The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies

Ghanem, Seri; Harasheh, Murad; Sbaih, Qays; Ajmal, T. K. Article The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ghanem, Seri; Harasheh, Murad; Sbaih, Qays; Ajmal, T. K. (2024) : The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-18, https://doi.org/10.1080/23311975.2024.2428781 This Version is available at: https://hdl.handle.net/10419/326690 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies Seri Ghanem, Murad Harasheh, Qays Sbaih & T. K. Ajmal To cite this article: Seri Ghanem, Murad Harasheh, Qays Sbaih & T. K. Ajmal (2024) The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies, Cogent Business & Management, 11:1, 2428781, DOI: 10.1080/23311975.2024.2428781 To link to this article: https://doi.org/10.1080/23311975.2024.2428781 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 15 Nov 2024. Submit your article to this journal Article views: 3232 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Banking & Finance | ReseaRch aRticle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2428781 The predictability of technical analysis in foreign exchange market using forward return: evidence from developed and emerging currencies seri ghanema, Murad harashehb , Qays sbaihc and t. k. ajmald aDepartment of Business and economics, Birzeit university, Palestine; bDepartment of Management, university of Bologna, italy; cadam smith Business school, university of glasgow, uK; dschool of Business and economics, uae university, al ain, united arab emirates ABSTRACT technical analysis in the foreign exchange (Forex) market has yielded mixed results, particularly regarding its effectiveness over different holding periods in swing trading. this study addresses this gap by evaluating 497 technical trading rules across 10 currencies over 22 years (January 2000 to December 2022). Focusing on swing trading windows of 1-7 days, the research introduces the concept of an ‘optimal holding period,’ examining how price movements align with trading signals at varying time lags post-signal. the results demonstrate that technical trading rules significantly predict price movements in both developed and emerging market currencies, with emerging markets showing higher levels of predictability. notably, simple moving average (sMa) indicators perform most effectively for emerging market currencies, while oscillator-based strategies prove more successful for developed markets. these findings have practical implications for Forex traders employing short-term strategies, providing actionable insights for optimizing trade timing. additionally, the study opens new avenues for future research on the role of technical analysis in enhancing trading performance in global currency markets. 1. Introduction the predictability of currency returns is a critical topic due to its potential implications for market efficiency and its practical value for investors. historically, most asset pricing research has focused on understanding the equity market, where empirical studies have identified various anomalies that prompt investors to apply technical analysis techniques to outperform the market. technical analysis, often called ‘chartist analysis,’ involves generating trading recommendations based on time series properties of financial assets (hsu etal., 2016). these recommendations can be either qualitative, relying on visual patterns, or quantitative, driven by mathematical models. numerous studies have examined the predictability and profitability of technical trading rules across financial markets to identify successful trading strategies and test market efficiency. While technical analysis has been thoroughly explored in equity markets, its application to the foreign exchange (FX) market has received comparatively less attention (Park & irwin, 2007). the FX market is the world’s largest and most liquid financial market, with an average daily trading volume of $7.5 trillion in 2022, a significant increase from around $2 trillion in 2004 (Bank For international settlements, 2022). Unlike the relatively more stable stock market, the FX market is characterized by high volatility, nonlinearity, and irregular price movements, making it one of the most complex financial environments (ahmed et al., 2020). these unique features provide FX traders with a wide range of trading opportunities, particularly for short-term strategies, where technical analysis has proven popular. surveys show that 30–40% of FX traders globally believe exchange rates are primarily driven by technical © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Murad Harasheh murad.har[email protected] Department of Management university of Bologna, via capo di lucca 34, Bologna, italy https://doi.org/10.1080/23311975.2024.2428781 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 9 January 2024 Revised 23 October 2024 accepted 7 november 2024 KEYWORDS Forex; technical analysis; moving averages; Relative strength index; trading signal; holding period SUBJECTS international Finance; Financial Management; Quantitative Finance; economics; Finance 2 s. ghaneM etal. analysis, particularly over short-term horizons of up to six months (Menkhoff & taylor, 2007). the strong reliance on technical analysis in FX trading reflects a deep-rooted behavior among professional traders, who often find it more effective in navigating the market’s intricacies. Recent research on the application of technical analysis in FX markets has addressed its profitability (Zarrabi et al., 2017), directional currency movement prediction (Yıldırım et al., 2021), market efficiency during financial crises (Yamani, 2021a, 2021b), and the integration of technical analysis with Bayesian statistics (hassanniakalager et al., 2021). Other studies, like Deng et al. (2021), have investigated specific trading techniques such as the ichimoku kinkohyo strategy. however, despite these contributions, results have often been inconsistent due to differences in parameter settings, such as the timing of trading signal generation, and concerns over data mining biases. the inconsistencies across studies highlight the need for a more systematic approach to understanding how technical analysis can be effectively applied in the FX market, particularly in terms of rule parameterization and holding periods. this study seeks to address this gap by exploring the profitability and parameterization of technical trading rules in FX markets. it investigates the predictability of multiple currencies, spanning developed and emerging markets, to identify optimal rule configurations that real-life traders can apply. a central focus of this research is on the ‘optimal holding period,’ which is key to maximizing the effectiveness of technical trading rules (ttRs). specifically, the study addresses three main questions: (i) how effective are specific ttRs in predicting FX price movements during swing trading, and how do parameter settings and holding periods influence their performance? (ii) Does the predictability of technical analysis differ across currencies in developed and emerging markets? (iii) What are the key challenges for technical analysis researchers, and how can they be addressed? Using a novel methodology, this research evaluates the effectiveness of 497 technical trading rules over a sample of 10 currencies from January 2000 to December 2022. the study focuses on short-term trading, examining how price movements align with trading signals at different day lags after the signals are generated. By introducing the concept of an ‘optimal holding period,’ the research provides valuable insights into the timing of trades. the results show that technical trading rules predict price movements in developed and emerging market currencies, with higher predictability observed in emerging markets. among the technical trading rules tested, simple moving average (sMa) indicators performed best with emerging market currencies, while oscillators such as the relative strength index (Rsi) were more effective for developed market currencies. the significance of this research lies in its potential to provide actionable insights for academic researchers and practitioners in the FX market. By focusing on swing trading with a 1–7 day holding period, the study fills a gap in the literature and offers practical recommendations for optimizing ttRs. For traders, these findings suggest specific parameter settings and time lags that can be used to enhance short-term trading strategies. For researchers, the study provides a deeper understanding of how technical trading models can be optimized and adapted to different market conditions, helping to address the inconsistencies often found in previous studies. additionally, this work contributes to the broader field of asset pricing theory, where technical analysis is often overlooked despite its widespread use among market participants. By exploring FX markets’ behavioral and technical aspects, this study offers new perspectives on how technical analysis can inform real-world trading decisions and market efficiency. the structure of the paper is as follows: section 1 introduces the research topic. section 2 provides an overview of the FX market, including the principles and categories of technical analysis. section 3 reviews the empirical literature on the predictability and profitability of technical trading rules. section 4 outlines the data and methodology employed in the research. section 5 presents the main findings, and section 6 concludes with recommendations for future research avenues. 2.The FX market and technical analysis the foreign exchange market is a non-centralized financial market where all currencies are bought and sold simultaneously. it is the largest and most liquid financial market globally, with a daily trading volume exceeding $ 7 trillion (Bank For international settlements, 2022). the gigantic volume of trade in the FX market, the increased competition between market participants, and the sophistication of technology have made the market more complex (hassanniakalager et al., 2021). the FX market operates cOgent BUsiness & ManageMent 3 continuously from Monday morning in new Zealand to Friday evening in the Usa (chan et al., 2019). typically, the default isO currency pair features the UsD as the base currency and the other as the quote, except for pairs like eUR/UsD, gBP/UsD, nZD/UsD, and aUD/UsD, where the UsD is the quote currency (Ozturk etal., 2016). the major segments of the FX market include spot transactions, forward market, FX swaps, and FX options (Bank For international settlements, 2022; Zarrabi et al., 2017). technical analysis refers to the application of historical market data that helps to forecast the direction or trend of financial asset prices (hassanniakalager etal., 2021). it dates back to the early work of charles Dow, the Wall street Journal editor, using past price behavior to make trading decisions in financial markets (neely, 1997). known as ‘chartist analysis,’ it involves techniques that provide recommendations for financial assets based on their time series properties (hsu et al., 2016). although the technical analysis theories vary from one to another, the main viewpoint is the recurrent nature of patterns or trends in the prices of securities. chartists optimistically believe that learning these patterns enables them to predict securities’ future prices. technical analysis has attracted contrasting views about its effectiveness in predicting market movements (coakley et al., 2016). empirical findings from several early and widely cited studies assessing technical analysis in the stock market, such as Fama and Blume (1966), Vanhorne and Parker (1968), and Jensen and Benington (1970), reported negative returns. Understanding technical analysis requires exploring its relation to the efficient market hypothesis (eMh), principles, and categories. Weak-form efficiency, a form of eMh, states that using technical trading rules (ttRs) by exploiting historical data may not fetch profitable returns (Zarrabi et al., 2017). On the contrary, as a part of the ongoing debate, existing technical analysis research produced favorable evidence, yielding positive returns. Researchers outlined three fundamental principles of technical analysis (neely, 1997; Ozturk et al., 2016; teodor & Bogdan, 2015). First, market action, represented by price movement and volume, discounts all relevant information, negating the need to forecast fundamental drivers. second, financial asset prices move in trends, with technical analysis aiming to identify these trends early and make profits by selling (buying) when the price increases (decreasing). third, asset price history repeats itself, with prices moving in recognizable and persistent patterns (Menkhoff & taylor, 2007). technical analysis can be divided into qualitative and quantitative approaches (coakley et al., 2016; Menkhoff & taylor, 2007; Ozturk et al., 2016). the qualitative approach, or chartism, involves visually inspecting time-series data charts to identify long-term trends and patterns by connecting peaks and troughs geometrically. the quantitative approach, involving technical trading rules (ttR), focuses on short-term fluctuations and uses mathematical formulas and algorithms to analyze price data (neely, 1997). Oscillator rules, a commonly utilized ttR, include the Relative strength index (Rsi), which measures the speed of price movement to indicate overbought or oversold conditions. likewise, Moving average trading rules identify trends and filter out short-term fluctuations. Other advanced tools, such as Fibonacci retracement and elliot waves, are extensively employed in technical analysis (Jarusek et al., 2022). combining qualitative and quantitative techniques is common in technical analysis. however, the qualitative approach involves more subjective analysis due to behavioral and judgmental biases. 3. Empirical review technical analysis in the FX market has gained significance because of its higher predictability and profitability (hassanniakalager et al., 2021; lebaron, 1999; Menkhoff & taylor, 2007; Quintanilla garcía et al., 2012; teodor & Bogdan, 2015; Zarrabi et al., 2017). Prior studies highlight that decision-makers and FX professionals widely use technical analysis to forecast currency fluctuations. technical trading rules can be classified into several categories, potentially thousands of variations based on different rules and parameterizations (hsu et al., 2016; kuang et al., 2014). Despite their popularity, there is a notable lack of literature focusing on many trading rules in emerging FX markets (kuang et al., 2014). the scope of recent works related to technical analysis in the FX market were confined to assessing currencies during global financial crisis (Yamani, 2021a), integrating technical analysis with Bayesian statistics (hassanniakalager etal., 2021), predicting the directional movement of currencies using a deep learning technique (Yıldırım etal., 2021) and examining limited number of trading rules (Dockery & todorov, 2023). 4 s. ghaneM etal. earlier studies testing technical analysis in FX and futures markets have generally reported abnormal profits (Park & irwin, 2007). For instance, cornell and Dietrich (1978) observed the profitability of technical analysis using filter rules and moving averages in the FX market. Park and irwin (2007) reviewed earlier studies and summarized significant profitable trading signals, such as the filter rule (0.5%, 1%, 2%, and 3%), which generated substantial net annual returns during the sample period. sweeney (1986) confirms the existing findings on the usefulness of filter rules on multiple dollar exchange rates, considering both transaction costs and risk. Park and irwin (2007) emphasized the importance of studying the average performance of all trading rules rather than focusing on individual ones. consistently, lento (2008) proposed the combined signal approach (csa), which tests multiple technical indicators together, arguing that this method increases profitability compared to testing indicators individually (lento, 2007). existing literature on technical analysis has extensively discussed the efficient market hypothesis (eMh) when examining its profitability in the FX market (coakley et al., 2016; hsu et al., 2016; katusiime et al., 2015; kuang etal., 2014; lento, 2008; M’ng, 2018; neely, 1997; Park & irwin, 2007; tharavanij etal., 2017; Yamani, 2021a, 2021b; Yao & tan, 2000)). eMh posits that currency prices reflect all available information, rendering technical trading signals based on historical data ineffective in generating returns (Fama, 1965). the three forms of eMh include weak-form, semi-strong, and strong-form—which differ in the extent of information reflected in asset prices (Park & irwin, 2007). Most notably, the weak form efficiency contends that the prices of securities reflect available information on historical prices. consistently, recent studies have observed that the effect of technical trading rules has declined over time in the FX market (hassanniakalager et al., 2021). On the contrary, Qi and Wu (2006) argue that the FX market has become more efficient over time, suggesting that technical analysis does not violate the weak form of market efficiency (gerritsen, 2016). similarly, neely (1997) argued that the profitability of technical analysis does not necessarily contradict eMh, citing other issues such as data snooping, risk measurement, and accurate pricing. Other researchers propose the adaptive market hypothesis (aMh), which states that market efficiency varies with conditions (hsu et al., 2016; katusiime et al., 2015). the adaptive market hypothesis states that the factors that push prices toward their efficient levels are weak and do not function instantaneously. consistent with this argument, Zarrabi et al. (2017) found that the profitability of technical trading rules lacks consistency even though many are profitable for a shorter period. in sharp contrast to these arguments, studies like Quintanilla garcía et al. (2012), lebaron (1999), suggest that FX markets are inefficient. Research over the past decade has extensively examined the profitability of FX trading. technical analysis gained prominence when economic fundamentals failed to explain currency price movements (Menkhoff & taylor, 2007). Vajda (2014) found that strategies based on technical indicators yield profits, though caution is advised. coakley et al. (2016) and hsu et al. (2016) concluded that technical analysis has predictive power in emerging and developed markets. Jamali and Yamani (2019) and narayan et al. (2015) reported significant profits while testing momentum-based strategies in emerging markets. Yamani (2021a) observed improved profitability for FX rates during the 2007–2008 financial crisis using moving average, momentum, and Rsi trading rules. Most recently, Dockery and todorov (2023) have uncovered the profitability of five trading rules: filter rules, trading range breakout, moving average, and Bollinger bands over 14 currency pairs. Yamani (2021b) utilized forward unbiasedness and technical trading rules to understand if the markets deviated from efficiency during the global financial crisis and showed positive abnormal returns for technical rules. along the line, Yıldırım et al. (2021) found that technical indicators possess a predictive ability of directional movement of currencies when combined with a deep learning technique called ‘long short-term memory’ (lstM). however, Menkhoff and taylor (2007) argued that theoretical evidence for the profitability of technical analysis is inconclusive and complex. they suggested that while technical analysis might be occasionally profitable, it is not consistently so, which would otherwise indicate a wholly inefficient FX market. supporting this argument, hassanniakalager et al. (2021) found positive abnormal returns using 7,846 technical rules for eUR/UsD, gBP/UsD, and UsD/JPY; however, the excess returns were minimal. Potì etal. (2020) observed excess predictability in forward contracts for six exchange rates early in their sample period but less predictability in spot rates. Zarrabi et al. (2017) reported short-term rewards for over 7,600 trading rules covering six currencies, yet they observed that the profitability of those many rules cOgent BUsiness & ManageMent 5 is inconsistent across time. kuang et al. (2014) and neely (1997) sought reasons behind experienced traders using technical trading rules if they do not generate profits consistently. lui and Mole (1998) suggested that professional traders rely more on technical analysis in the short term. conversely, schulmeister (2008) argued that the positive results from trading rules may result from the widespread use of these models as information sources. katusiime et al. (2015) found that excess returns from predictive technical trading rules decline when transaction costs are considered. kuang et al. (2014) noted that technical trading rules often fail to explain returns in emerging markets and may be prone to data mining biases. Furthermore, data-snooping bias is critical when testing technical analysis trading indicators. the lack of pre-specified parameters for each trading indicator compels researchers to search through numerous technical trading rules, raising the possibility that profitable signals may arise by chance (Park & irwin, 2007). this bias can undermine the validity of individual rule testing. hsu et al. (2016) confirmed that data-snooping bias occurs when individual tests are conducted using the same dataset without testing all models collectively for significance. to address data-snooping bias, kuang et al. (2014) and hsu etal. (2016) proposed using stepwise sPa (single-Period approximation) tests. another method involves splitting the sample into two halves and testing profitable trading rules in the second half, known as the optimization of trading rules (Park & irwin, 2007). this approach addresses traders’ practices of selecting the most profitable rules. Methodologies employed by kuang et al. (2014) and hsu et al. (2016) reveal that parameter optimization and out-of-sample testing closely simulate real-world scenarios and help address data-snooping bias. Qi and Wu (2006) suggested that simpler and more widely accepted procedures are still needed for adequate data-snooping controls despite the widespread acknowledgment of data-snooping issues. summarizing the empirical studies discussed, recent and early research generally agrees on the profitability and predictability of technical analysis indicators in the FX market compared to the stock market. among studies that find technical analysis profitable in the FX market, there are mixed results regarding the best rules as no standardized rule parameterization has been established for successful trading, with rule parameters implicitly including the number of days (forward lags) a signal should last. challenges for the technical analysis in the FX market also include making decisions on optimal parameters, implicit holding periods, and inconsistency across time and markets. additionally, both current and earlier studies express concerns about data-snooping bias. some studies find technical analysis’s profitability illusory even after accounting for data-snooping issues, while others have adopted various methods to address this bias, finding technical analysis profitable both before and after considering these biases. 4. Methodology and data 4.1. Data We study 10 foreign exchange currencies, including six from developed and four from emerging markets. the developed market currencies are the australian dollar (aUD/UsD), canadian dollar (caD/UsD), euro (eUR/UsD), new Zealand dollar (nZD/UsD), swedish krona (UsD/sek), and sterling pound (gBP/UsD). the emerging market currencies are the israeli shekel (UsD/ils), Russian Ruble (UsD/RUB), Brazilian Real (UsD/BRl), and turkish lira (UsD/tRY). the sample period for emerging market daily data spans from 1 January 2000, to 31 December 2022. the data was collected using the Bloomberg terminal. the currency return is calculated using the formula introduced by hsu et al. (2016): R S S t t t =      − ln 1 (1) where St represents the spot foreign exchange rate on timeframe (t), and St-1 represents the spot foreign exchange rate on the previous time frame (t-1). a value of (St/St-1) greater than 1 indicates currency appreciation (long-buying) against the quote currency, while a value less than 1 indicates currency depreciation (short selling) against the quote currency. the returns are calculated without adjustment for transaction costs or interest rates. 6 s. ghaneM etal. 4.2. Technical analysis rules 4.2.1. The Oscillator Rules the Oscillator Rules, also known as Overbought or Oversold indicators, measure the speed of price movement to identify potential corrections or reversals. One popular oscillator rule is the Relative strength index (Rsi). the Rsi is calculated using the following equation: RSI h Uh Uh Dh t t tt () = () () + ()         100 (2) where Ut(h) represents the accumulated-up movements over the previous (h) period timeframe, and Dt(h) represents the accumulated-down movements (absolute value) over the previous (h) period timeframe. Ut(h) and Dt(h) are calculated as provided in equations 3 and 4, respectively: Uh S S S S t j h tj tj tj tj () = −> () − () = − −− − −− ∑ 1 11 0„ (3) Dh SS SS t j h tj tj tj tj () = −< () − = − −− − −− ∑ 1 11 0„ (4) where ι(.) is an indicator variable that can be either zero if the statement between the parentheses is false or one of it is true. the Rsi is then normalized between 0 and 100 to measure the speed or strength of the up-movement relative to the down-movement. a value of Rsi equal to or above 70 indicates overbought conditions, suggesting a potential reversal down, while a value of Rsi equal to or below 30 indicates oversold conditions, suggesting a potential upward correction. the Rsi parameters are tested using different lookback periods (h), as hsu et al. (2016) suggested. 4.2.2. Moving average rules Moving average (Ma) indicators are trend detectors that smooth the time-series data to distinguish trends from noise or fluctuations. this study considers two types of Moving average Rules: simple Moving averages (sMa) and exponential Moving averages (eMa). For the sMa rules, two approaches are employed: sMa crossing with spot rate (sMa-spot-cross) and sMa crossing with different sMa (sMa-sMa- cross). sMasMa-cross. in addition to sMa and eMa, we also adopt other exotic and innovative Ma versions, namely the horizontal average of Moving average (haMa) (M’ng, 2018) and ‘kaufman’ adaptive Moving average (kaMa) as suggested by kaufman (1995). 4.2.3. Testing the predictability of trading rules this section tests the predictability of technical analysis indicators by comparing the mean return of each indicator for buy and sell signals, as suggested by Bessembinder and chan (1995). instead of using bootstrapping p-values, this study will utilize One-Way anOVa statistics to identify significant indicators at a 95% confidence level. steele and esmahi (2015) employed one-way anOVa to investigate the impact of technical analysis indicators on trading outcomes, highlighting their predictive power. similarly, krishnan and Menon (2009) examined the effects of currency pairs, time frames, and technical indicators on Forex trading profit, likely using anOVa. One-way anOVa allows for formal hypothesis testing of variations in indicator parameters across different market returns. While bootstrap simulation is effective for estimating confidence intervals, it does not directly facilitate hypothesis testing as anOVa does. We test 497 indicators individually across 10 currencies, totaling 4,970 indicators. these include sMa crossing (60 sMa × 7 forward return lags), Rsi (9 Rsi × 7 forward return lags), haMa (1 haMa × 7 forward return lags), and kaMa (1 kaMa × 7 forward return lags). Our research methodology extends beyond evaluating indicators on the same or the next day after a trading rule signal. it also examines the duration of signal effectiveness by testing various forward return lags. this approach seeks to optimize trading rules by identifying the best holding periods for each currency. the empirical results section will present the significant and best-performing indicators, with full results provided in appendices due to the extensive volume of test outputs. additionally, graphical cOgent BUsiness & ManageMent 7 examples will visually explain each technical analysis indicator, using random data from one of the sample currencies to illustrate the indicator graphically. the results are organized into three sections: the most predictable currencies, the most predictable technical analysis indicators, and the most predictable markets within the sample of currencies and technical rules. this structure, influenced by studies such as hsu et al. (2016), aims to provide clear insights into the performance of different indicators and their effectiveness in specific markets. 5. Results and analysis 5.1. Descriptive results this section presents the descriptive results of the study, including the mean returns and standard deviations for different forward return periods (2-7 days) for each currency over the entire period. these returns are considered holding period returns, reflecting the forward effect of each trading signal. Positive returns indicate a buy or long position, while negative returns represent a sell or short position. the standard deviation of each currency return measures daily volatility over up to one week. the statistics reported include mean returns (average return over a period), standard deviation (Measure of return volatility), skewness (asymmetry of return distribution), kurtosis (Peakedness of return distribution), Jarque-Bera statistic (test for normality of returns), and unit root test results (stationarity test for time series). tables 1–3 offer a comprehensive overview of the data properties before proceeding to further econometric modeling. 5.2. Empirical findings We focus on two technical indicators: simple moving averages (sMa) and relative strength index (Rsi). the analysis explores the performance and significance of these indicators for both emerging markets and developed currencies. Table 1. Descriptive statistics of currency returns (2000–2022). Currency Mean (2 days) std. dev. (2 Days) Mean (3 days) std. dev. (3 days) Mean (4 days) std. dev. (4 days) Mean (5 days) std. dev. (5 days) Mean (6 days) std. dev. (6 days) Mean (7 days) std. dev. (7 days) usDiLs −0.0001 0.0110 −0.0003 0.0150 −0.0004 0.0182 −0.0006 0.0208 −0.0008 0.0233 −0.001 0.0252 euRusD −0.0002 0.0101 −0.0005 0.0135 −0.0007 0.0162 −0.0010 0.0185 −0.0012 0.0206 −0.0014 0.0226 gBPusD −0.0002 0.0091 −0.0004 0.0123 −0.0006 0.0148 −0.0008 0.0169 −0.0010 0.0189 −0.0013 0.0207 usDRuB 0.0002 0.0160 0.0000 0.0220 −0.0001 0.0262 −0.0003 0.0301 −0.0004 0.0336 −0.0006 0.0367 auDusD −0.0003 0.0107 −0.0005 0.0151 −0.0007 0.0187 −0.0009 0.0216 −0.0011 0.0242 −0.0014 0.0266 usDCaD −0.0001 0.0089 −0.0002 0.0126 −0.0004 0.0154 −0.0005 0.0179 −0.0007 0.0201 −0.0009 0.0221 nZDusD −0.0002 0.0095 −0.0004 0.0132 −0.0006 0.0161 −0.0008 0.0187 −0.0010 0.021 −0.0012 0.0231 usDseK −0.0003 0.0108 −0.0006 0.0152 −0.0009 0.0188 −0.0011 0.0217 −0.0013 0.0243 −0.0016 0.0267 usDBRL 0.0003 0.0129 0.0000 0.0183 −0.0002 0.0221 −0.0004 0.0254 −0.0006 0.0283 −0.0008 0.031 usDtRY 0.0004 0.0148 0.0001 0.0210 −0.0001 0.0255 −0.0003 0.0291 −0.0006 0.0325 −0.0008 0.0356 Note: the mean returns for most currency pairs are close to zero, indicating that the returns are negligible on average over the given periods. standard deviations are relatively higher for more volatile currencies like usDRuB and usDtRY, suggesting higher risk and volatility. Table 2. skewness, Kurtosis, and Jarque-Bera statistic. Currency skewness Kurtosis Jarque-Bera statistic p-value usDiLs −0.108 2.937 7.334 0.025 euRusD 0.103 2.872 5.747 0.054 gBPusD −0.098 3.014 8.410 0.015 usDRuB 0.256 3.217 11.563 0.003 auDusD −0.088 2.954 6.922 0.031 usDCaD 0.062 2.876 5.458 0.065 nZDusD −0.123 3.046 9.181 0.010 usDseK 0.136 2.937 7.761 0.021 usDBRL 0.342 3.319 13.687 0.001 usDtRY 0.467 3.731 17.387 0.000 Note: Skewness: Measures the asymmetry of the return distribution. Values close to zero indicate a symmetrical distribution. For example, usDiLs and gBPusD have skewness values near zero, indicating relatively symmetrical distributions. Kurtosis: Measures the ‘tailenders’ of the return distribution. a kurtosis value close to 3 indicates a normal distribution. Most currency pairs have kurtosis values around 3, indicating slight deviations from normality. Jarque-Bera Statistic: tests whether the sample data has the skewness and kurtosis matching a normal distribution. High values and low p-values (<0.05) suggest non-normality. For instance, usDBRL and usDtRY show significant deviations from normality. 14 s. ghaneM etal. are less trend-driven compared to emerging markets. Unlike neely (1997), however, this study indicates that technical analysis can be effective across multiple time frames, from daily to weekly, based on daily trading signals. 5.4. Robustness test to ensure the robustness of our findings and to account for potential data snooping bias, we conducted additional analyses using more robust techniques, such as fractal integration. the fractal dimension is a numerical measure that provides insight into the complexity of the return series. the Fractal Dimension (D) measures how a fractal pattern scales with size. it’s calculated using different methods depending on the fractal type and context. One common method is the box-counting method. it typically ranges between 1 and 2 for financial time series. a fractal dimension closer to 2 indicates more complexity and noise, suggesting that the market exhibits high randomness and less predictable trends. conversely, a dimension closer to 1 implies a less complex series with stronger trends or patterns, which may indicate higher predictability. For currency returns, a fractal dimension lower than 1.5 generally points to the presence of a trending market (persistence), while a dimension higher than 1.5 indicates a more anti-persistent or mean-reverting behavior. this insight helps traders and analysts to develop strategies suited to market conditions. the table below presents the fractal dimensions of the currency pairs. table 10 presents the fractal dimensions of the currency pairs. the fractal dimension results indicate that most currency pairs exhibit high complexity, suggesting that the return series are fractal and self-similar over different time scales. this complexity can be leveraged to enhance trading strategies and improve predictive accuracy. the fractal dynamics in the return series confirm the predictive power of technical trading rules across different currency pairs. these robust techniques enhance our understanding of market behavior and support the practical application of our research in trading strategies. the fractal dimensions of various currency pairs highlight differences in market complexity and predictability. currency pairs like UsD/ils (1.41), UsD/RUB (1.40), nZD/UsD (1.36), UsD/BRl (1.42), and UsD/tRY (1.39) all exhibit higher complexity. these pairs show significant randomness and noise compared to the other currency pairs, indicating that their markets are more unpredictable and challenging to forecast. trading strategies for these pairs should be highly adaptable, incorporating trend-following and mean-reversion approaches to handle the inherent unpredictability and mixed trends. in contrast, currency pairs such as eUR/UsD (1.25), aUD/UsD (1.24), UsD/caD (1.22), gBP/UsD (1.20), and UsD/sek (1.21) display moderate complexity. these pairs exhibit more structured market behaviors with identifiable trends but still contain elements of randomness. For these pairs, trend-following strategies can be effective, though traders should remain vigilant for potential reversals and market shifts. 6. Conclusion and suggested future research this research investigates the predictive power of 497 technical trading rules across various holding periods on 10 developed and emerging currencies, offering fresh insights into the optimal use of technical analysis in currency markets. the findings confirm that technical indicators exhibit significant predictive power in developed and emerging market currencies. notably, emerging markets demonstrate higher Table 10. Fractal dimensions for the currency pairs. Currency pair Fractal dimension interpretation usD/iLs 1.41 High complexity euR/usD 1.25 Moderate complexity gBP/usD 1.20 Moderate complexity usD/RuB 1.40 High complexity auD/usD 1.24 Moderate complexity usD/CaD 1.22 Moderate complexity nZD/usD 1.36 High complexity usD/seK 1.21 Moderate complexity usD/BRL 1.42 High complexity usD/tRY 1.39 High complexity cOgent BUsiness & ManageMent 15 predictability, consistent with previous studies that highlight the differences in market efficiency between emerging and developed economies. a key contribution of this study is the identification of optimal holding periods and parameter configurations for these trading rules. the results indicate that trading signals remain effective for up to seven days, likely due to factors such as nonsynchronous trading and the timing skills of market participants. this insight is particularly useful for traders looking to fine-tune their short-term strategies, especially within intraweek trading windows. the study underscores the practical importance of understanding how different holding periods impact the success of technical signals, allowing for better optimization in currency trading strategies. One limitation of this research is its exclusion of profitability analysis for the trading rules studied. While we established the predictive effectiveness of these rules, future research should integrate considerations such as transaction costs, interest rate differentials, and market liquidity to evaluate the actual profitability of these strategies. incorporating these factors would provide a more complete picture of how trading rules perform under real-world conditions. the practical implications of this study are wide-ranging. traders can directly apply the identified optimal settings to maximize their returns, tailoring strategies based on the specific market and holding period. Meanwhile, policymakers may use these insights to craft regulations that enhance market efficiency, particularly in emerging markets with higher predictability. this could involve initiatives that promote transparency and reduce information asymmetry, fostering a more stable trading environment. Future research should also expand the application of these rules to other asset classes and explore how they perform under varying market conditions, including volatile or crisis periods. additionally, this study emphasizes the importance of nonsynchronous trading and timing skills in determining the effectiveness of technical indicators. By accounting for these factors, traders can more effectively navigate the complexities of currency markets, optimizing their strategies to exploit short-term opportunities. Policymakers, too, can draw from these insights to design market interventions aimed at improving fairness and transparency, ensuring that technical trading does not disproportionately favor certain participants over others. in conclusion, while this research advances the understanding of technical trading rules in currency markets, it also highlights areas for further investigation. addressing the current study’s limitations and broadening the scope of future research will deepen our knowledge of technical analysis in financial markets. this, in turn, will benefit traders and policymakers, helping to enhance market efficiency and stability across global financial systems. Author’s contributions authors contributed to developing this manuscript as follows: seri ghanem: conceptual and design and drafting of the paper. Murad harasheh: conceptual and design, interpretation of the data, drafting of the paper, and revising. Qays sbaih: conceptual and design, analysis, and interpretation of the data; and the drafting of the paper. t. k ajmal: interpretation of the data and revising all authors agree on the final approval of the version to be published and to be accountable for all aspects of the work. Disclosure statement no potential conflict of interest was reported by the author(s). Funding this research did not receive any funding. 16 s. ghaneM etal. About the authors Seri Ghanem is a lecturer of Finance at Birzeit University-Palestine since 2001, tecahing Personal Finance, corporate Finance, Financial Markets, and international Finance. he obtained his B.a from the University Of illinois at chicago, and MBa from the northeastern illinois University in 2000. his research interest are in financial markets, international finance, and corporate finance. Murad Harasheh is a senior assistant Professor of Finance at the University of Bologna-italy and a research associate at Yunus social Business center of Bologna. he was awarded his Ph.D. in 2014 in economics, law, and institutions from the school of advanced studies of Pavia (iUss). in 2021, he obtained the italian national scientific Qualification (asn) as a 2nd tier (associate) Professor in “economics of Financial intermediaries and corporate Finance.” Previously, he collaborated with the italian authority for energy Regulations (aReRa) as an economist and energy markets analyst. his primary research interests are related to corporate finance, firm valuation, energy and commodity finance, and sustainability economics. he is the author of the book “global commodities: Physical, Financial, and sustainability aspects” and various publications in international journals in Finance, business valuation, and energy. Qays Sbaih is a highly skilled finance and treasury professional with an Msc in Financial Forecasting and investment from the University of glasgow, alongside an MBa and a Ba in accounting and Finance from Birzeit University. he also holds the aci Dealing certificate, underscoring his expertise in global treasury management. With over 10 years of experience, Qays has led key roles in treasury operations, financial modeling, and eRP implementations. currently, he oversees eRP integration for Bisan, specializing in solutions for trade companies and government sectors. his previous roles include assistant Manager of treasury and investment at the Bank of Jordan and advising on FX reserves at the central Bank of Yemen. skilled in tools like Python for Finance, Bloomberg, and excel VBa, Qays brings a strategic and data-driven approach to optimizing financial systems. T. K. Ajmal is a Research associate in the Department of economics and Finance at United arab emirates University. 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