Does intraday technical trading have predictive power in precious metal markets?

Batten, Jonathan A.; Lucey, Brian M.; McGroarty, Frank; Peat, Maurice; Urquhart, Andrew

doi:10.1016/j.intfin.2017.06.005

Cited by 25 publications

(17 citation statements)

References 38 publications

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“…There is also evidence of significant results from technical trading in commodity futures markets (Miffre and Rallis 2007;Szakmary et al 2010;Narayan et al 2015;Han et al 2016), commodity spot markets (Batten et al 2018;Psaradellis et al 2019), bond markets (Shynkevich 2016) and commodity ETFs (Hudson et al 2017). Despite these findings, there is no clear consensus on the predictability of technical trading rules in the literature, with many papers indicating that technical trading rules do not offer any predictive power, especially once transaction costs have been accounted for (for instance Bessembinder and Chan 1998;Allen and Karjalainen 1999;Marshall et al 2008a, b;Bajgrowicz and Scaillet 2012;Yamamoto 2012;Urquhart et al 2015 ;Batten et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Technical trading and cryptocurrencies

Hudson

Urquhart

2019

Ann Oper Res

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This paper carries out a comprehensive examination of technical trading rules in cryptocurrency markets, using data from two Bitcoin markets and three other popular cryptocurrencies. We employ almost 15,000 technical trading rules from the main five classes of technical trading rules and find significant predictability and profitability for each class of technical trading rule in each cryptocurrency. We find that the breakeven transaction costs are substantially higher than those typically found in cryptocurrency markets. To safeguard against datasnooping, we implement a number of multiple hypothesis procedures which confirms our findings that technical trading rules do offer significant predictive power and profitability to investors. We also show that the technical trading rules offer substantially higher risk-adjusted returns than the simple buy-and-hold strategy, showing protection against lengthy and severe drawdowns associated with cryptocurrency markets. However there is no predictability for Bitcoin in the out-of-sample period, although predictability remains in other cryptocurrency markets.

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Section: Introductionmentioning

confidence: 99%

Technical trading and cryptocurrencies

Hudson

Urquhart

2019

Ann Oper Res

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“…In this study, we develop on the work of Brock et al [1992] and test whether two of the most popular trading rules, in the form of the moving average (MA hereafter) and the trading range break hold for the Bitcoin market using high-frequency trading data. Such analysis further demonstrates the evolution of this asset class, while investigating as to whether cryptocurrencies share similar characteristics to other asset classes that have presented evidence of strong support for both strategies, such as equity markets (Brock et al [1992]), futures markets (Raj and Thurston [1996]), foreign exchange markets (Taylor and Allen [1992]) and commodity markets (Marshall et al [2008]; Batten et al [2018]). Overall, our results provide important results for the technical strategies under observation.…”

Section: Introductionmentioning

confidence: 77%

The effectiveness of technical trading rules in cryptocurrency markets

Corbet

Eraslan

Lucey

et al. 2019

Finance Research Letters

100

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We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.

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“…For emerging economics, we use Sensex 30 index of India and CSI 300 index of China. As pointed out in (Batten et al 2018), the sample composition could have an impact on the model performance. In order to maintain the in-sample and out-of-sample periods have the same observations, we divide the sample from 2006-2011 and 2012-2017.…”

Section: The Datamentioning

confidence: 99%

“…More importantly, forecasting models based on the dividend and earnings yield may also have instability problems (see Lettau and Ludvigson 2001;Goyal and Welch 2003;Paye and Timmermann 2006;Cai et al 2015). Moreover, since our paper also focuses on developing trading strategies, the technical analysis paper such as (Park and Irwin 2007, Batten et al 2018and Jiang et al 2019 as well as the recent non-linear model works such as (Zhao et al 2019 andFacchini et al 2020) could also be relevant.…”

Section: Literature Overviewmentioning

confidence: 99%

Forecasting stock market return with nonlinearity: a genetic programming approach

Ding

Cui

Xiong

et al. 2020

J Ambient Intell Human Comput

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The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic Programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading. Keywords return forecasting • nonlinear models • genetic programming 1 IntroductionA crucial question for open discussions in finance is whether future stock returns are predictable (see Fama 1970), and this issue is also controversial (e.g. Ang and Bekaert 2006). A plethora of studies (such as Fama and French 1988;

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Does intraday technical trading have predictive power in precious metal markets?

Cited by 25 publications

References 38 publications

Technical trading and cryptocurrencies

Technical trading and cryptocurrencies

The effectiveness of technical trading rules in cryptocurrency markets

Forecasting stock market return with nonlinearity: a genetic programming approach

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