stated that volatility was range (Yamanaka, 2002). This means that through such after- recognized as relevant for investing by academics and hour announcements, the price range would increase and this practitioners after the crash 1987. In the late 1980’s empirical higher occurring volatility is included in the true range and theoretic studies were published to the issue of volatility. (Yamanaka, 2002). The ATR can therefore be seen as a The reason was that hedging potential occurred for volatility measure of price volatility. changes, which was the basis of the discussion. Brenner and For the computation of the true range, three scenarios are Galai (1989) introduced to hedging potential and volatility possible: (a) the current high and current low of the current changes a first reference volatility index in their study to cope price candle is considered if the previous price candle is with stochastic volatility of derivatives. In 1993 the Chicago smaller than the current price candle (price candle = high, low, Board Option Exchange (CBOE) introduced a volatility index opening, closing price); (b) current high and last close of the (VIX) that was based on the price movements of index options price candles are considered if the current price candle rises; (c) of the S&P 100 (Corrado & Miller, 2005). This means that the current low and last close of the price candles are considered if *Corresponding author: Dr.Ulrich R. Deinwallner PhD Management and Finance, Walden University, USA Dr.Ulrich R. Deinwallner., Average True Range: High Volatility as A Success Factor for Trading VIX is a volatility measure of the U.S. stock markets to allow information (Fama, 1970). This means that efficient stocks are responding to volatility change. traded at a fair price level (Fama, 1970). When looking at efficient markets, these stock markets often appear to be In regard of what drives the return volatility, the subsequent irrational and hard to predict for investors. Investors can only factors can be mentioned. Gulen and Mayhew (2000) reported achieve higher profits from efficient stock markets, by that volatility is lower during time periods when U.S. stock accepting for their investments a higher risk. Therefore, index futures face high open interest. Gulen and Mayhew saw efficient markets seem to be perfectly allocated markets, where a connection between high volatility during periods when all information are reflected in the stock prices. future volume was high. Clark (1973), Gallo and Pacini (2000), Andersen (1996) confirmed the connection between Further distinctions were made to the EMH in the scholarly volatility and volume, while Clark postulated the “mixture of literature by other scholars. Farma (1970) investigated a model distribution hypothesis” that posits a correlation between return to EMH and found three market efficiency forms of: (a) weak; volatility and volume that is influenced by a latent event of (b) semi; and (c) strong. Oprean, Tănăsescu, and Brătian information flow. Andersen argued that volume and latent (2014) differentiated if stock markets follow an evolutionary events impact directly the return volatility and past return pattern or simply follow a random walk. Jiang (2017) shocks become insignificant. This means that volatility tends presented findings to the issue of time horizons to the to be low if there is a large demand for U.S. securities; discussion for the EMH, where for long time horizons stock however, the volume plays a significant role. If the volume of markets appeared to be efficient and for short time horizons the buying and selling securities increases [decreases] through stock markets appeared to be inefficient. Akbas et al. (2016) latent events, then the volatility is assumed to increase as well. confirmed that stock market efficiency can vary over time, and saw reasons in the availability of arbitrage capital. Where, Moving Average sufficient capital flow was seen as relevant to equalize Much has been written, in regard of gaining excess returns with arbitrage anomalies in the investigated stock markets (Akbas et the application of a commonly used technical analysis method al., 2016). In conclusion, evidence was found in the of defining MA rules in the stock markets. In the scholarly researcher’s studies for a random walk price movement and for literature, publications occurred to the issue of MA trading the efficiency of stock markets. However, the efficiency varied strategies by for example Allen and Taylor (1990) who in the stock markets, depending on the markets time periods presented findings for technical and fundamental analyses; that are considered; available arbitrage capital; and depending Brock, Lakonishok, and LeBaron (1992) who reported in on sufficient capital flow in the stock markets. stochastic properties of stock returns; Antoniou, Ergul, Holmes, The current situation for the study is that several studies have and Priestley (1997), Blume, Easley, and Hara (1994) who been published to the issue of volatility, which was impacted presented findings to volume and technical trading; Gencay by volume increase and latent events, and using an ATR as a (1998a) investigated optimization and Gencay (1998b) and volatility measure to either price stop-loss orders of an Kwon and Kish (2002) analyzed the predictability of technical investment or to predict market turnaround points (see trading rules; Ready (2002) presented findings to the Yamanaka, 2002; Vervoort, 2009). profitability of technical trading rules, and Wong, Manzur, and Chew (2003) contributed with similar research to Singapore The general problem of the study was that if, according to the stock markets. The application of MA trading rules was in a EMH, the investors have a higher change of beating the market similar way applied in the studies, where either a price during inefficient market phases, then high volatility could be crossover or a double crossover MA signal was considered. In an indication to improve the profitability of a trading strategy, Ren and Ren (2018) and Deinwallner (2019) findings, an for which little has been investigated yet in regard of the ATR. alternative approach of applying MA rules was introduced. An The specific problem of the study was to understand the impact ANDOR strategy was more profitable to trade in the currency of using an ATR for investment strategies in U.S.
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