Navigating Energy and Financial Markets: A Review of Technical Analysis Used and Further Investigation from Various Perspectives

Abstract

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration.

1. Introduction

Technical analysis offers a structured method for forecasting price movements by examining historical market data [1,2]. This approach operates on the premise that past price trends often repeat, empowering traders and investors to pinpoint potential opportunities. Through the scrutiny of historical price and volume data, technical analysis facilitates the recognition of recurring patterns, which can be leveraged using various tools and methodologies associated with technical analysis [3]. In energy and financial markets, technical analysis helps traders, investors, and analysts make investment decisions, find market entry and exit points, and manage risk.

Fundamental principles within technical analysis (e.g., trend identification, support and resistance (S&R) thresholds, and a range of analytical tools and indicators) offer a structured approach to interpreting market information [4]. Trend analysis, for instance, aids in determining the trajectory of market movements, whether they are upward, downward, or sideways, utilizing tools like trend lines and moving averages. Support and resistance (S&R) denote crucial price levels where market trends may reverse or encounter obstacles. Although technical analysis is widely employed in stock trading, its relevance extends to other financial markets, including energy commodities like crude oil, natural gas, and electricity [5,6]. Despite the complexity of energy and financial markets, which are subject to influences such as geopolitical shifts, supply–demand imbalances, weather variations, and technological innovations, technical analysis remains a valuable tool for deciphering market patterns [7] and guiding strategic trading choices [8].

Fundamentally, technical analysis operates on the principle that historical price movements tend to repeat themselves due to human psychology [9]. This premise is rooted in the belief that market participants exhibit consistent and predictable behaviors, forming identifiable patterns on price charts. Technical analysts seek to predict forthcoming price shifts by recognizing these patterns and leveraging them to their advantage. Technical analysis also detects S&R thresholds, which indicate price movements and intensify buying or selling activity [10].

In energy and financial markets, technical analysis assumes even greater significance due to the influence of external factors on prices [11]. For instance, geopolitical tensions in oil-producing regions can trigger sudden spikes in crude oil prices [12], which may need to be adequately captured by fundamental supply and demand analysis alone. Technical analysis equips traders with timely signals and identifies potential reversal points [13], enabling them to navigate volatile market conditions effectively [14].

This paper offers a thorough review of technical analysis in energy and financial markets, structured into several critical sections for clarity and coherence. Beginning with the introduction, Section 1 sets the stage for the subsequent analysis. Section 2 discusses the theoretical foundations, illuminating the technical analysis methodologies. Following this, Section 3 reviews various techniques used in the literature, while Section 4 offers classifications for better understanding and differentiation. Section 5 delves into empirical evidence supporting the discussion with real-world applications and outcomes in energy and financial markets. Section 6 broadens the scope by exploring prospects from multiple perspectives within the research field. Additionally, some of the prospects that have been proposed and investigated are presented in Section 7, which may help individuals who are interested in these issues to explore and progress them further. The paper concludes in Section 8 with a summary of significant findings, reflecting on the insights gained and suggesting avenues for future research. This structured approach ensures a thorough and logical progression, facilitating a deep understanding of the complex dynamics of technical analysis in the energy and financial markets, as illustrated in Figure 1.

2. Theoretical Foundations of Technical Analysis

Technical analysis is a method used to forecast future price movements by analyzing past market data, notably price and volume [15,16,17]. Adherents of technical analysis maintain that by examining past price patterns and trends, they can forecast subsequent price movements. Detractors, however, criticize technical analysis as subjective and lacking empirical backing, while advocates argue that it offers valuable insights into market dynamics.

The efficient market hypothesis (EMH) forms a cornerstone of technical analysis, positing that asset prices include all relevant information, making historical price data analysis impossible to outperform the market [18] continually. Nonetheless, proponents of technical analysis suggest that while the EMH may hold over the long term, in the short to medium term, market prices may stray from their intrinsic values because of investor sentiment [19,20], market psychology [21,22], and irrational behavior [23,24]. Technical analysts employ various tools and methodologies, including trend analysis, to identify and evaluate the direction of price trends, believing that these trends persist and can guide trading decisions [25].

S&R levels are essential ideas in technical analysis [26], indicating price points where security tends to halt its upward or downward movement, respectively, based on traders’ actions [27,28]. Additionally, technical analysts employ stochastic oscillator (SOI) and relative strength index (RSI) technical indicators to pinpoint market conditions that are either overbought or oversold. These indicators validate or signal potential reversals in trends [29,30,31].

Technical analysis is precious in energy and financial markets due to their unique characteristics. These markets are impacted by a variety of factors, such as geopolitical shifts [32], supply and demand dynamics [33], and weather patterns [34], all of which can result in substantial price fluctuations [35,36,37]. This level of volatility makes it challenging to predict future price movements using only fundamental analysis since technical analysis may help investors make better decisions by analyzing price movements [38,39].

Technical analysts in the energy and financial markets use previous price data to predict future price changes [40]. These markets often exhibit cyclical patterns and trends, which technical analysts aim to identify and capitalize on for profitable trading opportunities [41]. Analysts can recognize patterns and trends by studying historical pricing data and forecast price changes [9,42]. Furthermore, technical analysis is well-suited to energy and financial markets’ fast-paced and volatile nature. Energy and financial prices can experience significant volatility, reacting swiftly to news and events. Therefore, by analyzing short-term price movements and trends, technical analysis assists traders and investors in navigating rapid price fluctuations and making timely decisions [43,44].

Additionally, technical analysis is preferred in energy and financial markets for its ability to provide clear and objective trading signals. Unlike fundamental analysis, which can be subjective, technical analysis relies on mathematical calculations and objective data. This objective nature makes it easier for market participants to make decisions according to technical analysis, as the signals generated by technical indicators are often unambiguous [45,46]. Despite criticism and limitations, many traders and investors view technical analysis as valuable for analyzing market behavior and trading decisions in energy and financial markets [15,40,47].

3. Review of Technical Analysis Used

Traders and investors in energy and financial markets frequently rely on technical analysis, a popular method for predicting future price movements using historical market data. This method uses trend analysis, chart patterns, and technical indicators to find trading opportunities. Moreover, momentum and contrarian investment strategies are commonly employed within technical analysis to capitalize on different market conditions [31,36].

Technical trend analysis assumes prices move in trends—upward, downward, or sideways. Traders use trend lines, moving averages, and other indicators to discover and confirm trends [20,39,48]. In energy and financial markets, trend analysis helps traders understand market direction, enabling them to make informed trading choices. Price changes create diverse chart patterns essential in technical analysis [49,50,51]. These patterns can indicate potential trend reversals or continuations, aiding traders in determining optimal entry and exit points. Additionally, technical trading rules, such as the RSI, SOI, and Ma trading rules, are mathematical calculations using historical price data [52,53,54,55], which would help identify over-reaction conditions, confirm trends, or signal reversals, supporting traders in their decision-making process.

In investment strategies, momentum strategies involve buying securities on upward trends and selling those trending downward. Moving averages are crucial momentum indicators in technical analysis for indicating trend direction [42]. Another momentum strategy is Trading Range Breakout (TRB), which views a breakout from a trading range as a sign of solid momentum in the breakout direction, potentially leading to profitable trading opportunities [19]. In contrast to momentum strategies, contrarian investment strategies involve buying currently unpopular securities in the market and selling those considered overvalued.

The RSI is a well-known contrarian indicator, gauging overbought or oversold phenomena in diverse markets, including financial and energy markets [30]. Contrarian investors use RSI to perceive potential market reversals and make trading decisions. Like the RSI, the SOI emits overbought or oversold signals to help traders identify trend reversals [56]. Bollinger Bands assess volatility and identify overbought and oversold conditions by utilizing a moving average and two standard deviations above and below it. Traders can then use these bands to generate buy or sell signals based on price movements about the bands [57,58].

4. Classifications for Technical Analysis

In energy and financial markets, technical analysis strategies can be classified into three main groups: price pattern analysis, indicator analysis, and market sentiment analysis [59,60], as depicted in Figure 2.

4.1. Price Pattern Analysis

This category encompasses strategies that identify and interpret patterns in price charts, such as head and shoulder patterns, triangles, flags, and pennants [61]. Traders utilize these patterns to predict price movements according to past price patterns. Candlestick patterns also fall into this category, providing graphical representations of price movements within specific periods, which assist traders in predicting future price movements based on the shape and formation of the candles. Price pattern analysis aids traders in anticipating market trends and making informed trading decisions [62,63].

Price pattern analysis is a foundational element of technical analysis in energy and financial markets, involving detecting and interpreting patterns in price charts [64,65]. These patterns, arising from the interplay of market forces, are thought to recur, offering traders insights into potential price shifts [4]. Among the well-known patterns is the head and shoulders, often signaling a change in trend. Traders frequently view the pattern as a signal for the price reversion [66,67].

Candlestick patterns represent another crucial aspect of price pattern analysis. Candlestick charts visually represent price movements within specific periods, each indicating the open, high, low, and close prices [49,68,69]. Traders analyze the shapes and formations of these candlesticks to forecast price movements. For instance, hammer candlesticks have a short body and extended lower wick, indicating that this pattern portends an upward trend [70]. Price pattern analysis, in general, is a valuable resource for traders, helping them predict market trends and make well-informed trading choices [71].

Energy and financial market technical analysis requires price chart pattern recognition and interpretation [9]. These patterns, which arise from the interaction of supply and demand, suggest they may recur over time, giving traders valuable insights into potential price movements [61]. The head and shoulders pattern is typical and indicates a trend reversal [72]. Traders often expect the price to reverse after this pattern [73].

4.2. Indicator Analysis

Indicator analysis helps traders understand market patterns and conditions [35,74]. These indicators can be broadly categorized into momentum and contrarian indicators, each serving a unique purpose in guiding trading decisions. In the realm of momentum indicators [13,42], MA plays a crucial role in smoothing out price data to reveal trends, which would help traders determine trend direction and potential reversal points [42,48]. The Moving Average Convergence Divergence (MACD) lines help traders determine trend strength, direction, momentum, and duration changes. Crossings between the MACD and signal lines are used as signals to buy or sell [75,76]. Trading Range Breakout (TRB) is a strategy that identifies breakouts from a trading range, signaling potential shifts in trend direction. TRB helps traders find entry and exit positions when prices break the trading range [19,77].

Regarding indicators that go against the prevailing trend, the SOI pinpoints instances where the security may be excessively bought or sold. Readings exceeding 80 suggest a potential overbought status, while those under 20 indicate possible overselling [78]. The RSI value is above 70 (below 30), indicating overbought (oversold) signals [79]. The BB consists of an MA and two outer bands set at standard deviations from the MA, aiding traders in spotting volatility and potential overbought or oversold scenarios. Movement beyond these bands could hint at either the continuation or reversal of the present trend [57,58,80].

By integrating these indicators into their investment approaches, market players can deepen their grasp of energy and financial markets and improve trading outcomes. The momentum and contrarian indicator synergy enables traders to discern trends, momentum shifts, and potential reversals, refining their strategies and boosting performance [36,81,82].

4.3. Market Sentiment Analysis

Market sentiment analysis is pivotal in trading, offering insights into the prevailing mood of market participants towards specific assets or the overall market [83,84]. Traders utilize a range of sentiment indicators to measure market sentiment accurately and make trading choices. For instance, the Put/Call ratio and the VIX (volatility index) are widely employed indicators [85]. The Put/Call ratio compares put options (anticipating price declines) to call options (expecting price increases). Bearish sentiment is indicated by a high put-to-call ratio, which suggests that a market decline is coming. A low ratio, on the other hand, indicates bullish sentiment and confidence in a market rise [86,87].

The VIX, often called the fear gauge, measures the market’s expectation of future volatility [88,89]. A high VIX indicates higher expected volatility, implying increased fear or uncertainty among investors and potentially signaling market downturns. Conversely, a low VIX suggests lower expected volatility, indicating investor complacency or confidence, potentially signaling stable or rising markets [90,91]. Examining market sentiment offers traders a window into the broader market landscape and possible trends, empowering them to adapt their trading strategies. This adaptability might mean a more cautious stance in times of heightened fear or a proactive approach during periods of investor complacency [92].

In momentum indicators [13,42], moving averages (MAs) are fundamental tools that help smooth out price data to reveal trends. There are two main types: Simple Moving Average (SMA) and Exponential Moving Average (EMA). MAs assist traders in identifying trend direction and potential reversal points [42,48]. The MACD helps traders gauge trend strength, direction, momentum, and changes in duration. Crossings between the MACD and signal lines are used as signals to buy or sell [75,76]. Trading Range Breakout (TRB) is a strategy that identifies breakouts from a trading range, signaling potential shifts in trend direction. TRB suggests entry and exit points when prices break the trading range [19,77].

Table 1. The description and critical techniques/indicators for three classifications.

Classifications	Description	Key Techniques/Indicators
Price Pattern Analysis	Focuses on identifying and interpreting visual patterns on price charts.	Head and shoulders, triangles, flags, pennants, candlestick patterns, etc.
Indicator Analysis	Utilizes various mathematical indicators to analyze market conditions.	Moving averages (MAs), Moving Average Convergence Divergence (MACD), relative strength index (RSI), stochastic oscillator indicator (SOI), etc.
Market Sentiment Analysis	Assesses the overall mood of the market to inform trading decisions.	Put/Call ratio, volatility index (VIX), etc.

presents the description and critical techniques/indicators for three classifications: price pattern analysis, indicators analysis, and market sentiment analysis. These classifications indicate their specific focuses and the key techniques/indicators associated with each category. This classification aids in clarifying how different analytical techniques are applied to understand and predict market behaviors.

5. Empirical Evidence for Application of Technical Analysis in Energy and Financial Markets

In this study, we present a thorough classification of research on the application and effectiveness of technical analysis in energy and financial markets. Our classification encompasses various aspects, including the type of energy market, geographical focus, period, methodology, and more. This classification has the potential to offer practitioners valuable insights into the practical implementation of technical analysis in energy and financial markets. As such, it may set a foundation for future research endeavors.

5.1. Types of Energy and Financial Markets

Empirical studies in energy markets encompass traditional fossil fuels like crude oil and natural gas [93,94]. Moreover, emerging renewable sources constitute a compelling body of evidence [95,96]. This research offers detailed analyses of price dynamics, market integration, and the impacts of geopolitical factors on crude oil markets, providing nuanced insights that underpin broader discussions on energy security and economic stability [97]. In natural gas markets, empirical research sheds light on supply–demand dynamics, pricing mechanisms, and the pivotal role of infrastructure, bolstering arguments for strategic investments in gas infrastructure to enhance market efficiency and resilience [98]. Similarly, studies in electricity markets contribute to the discourse on market power, pricing mechanisms, and the challenges and opportunities presented by the integration of renewables, offering compelling reasons for policy interventions to promote renewable energy adoption and grid modernization [99,100,101]. The appeal of research in renewables lies in its exploration of policy impacts, technological advancements, and market competitiveness, providing convincing evidence for accelerating the transition towards sustainable energy sources [102]. Collectively, these empirical studies deepen our understanding of energy markets and provide a solid foundation for informed decision making, policy formulation, and investment strategies that are crucial for navigating the complexities of the global energy landscape. Similarity, empirical studies in financial markets comprise numerous types, including stock markets for trading shares of public companies, bond markets for buying and selling debt securities, commodity markets for trading raw or primary products, foreign exchange markets for trading currencies, derivative markets for financial contracts derived from underlying assets, money markets for short-term borrowing and lending of funds, and so on.

5.2. Types of Geographical Areas

Empirical studies focusing on specific regions or countries, such as the USA, Europe, and Asia [103,104], contribute significantly to understanding regional energy and financial dynamics [105]. Research on the USA’s energy landscape, for instance, delves into the impact of the shale revolution, the integration of renewable energy, and regulatory frameworks, providing nuanced insights that underpin discussions on energy independence, sustainability, and climate change mitigation [106]. In Europe, empirical studies highlight the transition towards renewable energy sources, energy market liberalization, and cross-border energy trading, offering compelling reasons for further integration, infrastructure development, and policy harmonization to enhance energy security and market efficiency [107,108]. Meanwhile, in Asia, research focuses on the rapid growth of energy demand, challenges related to energy access, and the transition towards cleaner energy sources, providing convincing evidence for investments in energy infrastructure, technology innovation, and sustainable development strategies [109,110]. These region-specific studies deepen our understanding of regional energy dynamics and inform global energy policy discussions, highlighting the interconnectedness of energy markets and the imperative for coordinated efforts to address the global energy sector’s complex challenges. Regarding the types of geographical areas, financial markets exist across various geographical areas, including domestic markets that operate within a single country, regional markets that span multiple countries within a specific region, and global markets that encompass trading activities worldwide. In addition, this study states that each geographical area has its regulatory frameworks and market characteristics that influence market behavior and investment opportunities.

5.3. Data Period and Data Frequency

Empirical studies on energy and financial markets can be classified based on the time horizon they analyze, including short-term, medium-term, and long-term perspectives [111]. Short-term studies typically focus on intraday or daily price movements, examining the impact of factors such as market news, weather patterns, and geopolitical events on energy prices [112]. These studies are valuable for traders and market participants to grasp how to exploit profits on short-term price movements. Medium-term studies often take a few weeks to several months. They may involve the analysis of technical indicators, supply–demand dynamics, and seasonal trends [113], providing awareness of medium-range price drives and are helpful for market participants and analysts developing medium-term trading strategies. Long-term studies encompass several years or even decades and aim to understand fundamental shifts in energy markets, such as changes in energy policy, technological advancements, and shifts in global energy demand [114]. These studies are essential for long-term investors, policymakers, and energy companies planning strategic investments. Additionally, some studies focus on specific historical periods, such as the oil crises of the 1970s or the recent shale revolution, providing valuable lessons and insights into how energy markets respond to significant events and policy changes [115]. Understanding the time horizon of these empirical studies is crucial for interpreting their findings and applying them effectively in energy market analyses and decision-making processes.

Empirical energy and financial studies utilize various data frequencies, such as intraday, daily, weekly, and monthly, offering unique insights into market behavior [116]. Intraday data, with its high frequency, provide a granular view of market dynamics, enabling researchers to analyze price movements and volatility patterns within a single trading day [117]. This level of detail is beneficial for studying market microstructure, including price discovery mechanisms and the impact of high-frequency trading. While less granular than intraday data, daily data capture daily price changes and trends, offering a broader view of market performance over time [118] and analyzing the effectiveness of trading strategies and the influence of external elements (e.g., geopolitical events and economic indicators). Weekly data provide a more aggregated perspective, smoothing out daily fluctuations to highlight longer-term trends and seasonal patterns [119]. This frequency is valuable for understanding market trends over weeks or months, identifying cyclical patterns, and assessing the impact of policy changes or production disruptions on energy markets. Monthly data, with its even broader view, help analyze medium- to long-term trends, such as seasonal variations in energy demand or the impact of macroeconomic factors on energy prices [120,121]. The selection of data frequency is mainly based on the research questions and objectives, with each frequency offering unique insights into energy and financial market behaviors.

5.4. Methodologies and Performance Metrics

Empirical studies in energy and financial markets employ various methodologies to analyze and interpret data [122,123]. One common approach is to calculate average short- or long-term hold period returns, which involves measuring the average return on an investment over a specific period, providing insights into the profitability of different energy and financial assets [124]. Another method is to calculate average cumulative abnormal returns, which compares the actual returns of an asset with the expected returns based on market trends, helping identify abnormal returns potentially caused by external factors [125]. Additionally, the Sharpe ratio is employed to evaluate the risk-adjusted return of an investment, providing a measure of how well an asset has performed relative to its risk [126]. Moreover, researchers may analyze the maximum drawdown, which measures the most significant peak-to-trough decline in the value of a portfolio, indicating the maximum loss an investor could have experienced during a specific period [127].

These quantitative methods offer valuable insights into energy and financial assets’ performance and risk profile, helping investors, analysts, and policymakers make informed decisions in energy and financial markets [128,129]. In contrast, qualitative and mixed-method research in energy and financial markets often involves in-depth interviews, case studies, and surveys to understand better market dynamics, stakeholder perceptions, and regulatory environments [130]. These methods complement quantitative analysis by providing context and nuance to the empirical findings, enriching our understanding of the complexities of energy and financial markets and informing more holistic decision-making processes [131]. Combining quantitative and qualitative approaches can lead to more robust and insightful research in energy and financial markets, offering a comprehensive view of market trends, drivers, and challenges [132].

5.5. Technical Indicators Implicated Investment Strategies

In empirical energy and financial studies, momentum strategies often rely on technical analysis indicators such as MA, MACD, and TRB. MAs smooth price data and highlight trends over time, revealing market direction [20]. The MACD indicator assists traders in gauging trends’ strength, direction, momentum, and changes in duration [55]. TRB trades capitalize on price momentum and trends when the price breaks above or below a range [19].

On the other hand, contrarian approaches in energy and financial markets frequently utilize technical analysis indicators like the RSI, SOI, and BB. Traders use momentum oscillators like the RSI and SOI to recognize overbuying and overselling [29,30]. Bollinger Bands consist of volatility bands positioned above and below a moving average, with price shifts outside these bands potentially signaling trend reversals [57,80]. Contrarian strategies are valuable for traders seeking to profit from short-term price corrections or trend reversals in energy and financial markets.

Integrating momentum and contrarian approaches in empirical studies of energy and financial markets can substantially improve researchers’ comprehension of market dynamics and result in more efficient trading strategies. Each approach provides distinct perspectives on market trends, with momentum strategies concentrating on leveraging ongoing trends and contrarian strategies aiming to pinpoint potential reversals. These strategies have pros and cons but can provide a more complete market insight combined. By meticulously examining data and trends using these technical analysis tools, researchers can make better-informed choices, ultimately enhancing trading outcomes in energy and financial markets.

5.6. Market Efficiency and Market Anomalies

In the context of energy and financial markets, the efficient market hypothesis (EMH) plays a significant role in understanding the applicability of technical analysis. The EMH states that asset prices incorporate all available information, making technical analysis unlikely to outperform the market [18]. Studies examining the implications of the EMH on technical analysis in energy and financial markets found mixed results. Some research indicates that energy and financial markets exhibit varying degrees of efficiency, with specific periods or segments of the market being more efficient than others [36,133]. While technical analysis may not always be effective in energy and financial markets, specific conditions or anomalies could provide value.

Market anomalies are another area of interest when applying technical analysis in energy and financial markets. These anomalies refer to deviations from the EMH, where asset prices behave in a way inconsistent with market efficiency [134]. Studies investigating potential anomalies in energy and financial markets that could be exploited using technical analysis identified instances where specific technical indicators or patterns effectively predicted price movements [135]. However, the existence and persistence of these anomalies are debated, with some researchers arguing that they may result from data mining or random chance rather than actual market inefficiencies [136].

5.7. Behavioral Finance

In energy and financial markets, behavioral finance provides a valuable lens through which to analyze investor behavior and its implications for technical analysis. This field examines how psychological factors influence decision making, shedding light on how emotions, cognitive biases, and social cues shape market dynamics [137]. Investor sentiment, a key concept, reflects the overall attitude of investors towards energy and financial assets, influenced by factors such as news, rumors, and even weather patterns [92]. Studies demonstrate that shifts in investor sentiment can drive short-term price movements, causing deviations from fundamental values and creating opportunities for technical analysis to identify trends or reversals [138,139].

Market psychology, another critical aspect of behavioral finance, focuses on the collective behavior of market participants. It emphasizes how herding behavior, over-reaction, and under-reaction to information can impact price movements [140]. For example, while energy and financial markets exhibit high volatility for a period, investors may have herd behavior, following the actions of others without thoroughly analyzing the underlying fundamentals, thus creating price distortions that technical analysis may exploit by identifying patterns that indicate the market is overbought or oversold [29,141].

Behavioral biases, including loss aversion, confirmation bias, and anchoring, further contribute to market inefficiencies that technical analysis can exploit. Loss aversion, for example, describes investors’ tendency to avoid losses more than acquire equivalent gains, leading to suboptimal decisions [142]. Confirmation bias leads investors to seek and interpret information that confirms their beliefs, potentially distorting technical signals [143]. Anchoring bias occurs when investors rely too heavily on specific reference points when making decisions, leading to mispricing and offering opportunities for technical analysis to identify trends [144]. Incorporating insights from behavioral finance can thus enhance the effectiveness of technical analysis in energy and financial markets by offering a deeper comprehension of the psychological drivers of market behavior.

5.8. Comparison to Other Analyses

Comparative studies between technical and fundamental analyses in energy and financial markets offer valuable insights into the effectiveness of these two approaches. Fundamental analysis involves evaluating supply and demand dynamics, geopolitical factors, and macroeconomic trends to determine an asset’s intrinsic value. Conversely, technical analysis predicts price changes using prior price and volume data. Research comparing the two approaches has yielded varying outcomes. Fundamental analysis might surpass technical analysis in efficient markets by considering all relevant information in pricing assets. Nonetheless, technical analysis could be more successful, particularly in short-term trading or markets influenced heavily by psychological elements. For example, technical analysis proved more profitable in trading crude oil futures and markets than fundamental analysis [40,145].

In contrast, studies comparing technical analysis with quantitative analysis, particularly machine learning algorithms, in energy and financial markets have shown promising results for the latter. Machine learning algorithms can examine extensive datasets and detect intricate patterns that may not be evident to human analysts. This capability enables them to generate more precise predictions of price movements and formulate improved trading strategies. For example, compared the performance of machine learning algorithms with traditional technical analysis methods in predicting crude oil prices, the results showed that machine learning algorithms outperformed technical analysis [146,147], highlighting the potential of quantitative analysis methods in energy and financial markets.

6. Prospects from Multiple Perspectives

The prospects for technical analysis strategies in energy and financial markets are tied to technological advancements. Therefore, we outline several significant trends and advancements that are expected to influence the future of technical analysis in energy and financial markets.

6.1. Artificial Intelligence and Big Data

Integrating big data analytics and artificial intelligence technologies is poised to transform technical analysis in energy and financial markets. The sheer volume of data generated in these markets, encompassing supply, demand, geopolitical events, and weather patterns, presents a significant challenge for human traders to analyze effectively. Although these enormous datasets may be complex to observe, AI and machine learning systems may quickly identify minor patterns and trends [148,149]. These technologies enable traders to make informed decisions quickly [150].

AI’s impact on technical analysis extends beyond data processing speed [151]. It also enhances the recognition and interpretation of complex patterns within price charts. Automated AI systems can detect and assess various chart patterns, empowering market participants to make more precise predictions grounded in historical price actions [2,152]. Integrating big data and AI technologies will likely lead to more sophisticated and practical technical analysis strategies within energy and financial markets, potentially reshaping how traders approach market analysis and decision-making processes.

6.2. Algorithmic Trading

Algorithmic trading has garnered significant interest in energy and financial markets for its potential to boost trading efficiency and effectiveness. These algorithms can execute trades without human intervention, using predefined criteria such as technical indicators or price patterns [153]. By automating trading, algo trading can significantly reduce the time needed to execute trades, enabling traders to capitalize swiftly and efficiently on market opportunities.

Moreover, algorithmic trading offers improved precision by eliminating emotional influences that can sway human traders. Algos operates on predefined criteria, fostering disciplined trading approaches [154]. This can mitigate impulsive decisions rooted in fear or greed, resulting in more dependable trading results. Algorithmic trading is increasingly acknowledged as a beneficial asset for energy and financial market players aiming to refine their trading tactics and attain superior outcomes.

6.3. High-Frequency Trading

High-frequency trading (HFT) has surged in popularity within energy and financial markets thanks to its capacity to execute numerous trades rapidly. HFT firms leverage advanced algorithms to scrutinize market data and pinpoint short-term trading prospects. These algorithms can analyze immense datasets in milliseconds, enabling HFT firms to exploit even the smallest price differences [155]. Consequently, HFT can enhance market efficiency by narrowing bid–ask spreads and boosting liquidity [156].

Despite its advantages, HFT has sparked concerns regarding market stability and fairness. Critics contend that HFT firms enjoy an unjust advantage over traditional traders by accessing and analyzing market data more quickly. This capability, critics argue, can lead to market manipulation and heightened market volatility [157,158]. Moreover, the speed and complexity of HFT algorithms can increase the risk of market crashes, as seen in the 2010 Flash Crash, where HFT algorithms were implicated in exacerbating market volatility [159].

Regions implemented various measures to monitor and regulate HFT activity to address these concerns. For example, circuit breakers and trading halts can help mitigate the impact of excessive volatility caused by HFT [160]. Additionally, regulators have introduced rules requiring HFT firms to provide liquidity and maintain fair and orderly markets [157,158,161]. While HFT has the potential to improve market efficiency, it is essential to balance its benefits with the need to maintain market stability and fairness.

6.4. Blockchain Technology

Blockchain technology has garnered significant attention for its potential to enhance transparency and efficiency in energy and financial trading. By providing a decentralized and immutable ledger, blockchain can track the provenance of energy and financial commodities from generation to consumption, ensuring transparency and reducing the risk of fraud [162,163]. Moreover, blockchain can streamline trading processes by enabling peer-to-peer trading without intermediaries, reducing transaction costs, and increasing market accessibility [164]. These features of blockchain technology align well with technical analysis strategies, as they provide traders with reliable and transparent data for making informed trading decisions.

Furthermore, blockchain technology can improve energy and financial markets’ efficiency by facilitating the creation of smart contracts. These contracts are self-executing agreements with the terms directly encoded into the blockchain’s code. They automatically enforce the terms of the agreement, removing the need for intermediaries and decreasing the risk of fraud [165]. By integrating blockchain technology and smart contracts into energy and financial trading, market participants can automate trading processes, reduce transaction costs, and improve market efficiency, all of which can complement technical analysis strategies in energy and financial markets.

6.5. Quantitative Analysis

Quantitative analysis in energy and financial markets is on the brink of significant progress, driven by the increasing availability of data and advancements in computational tools. As these markets grow in complexity, quantitative analysis is expected to become more sophisticated, enabling the creation of intricate trading strategies based on technical analysis. For example, studies have demonstrated the effectiveness of machine learning algorithms like support vector machines and neural networks in forecasting energy and financial market prices and trends [166,167]. These algorithms can analyze vast datasets more efficiently than traditional statistical models, potentially leading to more precise forecasts and better-informed trading decisions [168]. Furthermore, advancements in high-frequency trading (HFT) technologies are anticipated to enhance quantitative analysis capabilities in energy and financial markets further. HFT enables traders to execute trades at exceptionally high speeds, utilizing algorithms to identify and seize fleeting market opportunities [169]. By integrating HFT with quantitative analysis, traders can gain a competitive advantage in rapidly evolving energy and financial markets.

Furthermore, quantitative analysis in energy and financial markets highlights efficiency and regulation issues. While sophisticated trading strategies can improve market liquidity and price discovery, they can also introduce risks, such as market manipulation and systemic instability [170]. Regulators are, therefore, tasked with balancing the benefits of quantitative analysis with the need for market integrity and investor protection. For instance, implementing circuit breakers and position limits aims to mitigate the risks associated with high-frequency trading and other quantitative strategies [171,172]. Additionally, regulators may need to enhance technological capabilities to monitor and regulate increasingly complex trading activities effectively [173]. Overall, as quantitative analysis continues to evolve in energy and financial markets, it will be essential for regulators, market participants, and researchers to collaborate in addressing the challenges and opportunities associated with these advancements.

6.6. Data Visualization

Data visualization tools are increasingly crucial in energy and financial markets, where vast amounts of complex data must be analyzed quickly and effectively. These tools enable traders to visualize market data through interactive charts and graphs, facilitating the identification of patterns and trends. For instance, candlestick charts are used in technical analysis to show price trends and reversals [71]. Similarly, heatmaps can visually depict market volatility and price changes across various periods and asset categories since heatmap visualization may help traders make proper decisions [39,42,52,57]. Furthermore, advancements in data visualization technologies, such as augmented reality (AR) and virtual reality (VR), hold promise for enhancing traders’ ability to analyze and interpret complex market data [174,175]. AR and VR can create immersive data visualization environments, enabling traders to explore market data in new and insightful ways.

Using data visualization tools in energy and financial markets also has implications for risk management and regulatory compliance. For instance, regulators may require traders to use specific visualization tools to monitor and report trading activities [176]. Moreover, data visualization can help identify and mitigate market manipulation and fraud risks by providing greater transparency and oversight [177]. As data visualization technologies continue to advance, it will be essential for regulators and market participants to collaborate in developing standards and best practices for their use in energy and financial markets.

6.7. Risk Management

Managing risk is crucial in energy and financial market trading, and research suggests that technical analysis can be instrumental in effective risk management. One critical application is identifying stop-loss levels, where technical analysis such as MAs and S&R levels can assist traders in setting appropriate levels to limit potential losses during unfavorable market conditions [178,179]. Furthermore, technical analysis aids in determining the proper position by enabling traders to evaluate the risk–return profile of their trades according to past prices [180]. By integrating technical analysis into risk management strategies, investors may make more proper selections and reduce market risk to their portfolios.

Technological advancements continue to refine risk management approaches in energy and financial markets. For instance, integrating machine learning and artificial intelligence algorithms into risk models bolstered the precision of risk assessments [181]. These advanced models can process extensive datasets, uncovering intricate patterns and correlations that enhance the reliability of risk evaluations for traders. Moreover, real-time data and advanced analytics tools can enable traders to assess and mitigate risks more effectively by providing timely insights into market conditions and trends [182]. By leveraging these technological advancements, traders can improve their risk management strategies and protect their portfolios from adverse market movements.

7. Some Prospects Proposed and Investigated

7.1. Measuring Performance Related to Investments

Investors typically evaluate the performance following trading signals generated by technical trading rules (i.e., measuring subsequent performance rather than performance in a given month), which differs from the widely discussed January effect or weekend effect. We contend that this focus on measuring subsequent performance aligns with real-world investment practices [36]. While trading signals are emitted by technical trading rules at time t, investors might not know the performance until time t + i. However, this concern, linked to the concept of investing, has yet to receive much attention despite its importance in previous studies.

Furthermore, investors may also consider the timing of their trades based on signals from technical trading rules, subsequently assessing performance [29,80]. Different technical trading rules, such as moving average (MA) or relative strength index (RSI) rules, may yield varying performance outcomes depending on when the signals are generated, whether in different quarters [13,19,30] or months [54]. This suggests that certain quarters (or months) may exhibit better subsequent performance than others. We infer that these above results may be attributed to factors such as the fluctuating demand and supply of various energy and financial sources, including fossil fuels and solar power [183,184], as well as economic factors [185].

7.2. Factors Influencing Performance Measured by Technical Analysis

Firm performance, traditionally assessed through metrics like stock return, firm value, and return on equity (ROE) in finance, accounting, economics, and business, can also be evaluated using technical analysis. For example, one approach involves measuring the frequency of days a stock price crosses above a “golden cross” following a moving average (MA) trading rule in a year, divided by the total trading days in a year, as a proxy for firm performance. Based on momentum technical trading rules, this proxy can then be investigated to see whether financial statement variables, corporate governance variables, and other pertinent variables influence it [186]. Similarly, contrarian technical trading rules can be used to measure firm performance, such as the frequency of days a stock price falls into the “overbought” zone following the application of a stochastic oscillator (SOI) or relative strength index (RSI) trading rule in a year, divided by the total trading days in a year [78,79]. Based on contrarian technical trading rules, these measurements can also be used as proxies for firm performance to see whether the abovementioned variables influence them. Due to the annual nature of these performance metrics and the need for sufficient samples, researchers may use the constituents of indices like energy and financial or stock indices, and varied panel data methods are frequently used to analyze such studies.

7.3. Flexibility and Extension of Using Technical Parameters

Although the default parameters are 80 (20) and 70 (30) for overbought (oversold) for SOI and RSI trading rules, different parameters might deliver better results. As such, while using big data analytics, investors might use a variety of parameters (such as 90, 85, 80, 75, and 70) instead of using 80 only for SOI overbought trading rules since the better results might derive from different parameters instead of default parameter [20,31], and vice versa for the RSI trading rule. By employing the above technique, investors might derive more information by analyzing data through big data analytics. In addition, some technical trading rules would use two parameters. For example, variable lag moving average (VMA) trading rules such as VMA (5, 20) using five days as short-term MA and 20 days as long-term MA [42,119] and Bollinger Band (BB) trading strategies such as BBTS (20, 2) that uses 20-day moving average and two standard deviations [57]. Following the wisdom mentioned above, we may use diverse VMA (n1, n2) using different n1 and n2 and varied BBTS (m1, m2) using various m1 and m2 and then present their overall performance (cumulative holding performance and average holding performance) in heatmap matrix based on mixed combinations of these two parameters. Consequently, such a study may cast light on the significance of decision making by utilizing big data analytics and heatmap visualization.

7.4. Concerning Diverse Price Movement Instead of Using Technical Analysis Alone

Following technical analysis such as momentum technical trading rules (MA and MACD trading rules) and contrarian technical trading rules (SOI and RSI), investors may trade financial instrument tracking energy and financial index (i.e., index ETFs) or energy and financial index futures following these technical trading rules. However, trading signals might be decided as some phenomena occurred, or trading practices employed by investors instead of following technical trading rules alone. For example, while using daily data, continuously rising (falling) prices for two days, three days, and four days would be employed as trading signals; sharply rising (falling) prices over 1%, 2%, 3%, and 4% might be employed for trading signals; the continuously rising price and continuously technical trading signals emitted for two days, three days, and four days might be employed as trading signals; and continuously bull candlesticks [20,49,69,187,188]. As per intraday data, we state that trading index futures such as stock index futures and energy and financial index futures, daily trading might be widely adopted by many investors and speculators due to the high-leverage characteristics of index futures, as such, different intraday rising (falling) significant index changes in one minute (e.g., rising 25, 50, 75 index changes in one minute) might be regarded as trading signals, since we state that rising significant index change in a short time might result from those who have market forces or unleased news [189]. In addition, we argue that we may treat index futures rising (falling) implicitly (e.g., rising (falling) in every minute and less than 2.5, 5, 7.5, and 10 points in five minutes) as trading signals since such movement, defined as an implicit rising (falling) phenomena, might contain hidden information, given that we state that the implicit phenomena might be an indication of manipulation by some professional investors and even insiders [190]. Both studies, using intraday index future data, show that investors may exploit profits in several cases.

8. Concluding Remarks

8.1. Conclusions

This study reached some critical conclusions about the review of technical analysis in the energy and financial markets. First, technical analysis is a crucial methodology for navigating energy and financial markets, offering traders and investors a systematic approach to predicting price movements based on historical market data.

First, the review underscored the fundamental principles and tactics of technical analysis, encompassing trend assessment, S&R levels, and various tools and indicators like moving averages, RSI, and MACD. Implementing technical analysis in energy and financial markets offers invaluable insights into market trends, aiding traders in making informed decisions, especially given these markets’ dynamic and volatile nature. Proficiency in and application of technical analysis strategies enables market participants to refine their trading approaches and maximize their effectiveness in energy and financial markets.

Second, theoretical foundations of technical analysis in energy and financial markets are rooted in the efficient market hypothesis (EMH) and the notion that historical price movements can forecast future prices. While critics argue that technical analysis lacks empirical evidence and is subjective, proponents contend that it provides valuable insights into market behavior, particularly in the short to medium term. Technical analysis in energy and financial markets scrutinizes price movements, trends, and patterns. It provides a systematic approach to analyzing market data, aiding in making well-informed trading decisions. This approach is particularly crucial in energy and financial markets, where many external factors influence prices.

Third, exploring technical analysis strategies in energy and financial markets unveils a spectrum of approaches, encompassing price pattern analysis, indicator analysis, and market sentiment analysis. Price pattern analysis centers on recognizing and interpreting patterns in price charts, whereas technical indicators are used to analyze past price and volume data. Market sentiment analysis assists traders in grasping the predominant sentiment among market participants, enabling them to make well-founded trading choices. By integrating these strategies, traders and investors can grasp energy and financial market trends and conditions, improving their ability to navigate dynamic and volatile market landscapes adeptly.

Fourth, this study offers a comprehensive classification of research on the application and effectiveness of technical analysis in energy and financial markets. The classification covers various aspects, including the type of energy and financial market, geographical focus, period, methodology, and more. Through this categorization, the study provides a structured framework for understanding the research landscape in this domain and identifies the existing literature gaps. This classification has the potential to provide practitioners with valuable insights into the practical implementation of technical analysis in energy and financial markets, enriching discussions on its role in this sector. Additionally, it sets a foundation for future research endeavors in this field.

Last, the future of technical analysis strategies in energy and financial markets hinges on technological progress. Incorporating big data analytics and AI technologies can transform technical analysis in energy and financial markets, providing traders with more profound insights into market behavior and enhancing the quality of decision making. Furthermore, algorithmic trading, high-frequency trading (HFT), and blockchain technology are expected to enhance trading efficiency and transparency, offering new opportunities for technical analysis strategies. As quantitative analysis continues to evolve, collaboration between regulators, market participants, and researchers will be essential in addressing the challenges and opportunities associated with these advancements. Overall, the future of technical analysis in energy and financial markets is promising, with technology playing a pivotal role in shaping its evolution and effectiveness.

8.2. Research Implications

In terms of theoretical implications, this study states that this review serves as a significant milestone in advancing the understanding of technical analysis in energy and financial markets. By synthesizing and highlighting key concepts, strategies, and indicators, it not only consolidates existing knowledge but also emphasizes the critical role of technical analysis in navigating the complexities of these markets. Furthermore, it sets the stage for future research to delve deeper into these concepts, refining them and exploring new avenues for integrating technical analysis with other analytical approaches. This could lead to a more comprehensive understanding of market behavior and better-informed trading decisions.

In the realm of methodological implications, this study argues that this review’s exploration of emerging trends in technical analysis, such as machine learning and AI, indicates a fundamental shift towards more data-driven and sophisticated analytical methods. This shift underscores the importance of methodological innovation in researching technical analysis strategies. Future studies could explore the application of advanced computational techniques to enhance the predictive power and accuracy of technical analysis models in energy and financial markets. Such innovations could revolutionize the field, potentially leading to more robust and accurate trading strategies.

Regarding practical implications, this review provides actionable insights for traders, investors, and analysts in energy and financial markets. By carefully evaluating the benefits and drawbacks of various technical strategies and emphasizing the importance of market trends and indicators, it offers a practical roadmap for making informed trading decisions. Professionals in the field can leverage these insights to integrate technical analysis into their trading strategies, potentially leading to enhanced performance and improved risk management. As such, this review may not only advance theoretical understanding but also offer practical tools for real-world applications in the dynamic energy and financial markets.

8.3. Limitations

While technical analysis offers numerous advantages, it is essential to acknowledge its limitations. One significant drawback is its subjective nature, which relies on interpreting historical price data and patterns. This subjectivity can result in varying conclusions and trading decisions among analysts, potentially leading to less-than-optimal outcomes. Another limitation involves its dependence on past data, which may not consistently forecast future price shifts, particularly in swiftly evolving energy and financial markets. Additionally, the technical analysis might only partially consider the effects of external elements, such as geopolitical shifts or regulatory alterations, which can significantly sway energy and financial prices. Nevertheless, despite these drawbacks, technical analysis is a practical resource for energy and financial market participants, offering a systematic method for analyzing market trends and facilitating informed trading choices.