Enhancing Investment Profitability: Study on Contrarian Technical Strategies in Brent Crude Oil Markets

Abstract

In the context of heightened oil price volatility, mastering technical trading strategies is essential for informed investment and sound decision making. This study explores the effectiveness of contrarian technical trading strategies in the Brent crude oil market, aiming to enhance returns in the face of persistent market fluctuations. Utilizing historical price data, this research formulates trading rules based on overbought and oversold signals derived from the Relative Strength Index (RSI) and the Stochastic Oscillator Indicator (SOI). It assesses their performance through a range of Average Holding Period Return (AHPR) metrics, emphasizing the 250-day AHPR as a proxy for one-year returns. The findings show that RSI-based strategies, especially those using a threshold of 25, are most effective in oversold conditions, achieving peak profitability of over 40% in Quarter 2. The conclusions highlight the importance of parameter flexibility, strategic timing, and responsiveness to market dynamics in optimizing the contrarian strategy performance. The implications suggest investors and managers can refine strategies by accounting for behavioral biases, market timing, and flexible parameters, while enhancing big data analytics in technical trading.

1. Introduction

The volatility of crude oil markets is a significant concern for investors, policymakers, and businesses globally. Crude oil markets are inherently volatile due to many elements, including geopolitical, supply and demand, and macroeconomic factors ([1,2]). Understanding this volatility is critical for making informed investment decisions and achieving profitability ([3,4]). Investors often rely on technical analysis and advanced trading strategies to navigate these fluctuations and maximize returns ([5]). Moreover, crude oil is a cornerstone of the global economy, affecting everything from transportation costs to the pricing of goods and services ([6]). Therefore, understanding the nuances of oil market volatility can offer significant economic advantages by effectively managing volatility spillovers ([7,8]). For example, the contrarian investment strategy involves trading against the prevailing market trends to exploit price overreactions. Technical indicators, like the Relative Strength Index (RSI) and the Stochastic Oscillator Indicator (SOI), are used to detect overbought and oversold conditions, signaling potential reversals ([9,10,11]). These tools help identify stock price overshooting driven by investor sentiment, enabling timely entry and exit decisions ([12,13]). The momentum investment strategy capitalizes on continuing price trends. Based on the moving average crossovers, the MA trading rules help identify the trend direction and signal entry or exit points ([14,15,16,17]). These rules guide investors in trend-following behavior to capture sustained market movements ([10]).

Technical analysis mainly employs historical data (e.g., oil price data) to establish various technical indicators and trading rules to grasp profits from crude oil price fluctuations ([18]). By recording historical price data, analysts can identify the patterns and the trends that inform trading decisions ([19]). Technical trading rules, such as contrarian (i.e., RSI and SOI) and momentum technical trading rules (moving average), are often used to predict future price movements and optimize trading strategies ([20,21]). Numerous financial websites, including Bloomberg, Investing.com, and MarketWatch, provide daily technical analysis information, highlighting the importance of this approach in financial markets ([22,23]). These platforms offer real-time data, charts, and expert analysis, enabling traders to stay informed and make strategic decisions. The widespread availability of such resources underscores the crucial role of technical analysis in directing the complex and volatile crude oil market ([24]).

Crude oil prices frequently exhibit overshooting ([25,26]), characterized by temporary and substantial deviations from their fundamental values. These distortions offer compelling opportunities for investors applying contrarian strategies that seek to profit from market overreactions. In this context, technical indicators, such as the Relative Strength Index (RSI) and the Stochastic Oscillator Indicator (SOI), are particularly effective in identifying overbought and oversold conditions, signaling potential mean reversion points where prices are likely to revert to their long-term averages ([24,27]). By capturing these signals, investors can position themselves advantageously ahead of the anticipated price corrections. This study is motivated by the potential of the RSI and SOI trading rules, specifically designed for the crude oil market, to enhance trading outcomes by exploiting the mean reversion patterns ([10,28,29]). Given the market’s inherent volatility and frequent price fluctuations, systematically applying these indicators can yield valuable strategic insights. Ultimately, this research demonstrates how contrarian strategies, guided by the RSI and the SOI, can effectively navigate the complexities of crude oil trading and provide a robust approach to achieving a superior investment performance.

An expanding body of research highlights the value of technical analysis in navigating volatile markets such as crude oil, with indicators like the RSI, the SOI, and moving averages proving effective for trend identification and forecasting price reversals ([18,19,28,30,31]). While the previous studies support the profitability of momentum and contrarian strategies ([10,17,21]), they often rely on static thresholds and overlook the impact of trading seasonality. This is a critical limitation, as crude oil prices frequently overshoot due to investor sentiment and macroeconomic shocks, making fixed rules less effective. Moreover, herding behavior and imitation effects are prevalent in crude oil markets, where traders often follow the actions of others to reduce uncertainty and avoid potential losses, especially in volatile conditions. This behavior is driven by information asymmetry, social influence, and fear of missing out on profitable opportunities, which leads traders to imitate market trends rather than relying solely on individual analysis ([32,33]). Additionally, the crude oil market data show that forecast-based strategies can enhance investor welfare, particularly for wealthier participants, though their effectiveness declines under high-price impact conditions ([34]).

Measuring trading performance is also crucial, and it is typically assessed using established metrics, such as the Average Holding Period Return (AHPR), which measures the average returns over a holding period ([35]); the Sharpe Ratio, which evaluates the risk-adjusted returns by incorporating volatility ([36]); Maximum Drawdown (MDD), which reflects the most significant peak-to-trough decline and captures the downside risk ([37]); and evaluation metrics that account for generalization ability, risk, and return consistency in statistical learning-based strategies ([38]). The AHPR is particularly appropriate for this study, as it captures the average performance across multiple trading signals and holding periods, offering a comprehensive view of the effectiveness of the SOI and RSI rules. To address these gaps, our study employs big data analytics to dynamically adjust the RSI and SOI parameters and evaluate their seasonal performance using the AHPR. In doing so, we offer marginal contributions that have been largely overlooked in prior research and challenge the theoretical foundations of the Efficient Market Hypothesis.

Consequently, this study examines the effectiveness of contrarian technical trading strategies in the Brent crude oil market by applying trading rules based on the RSI and the SOI to exploit the market inefficiencies associated with overbought and oversold conditions. The technical analysis literature defines overbought scenarios as occurring when the SOI’s K value exceeds 80 or the RSI surpasses 70, often signaling potential market downturns due to price overextension ([31,39]). In contrast, oversold signals emerge when the SOI’s K value falls below 20 or the RSI drops under 30, indicating possible rebound opportunities following market corrections ([40]). This study investigates whether adjusting these threshold parameters through big data analytics can enhance the trading performance, challenging the conventional reliance on fixed benchmarks. By analyzing the historical Brent crude oil price data, this research provides empirical insights into the contrarian strategies’ profitability and risk management potential. This approach contributes to advancing technical analysis by illustrating how adaptive applications of the RSI and the SOI can support informed decision making in volatile energy markets, offering valuable implications for investors and analysts alike ([24]).

Moreover, this study offers important and novel insights by demonstrating that integrating flexible RSI parameter settings with strategic trading timing significantly enhances the effectiveness of contrarian strategies in the volatile Brent crude oil market. In contrast to prior research that employs static trading rules, our approach reveals the empirical advantages of dynamic RSI adjustments and seasonal timing, contributing to the advancement of technical analysis and the development of more adaptive and profitable investment strategies in energy markets.

This study makes several key contributions to advancing the understanding of contrarian technical trading strategies in the volatile crude oil market. First, it pioneers the dynamic adjustment of the RSI and SOI thresholds using big data analytics rather than relying on static parameter settings, a limitation of much prior research. This innovation allows for trading signals to better adapt to rapidly changing market conditions, enhancing the responsiveness and effectiveness of contrarian strategies. Second, by employing Average Holding Period Returns (AHPRs) to evaluate the seasonal performance across various parameter adjustments, this study provides a more realistic and time-sensitive measure of profitability, which is vital for accurately assessing the timing of investment decisions. Third, this study identifies that the integration of flexible parameters with strategic trading timing—such as applying RSI25 for oversold signals in Quarter 2 and RSI75 for overbought conditions—yields superior outcomes, emphasizing that the combined effect of timing and parameter configuration is critical to enhancing trading profitability. Finally, this study provides compelling evidence that the RSI trading rules, particularly when the RSI threshold is set at 75, consistently outperform the SOI rules under overbought conditions. This highlights the critical role of optimizing indicator levels to enhance returns in contrarian trading. These findings further reveal that flexible parameter settings markedly boost the performance, especially in response to overreaction signals, underscoring the necessity of adopting adaptive strategies to effectively navigate complex and dynamic market environments. Together, these contributions provide practical, data-driven guidance for investors seeking to outperform the traditional technical strategies in energy markets.

2. Literature Review and Hypotheses Proposed

2.1. Theoretical Foundation

The theoretical foundation of technical analysis challenges the Efficient Market Hypothesis (EMH) by asserting that financial markets, including the oil market, are subject to inefficiencies and anomalies that can be systematically exploited. Technical analysis posits that price movements are not entirely random, but often follow discernible patterns influenced by investor psychology and cognitive biases, as emphasized in behavioral finance ([12,41,42,43]). In the context of oil markets, where prices are affected by geopolitical events, speculative behavior, and supply–demand dynamics, these inefficiencies become more pronounced. Analysts highlight that deviations from the fundamental values offer opportunities to identify overbought and oversold conditions, anticipate trend reversals, and implement rule-based trading strategies that go beyond the scope of fundamental analysis ([28,44,45]). Recent empirical studies have demonstrated that certain technical indicators, such as moving averages and the RSI, retain predictive validity and contribute to abnormal returns in Brent and WTI future trading ([46]). These findings reinforce the view that price anomalies persist in oil markets and that the assumptions of informational efficiency cannot fully capture market behavior. Therefore, technical analysis offers significant value by providing a systematic approach for interpreting market signals and exploiting inefficiencies, thereby challenging the EMH’s premise of fully rational pricing ([47,48]).

2.2. Contrarian Investment Strategies

Contrarian trading involves taking positions against the prevailing market trends or sentiments, capitalizing on price movements driven by market overreactions and inefficiencies ([49]). In the context of oil markets, where geopolitical factors and supply–demand shocks often influence volatility, this strategy leverages indicators, such as the RSI and the SOI, to identify overbought and oversold conditions and anticipate corrections or reversals ([10,12,28]). Recent research has shown that contrarian strategies can provide valuable diversification, particularly in times of heightened market uncertainty or during market overreactions, where conventional momentum-based strategies may underperform ([50]). Furthermore, the successful implementation of contrarian tactics in the oil market requires a nuanced understanding of market psychology to differentiate between temporary price fluctuations and fundamental shifts in sentiment ([51]). By challenging the prevailing market sentiment, contrarian strategies offer traders the potential for higher returns, especially when market inefficiencies create profit opportunities. This approach not only presents an alternative to the traditional trading methods, but also provides a framework for navigating market volatility and capitalizing on oil market anomalies.

2.2.1. SOI Trading Rules

The Stochastic Oscillator Indicator (SOI) is a widely used technical analysis tool to identify potential trend reversals or momentum shifts in financial markets, including oil ([52]). It evaluates the closing price within the high–low range over a set period, often nine or fourteen days, to indicate overbought (above 80) or oversold (below 20) conditions, suggesting market corrections ([18,31,53]). In the oil market, where price volatility is common, these signals can highlight potential buying opportunities during oversold conditions or warn of a downturn during overbought conditions ([54,55]). Traders frequently combine the SOI signals with other indicators to refine their entry and exit strategies, aiming to profit from short-term price movements ([9]).

2.2.2. RSI Trading Rules

The Relative Strength Index (RSI) is a widely used momentum oscillator in technical analysis, particularly effective in evaluating the speed and change in price movements in markets like oil ([56]). Calculated over 14 days, the RSI oscillates between 0 and 100 and is instrumental in identifying overbought and oversold conditions ([10,28]). A reading above 70 signals that the asset is overbought, suggesting a potential price correction, while an RSI below 30 indicates oversold conditions, often pointing to a buying opportunity as the price may rebound ([57]). In the volatile oil market, where price swings are common, the RSI helps traders capitalize on these fluctuations and validate market trends ([55,58]). By combining the RSI signals with other indicators, traders can confirm trends and make more informed decisions, enhancing the accuracy of short-term market predictions ([17]).

2.3. Hypotheses Proposed

2.3.1. Concerning Contrarian Trading Rules

The Stochastic Oscillator Indicator (SOI) and the Relative Strength Index (RSI) are valuable tools in technical analysis, each offering distinct advantages and disadvantages. The SOI dynamically identifies overbought and oversold conditions, incorporating recent price movements relative to the high–low range ([59]). This responsiveness may provide timely signals for market participants to profit on short-term price reversals in volatile markets like Brent oil. However, the SOI’s sensitivity to market noise can lead to more frequent false signals, requiring traders to filter out noise effectively ([60]).

In contrast, the RSI is renowned for its simplicity and effectiveness in highlighting broader momentum trends and market extremes. Its smooth oscillations and straightforward interpretation make it easier for traders to identify sustained trends and anticipate major reversals ([61]). The RSI’s calculation, based on average gains and losses over a specified period, typically 14 days, provides a more precise signal of market momentum ([57]). Its single line oscillates between 0 and 100, making it straightforward to interpret when it crosses thresholds like 30 (oversold) or 70 (overbought), facilitating easier decision making for traders ([62]).

The Efficient Market Hypothesis (EMH) suggests that financial markets reflect all available information, implying that technical analysis should not provide consistent profits ([63]). However, technical indicators like the Relative Strength Index (RSI) challenge this assumption by offering tools to identify market inefficiencies, particularly in volatile environments. As such, this study emphasizes the characteristics of the Relative Strength Index (RSI) as a trading tool in oil markets, highlighting its smoother calculation methodology and its capacity to generate more reliable overbought and oversold signals compared to that of the Stochastic Oscillator Index (SOI). One distinct characteristic of the RSI is its ability to filter out market noise. It is particularly effective in volatile environments where clarity in momentum signals is essential for informed decision making ([64]). The contributions of this study lie in its comparative analysis of technical indicators, offering insights into the superior performance potential of the RSI in capturing trend reversals and optimizing entry and exit points. Innovatively, this study integrates flexible RSI threshold settings and applies a seasonal performance evaluation approach, providing a more adaptive and data-driven strategy for enhancing contrarian trading outcomes in energy markets. Therefore, this study proposes H1.

H1. 

RSI-based trading strategies outperform SOI-based strategies in crude oil markets.

This study emphasizes the characteristics of rule-based trading strategies in the oil market, particularly their ability to respond to sentiment-driven mispricings through systematic entry and exit signals. These strategies challenge the Efficient Market Hypothesis (EMH), which posits that financial markets are efficient and that such mispricings should not persist. Specifically, using oversold signals to purchase Brent oil ETFs and overbought signals to initiate short positions reflects a disciplined approach to capturing price reversals and correcting inefficiencies ([65]). One prominent characteristic of this strategy is its reliance on technical indicators that react to momentum shifts, enabling traders to act decisively during periods of extreme investor behavior. The contributions of this approach lie in its potential to deliver a superior performance by consistently capitalizing on behavioral biases and short-term market dislocations in the Brent oil market ([66]). Innovatively, this study enhances the rule-based strategies by integrating seasonally segmented analysis and refining the signal thresholds to better accommodate the cyclical and sentiment-driven nature of the energy markets. By offering a replicable and adaptive framework, this strategy contributes to developing more effective trading systems tailored to the volatile nature of energy markets. Accordingly, this study proposes H2.

H2. 

Following contrarian trading rules, specifically buying Brent oil ETFs on oversold signals and short selling on overbought signals, results in a better performance.

2.3.2. Concerning the Adjustment of Parameters for Contrarian Trading Rules

This study identifies the key characteristics of adaptive contrarian trading strategies in oil markets, particularly their responsiveness to fluctuating market sentiment and evolving economic conditions, which contradict the EMH. Unlike rigid models that rely on fixed RSI thresholds (e.g., 30 and 70), adaptive strategies leverage big data analytics to recalibrate these parameters in real time, allowing for greater alignment with the current market dynamics ([67]). A distinctive characteristic of this approach is its flexibility, which enables traders to anticipate price reversals during periods of volatility better and shift investor behavior ([68]). The value of this method lies in its capacity to enhance trading accuracy and returns by tailoring strategies to the unique patterns of the oil market, which is often shaped by geopolitical risks, supply–demand imbalances, and macroeconomic shocks ([69]). By incorporating real-time data calibration and emphasizing contextual sensitivity, this study contributes an innovative framework that advances the precision and adaptability of contrarian strategies in energy trading. This study offers valuable insights into more robust and profitable decision-making frameworks by integrating adaptive mechanisms into contrarian trading models. Therefore, it proposes H3.

H3. 

Different parameters (i.e., adjusting the parameters above or below those recommended by these contrarian trading rules) can enhance the trading performance.

2.3.3. Concerning Contrarian Trading Rules with Trading Signals Issued at Varying Times

Implementing contrarian trading signals across different quarters can enhance the investment performance by capitalizing on market inefficiencies and emotional price swings rather than supporting the Efficient Market Hypothesis (EMH) ([70]). This study focuses on the characteristics of contrarian trading strategies, particularly their ability to exploit the emotional price swings and temporary inefficiencies often driven by cyclical demand, geopolitical disruptions, and speculative behavior in oil markets ([71]). The defining characteristic of these strategies is their tendency to move against prevailing market trends, enabling investors to detect price reversals and corrections when markets are driven by irrational exuberance or fear ([19]). Contrarian strategies can respond to varying macroeconomic conditions and sentiment shifts when implemented across different quarters, enhancing their effectiveness. The contributions of this approach lie in its potential to improve the portfolio performance by identifying profitable entry points during market mispricing and reducing exposure to herd-driven volatility ([72]). By integrating temporal adaptability into contrarian strategies and addressing the cyclical nature of oil markets, this study introduces an innovative quarterly-based framework that strengthens timing precision and enhances resilience in volatile investment environments. Therefore, this study proposes H4.

H4. 

Contrarian trading rules exhibit a superior performance as trading signals in certain quarters compared to others.

2.3.4. Concerning the Adjustment of Parameters for Contrarian Trading Rules with Trading Signals Issued at Varying Times

In line with challenging the Efficient Market Hypothesis (EMH), this study examines the characteristics of contrarian trading strategies with dynamically adjusted parameters, explicitly focusing on the quarterly variations in oil market behavior. Oil prices’ volatility and cyclical nature, influenced by geopolitical tensions, supply shocks, and seasonal demand shifts, make the market exceptionally responsive to adaptive strategies ([73]). By incorporating big data analytics to revise the traditional trading rule parameters, this approach enhances the precision of contrarian signals, especially during periods of elevated uncertainty or trend reversals when the conventional thresholds may underperform. One of the key characteristics of this adaptive method is its responsiveness to evolving market sentiment, which allows for traders to align their strategies with the current dynamics better. This study contributes innovatively by integrating data-driven quarterly recalibration into contrarian models, offering a more nuanced, context-sensitive framework that enhances predictive power and strategic flexibility in turbulent oil markets. By addressing the limitations of fixed-threshold models and providing empirical insights into time-sensitive market behaviors, this research advances the development of adaptive, performance-optimized trading strategies ([12,28]). Therefore, this study proposes H5.

H5. 

When the parameters are revised, the contrarian trading rules will exhibit a better performance as trading signs are triggered in particular quarters compared to other quarters.

3. The Design of This Research

3.1. Trading as Contrarian Trading Signals Produced

This study integrates contrarian strategies employing technical indicators, like the Stochastic Oscillator (SOI) and Relative Strength Index (RSI), offering practical benefits for investors. These strategies capitalize on market signals indicating overreactions, aiming to exploit inefficiencies and shifts in investor sentiment ([70,74]). Due to the increased volatility in crude oil prices caused by climate change impacts like extreme weather events, investors might benefit from reallocating their portfolios toward energy markets by including crude oil-related financial instruments ([40,75]), such as crude oil ETFs ([76]). These investments may capitalize on contrarian trading signals, offering potential advantages.

Our research explores whether implementing a strategic timing approach for Brent oil ETFs, guided by contrarian signals during specific quarters, can enhance profitability compared to that of a broader timeframe or other quarters. In our study centered on Brent oil ETFs, we utilize the SOI and RSI contrarian trading strategies to evaluate the profitability of trades, analyzing oversold and overbought signals generally and across different quarters.

3.2. Subsequent Performance Measured

This study assesses the performance following signs generated by contrarian SOI and RSI trading rules. Day 0 is the starting point when these technical rules trigger signals. Recognizing the variable pace of share price rebounds post-overbought or oversold signals, we use a spectrum of Holding Period Return (HPR) metrics to evaluate the investment outcomes. The short-term performance is analyzed through 10-day and 25-day HPRs, while the long-term performance is assessed with 100-day and 250-day HPRs, providing insights into the efficacy of the SOI and RSI trading rules. Given the typical 250 trading days in a year, we cap the maximum holding period at 250 days to avoid data overlap from the same days or months across consecutive years, addressing the potential issues that could arise with utilizing five-quarter HPRs as our longest metric.

Following that, by applying Equation (1), the calculation of the Holding Period Return (HPR) for various time intervals is as follows:

HPRn = [(1 + R1) × (1 + R2) × … (1 + Rn)] − 1

(1)

where Ri represents the daily return for the i-th day; i ranges from 1 to n; and n is set to 10, 25, 100, and 250.

Subsequently, this study calculates HPR10, HPR25, HPR100, and HPR250. Given the numerous trade signals generated by the SOI and RSI trading signals, this study then calculates the Average Holding Period Return (AHPR) using Equation (2):

$${\mathrm{AHPR}}_{\mathrm{n}}={\displaystyle \frac{\left({\mathrm{H}\mathrm{P}\mathrm{R}}_{\mathrm{n},1}+{\mathrm{H}\mathrm{P}\mathrm{R}}_{\mathrm{n},2}+\cdots \dots \dots {\mathrm{H}\mathrm{P}\mathrm{R}}_{\mathrm{n},\mathrm{m}}\right)}{\mathrm{m}}}$$

(2)

where AHPRn is the average of HPR for n days, with a total of m trades based on a specific contrarian trading rule.

This study can assess various subsequent performances (i.e., diverse AHPRs) based on Equation (2). While Brent oil can be acquired at market prices, this study argues that investors may purchase financial instruments tracking Brent oil price (i.e., Bernt ETFs). As such, this concern may offer valuable insights to traders who buy the ETFs that track the price movements of Brent oil.

Furthermore, our analysis acknowledges the importance of considering transaction costs associated with trading Brent ETFs. However, the transaction fees linked to these index ETFs are minimal and are not expected to impact their subsequent performance significantly.

4. Results

4.1. Sample Statistics

This study employs the daily Brent oil price data from Datastream from 1990 to 2023 (i.e., 34 years) as our sample. The mean (M), median (Med), standard deviation (SD), minimum (Min), and maximum (Max) of Brent oil prices are shown in

Table 1. Descriptive statistics. M, SD, Med, Mix, and Max for Brent oil prices from 1990 to 2023.

	Obs.	M	SD	Med	Min	Max
Panel A: Daily index
Brent	8623	51.97	32.84	46.57	9.1	143.95
Panel B: Daily index return
Brent	8623	0.05%	2.55%	0.05%	−47.47%	50.99%

. We then reveal the considerable difference between the maximum and minimum for the Brent oil price, representing the relative volatilities of Brent oil price as shown by its high standard deviation.

Moreover, Figure 1 illustrates the long-term trend of Brent crude oil prices from 1990 to 2023, revealing significant fluctuations influenced by geopolitical tensions, economic cycles, and supply–demand dynamics. Some notable events include the early 2000s surge, the 2008 financial crisis, the 2014 price collapse, and the COVID-19 shock and recovery. This historical perspective highlights the inherent volatility of the Brent market and provides a basis for assessing contrarian trading strategies within cyclical price movements.

4.2. Results of Oversold Trading Rules in Aggregate and Various Quarters

4.2.1. Results of Using RSI Oversold Trading Rules

Table 2. The subsequent Brent oil price performance for the diverse RSI oversold trading signals emitted. This study measures the subsequent Brent oil price performance, including the 10-, 25-, 100-, and 250-day Average Holding Period Returns (AHPRs) as oversold signals produced by the SOI and RSI regulations both in aggregate (Panel A) and in diverse quarters (Panel B). This study examines whether these AHPRs significantly differ from zero by using the t-test, providing p-values (p) for these AHPRs in different holding periods (HPs). Statistical significance is indicated by *, **, and ***, representing the 10%, 5%, and 1% levels, respectively.

Panel A: Performance in aggregate
				Holding period (HP)			Number (N)				Mean (M)				p value (p)				Sig. (S)
RSI25				10			295				−2.03%				0.0025				***
RSI25				25			295				−1.78%				0.1401
RSI25				100			295				8.12%				0.0003				***
RSI25				250			295				9.45%				0.0051				***
RSI30				10			669				−1.74%				0.0000				***
RSI30				25			669				−1.45%				0.0358				**
RSI30				100			669				6.10%				0.0000				***
RSI30				250			667				11.10%				0.0000				***
RSI35				10			1274				−1.05%				0.0000				***
RSI35				25			1271				−0.79%				0.0784
RSI35				100			1269				7.21%				0.0000				***
RSI35				250			1254				11.34%				0.0000				***
Panel B: Performance in diverse quarters
		Quarter 1					Quarter 2					Quarter 3					Quarter 4
	HP	N	M		p	S	N	M	p	S		N	M	p		S	N	M	p	S
RSI25	10	85	1.99%		0.1419		33	−1.10%	0.7105			47	−1.20%	0.2263			130	−5.19%	0.0000	***
RSI25	25	85	0.50%		0.8656		33	15.16%	0.0000	***		47	−3.09%	0.0113		**	130	−7.10%	0.0000	***
RSI25	100	85	26.15%		0.0000	***	33	42.85%	0.0000	***		47	−32.94%	0.0000		***	130	2.36%	0.1982
RSI25	250	85	30.39%		0.0004	***	33	45.63%	0.0022	***		47	−15.79%	0.0001		***	130	−4.30%	0.1073
RSI30	10	176	0.29%		0.7305		117	0.33%	0.7542			130	−0.32%	0.6677			246	−4.92%	0.0000	***
RSI30	25	176	−2.26%		0.1785		117	9.42%	0.0000	***		130	−2.68%	0.0340		**	246	−5.39%	0.0000	***
RSI30	100	176	16.98%		0.0000	***	117	26.15%	0.0000	***		130	−23.23%	0.0000		***	246	4.28%	0.0024	***
RSI30	250	176	16.56%		0.0004	***	115	27.25%	0.0000	***		130	−6.56%	0.0244		**	246	8.97%	0.0016	***
RSI35	10	312	0.54%		0.3589		259	0.74%	0.1982			259	0.34%	0.4913			444	−4.01%	0.0000	***
RSI35	25	312	−1.20%		0.2693		259	6.68%	0.0000	***		259	−1.11%	0.1679			441	−4.71%	0.0000	***
RSI35	100	312	13.49%		0.0000	***	259	26.91%	0.0000	***		259	−16.09%	0.0000		***	439	4.85%	0.0000	***
RSI35	250	306	12.61%		0.0000	***	250	21.05%	0.0000	***		259	−0.54%	0.8004			439	11.95%	0.0000	***

presents the Brent oil price performance as oversold signs emitted by various RSI rules. It measures the AHPRs for 10 and 25 days as the short-term performance and 100 and 250 days as the long-term performance after the generated oversold signs. Additionally, Panel A presents the overall best performance for the RSI oversold signs produced in general. At the same time, Panel B shows the best performance for oversold signs emitted in diverse quarters, which we used to evaluate whether the subsequent performance would be different or far different between different quarters.

Panel A in Table 2 indicates generally negative short-term returns (10- and 25-day AHPRs) and positive long-term returns (100- and 250-day AHPRs), with RSI35 showing a better long-term performance than RSI 30 and RSI25. Panel B provides the more detailed analysis of the best performance for the trading signs produced at different quarters (i.e., breaking down the results by different quarters (Q1–Q4)). This table may aid traders and analysts in understanding the effectiveness of the RSI oversold signals for predicting future oil price movements over various time horizons and market conditions.

Furthermore, in Panel A in Table 2, it is evident that a more extended holding period correlates with a higher AHPR, with AHPRs250 showing values of 9.45%, 11.1%, and 11.34% for the RSI25, RSI30, and RSI35 trading rules, respectively. These figures significantly surpass the risk-free rate (represented by the yield on the US 10-year government bonds). Therefore, investors who purchase financial instruments tracking the Brent oil price (such as Brent ETFs) in response to trading signals generated by diverse RSI oversold rules may achieve returns exceeding 9%, outperforming the stock above market benchmark and the risk-free rate.

Following this, we examine whether investors can achieve higher returns when the trading signals are generated in a specific quarter compared to other quarters. Panel B in Table 2 indicates that AHPRs250 exceed 45%, 27%, and 21% after the oversold signals generated by RSI25, RSI30, and RSI35, respectively, in Quarter 2; however, the almost negative AHPRs are shown in Q3 as trading signs produced by these RSI oversold trading rules.

The revealed results will be valuable to investors since trading timing matters for the trading performance, as shown by the fact that investors will derive a much better performance than the subsequent performance in Q2. In contrast, investors might suffer a loss when trading Brent ETFs as the oversold trading signals were emitted in Q3. As such, trading timing must be emphasized.

4.2.2. Results of Using SOI Oversold Trading Rules

When adopting the SOI oversold rules, Panel A in

Table 3. The subsequent Brent oil price performance for the diverse SOI oversold trading signals emitted. This study measures the subsequent Brent oil price performance, including 10-, 25-, 100-, and 250-day AHPRs, as oversold signs produced by the SOI regulations in aggregate (Panel A) and diverse quarters (Panel B). This study examines if these AHPRs significantly differ from zero by using the t-test, providing p-values (p) for these AHPRs in different holding periods (HPs). Statistical significance is indicated by *, **, and ***, representing the 10%, 5%, and 1% levels, respectively.

Panel A: Performance in aggregate
			Holding period (HP)					Number (N)				Mean (M)				p value (p)					Sig. (S)
K15			10					1017				0.05%				0.8513
K15			25					1014				−0.16%				0.7360
K15			100					1012				6.68%				0.0000					***
K15			250					1003				11.01%				0.0000					***
K20			10					1461				−0.10%				0.6474
K20			25					1455				−0.01%				0.9740
K20			100					1446				6.37%				0.0000					***
K20			250					1431				11.09%				0.0000					***
K25			10					1862				0.01%				0.9740
K25			25					1856				0.28%				0.4433
K25			100					1841				6.32%				0.0000					***
K25			250					1820				11.12%				0.0000					***
Panel B: Performance in diverse quarters
		Quarter 1					Quarter 2						Quarter 3					Quarter 4
	HP	N		M	p	S	N		M	p	S		N	M	p		S	N	M	p		S
K15	10	247		−0.21%	0.7553		207		2.43%	0.0000	***		222	2.01%	0.0001		***	341	−2.48%	0.0000		***
K15	25	247		0.06%	0.9568		207		6.40%	0.0000	***		222	1.61%	0.0661			338	−5.52%	0.0000		***
K15	100	247		14.37%	0.0000	***	207		21.57%	0.0000	***		222	−11.86%	0.0000		***	336	4.10%	0.0012		***
K15	250	244		16.40%	0.0000	***	201		18.85%	0.0000	***		222	2.33%	0.3171			336	8.16%	0.0009		***
K20	10	351		−0.40%	0.4648		296		1.83%	0.0000	***		323	1.53%	0.0001		***	491	−2.12%	0.0000		***
K20	25	351		0.28%	0.7596		296		5.54%	0.0000	***		323	1.58%	0.0229		**	485	−4.67%	0.0000		***
K20	100	351		11.92%	0.0000	***	296		19.55%	0.0000	***		321	−9.84%	0.0000		***	478	5.03%	0.0000		***
K20	250	344		13.03%	0.0000	***	288		16.91%	0.0000	***		321	3.52%	0.0693			478	11.27%	0.0000		***
K25	10	443		−0.36%	0.4567		396		2.02%	0.0007	***		419	1.15%	0.0006		***	604	−1.84%	0.0000		***
K25	25	443		0.38%	0.6242		396		5.60%	0.0000	***		419	1.43%	0.0146		**	598	−4.11%	0.0000		***
K25	100	443		10.37%	0.0000	***	396		18.76%	0.0000	***		417	−8.79%	0.0000		***	585	5.62%	0.0000		***
K25	250	433		11.38%	0.0000	***	385		17.51%	0.0000	***		417	3.97%	0.0178		**	585	11.80%	0.0000		***

displays that the annualized Holding Period Returns (AHPRs250) are similar: 11.01% for RSI25, 11.09% for RSI30, and 11.12% for RSI35. This suggests that investors might not observe significant differences when evaluating the subsequent performance using these various SOI trading rules, regardless of which SOI oversold trading rule is used.

Next, we investigated whether investors can achieve higher returns when the trading signals are generated in a specific quarter compared to those in other quarters. Panel B in Table 3 reveals that AHPRs250 are 18.85%, 16.91%, and 17.51% for the oversold signals generated by KI5, K20, and K25, respectively, in Quarter 2. However, inferior AHPRs250 (less than 5%) and even negative AHPRs100 are shown in Quarter 3 for these SOI oversold trading rules. Thus, the results indicate significant differences depending on the quarter in which the trading signals are emitted, as evidenced in Panel B in Table 3. This highlights the importance of considering trading timing.

Additionally, it is noted that when overbought signals are emitted, market participants are advised to short sell stocks rather than buy them. However, this study shows that adopting a long position yields better results than a short position, contradicting the short-selling strategies typically suggested by the overbought signals generated by the SOI trading rules. This indicates that short selling stocks might not be appropriate for holding long-term periods, such as 250 trading days (approximately one year).

4.3. Results of Overbought Trading Rules in Aggregate and Various Quarters

4.3.1. Results of Using RSI Overbought Trading Rules

Regarding the RSI overbought trading rules,

Table 4. The subsequent Brent oil price performance for the diverse RSI overbought signs emitted By measuring the subsequent Brent oil price performance, including 10-, 25-, 100-, and 250-day AHPRs as overbought signs produced by RSI regulations in aggregate (Panel A) and in diverse quarters (Panel B), this study uses the t-test to examine whether these AHPRs differ from 0, provides p-values (p) for these AHPRs in different holding periods (HPs), and displays *, **, and ** to represent 10%, 5%, and 1% statistical significance.

Panel A: Performance in aggregate
			Holding period (HP)						Number (N)				Mean (M)			p value (p)				Sig. (S)
RSI65			10						1713				0.34%			0.0645
RSI65			25						1713				1.05%			0.0002				***
RSI65			100						1688				3.39%			0.0000				***
RSI65			250						1667				8.74%			0.0000				***
RSI70			10						925				0.39%			0.1019
RSI70			25						925				1.26%			0.0010				***
RSI70			100						906				3.80%			0.0000				***
RSI70			250						896				9.95%			0.0000				***
RSI75			10						428				1.23%			0.0006				***
RSI75			25						428				2.99%			0.0000				***
RSI75			100						419				6.32%			0.0000				***
RSI75			250						418				15.00%			0.0000				***
Panel B: Performance in diverse quarters
		Quarter 1					Quarter 2					Quarter 3						Quarter 4
	HP	N		M	p	S	N	M		p	S	N		M	p		S	N	M		p	S
RSI65	10	434		0.81%	0.0291	**	474	0.55%		0.0732		450		0.54%	0.1864			355	−0.79%		0.0226	**
RSI65	25	434		2.53%	0.0000	***	474	0.55%		0.1638		450		2.96%	0.0000		***	355	−2.52%		0.0000	***
RSI65	100	434		8.40%	0.0000	***	474	1.06%		0.2561		428		1.61%	0.0786			352	2.53%		0.0644
RSI65	250	434		7.46%	0.0000	***	465	7.91%		0.0000	***	416		10.35%	0.0000		***	352	9.54%		0.0000	***
RSI70	10	222		0.28%	0.4868		242	0.83%		0.0592		254		1.00%	0.0764			207	−0.78%		0.0649
RSI70	25	222		2.26%	0.0049	***	242	0.68%		0.2176		254		4.00%	0.0000		***	207	−2.51%		0.0003	***
RSI70	100	222		8.76%	0.0000	***	242	0.21%		0.8682		237		2.06%	0.0859			205	4.69%		0.0065	***
RSI70	250	222		4.07%	0.0879		237	12.93%		0.0000	***	232		11.73%	0.0000		***	205	10.85%		0.0000	***
RSI75	10	91		1.72%	0.0008	***	116	2.00%		0.0018	***	127		1.08%	0.2397			94	0.03%		0.9579
RSI75	25	91		3.70%	0.0096	***	116	1.90%		0.0227	**	127		5.85%	0.0001		***	94	−0.23%		0.8064
RSI75	100	91		10.75%	0.0000	***	116	2.78%		0.0785		118		3.33%	0.0545			94	10.13%		0.0002	***
RSI75	250	91		10.95%	0.0027	***	115	22.48%		0.0000	***	118		12.79%	0.0000		***	94	12.56%		0.0001	***

shows that the RSI 75 trading rule outperforms the RSI 70 and RSI 65 trading rules. Our findings indicate that adhering to the suggested parameter for RSI oversold trading rules (i.e., RSI 70) might not outperform the overbought trading rules with parameters higher than 70 (i.e., RSI 75). Therefore, fine-tuning the parameters through big data analytics could lead to a better performance than sticking with the parameters traditionally advised by the RSI rules.

When comparing the subsequent performance (AHPRs250) of the RSI overbought signs produced in Quarter 2, this study found that the RSI 75 achieves a performance of 22.84%, significantly higher than 12.91% for the RSI 70 and 7.91% for the RSI 65. Additionally, the trading performance using the RSI 75 rule in Quarter 2 (22.84%) is much better than its overall performance (15%), highlighting the importance of trading timing. In contrast, the trading performance in Quarter 1 for the RSI 75, the RSI 70, and the RSI 65 are 10.95%, 4.07%, and 7.46%, respectively, inferior to the performance observed in Quarter 2. These results suggest that investors should pay attention to trading timing.

4.3.2. Results of Using SOI Overbought Trading Rules

Table 5. The subsequent Brent oil price performance for the diverse SOI overbought signs emitted. By measuring the subsequent Brent oil price performance, including 10-, 25-, 100-, and 250-day AHPRs as oversold signs produced by SOI regulations in aggregate (Panel A) and in diverse quarters (Panel B) this study uses the t-test to examine whether these AHPRs differ from 0, provides p-values (p) for these AHPRs in different holding periods (HPs), and displays *, **, and ** to represent 10%, 5%, and 1% statistical significance.

Panel A: Performance in aggregate
			Holding period (HP)						Number (N)				Mean (M)			p value (p)				Sig. (S)
K75			10						2500				1.00%			0.0000				***
K75			25						2500				1.44%			0.0000				***
K75			100						2481				3.58%			0.0000				***
K75			250						2442				10.77%			0.0000				***
K80			10						1992				1.09%			0.0000				***
K80			25						1992				1.43%			0.0000				***
K80			100						1976				3.71%			0.0000				***
K80			250						1943				10.78%			0.0000				***
K85			10						1443				1.27%			0.0000				***
K85			25						1443				1.51%			0.0000				***
K85			100						1429				3.68%			0.0000				***
K85			250						1405				11.23%			0.0000				***
Panel B: Performance in diverse quarters
		Quarter 1					Quarter 2					Quarter 3						Quarter 4
	HP	N		M	p	S	N	M		p	S	N		M	p		S	N	M		p	S
K75	10	673		0.87%	0.0007	***	655	1.53%		0.0000	***	676		1.24%	0.0001		***	496	0.17%		0.5166
K75	25	673		2.25%	0.0000	***	655	1.86%		0.0000	***	676		1.81%	0.0004		***	496	−0.72%		0.1262
K75	100	673		9.23%	0.0000	***	655	2.12%		0.0162	**	657		1.34%	0.0934			496	0.83%		0.4926
K75	250	662		11.24%	0.0000	***	646	10.68%		0.0000	***	638		11.82%	0.0000		***	496	8.89%		0.0000	***
K80	10	538		0.71%	0.0124	**	526	1.60%		0.0000	***	538		1.34%	0.0001		***	390	0.58%		0.0442	**
K80	25	538		1.98%	0.0002	***	526	1.70%		0.0011	***	538		2.02%	0.0006		***	390	−0.50%		0.3582
K80	100	538		9.30%	0.0000	***	526	1.99%		0.0414	**	522		1.24%	0.1677			390	1.63%		0.2349
K80	250	530		10.54%	0.0000	***	518	10.95%		0.0000	***	505		11.67%	0.0000		***	390	9.73%		0.0000	***
K85	10	383		0.69%	0.0338	**	387	1.79%		0.0000	***	400		1.75%	0.0000		***	273	0.62%		0.0646
K85	25	383		1.62%	0.0070	***	387	1.89%		0.0016	***	400		2.52%	0.0005		***	273	−0.67%		0.3021
K85	100	383		9.19%	0.0000	***	387	1.54%		0.1307		386		0.91%	0.4001			273	2.93%		0.0683
K85	250	380		10.32%	0.0000	***	379	12.04%		0.0000	***	373		11.11%	0.0000		***	273	11.57%		0.0000	***

Note: The same note is shown in Table 2.

analyzes the three trading rules (K75, K80, and K85 overbought trading rules) applied to Brent crude oil prices from 1990 to 2023. AHPRs250 are quite similar, at 10.77%, 10.78%, and 11.23% for K75, K80, and K85, respectively. Additionally, when comparing the performances of the trading signs produced in diverse quarters, AHPRs250 are consistently around 10% across various quarters. This indicates that the results are similar when the SOI overbought signs are produced in diverse quarters. These findings contrast those obtained using the RSI trading rule, but show consistent results for the SOI overbought trading rule.

Moreover, since we used the long-term data from 1990–2023, we then used subperiods before and after 2008 and the whole period, as presented in Table 2, Table 3, Table 4 and Table 5. After processing by using two subperiod data, we found that the results revealed are similar to the results shown in this paper.

5. Discussion

Investing in the crude oil market primarily aims to achieve significant profits. To explore this, we thoroughly evaluated the effectiveness of contrarian strategies using the technical trading signals from the SOI and the RSI, focusing on using flexible parameters rather than strictly adhering to the suggested parameters from the SOI and RSI trading rules. Based on these considerations, we propose several hypotheses outlined in the Literature Review section and discuss their acceptance or rejection by analyzing Table 2 and Table 3 for the oversold results and Table 4 and Table 5 for the overbought results.

Regarding H1, the empirical results show that under contrarian trading strategies, the RSI and the SOI perform similarly in oversold conditions, as both effectively capture the mean reversion opportunities. However, when identifying the overbought conditions, the RSI at a threshold of 75 outperforms the other RSI and SOI rules. This suggests that a higher RSI threshold better captures significant price overextensions in bullish trends, avoiding premature signals and enhancing profit potential. The findings partially support H1, highlighting the importance of adapting trading thresholds to market dynamics for more precise and profitable contrarian strategies.

Concerning H2, the contrarian trading rules recommend buying when oversold signals are triggered by the RSI and the SOI and short selling during overbought signals. Our findings indicate that investors benefit from buying stocks linked to Brent oil based on oversold signals.

Interestingly, the results reveal that the buying strategies based on oversold signals yield more favorable outcomes than the short-selling strategies triggered by overbought signals, diverging from the conclusions of previous research ([58]). This finding is consistent with Ni et al. [12], suggesting that the profitability of technical strategies is context-dependent and influenced by market-specific dynamics ([10,77]). One plausible explanation for the underperformance of short selling in overbought conditions is the presence of strong upward momentum in the crude oil market, which often extends price rallies beyond the traditional overbought thresholds before reversal occurs. Additionally, geopolitical uncertainties, supply shocks, and speculative behavior can sustain high prices, making early short-selling positions vulnerable to losses. Thus, purchasing Brent oil ETFs during oversold conditions supports H2 for the oversold cases, while short selling during overbought conditions does not, thereby not supporting H2 for the overbought cases.

Regarding H3, this study explores the impact of flexible parameters on trading rules by adjusting the conventional RSI and SOI thresholds (e.g., RSI30 and K80 for overbought; and RSI30 and K20 for oversold). The results indicate that for the oversold signals, altering the parameters (e.g., RSI25 and RSI35; K15 and K25) does not significantly affect the performance, suggesting that stricter adherence to the traditional thresholds may be preferable in these conditions. However, when analyzing the overbought signals, the adjusted parameters, particularly using RSI75, outperform the other RSI and SOI strategies, leading to higher profitability. This finding supports H3, emphasizing the potential of customized parameter settings to improve returns in overbought conditions. The empirical results highlight the importance of flexibility in trading strategies, especially in volatile markets, where adapting to changing conditions can optimize the performance. While oversold signals may not benefit from parameter adjustments, the fine-tuning strategies for overbought conditions prove advantageous, offering practical insights for traders looking to maximize profits by adapting their approaches based on market dynamics ([78,79]).

With respect to H4, this study shows that incorporating trading timing can significantly enhance profitability, particularly for contrarian strategies based on the RSI and SOI regulations ([80,81]). Specifically, adopting buying strategies with RSI30 and SOI20 in Quarter 2 yields a superior performance compared to those of the other quarters, supporting H4. However, for the overbought conditions indicated by the SOI and the RSI, the AHPR250 values are similar across most quarters, except for Q1 when using the RSI70 rules. This partial support for H4 suggests that trading timing is crucial in optimizing strategies, especially when applied to energy markets. These findings highlight that market conditions and seasonal factors can influence the success of trading rules, emphasizing the importance of adapting strategies to specific timeframes ([74,82]).

Relating to H5, this study delves into how market participants’ consideration of trading timing and parameter adjustments can influence profitability. For oversold signals, an impressive 45% AHPR250 is achieved in Quarter 2 using the RSI25 rule, significantly outperforming the other quarters using the RSI and SOI trading strategies. This suggests that specific parameter settings in particular quarters can optimize the performance, supporting H5. Similarly, the RSI75 rule in Quarter 2 achieves an AHPR250 of over 22% for overbought signals, surpassing the results from the other quarters using the RSI and SOI rules. This indicates that trading timing and proper parameter selection play a critical role in maximizing returns. The empirical evidence reinforces the notion that market timing is a key factor in the success of contrarian strategies, highlighting the importance of adaptability in trading approaches. These findings underscore that profitability depends not solely on static strategies, but also on strategic timing and parameter adjustments ([81,83]).

6. Conclusions Remarks

6.1. Main Conclusions

This study presents groundbreaking insights that not only shed light on overlooked aspects in the previous studies, but also have the potential to contribute significantly to the existing body of literature.

First, the results reveal that RSI-based trading rules perform comparably to SOI under oversold conditions, but outperform them under overbought conditions, particularly when the RSI is set at 75. This partially supports H1 and confirms that RSI75 is especially effective in identifying profitable opportunities in overbought markets ([39]), offering a strategic edge for crude oil investors ([24]). These findings underscore the importance of selecting suitable technical indicators and tuning their thresholds for an optimal performance.

Second, this study emphasizes the need for flexibility in technical trading. While parameter adjustments have minimal impact on oversold signals, they notably enhance the overbought signal performance, particularly with RSI75. This finding suggests that rigidly adhering to the traditional settings may limit returns and that dynamic strategy adjustments based on market conditions can improve profitability ([40,78,79]).

Third, trading outcomes are significantly influenced by seasonal timing, with contrarian strategies producing notably higher returns in the second quarter, especially when responding to oversold signals. In contrast, returns for overbought signals remain relatively stable across quarters, except for a notable increase in Q1 using RSI70, emphasizing the need for continuous market monitoring and timely strategy adjustment ([74,80,81]).

Fourth, this analysis reveals significant variation in the effectiveness of the trading strategies. The contrarian RSI and SOI signals consistently generate statistically significant positive returns over a 250-day holding period, aligning with previous research on the performance of technical indicators ([84]). The RSI trading rules, with an AHPR250 surpassing 20%, demonstrate substantial potential as a trading signal. Notably, the RSI25 strategy achieves an AHPR250 above 40%, emphasizing its ability to capture profitable opportunities through well-timed entries in mean-reverting market conditions.

Finally, integrating trading timing with flexible parameter settings leads to improved outcomes, particularly when applying the oversold signals in Quarter 2 using RSI25 and the overbought signals with RSI75. These findings show that aligning parameter adjustments with appropriate timing strategies enhances profitability and more accurately captures the dynamic nature of crude oil markets ([81,83]). Investors are advised to refine their strategies to maximize returns and adapt to evolving market conditions ([85]).

6.2. Research Implications

This research offers several treasured implications. Initially, this study enhances the theoretical understanding of contrarian trading strategies within the context of crude oil markets by validating the efficacy of the RSI and SOI indicators. It challenges the Efficient Market Hypothesis by demonstrating that market anomalies, such as overbought and oversold conditions, can be systematically exploited for profit. This study underscores the importance of behavioral finance and cognitive biases, suggesting that investor behavior leads to predictable patterns that technical analysis may capitalize on crucial oil markets. This adds a nuanced perspective to the ongoing discourse on market efficiency, emphasizing that price movements are not solely driven by fundamental values, but also by psychological factors and sentiment. Consequently, this study contributes to a deeper understanding of how technical indicators can be reliable tools for predicting market reversals and optimizing trading strategies.

In addition, this study provides actionable insights for traders and investors by demonstrating the superior performance of the RSI-based contrarian trading strategies over the SOI-based ones in the Brent crude oil market. By identifying specific RSI thresholds (e.g., RSI75 for overbought conditions) that yield higher profitability, this study offers a pragmatic guide for optimizing trading decisions. The findings suggest that traders adopt flexible parameters and adjust their strategies dynamically in response to the market conditions. This practical approach enhances the trading performance and aids in risk management by providing more precise signals for market entry and exit. Thus, this study serves as a valuable resource for investors aiming to navigate the volatile crude oil market effectively and maximize returns.

Furthermore, for financial managers and policymakers, this study underscores the necessity of integrating advanced technical analysis tools and flexible trading strategies into investment portfolios. By highlighting the effectiveness of contrarian trading rules and the importance of adjusting parameters based on market dynamics, this research offers a strategic framework for managing investments in the crude oil market. Managers can leverage these insights to develop more robust investment strategies that account for market volatility and optimize profitability. Additionally, this study’s findings on the timing of trades across different quarters provide a nuanced comprehension of market cycles, enabling managers to make informed decisions regarding asset allocation and portfolio diversification.

Moreover, this research advances big data analytics in refining the technical trading rules. By employing data-driven approaches to adjust the RSI and SOI parameters, this study demonstrates how real-time analytics can enhance the accuracy and effectiveness of trading signals. This methodological innovation offers a blueprint for future research on applying adaptive trading strategies in volatile markets. It also highlights the importance of combining traditional technical analysis with modern data analytics to uncover deeper insights and improve the trading outcomes. Consequently, this study sets a precedent for integrating advanced analytical techniques in financial market research.

Last, but not least, this study provides a comprehensive framework for market participants to profit from market inefficiencies in the crude oil market. By detailing the conditions under which the RSI and SOI trading rules perform optimally, this research offers a strategic roadmap for implementing contrarian investment strategies. Investors can leverage these insights to develop tailored trading plans that exploit overbought and oversold conditions, thereby achieving superior returns. Additionally, the emphasis on flexible parameter adjustment and the importance of trading timing highlights the need for adaptive and responsive strategies in dynamic market environments. This strategic approach enhances profitability and provides a competitive edge in the highly volatile crude oil market.

6.3. Limitation and Future Research

While this study offers valuable insights into the effectiveness of contrarian trading strategies for Brent crude oil, several limitations remain. First, depending on the historical data, we need to fully grasp market conditions or account for geopolitical and economic changes affecting the current Brent crude oil market. Second, other potentially influential technical indicators and macroeconomic factors were not considered; instead we focused on the RSI and the SOI. Future research should address these limitations by incorporating real-time data and examining various indicators and economic variables. Expanding this study to include different market environments and periods could provide a more comprehensive assessment of contrarian strategies. Employing advanced analytical techniques such as machine learning and incorporating geopolitical analysis may further enhance the robustness and adaptability of trading strategies. Exploring the interplay between technical indicators and fundamental factors could offer deeper insights into optimizing contrarian trading strategies in the volatile energy market.