An Analysis of General Motors (GM) Stock Based on Elliott Wave Theory
Ethereum Analysis Based on the Elliott Wave Principle
Research Article
Ethereum Analysis Based on the Elliott Wave Principle
ABSTRACT
This study analyzes Ethereum's price movements within the framework of the Elliott Wave Principle, aiming to evaluate the applicability of the theory in cryptocurrency markets. Supported by technical tools such as Fibonacci ratios and the 200-day exponential moving average (EMA), the study seeks to forecast Ethereum's long-term price trends. The analyses indicate that Ethereum has been moving within a contracting triangle formation, which could potentially lead to an upward momentum upon completion. Notably, the success of Fibonacci ratios in price predictions and the EMA's role in trend validation have provided significant guidance in understanding Ethereum's price dynamics. The effectiveness of the Elliott Wave Principle in analyzing market behavior has been highlighted, while also emphasizing that such analyses are inherently subjective and should be complemented with other technical tools. The study demonstrates that the Elliott Wave Principle could serve as a strategic guide for investors dealing with highly volatile assets like Ethereum.
1. INTRODUCTION
Studies on the applicability of the Elliott Wave Theory in today's financial world indicate that this theory serves as an effective tool for predicting cyclical market movements. It has been particularly emphasized as a modern analytical method commonly used in stock and commodity markets (Frost and Russell, 1996).
The Elliott Wave Theory is one of the most important technical analysis tools used to understand the dynamics of financial markets. Developed by Ralph Nelson Elliott, this theory suggests that price movements are based on a specific pattern. By analyzing the psychological behavior of market participants, the theory provides a method for interpreting price movements and predicting future price targets (Prechter & Frost, 1978). Furthermore, the Elliott Wave Theory is regarded as a comprehensive investment system capable of analyzing markets across all time frames (hourly, daily, weekly…) and incorporating all price movement patterns to inform investment decisions (Gunn, 2009).
Occasionally, wave patterns or models in the market may deviate from expectations, resulting in unrecognized movements. However, such instances are rare, and the vast majority of stock market movements, regardless of scale, can be reasonably interpreted within the framework of the Elliott Wave Principle (Frost and Russell, 1996).
This study examines the application of the Elliott Wave Theory by analyzing Ethereum's price movements. Specifically, it evaluates the possibility that Ethereum, currently moving within a higher-degree triangle formation, could rise to the level of 10,000 USD following the completion of its E wave.
2. LITERATURE REVIEW
The accurate application of wave analysis allows investors to comprehend market trends and forecast future price movements. This method is particularly valuable as a tool for long-term investment strategies (Mendez and Sociales, 2001:12).
According to Volna and Kotyrba (2018), the Elliott Wave Principle suggests that crowd psychology oscillates sequentially between pessimism and optimism, forming specific and measurable patterns. Financial markets provide one of the most accessible domains for observing the functioning of the Elliott Wave Principle, as shifting investor sentiment is recorded in the form of price movements. If an individual can identify the recurring patterns within prices and determine their current position within those patterns, they can predict the future direction of the trend.
Elliott wave patterns do not always form in identical ways and may exhibit minor variations (Volna et al., 2013). These patterns can differ in amplitude and duration. A seemingly similar pattern might, in fact, differ, or a visually distinct pattern might share the same underlying structure. Moreover, these patterns may not encompass every point within a time series. However, methods have been developed to analyze wave movements and correctly interpret these differences.
Elliott wave patterns can be classified as upward or downward trend patterns. Upward trend patterns indicate that prices will move higher, while downward trend patterns suggest that market prices will decline. The Elliott wave pattern is associated with Fibonacci sequence numbers and the golden ratio, implying that every market fluctuation is confined within a finite range but regulated within a specific boundary. Understanding the relationship between Elliott waves, mathematical ratios, and the peak and trough points of price movements can make this approach more explicit and effective (Wang et al., 2013).
This theory accurately explains the workings of stock markets and has become a significant tool in securities trading. The Elliott Wave Theory, linked with the Fibonacci sequence and the golden ratio, comprises five advancing waves and three corrective waves (Duan et al., 2018). The primary pattern suggests that market prices will enter a downward trend, while the corrective waves (counter patterns) indicate a transition to an upward trend in market price movements.
3. METHODOLOGY
3.1 The Elliott Wave Theory
The Elliott Wave Theory was introduced by Ralph Nelson Elliott in the 1930s. Elliott believed that stock market trends followed a recurring pattern, which could be predicted in both the short and long term. He shared these ideas in his 1938 book, The Wave Principle.
Elliott argued that what might appear to be chaotic movements in stock market data actually reflected a harmony found in nature. His discoveries were entirely based on observation, yet he sought to explain his findings through psychological reasoning. The core principle of his theory is that a pattern consists of eight waves.
As clearly illustrated in the diagram, waves 1, 3, and 5 follow the general trend, while waves 2 and 4 correct this trend. The a, b, and c waves also serve to correct the general trend, with waves a and c continuing the correction and wave b offering resistance. Elliott observed that each wave consists of smaller waves, which follow the same pattern, creating what is known as a super-cycle, a larger model of market behavior.
The numbers in the diagram represent the wave count at different scales. For instance, the entire diagram contains two major waves: the impulse wave and the corrective wave. The impulse wave consists of 5 waves, while the corrective wave comprises 3 waves. The 5 waves within the impulse wave are divided into 21 sub-waves, and these sub-waves are further divided into 89 smaller waves. Similarly, the corrective wave contains 13 sub-waves, which are further divided into 55 smaller waves.
These numbers are part of the Fibonacci sequence, which is widely observed in various aspects of nature.
3.1.2 Impulse Waves
Wave 1:According to Person (2007), the first wave emerges from a consolidation phase following a prolonged price decline, serving as the starting point of a new trend. These waves are often perceived as minor corrective movements and are the smallest among the impulse waves. Technical analysts commonly refer to this stage as the "accumulation phase." Tirea and Negru (2016) describe the first wave as a period when market sentiment is still negative, and only a small number of investors show interest in the market, yet prices begin to move upward.
Şengöz (2014) states that the first wave breaks the downtrend and initiates an uptrend, while most market participants remain bearish. This wave is characterized as speculative in nature. Poser (2003) highlights that identifying the first wave at its inception is often challenging. Market participants typically doubt whether this wave signals the beginning of a new bull market.
During the first wave, market news is generally negative, and investors remain influenced by the preceding downtrend. However, gradual increases in trading volume can be observed during this phase, accompanied by slow and steady price rises as short positions are closed. Rejnuš (2008) considers the first wave as the initial phase of a new upward cycle, often described by the market as a "bottom reversal."
From an economic perspective, the first wave reflects a period when investors receive the initial signal that prices are unlikely to fall further, prompting them to take positions. However, during this phase, the overall market volume remains relatively low, and uncertainty persists among participants.
Wave 2:According to Person (2007), the second wave typically retraces approximately 61.8% of the first wave’s movement, indicating the market's adherence to Fibonacci ratios. Person emphasizes that this ratio provides a significant clue that the market is likely to sustain the five-wave pattern. Akdemir and Yu (2009) describe the second wave as a confirmation of the first wave and a precursor to the third wave, highlighting the importance of taking positions during this phase.
Poser (2003) explains that the second wave generally retraces between 38% and 62% of the gains achieved by the first wave. During this wave, trading volume is often low, and market participants tend to perceive the continuation of the previous downtrend. However, a key rule of the second wave is that it cannot fall below the starting point of the first wave, serving as a critical guideline to prevent misidentification.
According to Tirea and Negru (2016), the second wave corrects the movement of the first wave but does not drop below its starting point, signaling potential profit opportunities for investors. Şengöz (2014) notes that the second wave is often a sharp correction but reiterates that it does not fall below the starting level of the first wave.
Rejnuš (2008) describes the second wave as generally a testing phase, emerging as a corrective movement following the first wave. During this wave, economic conditions often deteriorate, and prices sometimes return to the previous low level. However, this correction does not create a new low. Technically, this wave represents a phase where investors strengthen their positions by taking on greater risk.
Wave 3:This wave represents a phase where the overall upward momentum in the market accelerates significantly. Rejnuš (2008) notes that the third wave is typically the strongest wave in terms of both length and volume. During this period, as investor expectations for economic growth rise, prices generally surpass the previous peak, and growing optimism is observed among market participants.
Person (2007) emphasizes that the third wave is the most important wave in the Elliott Wave Theory, serving as the phase where the trend is confirmed. This wave is usually accompanied by high trading volume, the dissemination of positive news, and rapid price increases. Person also underscores the rule that the third wave can never be the shortest wave, highlighting its significance in identifying wave patterns.
Akdemir and Yu (2009) describe the third wave as the longest and most powerful wave, recommending that positions should be maintained during this phase. According to Tirea and Negru (2016), the third wave is often the longest and most significant, characterized by rapid price increases supported by positive news. Poser (2003) similarly identifies the third wave as the strongest and longest wave in the Elliott Wave Principle, during which prices rise sharply, and trading volume increases substantially.
Typically, the length of the third wave can extend up to 1.618 times the length of the first wave. In technical analysis, momentum indicators often validate the price peaks during this phase, further solidifying investor confidence in the bull market. Şengöz (2014) describes the third wave as the primary phase of market growth, noting that fears subside during this period, and upward momentum intensifies.
Wave 4:Person (2007) describes the fourth wave as a corrective wave that typically retraces part of the gains achieved by the third wave. According to Person, the fourth wave often appears in chart patterns such as triangles, pennants, or flags, and its low point can never breach the peak of the first wave.
Akdemir and Yu (2009) characterize the fourth wave as a reflection of nature, explaining that the length and strength of the third wave may cause exhaustion among market participants. According to Tirea and Negru (2016), the fourth wave corrects the movement of the third wave and provides opportunities for new investors to enter the market. Similarly, Şengöz (2014) describes the fourth wave as a corrective phase dominated by emotional anxiety.
Poser (2003) explains that the fourth wave has a corrective structure and typically retraces around 38% of the third wave. During this wave, trading volume remains low, and price movements are generally sideways. The fourth wave offers an opportunity for profit-taking for existing investors while providing new investors the chance to enter at lower levels. However, the duration of the fourth wave may be longer compared to previous impulse waves.
Rejnuš (2008) describes the fourth wave as a correction phase in which investors experience disappointment, believing the market rally may have ended. During this phase, prices typically move sideways but never return to the starting level of the first wave. Technically, the fourth wave is considered a consolidation period that retraces a portion of the previous gains.
Wave 5:According to Person (2007), the fifth wave is often the strongest in commodities such as gold, crude oil, and currencies. However, during this phase, prices begin to slow, and the market starts losing momentum. Person highlights that oscillators and indicators like MACD may show overbought/oversold signals during this wave. Poser (2003) notes that the fifth wave is typically accompanied by declining momentum and excessive optimism among investors. Trading volume during this wave may be lower than that of the third wave. The fifth wave often marks the peak of a bull market, creating a false sense of confidence among market participants that the upward trend will continue indefinitely.
Akdemir and Yu (2009) describe the fifth wave as a precursor to an impending bearish trend, noting that this wave is not as intense as the third wave. Tirea and Negru (2016) emphasize that the fifth wave should surpass the peak of the third wave but often signals an overvalued market position, indicating potential trend reversals. Şengöz (2014) characterizes the fifth wave as nostalgic, fictional, and speculative, noting that technical divergences may form during this phase.
Rejnuš (2008) describes the fifth wave as the final stage of an upward cycle. During this phase, prices typically exceed the peak of the third wave, but speculative trades and excessive optimism dominate among market participants. Although economic data and market indicators may not be as strong as in the third wave, this wave serves as a warning to investors about potential future price declines.
3.1.3 Corrective Waves
Wave A:According to Prechter and Frost (1978), the A wave emerges at the beginning of a bear market and is often perceived by investors as merely a pullback. During this phase, there is a general expectation that the market will continue its upward trend. However, technical deterioration begins to appear, laying the groundwork for the next market move.
Magazzino (2012) describes the A wave as a short-term corrective movement following a strong bull market, often interpreted by investors as a temporary retreat. Nevertheless, this wave represents the onset of a broader corrective process. Rejnuš (2008) notes that the A wave often provides the first signal that the bull market has ended. At this stage, investors may still believe the prevailing trend will continue, leading to indecisive market behavior.
During the A wave, prices begin to exhibit a downward tendency, but investors often perceive this as a temporary correction. Patel and Modi (2018) emphasize that the A wave provides an early signal of the end of the previous trend, though this signal is usually tested by the subsequent B wave. Poser (2003) states that the A wave is the initial indication of the conclusion of the previous bull market and is characterized by increased market volatility.
Wave B:According to Prechter and Frost (1978), the B wave is a deceptive wave that creates false optimism among investors. This wave is typically fully retraced by the C wave and demonstrates a technically weak structure. Magazzino (2012) describes the B wave as misleading, convincing investors that the prevailing trend will continue. During this wave, much of the decline caused by the A wave is retraced, but this optimism is usually reversed by the subsequent C wave.
Patel and Modi (2018) note that the B wave attempts to counteract the effects of the A wave and generally retraces between 38% and 138% of the A wave. However, this wave is often misleading. Poser (2003) emphasizes that the B wave is the most deceptive of all corrective waves and is typically characterized by low trading volume.
Rejnuš (2008) defines the B wave as a phase where investors are falsely convinced of the continuation of the uptrend. This wave partially retraces the losses caused by the A wave and creates temporary optimism among market participants. However, the B wave is usually associated with low trading volume, and its movement is often fully reversed by the C wave.
Wave C:Prechter and Frost (1978) describe the C wave as the most impactful phase of a bear market, where investors’ illusions about the market are entirely dispelled. During this wave, fear dominates the market, and it becomes increasingly difficult for investors to find safe opportunities.
Rejnuš (2008) considers the C wave to be the strongest and most decisive movement in the corrective process. It completes the downward trend initiated by the A wave and reflects a period dominated by fear in investor psychology. The C wave causes a significant drop in prices, representing the end of false expectations about the market.
Magazzino (2012) explains that the C wave is the strongest and most prominent phase of the corrective structure. During this wave, the market concludes the corrective process and often signals the beginning of a new trend. Patel and Modi (2018) emphasize that the C wave is the most critical of all corrective waves and is often associated with high volatility.
Poser (2003) notes that the C wave is the strongest wave in the corrective structure, with prices making a sharp and decisive move against the previous trend.
3.2 Fibonacci Number Sequence
The Fibonacci numbers were defined by the Italian mathematician Leonardo Fibonacci (Nishu et al., 2020). In the Fibonacci sequence, each number is the sum of the two preceding numbers, and the sequence progresses as follows: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, …
1 + 1 = 2
2 + 1 = 3
3 + 2 = 5
5 + 3 = 8
8 + 5 = 13
The golden ratio, approximately equal to 1.618, is a unique value represented by the Greek letter "Phi." When any two consecutive Fibonacci numbers are divided, the resulting ratios fluctuate around the golden ratio (Nishu et al., 2020). For example:
1 / 1 = 1
2 / 1 = 2
3 / 2 = 1.5
5 / 3 = 1.666
8 / 5 = 1.6
Schneider (2016) provides a detailed explanation of the fundamental properties of the Fibonacci sequence. The golden ratio and its variations have been widely applied in technical analysis, such as through Fibonacci retracements, fans, and projections. Hoffer (1987) notes that the golden ratio (0.618034, the ratio of 1 to itself) has been considered the mathematical basis for the design of the Parthenon, the arrangement of sunflower seeds, snail shells, Greek vases, and even spiral galaxies in space. He further explains that the ancient Greeks used this ratio in designing many of their artistic and architectural works, referring to it as the "golden ratio."
The golden ratio appears across a vast range of scales, from microscopic structures like the microtubules in the brain and DNA molecules to macroscopic scales such as interplanetary distances and cycles. The golden ratio is found in semi-crystalline arrangements, light reflections on glass, the brain and nervous system, as well as in the structural designs of plants and animals. For example, when holding a book, you use two of your five limbs, each consisting of three-jointed parts, with five fingers at their ends. These fingers also consist of three-jointed segments. This 5-3-5-3 pattern strongly correlates with the Elliott Wave Principle (Prechter & Frost, 1978).
Sarma and Kanta (2018) state in their study that the golden ratio is approximately equal to 1.618. This number, known as the "phi" constant, is an irrational mathematical constant and is symbolized by the Greek letter 𝜑. In its simplest form, the golden ratio refers to a line being divided in such a way that the resulting segments produce an aesthetically pleasing fraction. Widely used in art and architecture, the golden ratio is also found in different areas of the human body and plant anatomy.
In this study, the primary focus is on Fibonacci extension levels, as the three impulse waves (1, 3, and 5) are closely associated with Fibonacci mathematics. These relationships typically involve ratios of 1.618 or 2.618, as well as their inverses, 0.618 and 0.382 (Prechter & Frost, 1978). Additionally, according to Neely (1990:5-35), Fibonacci ratios serve as a fundamental tool for understanding the dimensions, durations, and price relationships of corrective waves. These ratios are used to confirm relationships between waves and to verify the structural accuracy of corrective formations.
4. FINDINGS
The findings from the analysis of Ethereum's price movements within the framework of the Elliott Wave Theory are as follows:
4.1. Triangle Formation:
Triangles consist of a five-wave structure and are labeled with the letters A, B, C, D, and E. Triangular formations appear in fourth waves, B waves, and X waves within complex corrections. In general, there are four types of triangle formations: contracting triangle (Figure 1), expanding triangle, barrier triangle, and running triangle.
In the case of Ethereum, the chart demonstrates the presence of a contracting triangle, which will be the focus of this study. For information on other triangle types, please visit our website. Contracting triangles are the simplest type of five-wave triangles. In this formation, the A wave must be the largest, and each subsequent leg of the triangle must be smaller than the previous one (Sinclair, 2018).
According to Neely (1990), triangle formations within the Elliott Wave Theory are among the most complex yet critical structures for analyzing market movements. Triangles do not have a fixed completion time, and predicting the direction of price movement following a triangle formation is often challenging. However, once a triangle is complete, it provides valuable insights into the current market position and offers clues about the behavior of price movements after the triangle.
Triangles consist of five segments labeled a, b, c, d, and e. This rule applies regardless of whether the segments are simple or complex. Each segment represents a completed corrective wave and follows a "3-3-3-3-3" structure. This characteristic is a distinctive feature of triangles and differentiates them from other wave formations.
Neely (1990) further explains that in triangles, price movements frequently fluctuate within the same price region, often showing a tendency to either expand or contract within the boundaries of the triangle. He also notes that the upper or lower boundaries of the triangle may exhibit slight upward or downward slopes. However, Neely emphasizes that such minor deviations do not alter the overall rules governing triangle formations.
5. DISCUSSION
In this study, Ethereum's price movements were analyzed using the Elliott Wave Theory and Fibonacci ratios. The analyses conducted on the chart indicate that Ethereum is in the preparatory phase of its final impulse wave in its long-term price trend.
E waves signify a period in which the psychological boundaries of the market are tested. During these waves, the emotional influences on investors’ decision-making processes may become more pronounced. While the article emphasizes that the E wave serves as a structure testing the confidence of market participants, it is also necessary to analyze how this wave may vary under different market conditions. For instance, the impact of negative news could lead to a deeper correction than anticipated, potentially harming the market's long-term trend.
Upon examining the chart in Figure 2, it becomes evident that the wave structures are closely tied to Fibonacci ratios. It was observed that the higher-degree (3)rd Wave aligns with the Fibonacci 2.618 ratio of the (1)st Wave (depicted in red), while the lower-degree 3rd Wave corresponds to the Fibonacci 1.618 ratio of the 1st Wave (depicted in blue). Furthermore, it was noted that the blue-colored 2nd Wave corresponds to the Fib. 0.382 level, while the blue-colored 4th Wave aligns with the Fib. 0.618 ratio.
On the other hand, Frost’s (1979) assertion that the Dow Jones Index moves according to a natural law supports the idea that Ethereum’s wave structures exhibit a natural order consistent with Fibonacci ratios.
In the Elliott Wave Principle, the 5th wave is typically described as a phase where the market tends to reach its peak and exhibits a speculative nature. The projected target of $10,000–15,000 for Ethereum could generate excessive optimism among investors. However, this optimism, combined with a decline in momentum, may increase market risks. During this period, indicators such as market liquidity and trading volume should be closely monitored.
While the effectiveness of Fibonacci ratios in predicting price movements is widely recognized, it is essential to remember that these ratios may not always operate with the same level of accuracy under all market conditions. Sudden price movements, which are common in cryptocurrency markets, could lead to deviations from or exceedance of Fibonacci-derived targets. This limitation underscores the importance of not relying solely on these ratios when making investment decisions.
The Elliott Wave Principle is inherently subjective, and different analysts may interpret the same chart differently. To enhance the robustness of the analysis, it is recommended to complement Elliott Wave analysis with other technical tools. For instance, incorporating additional indicators like the Awesome Oscillator (AO) or Moving Average Convergence Divergence (MACD) could improve the accuracy of predictions.
The cryptocurrency market possesses unique dynamics that differ from traditional markets. These differences may require certain adjustments when applying the Elliott Wave Principle. For assets like Ethereum, price movements are often influenced by speculative news, mining activities, and market manipulation. Incorporating these factors into wave analysis can make the analyses more comprehensive and realistic.
The Elliott Wave Theory serves as an essential tool for understanding the impact of investor psychology on price movements. In this study, Ethereum’s movements within triangle formations could be further explored to demonstrate how crowd psychology shapes market behavior. The effects of FOMO (fear of missing out) and panic selling on wave structures, in particular, warrant deeper analysis.
According to the chart analysis, Ethereum’s price has moved within a higher-degree triangle formation, completing the D wave at the $4,000 level. During this process, the break above the 200-day exponential moving average (EMA) confirmed an upward trend change. The E wave, expected to form after the D wave, is projected as a corrective phase preceding the 5th wave (Figure 7).
Following the D wave, E waves are often perceived by investors as the beginning of a strong bearish trend in the market. These waves frequently coincide with news that supports a downward movement, fostering a strong belief among market participants in a sell-off. Additionally, E waves typically represent a turning point in the market. As a termination wave, E waves are associated with intense emotional reactions, similar to those observed in fifth waves, from the perspective of market psychology. During this stage, the risk of investors misinterpreting market conditions and making flawed decisions is significantly high (Prechter & Frost, 1978).
6. CONCLUSION
This study has analyzed Ethereum's price movements within the framework of the Elliott Wave Theory, demonstrating the theory's applicability to cryptocurrency markets. The integration of the Elliott Wave Principle with technical tools such as Fibonacci ratios and the 200-day exponential moving average (EMA) has proven to provide investors with robust guidance in understanding market movements and making informed decisions.
The E wave is expected to form either in a zigzag structure or as a triangular model. It is anticipated that Ethereum’s price retracement from the D wave could extend to the 200-day exponential moving average. Additionally, E waves frequently coincide with news that supports bearish sentiment, creating a strong belief in a sell-off among market participants (Prechter & Frost, 1978).
Moreover, the comprehensive framework provided by the Elliott Wave Theory offers a significant advantage in understanding market trends by analyzing the behavior of market participants. The mathematical precision provided by Fibonacci ratios and the trend-defining characteristic of the EMA have created a powerful synergy in forecasting Ethereum's future price targets.
The success of the Elliott Wave Principle in predicting market behavior aligns with Frost’s (1979) observations that the Dow Jones Index moves according to natural laws. The findings of this study, derived from Ethereum’s price movement analysis, support Frost’s argument and reinforce the applicability of the theory to the cryptocurrency market, particularly for Ethereum. This article highlights the significance of Fibonacci levels and EMA in understanding market trends, providing investors with a valuable guide for identifying accurate entry and exit points. However, the reliance of the Elliott Wave Theory on analysts’ interpretations may introduce subjectivity and lead to varying outcomes. Therefore, analyses based on the Elliott Wave Principle should incorporate additional technical tools.
In conclusion, this study demonstrates that the Elliott Wave Theory is a valuable tool for understanding the applicability and potential benefits of the theory in cryptocurrency markets. However, it should be noted that the theory and its technical indicators are subject to interpretation and may vary with different perspectives. Investors should adopt a multi-faceted approach, supported by other technical tools, and prioritize risk management alongside these analyses.
The findings confirm that the Elliott Wave Principle can play a strategic role as a guide in highly volatile cryptocurrencies like Ethereum. For further insights into the E wave and Ethereum, follow our website.
7. REFERENCES
Akdemir, B., & Yu, L. (2009). Elliot waves predicting for stock marketing using euclidean based normalization method merged with artificial neural network. ICCIT 2009 - 4th International Conference on Computer Sciences and Convergence Information Technology, 562–567. https://doi.org/10.1109/ICCIT.2009.296
Dr. Elder, A. (1993). Trading For Aliving
Duan, H., Xiao, X., Yang, J., & Zeng, B. (2018). Elliott wave theory and the Fibonacci sequence-gray model and their application in Chinese stock market. Journal of Intelligent and Fuzzy Systems, 34(3), 1813–1825. https://doi.org/10.3233/JIFS-17108
Frost, A. J., & Russell, R. (1996). The Elliott Wave Writings of A.J. Frost and Richard Russell, Edited by Robert R.Prechter, Jr. New Classic Library.
Frost, A. J. (1979, October 27). Wall Street crash not till '83: Analyst. Victoria Times.
Gunn, M. (2009). Elliott Wave Principle. John Wiley & Sons Ltd. https://doi.org/10.1002/9781119207801.CH8
Gurrib, I. (2022). Technical Analysis, Energy Cryptos and Energy Equity Markets. International Journal of Energy Economics and Policy, 12(2), 249–267. https://doi.org/10.32479/ijeep.11015
Hoffer, W. (1987). Graphic and Visual Communication. In IEEE TRANSACTIONS ON PROFESSIONAL COMMUNICATION (Issue 2).
Jiang, R., & Szeto, K. Y. (2003). Extraction of Investment Strategies based on Moving Averages: A Genetic Algorithm Approach.
Klinker, F. (2011). Exponential moving average versus moving exponential average. Mathematische Semesterberichte, 58(1), 97–107. https://doi.org/10.1007/s00591-010-0080-8
Magazzino, C., Mele, M., & Prisco, G. (2012). The Elliott’s Wave Theory: Is It True during the Financial Crisis? In Journal of Money, Investment and Banking.
http://ssrn.com/abstract=2333446http://www.journalofmoneyinvestmentandbanking.com
Mahadewa, A., Aryawan, M. G., & Prasetyo Utomo, P. E. (2018). Peramalan Indeks Harga Prulink Rupiah Equity Fund Dengan Metode Exponential Moving Average. Jurnal Sistem Informasi Dan Komputer Terapan Indonesia (JSIKTI), 1(2), 87–96. https://doi.org/10.33173/jsikti.18
Neely, G. (1990). Mastering Elliott Waves, (Version 2.0).
Nishu, M., Neha, M., Singh, M. J., & Parveen Mor, M. (2020). FIBONACCI RETRACEMENT IN STOCK MARKET. https://ssrn.com/abstract=3701439
Patel, H., & Modi, H. (2018). The Elliott Wave Principle and its Applications in Security Analysis. 1–6. www.stmjournals.com
Person L, J. (2007). Elliott Wave Theory.
Poser, S. (2003). Applying Elliott Wave Theory Profitably (W. John, Ed.).
Prechter, R. Jr., & Frost. (1978). Elliott Wave Principle.
Rejnuš, O. (2008). TEORIE ELLIOTTOVÝCH VLN-JEDNO Z TEORETICKÝCH VÝCHODISEK TECHNICKÉ ANALÝZY INVESTIýNÍCH INSTRUMENT.
Sarma, S., & Kanta Bhuyan, R. (2018). Fibonacci Number, Golden Ratio and their Connection to Different Floras. In International Journal of Mathematics Trends and Technology (Vol. 61, Issue 2). http://www.ijmttjournal.org
Schneider, R. (2016). Fibonacci numbers and the golden ratio. http://arxiv.org/abs/1611.07384
Şengöz, T. (2014). Elliott Dalga Prensibi.
Sociales, A., Calvo Espinal, C., Cristóbal, J., Méndez, J., & Ricardo, E. (2001). INNOVAR. Revista de Ciencias. Revista Innovar Journal. http://www.redalyc.org/articulo.oa?id=81801802
Tirea, M., & Negru, V. (2016). Behavioral Trading System-Detecting Crisis, Risk and Stability in Financial Markets. https://doi.org/10.1109/SYNASC.2016.45
Volná, E., Kotyrba, M., & Jarusek, R. (2013). Multi-classifier based on Elliott wave’s recognition. Computers and Mathematics with Applications, 66(2), 213–225.
https://doi.org/10.1016/j.camwa.2013.01.012
Volná, E., Kotyrba, M., Oplatková, Z. K., & Senkerik, R. (2018). Elliott waves classification by means of neural and pseudo neural networks. Soft Computing, 22(6), 1803–1813.
https://doi.org/10.1007/s00500-016-2097-y
Wang, L., Rajapakse, J. C., Fukushima, K., Lee, S., Yao, X., Hang, R., Szeto, K. Y., & Abstract, ~. (2002). DISCOVERING INVESTMENT STRATEGIES IN PORTFOLIO MANAGEMENT: A GENETIC ALGORITHM APPROACH (Vol. 3).
https://web.archive.org/web/20170812043234id_/http://bioinfo.au.tsinghua.edu.cn/member/ruijiang/publications/2005/ICONIP2002.pdf
Wang, Z., Che, W. G., Xiao, Y., & Yang, C. C. (2013). Research of the Elliott Wave Theory applications based on CBR. Proceedings of the 2013 3rd International Conference on Intelligent System Design and Engineering Applications, ISDEA 2013, 1137–1140. https://doi.org/10.1109/ISDEA.2012.268
Analysis of IBM Stock Price Movements Using The Elliott Wave Principle
ANALYSIS OF IBM STOCK PRICE MOVEMENTS USING THE ELLIOTT WAVE PRINCIPLE
ABSTRACT
This study aims to analyze IBM's stock using the Elliott Wave Principle. The structures comprising impulse and corrective waves in Elliott Wave Theory were examined across four different timeframes (2 months, 2 weeks, 1 week, and 1 day), and the alignment of price movements with market psychology was evaluated. During the analysis, technical indicators such as Fibonacci levels and the 200-day EMA were utilized to support wave structures. The findings reveal that IBM's stock is in a long-term upward trend, which exhibits a predictable pattern in line with the Elliott Wave Principle. Additionally, the impact of economic and social events on market waves was assessed, showing that such events primarily act as accelerators rather than initiators of trends. In conclusion, the Elliott Wave Principle is identified as an effective tool not only for analyzing past price movements but also for forecasting future price movements and making strategic investment decisions.
1. INTRODUCTION
The Elliott Wave Principle is an analytical method that suggests market movements progress in waves and reflect investor psychology (Prechter & Frost, 1978). According to this theory, price movements are categorized into two main types: impulse waves and corrective waves, providing a robust tool for understanding market cycles (Casti, 2002). This study aims to demonstrate the predictive power of the Elliott Wave Principle by applying it to IBM stock. Charts across four different timeframes (2 months, 2 weeks, 1 week, and 1 day) were analyzed, revealing that the stock is in a long-term upward trend. The findings indicate that the Elliott Wave Principle can be used not only to analyze past movements but also to forecast future price trends (Cristina & Ribeiro, 2019). In this context, it is emphasized that investors need to conduct accurate wave counts and integrate technical tools such as Fibonacci levels (Atsalakis et al., 2011). Throughout the analysis, historical price data, market psychology, and factors such as the 200-day EMA were considered to support wave structures.
2. LITERATURE REVIEW
The Elliott Wave Principle is an effective technical analysis method used to understand the dynamics of financial markets and predict future price movements. In the literature, this theory is widely regarded as a powerful tool that combines psychological and mathematical approaches to analyze market behavior.
Poser (2003) elaborated on the applicability of the Elliott Wave Principle, explaining how this method can be utilized to make profitable trading decisions. The study emphasizes the importance of accurate wave counts and the role of Fibonacci ratios in wave forecasting.
Gunn (2009) considers the Elliott Wave Theory a comprehensive investment method for market analysis, encompassing all timeframes and market conditions. According to Gunn, this theory serves as a complete methodology for analyzing price movements, rooted in an understanding of volatility cycles.
Jarusek, Volna, and Kotyrba (2013) demonstrated through experiments that the Elliott Wave Principle is beneficial in investment settings and provides higher profitability compared to traditional forecasting techniques and classic technical analysis methods. They concluded that the Wave Principle significantly contributes to predicting market fluctuations.
Fernández and Crespo (2022) investigated whether the Elliott Wave Principle can be used to forecast the future direction of market trends. They concluded that trend predictions can be made using the Wave Principle and that this principle offers significant value in forecasting future trends.
Guerra (2021) aimed to identify Elliott waves using historical market data. By identifying various wave types and grouping similar waves, the study calculated the probabilities of each wave. The results demonstrated that the Elliott Wave Principle is sufficient for understanding market trends, enabling profitable outcomes through real-time trading. Even partial waves, rather than complete ones, were found to be adequate for achieving profitable trading results.
The Elliott Wave Principle is a technical analysis method that examines the behavior of stock prices and asset prices in financial markets. Based on market behavior, it has been proven that stock prices develop in waves and exhibit predictable patterns. These patterns can assist investors in making profitable decisions in terms of timing and earnings (Tirea & Negru, 2016).
Ivanova (2019) characterized the Elliott Wave Principle as a popular method for trend analysis.
3. METHODOLOGY
This study aims to analyze IBM stock in accordance with the Elliott Wave Principle across different timeframes. The price data used were obtained from the www.tradingview.com platform. Price movements were analyzed using 2 month, 2 week, 1 week, and daily charts to identify wave structures from higher to lower degrees.
The fundamental principles of the Elliott Wave Principle and Fibonacci ratios were utilized to identify wave structures and determine price targets. On the daily chart, the 200-day EMA was employed as a critical tool to evaluate the direction of the long-term trend and identify support/resistance levels. It was observed that the price remained above the 200-day EMA, confirming that the stock was in an upward trend.
The impact of social and economic events on waves was evaluated using examples such as the Covid-19 pandemic and the 2024 U.S. Presidential Election. It was determined that these events accelerated existing trends rather than initiating new wave structures. Wave structures were identified in compliance with the rules of the Elliott Wave Principle.
4. FINDINGS
4.1. Analysis of the 2-Month Chart
IBM Stock 2-Month Chart
The 2-month chart provides a basis for examining IBM's long-term trends in accordance with the Elliott Wave Principle. On this chart, the major trends of the stock are clearly discernible. According to the Elliott Wave Principle, the waves are structured as follows:
4.1.1. Internal Structure of the Higher-Degree Wave 1:
Wave (I): September 1974 – April 1979
Wave (II): April 1979 – October 1981
Wave (III): October 1981 – April 1986
Wave (IV): April 1986 – January 1987
Wave (V): January 1987 – August 1987
4.1.2. Internal Structure of the Higher-Degree Wave 2 (ABC):
Wave A: August 1987 – December 1989
Wave B:December 1989 – February 1991
Wave C: February 1991 – August 1993
4.1.3. Higher-Degree Waves 3, 4, and 5:
The 3rd Wave began in August 1993 and concluded in July 1999. Spanning approximately six years, this wave represents a period during which IBM solidified its leadership in the technology sector, gained market confidence, and experienced significant stock price increases.
The 4th Wave occurred between 1999 and 2008, forming a flat correction with an ABC internal structure. The bursting of the dot-com bubble and the effects of the 2008 financial crisis reflected market uncertainties during this period.
The 5th Wave was completed between 2008 and 2013. IBM's focus on strategic areas such as cloud computing and artificial intelligence contributed to the renewed rise in stock prices during this period.
With the completion of these five waves, it is suggested that the Wave (1), which began in September 1974, concluded in 2013.
4.2. Analysis of the 2-Week Chart
IBM Stock 2-Week Chart
On the 2-week chart, the details of the higher-degree Wave (2) were analyzed. This wave formed a zigzag pattern (ABC) between 2013 and 2020:
Wave (A) : Developed with a five-wave internal structure between March 2013 and February 2016.
Wave (B): Completed as a WXY complex structure between February 2016 and February 2020.
Wave (C): Occurred as a sharp and destructive wave between February 2020 and March 2020.
The retracement level of 2nd Waves typically corresponds to the Fibonacci level of 0.618 of the 1st Wave's length (Baranauskas, 2011). As illustrated in Figure 1, it is evident that the Wave (II) within the 1st Wave retraced to the 0.618 Fibonacci level, and similarly, the higher-degree Wave (2) also retraced to the 0.618 Fibonacci level. The alignment of IBM stock prices with the Fibonacci level of 0.618 validates our wave analysis.
4.3.Analysis of the Weekly Chart
IBM Stock Weekly Chart
After the completion of higher-degree Waves (1) and (2). it is assumed that the Wave (3) has begun. Since the Wavewill (3) also consist of five internal structures, the price is currently within the 1st Wave of a lower timeframe.
1st Waves typically consist of five internal structures and are formed at the beginning of a consolidation phase following a prolonged price decline. Initially, this wave may appear as a minor correction to the preceding downtrend. In terms of price movement, the 1st Wave is generally the smallest of the three impulse waves. In technical analysis, this phase is often referred to as the "accumulation phase" (Person, 2007).
The Wave (I) within the 1st Wave developed as a leading diagonal structure between March 2020 and January 2022. The Wave (II) formed a flat correction with a 3-3-5 internal structure between January 2022 and May 2023.
It is believed that IBM's stock is currently within the Wave (III). Since the Wave (III) will also consist of five internal structures, it is observed that the I, II, III, and IV Waves have been completed. The next expected move is the formation of the Wave V, which will complete the Wave (III). Subsequently, a Wave (IV) correction is anticipated.
4.4. Analysis of the Daily Chart
IBM Stock Daily Chart
On the 1-day chart, it is considered that the Wave V could form in two different structures: a terminating diagonal or an impulse wave. The analysis assumes that the Wave V forms as an impulse wave. It is projected that the first and second waves of the lower timeframe have been completed and that the third wave has commenced. However, the invalidation level for this count is set at 214.67 USD. If the price falls below this level or touches it, the current wave count will be invalid, as second waves cannot fully retrace the entirety of the first wave.
The target for the third wave is determined to be 271.73 USD. This target is calculated based on the principle that third waves are typically 1.618 times the length of the first wave.
5. DISCUSSION
Human activities follow a rhythmic order, and future events can be predicted with a high degree of certainty using historical data. These cycles manifest as waves that repeat at regular intervals or as series of impulses formed in specific patterns and numbers (Elliott, 1946). The analysis conducted on IBM stock demonstrates how the Elliott Wave Principle aligns with social and economic perspectives. Elliott wave structures clearly highlight critical points where changes in the collective sentiment of market participants coincide with economic conditions.
Three of the five waves determine the direction of the trend, while the other two occur in the opposite direction, correcting the main trend. The first, third, and fifth waves indicate the primary direction of the trend, whereas the second and fourth waves act as corrections to the main trend. A sequence of five waves at one degree becomes the first wave of the next larger movement. For instance, the five-wave sequence of a movement is considered the first wave of the next higher-degree trend. This demonstrates how waves are interconnected and how movements evolve (Elliott, 1938). As seen in IBM's 2-month chart, the 1, 2, 3, 4, and 5 Waves collectively form the higher-degree Wave (1).
The Elliott Wave Principle is based on the cyclical nature of market participants' behaviors. This principle enables the understanding and prediction of market movements through the combination of factors such as economic conditions, socionomic effects, and investor psychology. The analysis of IBM stock reveals how the sentiment of market participants shapes over different timeframes and how these changes influence wave structures.
The higher-degree waves (I) and (III) represent the increasing optimism of market participants and periods of market uptrend, whereas the waves (II) and (III) reflect correction periods and the indecisiveness of investors. Notably, the internal structures of corrective waves (e.g., the formation of the wave (IV) as a flat correction) clearly illustrate market participants’ responses to economic uncertainties.
In the Elliott Wave Principle, C Waves represent the most severe part of corrections and often involve collective panic reactions from market participants (Elliott, 1946). In the analysis of IBM stock, the (C) Wave within the (2) Wave corresponds to a period when the market hit its lowest point. The decline between February 2020 and March 2020 was directly related to the global economic impact of the Covid-19 pandemic. The increasing uncertainty in the markets at the onset of the pandemic triggered risk-averse behavior among investors (Baker, Bloom, Davis & Terry, 2020). During the same period, volatility rapidly increased across financial markets, and the stock prices of many major companies experienced significant declines (Albulescu, 2021; Zhang, Hu & Ji, 2020).
The destructive impact of the Wave (C) clearly demonstrates the collective panic reactions of market participants to the economic crisis. However, such declines often lay the groundwork for the market trend to resume its upward movement. Indeed, following the initial shock of the Covid-19 pandemic, markets began to recover, and companies like IBM returned to an uptrend.
The expectations surrounding economic and tax policies after Trump’s victory in the 2024 U.S. Presidential Election (The Wall Street Journal, 2024) created a strong sense of confidence among market participants. In particular, expectations for increased infrastructure spending and accelerated economic growth supported various sectors, including the technology sector. This optimism contributed to the strong upward formation of subwave structures within the Wave (III) on the daily chart.
However, as Elliott Wave theorists, we recognize that the price is moving within the higher-degree Wave (3), rendering these news events irrelevant. News merely serves to accelerate the existing trend. These events only helped accelerate the rise in IBM stock prices, which were already within an impulse wave.
6. CONCLUSION
The stock market is a reflection of human behavior, exhibiting regular, measured, and harmonious movements that follow a specific wave principle (Elliott, 1946). This study on IBM stock demonstrates that the Elliott Wave Principle is an effective tool for understanding the dynamics of financial markets and predicting future price movements. Elliott wave structures clearly illustrate how changes in the collective sentiment of market participants are reflected in market prices. Analyses conducted across different timeframes validate that each wave structure develops in alignment with the cyclical nature of markets, shaping market trends within these structures.
During the analysis, intersections of price movements with economic and political developments were also examined. However, from the perspective of the Wave Principle, such developments are considered accelerators of existing trends rather than initiators. For instance, the sharp decline observed in prices between February 2020 and March 2020 due to the impact of the Covid-19 pandemic was part of the Wave (C) within the corrective structure of the higher-degree Wave (2). This decline occurred sharply in accordance with the nature of the wave. The pandemic accelerated this process but did not alter the wave structure.
Similarly, the optimism in the markets following Donald Trump’s re-election as President in the 2024 U.S. Presidential Election (The Wall Street Journal, 2024) contributed to the strong continuation of the higher-degree Wave (3). As Elliott Wave theorists, we recognize that market prices follow a natural order within wave structures, with such news serving as factors that accelerate existing trends. In this context, the behavior of market participants and their collective sentiment are the primary determinants shaping wave structures.
The analyses indicate that IBM stock is currently within the higher-degree Wave (3). According to the Elliott Wave Principle, this wave is typically the most powerful and pronounced impulse wave, suggesting that prices are in a long-term uptrend. In the daily chart analysis, it is observed that the subwaves within the Wave V are still forming, indicating the continuation of this trend. The fact that prices are above the 200-Day EMA supports this view. It has been determined that prices are currently in the third subwave of the Wave V, with a price target of 271.73 USD. The invalidation level of 214.67 USD is considered a critical level for maintaining the current trend.
The Elliott Wave Principle is not merely a tool for predicting market prices but also provides a robust theoretical framework for understanding the psychology of market participants and deciphering the cyclical nature of financial market movements. This study demonstrates that the Elliott Wave Principle is a valuable tool for forecasting future market movements and making strategic investment decisions.
7. REFERENCES
Albulescu, C. T. (2021). COVID-19 and the United States financial markets’ volatility. Finance Research Letters, 38. https://doi.org/10.1016/j.frl.2020.101699
Atsalakis, G. S., Dimitrakakis, E. M., & Zopounidis, C. D. (2011). Elliott Wave Theory and neuro-fuzzy systems, in stock market prediction: The WASP system. Expert Systems with Applications, 38(8), 9196–9206. https://doi.org/10.1016/j.eswa.2011.01.068
Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020). COVID-Induced Economic Uncertainty. www.worlduncertaintyindex.com,
Baranauskas, S. (2011). Elliott wave and fibonacci level mutual relationship and applying in a stock market. Business: Theory and Practice, 12(4), 390–397. https://doi.org/10.3846/btp.2011.40
Casti, J. L. (2002). The waves of life: The Elliott wave principle and the patterns of everyday events.Complexity, 7(6), 12–17. https://doi.org/10.1002/cplx.10051
Cristina, S., & Ribeiro, A. (2019). ELLIOTT’S WAVE THEORY IN THE FIELD OF ECONOPHYSICS AND ITS APPLICATION TO THE PSI20 IN THE CONTEXT OF CRISIS. Estudios de Economía Aplicada, 37(2), 41–53. www.revista-eea.net
Elliott, R. N. (1938, October). The Wave Principle.
Elliott, R. N. (1946). Nature’s Law.
Fernandez Molina Reinaldo, & Crespo Pena Manuel Daer. (2022). Forecasting the future trend of the EUR/USD exchange rate, using advanced technical analysis tools.
Guerra, M. A. (2021). REAL-TIME ANALYSIS OF THE ELLIOTT WAVE PRINCIPLE UTILIZING HISTORICAL MARKET DATA.
Gunn, M. (2009). Elliott Wave Principle. John Wiley & Sons Ltd. https://doi.org/10.1002/9781119207801.CH8
Ivanova, I. (2019). The Dynamics of Financial Markets: Fibonacci numbers, Elliott waves, and solitons. https://ssrn.com/abstract=3506517
Person L, J. (2007). Elliott Wave Theory.
Poser, S. (2003). Applying Elliott Wave Theory Profitably (W. John, Ed.).
Prechter, R. Jr., & Frost. (1978). Elliott Wave Principle, Key to Market Behavior.
The Wall Street Journal. (2024, November 5). Election 2024: Donald Trump Is Elected 47th U.S. President, Harris Concedes. https://www.wsj.com/livecoverage/trump-harris-election-day-results-2024
Tirea, M., & Negru, V. (2016). Behavioral Trading System-Detecting Crisis, Risk and Stability in Financial Markets. https://doi.org/10.1109/SYNASC.2016.45
Volna, E., Kotyrba, M., & Jarusek, R. (2013). Multi-classifier based on Elliott wave’s recognition. Computers and Mathematics with Applications, 66(2), 213–225. https://doi.org/10.1016/j.camwa.2013.01.012
Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36. https://doi.org/10.1016/j.frl.2020.101528
1987 Black Monday: An Analysis of a Market Crash through the Elliott Wave Principle
1987 Black Monday: An Analysis of a Market Crash through the Elliott Wave Principle
ABSTRACT
This study examines the crash of the S&P 500 index in October 1987 within the framework of the Elliott Wave Principle. Pre-crash wave counts indicated that this movement represented the IVth Wave of Cycle degree. Analyses reveal that the crash was not an exaggerated crisis but rather a natural corrective movement anticipated by the wave principle. Post-crash evaluations indicate that the trend resumed from where it left off, continuing the market's long-term bullish trajectory. The study highlights the predictive power of the Elliott Wave Principle in understanding market movements.
1. INTRODUCTION
The crash of the S&P 500 index in October 1987 made a profound impact on the financial world and is widely referred to in the literature as "Black Monday" (WSJ, 2022). In a short period, the market lost over 30% of its value, triggering similar declines in global markets. However, was this crash merely a crisis, or was it a natural part of a broader cycle?
The Elliott Wave Principle interprets market movements within the framework of natural cycles and explains price actions as a series of waves. In this study, the waves observed in the S&P 500 index before and after the 1987 crash are analyzed in detail, identifying the crash as the IVth Wave of Cycle degree during a bullish season. The continuation of the trend in the same direction post-crash supports this view. Consequently, the 1987 crash is reinterpreted as part of the inherent fluctuation process within the market's nature.
2. LITERATURE REVIEW
Academic studies on fluctuations and crises in financial markets have developed various theories to understand and predict market movements. Among these theories, the Elliott Wave Principle holds a significant place and serves as an effective tool for analyzing market cycles.
Karthikeyan and Chendroyaperumal (2011) evaluated the October 1987 crash as a major market movement and argued that this crash was a natural consequence of fluctuations within the financial system. Their study discussed how markets could be anticipated using fundamental and technical indicators.
Gunn (2009) described the Elliott Wave Theory as one of the most comprehensive investment approaches in market analysis, capable of accounting for all timeframes and regimes of price movements. Additionally, he highlighted that the method offers a highly holistic system for investment analysis.
In his thesis, Hayashi (2002) stated that Elliott Waves function as a technical analysis system that helps forecast market predictability. The system provides information indicating the proximity of trend changes while also determining the potential direction, target price, and timing of movements.
Frost and Russell (1996) emphasized that the wave principle is a rational theory.
Russell (1976) noted in his study that the wave principle is largely based on the Fibonacci sequence.
Beckman (2014) stated that once a distinct wave set is identified, the timing and magnitude of these waves can be predicted using specific rules and classification methods. He further indicated that each new wave affects price movements and determines the formation of subsequent waves, suggesting that every completed wave establishes a foundation for the waves that follow.
3. FINDINGS:
1st Wave: The Beginning of the Uptrend (September 30, 1974 – September 20, 1976)
The uptrend that began after the market reached its bottom on September 30, 1974, marks the formation of the first wave according to the Elliott Wave Principle. This period represents a phase where prices recovered from their lows and gradually started to rise. The 1st Wave typically signifies a time when investors overcome their fears and begin to regain confidence in the market.
During this wave, the initial signs of a bullish season become evident, and prices progressively increase. The uptrend continued until September 20, 1976, creating a robust first wave formation. This phase indicates a general trend reversal in the market and the emergence of upward momentum.
2nd Wave: Retracement (September 1976 – February 1978)
Following the completion of the 1st Wave, the market entered a retracement phase identified as the 2nd Wave. This corrective movement, which spanned from September 1976 to February 1978, was marked by a decline in prices aligning with Fibonacci retracement levels.
An examination of Fibonacci levels reveals that prices retraced to approximately 38.2% (0.382) and 50% (0.50) levels. Such a retracement is a characteristic feature of corrective waves and reflects a period of indecision among investors as they reassess market conditions. However, this type of pullback at Fibonacci levels can also be interpreted as a preparatory phase for a new bullish season.
By February 1978, the 2nd Wave concluded, and the market transitioned into a strong bullish phase. According to the Elliott Wave Principle, the uptrends that follow corrective waves typically result in more pronounced and robust trends. In this context, increasing confidence and optimism among market participants fueled a rapid rise in prices.
The subsequent analysis examines the period between March 1980 and 1984, focusing on the waves of Supercycle and Cycle degrees.
Supercycle Degree (II) Wave: November 1980 – August 1982
The (II) Wave, which followed the (I) Wave, marked a retracement period lasting from November 1980 to August 1982. According to Fibonacci levels, this correction extended to the 61.8% (0.618) retracement level. The correction took the form of an ABC zigzag pattern, indicating a brief consolidation phase in the market.
Notably, the C wave developed within a terminating diagonal formation, highlighting the complex structure of the retracement process. Such formations typically signify the nearing conclusion of a correction and indicate a likely upward reversal in the trend.
Cycle Degree I Wave: August 1982 – Summer 1983
The Cycle Degree I Wave began in August 1982 and ended in the summer of 1983. This wave reflected the market's strong recovery following the correction and the reestablishment of investor confidence. This period was characterized by a robust upward trend in prices, signaling a resurgence of market optimism and momentum.
Cycle Degree Wave II: Summer 1983 - July 1984
The Cycle degree Wave II began in the summer of 1983 and concluded in July 1984. This corrective movement retraced 38.2% (0.382) according to Fibonacci levels. Being relatively shallow, this correction indicated that the market maintained its overall upward momentum, laying the groundwork for a prolonged bull market. Following the end of Cycle Wave II, Cycle Wave III commenced with a robust upward movement, marking the onset of a strong bull trend characterized by increased momentum and peak investor confidence. The Fibonacci extension level for Cycle Wave III rose as high as 2.618 times that of the preceding Cycle Wave I, reaffirming the reliability of Fibonacci levels as a guiding tool.
Internal Structure of Cycle Wave III:
Primary Wave 1: Summer 1984 - Summer 1985
Primary Wave 1, beginning in the summer of 1984 and continuing through mid-summer 1985, represented the initial phase of the bull trend. This wave fostered an atmosphere of optimism among investors.
Primary Wave 2: Summer 1985 - September 1985
Following the first wave, the second wave indicated a corrective phase within the market, concluding in September 1985. This phase laid the foundation for a stronger third wave.
Primary Wave 3: September 1985 - Summer 1986
The third wave, from September 1985 to the summer of 1986, is typically the most powerful and longest wave. High trading volume and rapidly rising prices defined this phase, which embodied the essence of a strong bull market.
Primary Wave 4: Summer 1986 - Autumn 1986
The fourth wave, a corrective wave, partially retraced the gains of the third wave without altering the overall bullish trend. A notable alternation occurred between the second and fourth waves, with the latter experiencing a sharper retracement compared to the former.
Primary Wave 5: Summer 1987
The fifth wave marked the peak of the bull trend, concluding in the summer of 1987. With the completion of this wave, Cycle Wave III also came to an end.
Cycle Wave IV: Projections and Retracement
Cycle degree Wave IV naturally followed the completion of Cycle degree Wave III. According to Elliott Wave Principle, second and fourth waves tend to exhibit contrasting characteristics. Since Cycle Wave II retraced 38.2% (0.382) according to Fibonacci levels, it was anticipated that Cycle Wave IV would retrace to the 61.8% (0.618) level. When second waves are shallow and horizontal, fourth waves often exhibit sharper corrections, and vice versa. In summary, the 1987 market crash (Black Monday) can be viewed as a reflection of Cycle degree Wave IV, representing a natural corrective phase in the market.
As observed in the graph, the bull market that began in the final months of 1990, corresponding to the higher-degree blue Wave 3, continued until September 2000.
In the chart below (Chart Link), the consolidation period starting in September 2000 and lasting until March 2009 is illustrated. This period includes two significant crises: the 2001 crisis and the 2008 financial crisis.
In 2001, the United States faced a significant economic downturn. Known as the "dot-com bubble," this period saw a dramatic collapse in the stock market due to the overvaluation of technology companies. As tech stocks experienced sharp declines, investors panicked, and the U.S. economy entered a recession. Furthermore, the September 11, 2001, terrorist attacks exacerbated economic uncertainty. Companies cut back on spending, unemployment rose, and the Federal Reserve attempted to stimulate the economy by lowering interest rates. However, the fear in the markets did not dissipate immediately. As a result, 2001 was far from a stable year for the U.S., leaving long-lasting impacts on financial markets.
However, this crisis corresponded to the A wave within the blue Wave 4 (Flat structure) and consisted of three internal forms. It is also expected that the B wave would take on a three-wave structure since Flat patterns are typically formed in a 3-3-5 configuration. The diagram below provides a clearer view of how the Flat structure forms:
The 2008 crisis, on the other hand, was triggered by the bursting of the U.S. subprime mortgage bubble. Banks issued high-interest mortgage loans to individuals with poor creditworthiness. Trusting in the continuous rise of housing prices, banks securitized these loans into financial derivatives and sold them globally. However, as real estate prices fell, many borrowers defaulted, rendering these financial products nearly worthless. Major banks and insurance companies approached insolvency, prompting governments to intervene with bailout packages. The collapse of Lehman Brothers in the U.S. further escalated the crisis. This turmoil spread worldwide, leading to a global economic downturn and rising unemployment.
According to the Elliott Wave Principle, the financial crisis during this period represents the C wave. Consequently, the crises and collapses experienced from 2000 to 2009 formed the blue Wave 4 within a Flat pattern. Therefore, these declines were anticipated within the broader wave structure.
4. DISCUSSION
In the discussion section, the October 1987 crash of the S&P 500 index, commonly known as "Black Monday," is evaluated in light of the analyses presented in the findings section. Within the context of the Elliott Wave Principle, this assessment emphasizes that market movements should be viewed not as crises, but as a natural part of the market cycle.
According to the findings, pre-crash market movements were identified as part of the Cycle degree Wave IV. This wave represents one of the corrective phases inherent in market dynamics, which is essential for the healthy continuation of the preceding trend. The market’s loss of more than 30% of its value highlights both the depth of this corrective wave and its psychological impact on market participants.
The Elliott Wave Principle suggests that such movements occur in an orderly manner and are often associated with Fibonacci ratios. The findings indicate that Wave IV retraced to the 61.8% level, a ratio frequently observed in corrective waves. Furthermore, the retracement of Wave II to the 38.2% Fibonacci level provided a predictive basis for the retracement depth of the subsequent Wave IV. This alternation, where one wave is horizontal and the other more pronounced, reinforces the predictive reliability of the theory.
The analyses in the findings section reveal that each wave reflects the emotional responses of market participants. For instance, the retracement during Wave IV induced fear and uncertainty among investors, yet this period ultimately laid the groundwork for a new upward trend. The bull market observed after the correction demonstrates the dynamic nature of the market and its alignment with wave principles.
The 1987 crash should be interpreted as a natural outcome of market cycles. The data presented in the findings section demonstrate that this crash was merely a reflection of market dynamics and did not alter long-term market trends. This reinforces the reliability of the Elliott Wave Principle as a tool for market analysis. The overarching framework of this discussion aligns with the analyses in the findings section, underscoring the predictable and cyclical nature of market movements.
5. CONCLUSION
This study examined the 1987 "Black Monday" crash of the S&P 500 index within the framework of the Elliott Wave Principle, revealing significant insights into the natural cyclical structure of market movements. The findings demonstrate that this crash was consistent with a corrective movement of Cycle degree Wave IV, supporting the predictable nature of market cycles.
The analyses highlight that the emotional responses of market participants are clearly reflected in price movements and align with the forecasts of the Elliott Wave Principle. The continuation of the bull trend following the crash further validates the theory's effectiveness in understanding long-term market movements. Moreover, the influence of Fibonacci ratios on wave structures has reinforced the regularity of market cycles and the practical applicability of the wave principle.
In this context, the Elliott Wave Principle is evaluated as a robust tool for analyzing financial market cycles and forecasting future movements. The 1987 crash serves as a critical example in understanding the natural cycles of financial markets, demonstrating the theory's academic and practical value. The study's findings underscore the importance of theoretical models in comprehending market dynamics and supporting strategic decision-making processes.
For further insights into future trends in the S&P 500 index, visit www.ew-strategy.com !
6. REFERENCES
Beckman, R. C. . (2014). Super Timing: The Unique Elliott Wave - Keys to Anticipating Impending Stock Market Action (2nd Edition). Harriman House Ltd.
Chendroyaperumal, C., & Karthikeyan, B. (2011). Empirical Verification of Elliott Wave Theory in Indian Stock Market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1887789
Frost, A. J., & Russell, R. (1996). The Elliott Wave Writings of A.J. Frost and Richard Russell, Edited by Robert R.Prechter, Jr. New Classic Library.
Gunn, M. (2009). Elliott Wave Principle. John Wiley & Sons Ltd. https://doi.org/10.1002/9781119207801.CH8
Hayashi, A. D. (2002). APLICAÇÃO DOS FRACTAIS AO MERCADO DE CAPITAIS UTILIZANDO-SE AS ELLIOTT WAVES Dissertação de Mestrado.
Russell, R. (1976). The Elliott Wave Principle in Richard Russell’s Dow Theory Letters: Russell on Stocks & Bonds. Dow Theory Letters.
An Analysis of General Motors (GM) Stock Based on Elliott Wave Theory
Review Article
An Analysis of General Motors (GM) Stock Based on Elliott Wave Theory
1) Psychological Dynamics Behind Wave Movements
Financial market movements cannot be explained solely by economic data. The market is a reflection of collective investor psychology, which manifests itself in waves (Elliott, 1938). The Elliott Wave-based chart of General Motors (GM) stock also exhibits traces of this wave structure. This chart is not merely a product of technical analysis but also a tangible representation of investor sentiment.
According to Elliott Wave Theory, market movements follow a specific cycle consisting of a five-wave impulsive move and a three-wave corrective move (Elliott, 1938). Examining GM’s current price movements reveals that prices have been progressing in a distinct five-wave structure. These waves provide crucial insights into how investor sentiment evolves over time and how the market reacts to these shifts.
2) The Third Wave: A Sign of Strong Momentum
In Elliott Wave Theory, the third wave is typically the phase where the strongest momentum is observed and investor interest reaches its peak (Gorman & Kennedy, 2013). GM’s chart also demonstrates that the third wave has exhibited a significantly larger upward movement compared to other waves. Additionally, one can observe the presence of gap formations in every third wave. This indicates that the market is progressively entering a more optimistic sentiment phase, leading investors to take on greater risks.
3) The Fifth Wave and Speculative Surge
According to Elliott’s theory, the fifth wave is generally characterized by heightened investor optimism, yet technical indicators tend to weaken in this phase (Prechter, 2009). In GM stock’s recent movements, it is evident that while the price has resumed an upward trajectory, trading volume has been declining. This is often regarded as a "final surge," after which the market is expected to enter a corrective phase. Notably, on November 25, 2024, despite the price experiencing a final increase, momentum failed to support this rise. As Elliott analysts, we protected our premium members from the anticipated downturns. Following November 25, 2024, we expected a three-wave corrective movement, which indeed materialized. The ABC zigzag correction reached the 200-day EMA moving average. With the break of the downtrend, we anticipate the stock price to initiate a new uptrend.
Elliott Wave Theory is reinforced by Fibonacci ratios, allowing for more precise predictions (Prechter & Frost, 2017). In GM’s analysis, the application of Fibonacci levels reveals that prices have established strong support zones around the 0.50 level. Additionally, it is observed that the price is currently finding support at the 200-day EMA moving average. All these factors indicate that waves move within a mathematical order and that market psychology aligns with these Fibonacci ratios.
5) The Role of Market Sentiment: Herd Psychology and Investor Decisions
Herd psychology plays a significant role in financial markets, contributing to the formation of wave patterns in price movements (Jiménez Méndez & Calvo Espinal, 2001). The wave analysis of GM stock suggests that investors tend to follow the general market direction, reinforcing trends under the influence of herd behavior. This confirms that price movements are not solely dependent on economic data but are also driven by the collective psychology of investors.
6) Conclusion: Predictability of Wave Movements and Investment Strategies
When analyzed through the lens of Elliott Wave Theory, GM stock demonstrates that investor psychology follows a structured pattern, manifesting in waves. Just as every rally in the market is followed by a correction, every decline is eventually met with a recovery. Elliott Wave Theory provides investors with the opportunity to determine the market phase and formulate strategies accordingly (Močan, 2019).
In conclusion, we believe that GM’s current price movements indicate the completion of the three-wave corrective phase following the five-wave impulsive move. Therefore, we anticipate that the stock price will rise to complete the higher-degree (III). Wave, supported by the 200-day EMA moving average. GM stock aligns with the Elliott Wave Principle, demonstrating a direct correlation with investor sentiment and market psychology. By analyzing Fibonacci ratios and wave structures, more informed projections of future price movements can be made. However, as with any technical analysis method, Elliott Wave Theory does not provide absolute certainty on its own; investors should incorporate this theory with other technical and fundamental analysis tools for more comprehensive decision-making.
References
Elliott, R. N. (1938). The wave principle. Robert R. Prechter Archive.
Jiménez Méndez, J., & Calvo Espinal, A. (2001). Elliott wave theory and market psychology. Financial Research Journal.
Prechter, R. R. (2009). Conquer the crash: You can survive and prosper in a deflationary depression. John Wiley & Sons.
Prechter, R. R., & Frost, A. J. (2017). Elliott wave principle: Key to market behavior. New Classics Library.
Močan, M. (2019). The effectiveness of Elliott wave theory in financial markets. Journal of Technical Analysis.
Gorman, D., & Kennedy, T. (2013). Applying Elliott wave theory to market forecasting. Financial Analysts Journal.