What Is Adjusted Moving Average?
An Adjusted Moving Average is a type of moving average that modifies its calculation to account for specific market conditions, such as market volatility or the relevance of recent price action. Unlike a simple moving average (SMA) which gives equal weight to all data points in its calculation period, an Adjusted Moving Average aims to be more responsive to current price changes or to filter out market "noise" more effectively. This concept falls under the broader category of technical analysis, a methodology used to forecast financial market directions by studying past market data, primarily price and volume. This adjustment makes the Adjusted Moving Average a versatile indicators for traders and analysts seeking to gain a clearer view of underlying trends.
History and Origin
The foundational concept of moving averages dates back centuries, with forms of calculation appearing in statistical analysis even before their widespread application in financial markets. Early examples include their use by Japanese rice traders in the 18th century to analyze market trends14. The modern mathematical formula for moving averages is often attributed to British statistician R. H. Hooker in 1901, though it was initially referred to as "instantaneous average." By the 1920s, pioneers in financial analysis, such as Charles H. Dow, a founder of Dow Theory, began to apply moving averages to study trends in stock markets, cementing their role in the emerging field of technical analysis13. The evolution from simple averages to "adjusted" forms, such as weighted moving average and exponential moving average, arose from the desire to make these tools more responsive and relevant to dynamic market conditions. These adjustments aim to reduce the inherent lag of traditional moving averages, providing a more timely reflection of price trends.
Key Takeaways
- An Adjusted Moving Average (AMA) modifies its calculation to improve responsiveness to current market conditions or to filter out noise.
- Unlike simple moving averages, AMAs typically assign greater importance to recent price data or adapt their calculation period based on volatility.
- Common types of AMAs include the Exponential Moving Average (EMA) and the Linearly Weighted Moving Average (LWMA).
- AMAs are widely used in trend following strategies and for generating trading signals.
- While more responsive, AMAs can still produce false signals in choppy or sideways markets.
Formula and Calculation
An Adjusted Moving Average does not refer to a single, universal formula but rather a class of moving averages that incorporate an adaptive or weighting mechanism. The core idea is to make the average more sensitive to recent price changes or to dynamically adjust its period based on market characteristics like volatility.
One common way to "adjust" a moving average is through exponential weighting, as seen in the Exponential Moving Average (EMA). The EMA formula places a greater emphasis on the most recent data points, causing it to react more quickly to new information than a Simple Moving Average (SMA). The calculation for an EMA involves a smoothing factor or multiplier, which determines the weight applied to the most recent price.
The formula for the Exponential Moving Average (EMA) is:
Where:
- (\text{Price}_{\text{current}}) = The current closing price or chosen price for the period.
- (\text{EMA}_{\text{previous}}) = The Exponential Moving Average of the previous period.
- (\text{Multiplier}) (or Smoothing Factor) = Calculated as (2 \div (\text{Number of Periods} + 1)).11, 12
Another form of adjustment can involve dynamically changing the number of periods used in the average based on market volatility. For instance, a volatility-adjusted moving average (V-MA) may use a shorter period during low volatility to hug prices closely and a longer period during high volatility to avoid frequent whipsaws and filter out noise9, 10. This adjustment makes the average more robust across varying market conditions.
Interpreting the Adjusted Moving Average
Interpreting an Adjusted Moving Average (AMA) largely follows the principles of other moving averages, but with an enhanced focus on responsiveness and trend clarity. When the price of an asset consistently stays above an upward-sloping Adjusted Moving Average, it typically signals an uptrend. Conversely, prices consistently below a downward-sloping AMA suggest a downtrend. Due to its inherent adjustment for factors like recent price relevance or volatility, an AMA tends to provide earlier indications of trend changes compared to a traditional SMA.
Traders often use the crossover of an AMA with the price, or the crossover of two AMAs of different periods, to generate potential trading signals. For example, if a short-term AMA crosses above a longer-term AMA, it can be interpreted as a bullish signal. The slope and direction of the Adjusted Moving Average line are crucial for gauging the strength and direction of the trend. A steeper slope indicates a stronger trend, while a flattening line may suggest consolidation or a weakening trend. This dynamic nature helps market participants better understand shifts in market sentiment and potential support and resistance levels.
Hypothetical Example
Consider a hypothetical stock, "GrowthCo (GCO)," with the following closing prices over 10 trading days:
Day 1: $100
Day 2: $102
Day 3: $105
Day 4: $103
Day 5: $107
Day 6: $110
Day 7: $109
Day 8: $112
Day 9: $115
Day 10: $113
Let's calculate a 5-day Exponential Moving Average (EMA), a common type of Adjusted Moving Average, for GCO.
First, we need an initial SMA for the first 5 days to begin the EMA calculation:
SMA for Day 1-5 = ($100 + $102 + $105 + $103 + $107) / 5 = $517 / 5 = $103.40
Next, calculate the Multiplier for a 5-day EMA:
Multiplier = (2 \div (\text{Number of Periods} + 1) = 2 \div (5 + 1) = 2 \div 6 \approx 0.3333)
Now, calculate the EMA for subsequent days:
-
Day 6 EMA: (Current Price - Previous EMA) * Multiplier + Previous EMA
- Since Day 5 is the first EMA (calculated as SMA), ( \text{EMA}_{\text{previous}} ) for Day 6 is $103.40.
- Day 6 EMA = ($110 - $103.40) * 0.3333 + $103.40
- Day 6 EMA = ($6.60 * 0.3333) + $103.40 = $2.20 + $103.40 = $105.60
-
Day 7 EMA:
- Day 7 EMA = ($109 - $105.60) * 0.3333 + $105.60
- Day 7 EMA = ($3.40 * 0.3333) + $105.60 = $1.13 + $105.60 = $106.73
-
Day 8 EMA:
- Day 8 EMA = ($112 - $106.73) * 0.3333 + $106.73
- Day 8 EMA = ($5.27 * 0.3333) + $106.73 = $1.76 + $106.73 = $108.49
-
Day 9 EMA:
- Day 9 EMA = ($115 - $108.49) * 0.3333 + $108.49
- Day 9 EMA = ($6.51 * 0.3333) + $108.49 = $2.17 + $108.49 = $110.66
-
Day 10 EMA:
- Day 10 EMA = ($113 - $110.66) * 0.3333 + $110.66
- Day 10 EMA = ($2.34 * 0.3333) + $110.66 = $0.78 + $110.66 = $111.44
As shown, the Exponential Moving Average continuously adapts, giving more weight to the recent closing price action and providing a smoothed yet responsive view of the stock's trend.
Practical Applications
Adjusted Moving Averages are versatile tools with numerous practical applications across various aspects of financial markets and analysis. One primary use is in trend following strategies, where traders identify the direction and strength of price movements. An Adjusted Moving Average can smooth out short-term fluctuations, allowing analysts to discern the underlying trend more clearly. A rising average indicates an uptrend, while a declining average suggests a downtrend8.
They are frequently employed to generate trading signals for entry and exit points. For instance, a common strategy involves observing crossovers: when a shorter-term Adjusted Moving Average crosses above a longer-term one, it can be a bullish signal, and vice-versa7. This helps traders make informed decisions about when to buy or sell an asset.
Furthermore, Adjusted Moving Averages can act as dynamic support and resistance levels. Prices often tend to bounce off or reverse around these average lines, providing potential areas for traders to consider. They are also integral components of more complex indicators like the Moving Average Convergence Divergence (MACD) and Bollinger Bands. In a broader economic context, understanding market movements and investor sentiment is crucial for policymakers. For example, the Federal Reserve monitors various market dynamics, including indicators of market volatility, which can influence their monetary policy decisions6. The responsiveness of adjusted moving averages can offer a more immediate reflection of market sentiment changes that might be relevant for broader market analysis and risk management considerations.
Limitations and Criticisms
Despite their widespread use, Adjusted Moving Averages, like all tools within technical analysis, come with inherent limitations and criticisms. A primary concern is that they are lagging indicators5. By definition, moving averages are calculated based on past price data, meaning they always reflect what has already happened, rather than predicting future movements. While adjustment mechanisms aim to reduce this lag, they do not eliminate it entirely. This can lead to delayed signals, potentially causing traders to enter or exit a position after a significant portion of the price move has already occurred.
Another critique stems from the "self-fulfilling prophecy" argument. If enough traders use the same Adjusted Moving Average settings and act on the signals generated, their collective actions can, in part, influence price movements to conform to the indicator's predictions. This can make the indicator appear more effective than it inherently is. However, this phenomenon alone is generally insufficient to sustain trends over the long term.
Furthermore, Adjusted Moving Averages can generate false signals or "whipsaws," especially in choppy or sideways markets where a clear trend is absent. In such conditions, frequent crossovers between price and the average, or between multiple averages, can lead to unprofitable trades4. This highlights that no single indicator should be relied upon in isolation. Critics also point to the random walk hypothesis and efficient market hypothesis, which suggest that financial markets are inherently unpredictable and that all available information is already reflected in prices. From this perspective, analyzing historical price chart patterns to predict future prices is largely futile2, 3. Therefore, many analysts advocate combining Adjusted Moving Averages with other forms of analysis, such as fundamental analysis, to achieve a more comprehensive market view and improve risk management.
Adjusted Moving Average vs. Exponential Moving Average
The terms "Adjusted Moving Average" and "Exponential Moving Average" are related but not interchangeable. An Adjusted Moving Average (AMA) is a broader conceptual category referring to any moving average that modifies its calculation to account for specific market conditions or to give more weight to recent data. The goal of an AMA is to improve responsiveness or reduce noise compared to a simple, unweighted average.
The Exponential Moving Average (EMA) is a specific and widely used type of Adjusted Moving Average. What makes the EMA "adjusted" is its unique weighting mechanism: it assigns exponentially decreasing weights to older data points, meaning the most recent prices have the greatest impact on the current average1. This inherent weighting makes it significantly more responsive to current price changes than a simple moving average.
Therefore, while all EMAs are a form of Adjusted Moving Average, not all Adjusted Moving Averages are EMAs. Other types of Adjusted Moving Averages include the Linearly Weighted Moving Average (LWMA), which applies a linear weighting scheme, or adaptive moving averages that dynamically alter their calculation period based on market volatility. The key distinction lies in the EMA being a concrete, defined calculation method within the broader, more conceptual framework of an Adjusted Moving Average.
FAQs
What is the main purpose of an Adjusted Moving Average?
The main purpose of an Adjusted Moving Average is to provide a more responsive and accurate reflection of an asset's price trend following by giving more importance to recent data or adapting to changing market conditions like market volatility. It aims to reduce the lag inherent in simpler moving averages.
How does an Adjusted Moving Average differ from a Simple Moving Average?
An Adjusted Moving Average differs from a simple moving average because it does not treat all data points equally. Instead, it assigns varying weights, typically giving more emphasis to recent prices, or it may dynamically adjust its calculation period. A Simple Moving Average, by contrast, gives equal weight to all prices within its specified period.
Can Adjusted Moving Averages be used for short-term trading?
Yes, Adjusted Moving Averages are often favored for short-term trading due to their increased responsiveness to recent price action. Their ability to reflect immediate shifts in momentum can help traders identify potential trading signals more quickly than slower, unadjusted averages.
Are there different types of Adjusted Moving Averages?
Yes, there are several types of Adjusted Moving Averages. The most common is the Exponential Moving Average (EMA), which applies exponential weighting. Other variations include the Linearly Weighted Moving Average (LWMA) and various adaptive moving averages that incorporate measures of volatility into their calculations.
Is an Adjusted Moving Average a leading or lagging indicator?
An Adjusted Moving Average is still considered a lagging indicator because it is based on historical price data. While its adjustments aim to minimize this lag and make it more responsive than a Simple Moving Average, it does not predict future price movements but rather confirms existing or emerging trends based on past information.