The Pitfalls of Support and Resistance for Determining Entry and Exit Levels
Support and resistance levels are among the most widely used tools in technical analysis, often relied upon to predict price reversals and determine optimal entry and exit points. While these concepts seem straightforward and practical, they can also be misleading. A deeper analysis reveals the potential pitfalls of relying solely on support and resistance for trading decisions.
Support and resistance lines can give traders the impression that price will consistently respect these levels, leading to a sense of predictability. However, human psychology is naturally wired to detect patterns, even in random data. This cognitive bias often causes traders to assign undue significance to levels that may not hold any real predictive value.
A perfect demonstration of this bias can be seen in a video experiment conducted by Rayner Teo (below), where he randomly draws lines on a price chart with the actual price data temporarily hidden. When the price data is revealed, many of these arbitrary lines seem to align with price movements, creating the illusion that they act as legitimate support or resistance levels. This highlights how easily traders can be misled into believing in the importance of levels that are purely coincidental.
This realization raises questions about the reliability of support and resistance, especially when used without considering broader market context. Over-reliance on these levels, without integrating other factors like volume or market conditions, can lead to poor trading decisions and false confidence in the predictability of price movements.
The Fractal Nature of Markets
Markets exhibit fractal behavior, meaning patterns and movements repeat across different timeframes. This fractal nature can cause short-term support and resistance levels to appear stronger than they are, leading to inaccurate signals. What might appear as strong support on an hourly chart could be meaningless on a higher timeframe, causing traders to misjudge the importance of the level.
Moreover, markets are influenced by a variety of factors, including institutional orders, economic events, and geopolitical shifts. These elements can make traditional support and resistance levels less reliable, particularly when new participants enter the market and cause price shifts that invalidate previously identified levels.
The Issue of Adjusted Data
One significant problem with using support and resistance over long timeframes is the distortion caused by adjusted data. Both stock prices and futures contracts are subject to adjustments that can drastically alter historical price levels:
Adjusted Share Data: Stocks are often adjusted for dividends, stock splits, and other corporate actions. These adjustments create a smooth historical price series, but they also shift historical prices downward. As a result, levels that once appeared as strong support or resistance may no longer align after the data has been adjusted.
Adjusted Futures Data: Futures contracts face similar issues. Methods like back-adjusting or ratio-adjusting are used to account for differences between expiring contracts and new ones. Over time, these adjustments can cause historical support and resistance levels to shift, distorting traders’ perception of where price levels were significant in the past.
This makes it difficult to substantiate the reliability of support and resistance levels over longer horizons, as the adjusted data may no longer reflect the actual historical price action.
Weakening of Support and Resistance Over Time
A common misconception is that the more times a level is tested, the stronger it becomes. In reality, the opposite is often true. As a price tests a level repeatedly, the orders that previously held the level get absorbed, weakening its strength. Without fresh buying or selling pressure, the likelihood of a breakout increases. This erosion of strength is why relying on over-tested support or resistance levels can lead to failed trades.
A More Comprehensive Approach
For traders who still want to incorporate support and resistance in their models, it’s essential to combine this approach with additional analysis techniques to avoid the pitfalls mentioned above. Here’s how:
Volume Analysis:
Volume is crucial in confirming the strength of support and resistance levels. A level tested with high volume is more likely to hold, whereas low volume may indicate weakness. Volume also helps distinguish between false breakouts and genuine moves, as strong volume during a breakout suggests commitment from market participants.Supply and Demand Zones:
Rather than focusing on narrow support or resistance lines, consider analyzing supply and demand zones. These zones reflect areas where large institutional orders have been placed, making them more reliable for predicting price reactions. Supply and demand zones are often more dynamic and account for the real forces driving market behavior.Multiple Timeframe Analysis:
Analyzing support and resistance across multiple timeframes can provide a clearer view of the market. A level that aligns on both the daily and weekly charts is more likely to be significant compared to one that only appears on a shorter timeframe. This reduces the likelihood of reacting to false signals from short-term fluctuations.Understanding Data Adjustments:
Traders should be cautious when using adjusted data, particularly over long timeframes. Understanding how back-adjusting or ratio-adjusting works, and how it impacts historical price levels, is essential for avoiding misleading signals. By being aware of these adjustments, traders can make more informed decisions and avoid basing trades on distorted data.
Wrapping Up
Support and resistance levels can be useful tools in trading, but they should not be relied upon in isolation. The illusion of patterns, the fractal nature of markets, and issues with adjusted data all challenge the long-term reliability of these levels. Traders must incorporate additional methods like volume analysis, supply and demand zones, and multiple timeframe analysis to refine their strategies. This multi-faceted approach will provide better trading outcomes and a more nuanced understanding of market movements.