In the realm of financial analysis, the advent of technical indicators has spurred a plethora of academic inquiries. The pursuit of understanding their viability for profitable trading has yielded mixed conclusions. While certain studies champion the efficacy of technical indicators, others vehemently reject this notion.
The historical disdain of academics toward technical analysis, as eloquently expressed by Malkiel (1981), has undergone a profound transformation. The skepticism, rooted in the belief in the efficient market hypothesis (EMH), once deemed technical analysis false and easy to criticize. The EMH, however, has not escaped heated debates, with emerging studies revealing inefficiencies and memory in market prices, advocating for the relevance of technical analysis.
Newcomers to the trading arena find themselves drawn to the simplicity and popularity of technical indicators, despite facing criticism. These indicators, often highlighted in educational posts by brokers and readily available on trading platforms, become indispensable tools for traders.
Evolution of Technical Indicators
Historical prices narrate a time-series/digital signal, allowing tools designed for time-series analysis and digital signal processing to interpret them. The simplicity of early technical indicators, such as moving averages, rolling variance/standard deviation, and momentum oscillators, has evolved with technological advancements. Access to historical prices has become more straightforward, democratizing the creation and usage of technical indicators.
The introduction of programming languages tailored for crafting technical trading tools, exemplified by Pinescript, marks a pivotal juncture. Traders now wield the ability to create and share their technical indicators, ushering in a era of more complex calculations and graphical elements.
Technical Indicators Performances
A myriad of technical indicators populates the financial landscape, showcasing a dynamic practice of innovation. Yet, a definitive set of technical indicators with proven performances remains elusive. Traditional indicators like simple/exponential moving averages, momentum oscillators, stochastics, and Bollinger Bands persist in usage, despite their failure to account for the ever-changing conditions in market prices.
Adaptive indicators, such as Kaufman adaptive moving average (KAMA) and fractal adaptive moving average (FRAMA), attempt to address this limitation. However, adaptivity fails to significantly enhance profitability over fixed-length moving averages, possibly due to the continued reliance on user settings.
User settings pose a substantial challenge, demanding constant optimization. The search for a universally profitable technical indicator becomes an intricate task, considering the changing market conditions. The question shifts from “Is there a profitable technical indicator?” to the more nuanced “What is the best technical indicator under certain market conditions?”
Are Popular Indicators Better Than Less Popular Ones?
The popularity of a technical indicator may not always align with its effectiveness. External factors such as marketing, author popularity, and consumer behavior play a pivotal role in determining popularity. The visual aspect of technical indicators, relying on their digital appeal, adds another layer to their popularity.
Psychological studies on color perception highlight its influence on consumer behavior. More vibrant indicators may be perceived as complex, leading users to anticipate a higher quantity of information, potentially improving their trading decisions. However, the focus on visually appealing yet technically simple indicators, driven by advertising and endorsements, may not necessarily correlate with positive performances.
Redundant Information
A proficient technical indicator aims to provide abundant, easily accessible, and non-redundant information while minimizing user interaction. However, the redundancy problem persists, impacting the indicator’s core goals. Take the momentum oscillator, for instance, with its ability to determine trends, show divergences, and reveal changes in a simple moving average of the same period.
Certain indicators, like ribbons consisting of multiple moving averages with different periods, often return excessive redundant information. The challenge lies in the selection of moving averages and their periods, potentially rendering the information from the ribbon redundant and challenging to analyze.
Repainting and Non-Causality
The visual allure of an indicator may attract user interest, but indicators demonstrating excellent entry points garner the most attention from traders. Over the years, “repainting” indicators have exhibited enticing results. These indicators, subject to changes in past values over time, prove useful in non-real-time applications.
However, their utility diminishes when determining entry points in real-time, often succumbing to delayed decision timing. Repainting indicators, similar to lagging indicators, present challenges in real-time decision-making, akin to horizontal support and resistance indicators relying on pivots.
Conclusions
Endorsing the profitability of automated strategies based on technical indicators proves challenging. Trend-following strategies thrive in clear trends, while contrarian strategies excel in stationary markets. The worth of a technical indicator lies not in its ability to independently generate profits, given the complexity of market price variations.
However, for an experienced trader, a technical indicator can serve as a supportive tool for decision-making. The usefulness of an indicator hinges on the quantity of non-redundant and useful information it imparts, rather than its proficiency in providing early and accurate entry points—a feat harder to achieve. Crafting a “universal” indicator remains challenging, necessitating user interaction for optimal use.