Never fall in love with a tool!

In the year 2000, I conducted an interview with a gentleman known as AKR, who, during a teleconference about the derivatives market in India, advised, "Never fall in love with a tool." He was responding to my fervent exposition on the analytical tools I employed to forecast market trends. His remark took me aback, challenging and cautioning me simultaneously about my reliance on these measuring devices. In the financial markets, everyone has their preferred tools—be it a scale, a sentiment gauge, an intuition. AKR’s observation was a universal caution: if you become enamoured with your tools, you are destined for failure.

I am not sure if AKR’s words biased me permanently, but from that moment, my professional life transformed from affection to detachment as I progressed in my understanding of market dynamics. I experienced a cycle of falling in love with and then ruthlessly abandoning tools as I discovered their inherent limitations. Even when a tool seemed effective, its vulnerabilities—its Achilles' heels—rendered it insufficient in my eyes. I resolved never to fall in love with a tool again. The process of abandoning them was challenging, but it felt as though I was driven by a perpetual chant that haunted me. It was as if I was cursed to love and then forsake.

Years later, I re-encountered the truth in AKR's statement as I read about the Campbell and Goodhart curse. Every variable known to mankind is doomed to fail. This notion was further confirmed by Clive Granger, who posited that for a factor to be effective, it must exist in a dynamic state of simultaneous success and failure. Kenneth Boulding, an academic who significantly shaped modern finance, articulated a similar idea, noting that information is destined to oscillate between relevance and irrelevance. The overarching message was clear: history is bound to be forgotten, and humans are doomed to rely on their tools and emotions, even if they lead to their demise.

Charles Goodhart and Donald T. Campbell both developed concepts regarding the predictive failure of variables when used as targets, but Goodhart's formulation came slightly earlier. Goodhart, an economist, first introduced what would later be known as Goodhart's Law in 1975, stating that "When a measure becomes a target, it ceases to be a good measure." This observation was primarily related to monetary policy, where he noticed that once a statistical measure was targeted by policy makers, it tended to lose its effectiveness due to the alterations in behavior it induced. A year later in 1976, Donald T. Campbell, a social scientist, articulated a similar concept known as Campbell's Law. His principle highlighted that the more a quantitative social indicator is used for decision-making, the more it is susceptible to corruption and manipulation, thereby distorting the processes it intends to monitor. While both theories address the distortive effects of using measures as targets—Goodhart in the realm of economics and Campbell in social sciences—their core insight is that targeting a measure for policy or performance can undermine its reliability. Thus, though both contributed profoundly to understanding this phenomenon, it was Charles Goodhart who first articulated this principle.

Not long after, I relocated to Mumbai and found myself traveling home to my apartment in Andheri after a session of Technical Analysis at the Bombay Stock Exchange. These sessions focused on the study of stock market price charts to predict future trends. At that time, I was employed at Refco, a commodity clearing company with an office at Lower Parel, so my daily routine involved commuting from there to the Exchange for classes after work, then back home to northwest Mumbai. Despite the hours spent commuting as a strap-hanger on the Mumbai metro, my passion and energy for learning remained undiminished. I revered Bloomberg Markets, a magazine that, at the time, was a primary source of Wall Street news. Somehow, I managed to obtain the latest issues through friends in the industry who had access to Bloomberg terminals.

Dr. C.K., one of my first mentors in technical analysis, offered to drop me at Bandra Station after our sessions. As the name suggests, the subject was technical, involving detailed chart analyses focused on market trends and potential reversals. At that time, I was unaware that many Wall Street fundamentalists, who were considered the mainstream analysts, despised charting, viewing it as almost occult. Numerous authors criticized this subject, but even if I had known this, it would not have deterred me. I had already been charting for over a year and found the discipline fascinating. My affinity for charts was intuitive, perhaps because of my skill in sketching; I could draw a straight line without a ruler and a perfect circle without a compass. I was eager to master technical analysis as it complemented my derivatives background and enhanced my ability to forecast and issue research calls.

Evidence-based Technical Analysis" by David Aronson critiques subjective technical analysis in financial markets, advocating for a more scientific, data-driven approach to understanding market trends. Aronson is skeptical of traditional methods that lack empirical validation and are prone to cognitive biases. This sentiment is echoed by other prominent scholars like Burton Malkiel, whose book "A Random Walk Down Wall Street" argues that stock prices follow random paths, challenging the core premise of technical analysis that past price movements can predict future ones. Malkiel supports the Efficient Market Hypothesis (EMH), suggesting that stock prices reflect all available information, thus negating the effectiveness of technical analysis. Similarly, Eugene Fama’s work on EMH reinforces this view by proposing that consistently outperforming the market using historical data is not feasible. These critiques are further supported by the field of behavioral finance, with figures like Robert Shiller acknowledging that while markets exhibit inefficiencies, the psychological and social dynamics of market participants complicate the reliance on technical strategies. Collectively, these perspectives highlight significant skepticism towards technical analysis, promoting a deeper, more critical examination of market behaviors and the validity of using past price data to forecast future market movements.

Dr. C.K. drove me in his dark brown Santro, always polite, courteous, and a gifted teacher. All a passionate student needed was a mentor to fill his life with joy. I placed the Bloomberg magazine on the broad dashboard, starting a conversation with Dr. C.K. about various topics as he navigated the slow drive to Bandra. I couldn't contain my excitement about the recent cover story featuring Robert Prechter, a renowned Elliottician, which was just published in the magazine. Dr. C.K. did not even blink and said, "Prechter is a genius," as he parked at the station to let me off. I wanted Dr. C.K. to read the article, but not wanting to part with the magazine, I tore out the three-page article on Prechter and handed it to him. Thus began my love story with Elliott Wave analysis back in 2000.

Robert Prechter was the guru of the decade in 1980s and a living practitioner of Elliott waves, a method of price analysis created by Ralph N. Elliott. Elliott, who was terminally ill and had retired from his bookkeeping profession, took up the study of markets and contemporary theories like the Dow Theory, developed by Charles Dow. He built upon Dow's work, recognizing that markets move in a wave-like structure at every time frame, from the smallest to the largest. This wave structure was recurrent, with trends typically unfolding in five waves and counter-trends in three waves. For the trend to continue, the five waves needed to cover a greater net distance than the counter-trends, which were designed to pause and confuse but never to challenge the overarching trend set by the five-wave structure.

My affection for Dr. C.K. was greater than my love for the magazine, even though it pains me to admit that I tore out those pages. 24 years later, I would do it again. When I fall in love with a subject, it is an all-consuming passion. My approach to anything that intrigues me has never been half-hearted. If a subject fascinates me, the difficulty level is irrelevant, even if it involves learning a new language; I dive right into it.

And as I was learning charting and immersing myself in the subject, my focus was on conventional charting—a diverse study from price patterns to indicators, and specialized subjects like Candlesticks and Point and Figure. There was an expert for every area of charting. More books existed on charting than there were days in the year, and it all began with Charles Dow and the Sokyo Homma, the mythic Osaka rice trader who was rumored to have correctly predicted 100 trades in a row.

The Dow Theory was built from observations Charles Dow wrote in his editorials. He observed the ebb and flow of market trends over time, with ripples representing short-term trends, transitioning into waves as intermediate trends, all guided by larger tides which dominated the overall market movement. Many followers extended Dow's work through market letters that discussed the confirmation and non-confirmation between the railroad index and the industrials.

Then there was Elliott waves. It is believed that for every 100 people who study charts, 10 practice the Dow Theory and only one works with Elliott waves. The purity of Elliott wave theory, in its strict focus on price action, represents a profound simplification, but it also demands a unique skill set. One needs to become a master pattern recognizer, able to decipher the larger trend, the intermediate trends within it, and the short-term movements. It is about hierarchical pattern recognition. After observing the patterns for a few years, they begin to speak to you, revealing their future intentions.

Humans are biologically wired as pattern recognition machines, a trait that has been crucial for survival, enabling us to quickly identify environmental patterns, from recognizing faces to spotting potential dangers. However, this same capability leads us to perceive patterns where none exist, a phenomenon known as apophenia. This tendency is rooted in our cognitive processes, driven by the need to impose order and predictability on the world. It manifests across various contexts, from gamblers seeing non-existent "lucky streaks" to conspiracy theorists linking unrelated events. Neurologically, this ability is essential for processing sensory information and making rapid decisions, which could have once meant the difference between life and death. It also aids in navigating complex social systems and problem-solving by applying known patterns to new challenges. Despite its benefits, this instinct for pattern recognition can lead to cognitive biases, where random noise is misinterpreted as significant, impacting decisions negatively. This underscores the importance of fostering critical thinking and scientific skepticism to balance instinctual pattern recognition with rational analysis, helping prevent misinterpretations that our pattern-seeking brains can produce.

Was it the subject itself, its purity, or the sheer charisma of feeling in command of the market that captivated me? It was intoxicating. Combining my knowledge of waves with derivatives was potent. The markets had become an addictive drug. While all this was happening, charting tools were gaining popularity; physical point and figure charts were being replaced by electronic systems, and Ralph Acampora, one of the founders of the Market Technicians Association, helped bring the institution into mainstream recognition, earning it approval from the SEC and NASDAQ. Now, there were officially two types of analysts: fundamental and technical. By then, I had earned my CMT charter and proudly held the title of chief Elliottician at my Investment Advisory.

The recognition of the Chartered Market Technician (CMT) designation by FINRA, under the oversight of the SEC, formally acknowledges technical analysis as a valid approach in the securities industry. This acceptance allows individuals with the CMT designation to perform analytical duties legally within the regulatory framework, demonstrating regulatory approval of technical analysis. Although the SEC does not endorse specific methodologies, the incorporation of the CMT into FINRA’s qualifications highlights the legitimacy of technical analysis as a professional discipline in financial markets.

“Adgal Ghagri Chalkat Jai”—a little knowledge is a dangerous thing. There is no escaping leverage when you are trading. And investing is a slow and gradual process designed to be less risky, boring, and therefore not as appealing to the youth, who are drawn to speed and risk. Trading also ensnared me, and I had to navigate through my experiences, mixing leverage with my few years of learning Elliott waves. I lost money during this period, with a few friends who had pooled together trading capital for me. I had a choice to blame the tool, or to accept that I was still a novice and had not yet mastered the tool. In that moment, I re-embraced the subject, writing to Robert Prechter to thank him for all his books and telling him how his work had been invaluable to my life. I was determined and resolved to work harder to change the outcome. I was not ready to give up on my love; I had to reach the summit of the mountain.

The learning continued, and my risk management skills sharpened. My stock rose as an expert. I proved my perseverance with the tool. I started publishing market letters on a daily basis, calling it “Waves.” It’s easy to develop a market following when you are a forecaster. People adopt a guru because it somewhere confirms their opinion about the markets.

I had made a name for myself in India, and now I was doing the same in Romania. My reports were filled with “Anticipated and Happened” case studies and “Accuracy reports.” The focus was on multi-week trends, more than a few weeks long but less than a few months long. The trading improved. Clients started coming. Things went so well that I had a group of fellow technicians from India and Romania wanting to learn the craft from me. I was the new torchbearer. I was making bold calls and earning top credibility. My fame spread to LA, and Vinni, a top executive at NYMEX, subscribed to my intraday research on oil. I was ticking, counting waves, from a tick to year-long trends. But then, the voice of AKR kept ringing in my head: “Never fall in love with a tool.” I had climbed the perilous mountain surface and was near an overhang.

In the context of financial trading, a "tick" can also refer to any single trade or price change, regardless of the actual change in price. This definition emphasizes the time element in trading, where a tick represents the smallest increment of time in which any trading activity occurs, often measured in milliseconds or seconds in modern electronic markets. Each tick captures the exact moment a transaction happens, providing a granular view of trading activity. This is critical in high-frequency trading (HFT), where algorithms execute orders and analyze market data within these tiny fractions of a minute to capitalize on rapid price movements. Understanding ticks in this time-sensitive context is crucial for traders who need to respond instantaneously to market changes, aiming to exploit minor discrepancies in price for profit. This detailed tick-by-tick data is indispensable for a precise assessment of market dynamics and for developing strategies that require real-time execution.

The calm near the top did not last long. I received an email about a conference on time cycles in Vienna. A trip to Schönbrunn Palace and the heart of Austria gave me a sense of the Austro-Hungarian empire that stretched into Transylvania. The time cyclists presenting at the event came from diverse backgrounds. One was a man named Richard Mogey, who had worked under Edward R. Dewey, the chief economist for President Hoover during the Great Depression. The story goes like this: Hoover asked Dewey what caused the Great Depression, and after much research and conducting interviews, Dewey came back and told him that the Great Depression was a cyclical event and that it would happen again. Intrigued by time cycles, Dewey started the “Foundation of Cycles.”

I also met Theodore Modis, a physicist from the CERN particle accelerator who was based out of Lugano and was reviving the work of Pierre François Verhulst and the S-curve from oblivion. And Bill Meridian, another U.S.-educated cyclist, had also written more than a few books on cycles.

After spending a few thousand dollars on reading materials and a few months going through the tomes of literature in early 2007, studying Kitchin/Juglar/Berry/Strauss & Howe/Kondrateiff/Kuznet, and everything at the core of time cycles, I asked myself why the Foundation of Cycles had not merged with the Market Technician’s Association. If top Elliotticians like Prechter were articulating that Elliott was not so good at timing, but was more about form, then why was there not a merger of tools, cycles with Elliott?

The "S Curve" is a graphical model depicting the growth of a variable in terms of time, often used to represent the lifecycle of a product, technology adoption, or population growth. Characterized by its S-shape, the curve starts with a slow initial growth rate (early phase), accelerates during the middle (growth phase), and then levels off as it reaches a saturation point (maturity phase). This model helps businesses and analysts predict performance trends, manage expectations, and strategize around different lifecycle stages. The S Curve is particularly useful in strategic planning and market analysis, enabling decision-makers to anticipate changes and adjust their approaches accordingly.

Equipped with cycles, expertise in Elliott, trading, I was now at my peak best with forecasting and trading. My bookshelf was filled with more than 100 books on everything from point and figure, to Gann, to cycles, to psychology, to commodities, to strategies, to cycles, along with every book Prechter had written from Socionomics to Elliott to Fibonacci, everything. I had a library with a rocking office chair. And it was during these days that I started training Olga. A trader from Bucharest working for BRD, a large private bank soon to be bought by Socgen (Société Générale), one of the large French banks. A no-nonsense technician, she used to fly on Friday from Bucharest to Cluj, at her own expense, find a place to live in the city, and start training on Saturday morning with me all the way into the evening, and leave back the same day for Bucharest.

Teaching is fun when you meet a great student. She was inspired, diligent, and meticulous. This to and fro happened for a month, and one day, during our training sessions, she mentioned something she had read. What separates good teachers from great teachers is that great teachers know that they have to learn from their students. I was not a great teacher, but aspired to be one. And my inquisitive nature forced me to ask more. This is how I was introduced to Tony Plummer, a veteran technician from the UK society of technical analysts called CFTe. He had written more than a few books, and one of them specifically focused on explaining Elliott fractals through cycles. But this was such a nascent area that even experienced cyclists admit they had never thought about Elliott being a footnote in the larger subject of cycles.

Tony Plummer’s book Forecasting Financial Markets: The Psychology of Successful Investing offered an in-depth assessment of the phenomenon of cycles, patterns of economic and financial activity, and how to use cycles as a forecasting tool.

By mid-2007, I was getting ready for the great financial crash. And I had a sense that the world would change. I was also clear about a few more things, I did not want to die sitting on a chair doing Elliott waves and patterns. I wanted to teach machines to do what I could do. I did not see myself getting old as a trader. And the only sophisticated financial solution I saw myself doing was a long-short strategy. I had found my risk preference, something not easy to find. You have to look hard, to know what aspect of the market relates to the risk preference you vibrate at. A union of the mind with the market.

Most day traders don’t know if they are designed to be day traders. Most investors don’t know if they are designed to be value investors. Most value investors don’t know the risk that comes with investing, and most investors don’t know the challenges of size concentration. There are only a few investors in the world, who find themselves in the market, understanding who they are, why they are there, who they want to be, and what aspect of the market they want to deal with comes with a lot of introspection.

I knew that my Elliott love was slowly deserting me. AKR was grinning, having been proven right again. But I felt a lot richer in my mind, having experienced the world of patterns, the fame, the elations brief periods of conquering the market brought, falling in love with an art form and best of all finding myself, my risk purpose in a long-short spread and my journey to find the tool that was forecastable and non-forecastable at the same time.

Understanding risk preference comes before finding a risk purpose. The rush for wealth creation is meaningless, if we don’t find ourselves. Mastering the mind is always more valuable than mastering the markets. The beauty happens when we find both together, unearthing our purpose with markets and our connection with material wealth.

Before the great crash, near the peak, I had abandoned Elliott and had moved on to intermarket spreads, herding mechanisms, cantor clocks, and indexes. The clocks were designed to execute long-short trades in the future. A hedge fund in London was testing them in real-time as the future unfolded. I had transitioned from Elliott, cycles, and charting to machines. However, I sometimes reminisced about my lost love and exchanged notes with Bob (Robert Prechter). I had earned the right. I met him in 2012 at the 40th Anniversary of the MTA annual symposium. I told him how overjoyed I was to meet him in person. We sat next to each other. He said, “Mukul, you will get over this moment.” Bob was always human and knew that fame came with its own baggage.

The same year in March 2012, we exchanged notes over email. I had published a recount of Bob's historical work published in Technically Speaking, MTA, explaining how Dow was headed higher for a decade into 2015.

https://cmtassociation.org/development/technically-speaking/

Later I updated the call to Dow 36,000 [1] (Which crossed recently). By 2022, my work in Smart Beta also proved that indexes can indeed be redesigned, which meant the count could change for an underlying index and hence the forecast. This challenged the whole thesis of Elliott waves because alpha of 5% above the benchmark was an entity in itself which when compounded created a world dramatically different from the world of Dow Theory and Elliott Waves. Machines cared less what the count was, they cared about recovery, contained drawdown, and juicing the basket with different weights and rebalancing a new generational thought which transcended beyond human pattern watching capabilities.

The machine challenge was real. Good forecast is a curse in disguise because for every good forecast there are many bad infamous ones. I had peace in my mind, not because I could challenge my Guru but because I had great reverence for a tool for what it taught me and gratitude for my teacher who had patience with me, who gave me everything to enable me to move ahead.

There are different ways for us to find ourselves. This was my way and your way can be a lot different. I am designed to learn and unlearn and learn something new again. But there is one thing you should never forget, it's not the forecasts that make money, it is the risk management. And if you are in love with a forecasting tool, you are biased, and probably not paying enough attention to the underlying risk.