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How exactly do markets adapt?
█ How exactly do markets adapt? Evidence from the moving average rule in three developed markets.
Brought forth by Fama in the 1960s, the EMH suggests that it is impossible to consistently achieve returns over the average market on a risk-adjusted basis, given that price changes should only arise due to new information entering the market.
According to the weak form of EMH, this information includes historical price movements. That, by extension, renders technical trading strategies based on past price data theoretically ineffective. However, the dynamic nature of financial markets has given rise to an alternative perspective known as the Adaptive Market Hypothesis (AMH), proposed by Andrew Lo in 2004.
The AMH posits that the degree of market efficiency can vary over time due to the interactions of market participants, each adapting to changes within the market environment. This hypothesis allows for the potential profitability of trading rules during periods when markets are less efficient.
The moving average (MA) rule serves as a litmus test for the validity of both EMH and AMH. Historically, this rule has enjoyed periods of significant predictive power, famously demonstrated by Brock, Lakonishok, and LeBaron in 1992.
The primary objective of this study was to investigate the ongoing effectiveness of the moving average (MA) rule in predicting stock market prices post-1986. Andrew et al. focused on three developed markets: the DJIA in the United States, the FT30 in the United Kingdom, and the TOPIX in Japan.
█ Conclusion: The study concluded that the predictive power of the MA rule has significantly diminished in all three markets examined since 1986. This decline in effectiveness aligns with the Adaptive Market Hypothesis (AMH), which posits that market efficiency is not a static condition but evolves as market participants adapt to exploiting profitable opportunities.
█ Methodology
⚪Data Set and Timeframe
⚪Analytical Techniques Used
The study used a comparative analysis of the MA rule against a traditional buy-and-hold strategy. It serves as a benchmark for market performance over time. By evaluating the returns generated by following the MA signals versus simply holding stocks, it aimed to determine the rule's effectiveness in generating excess returns.
Additionally, the analysis included a detailed examination of market reactions to buy and sell signals generated by the MA rule. This approach assessed the immediate impact of these signals on stock prices and looked at how quickly and efficiently the markets absorbed this information.
█ Key Findings
Across all three markets studied—DJIA, FT30, and TOPIX—the findings consistently showed a decline in the predictive power of the MA rule post-1986. This trend was evident in the reduced profitability of strategies based on this rule.
⚪Market Adaptation to Trading Signals
The study revealed significant insights into how markets have adapted to trading signals. It appears that as market participants have become more sophisticated, the ability of traditional trading rules like the MA to outperform simpler strategies has decreased.
This adaptation may be partly due to the increased predictability of market reactions to known trading signals, leading to quicker adjustments in stock prices.
⚪Anticipation of MA Signals and Shift in Strategy
One of the more novel findings from the study was the shift in how traders anticipate MA signals. Traders, aware of the historical profitability of these signals, have begun to preemptively act on expected signals rather than waiting for the signals to be formally generated.
This anticipation leads to a scenario where actual trading on the anticipated signals the day before their formal generation often yielded superior profits compared to following the signals post-generation.
This shift in strategy underscores a more proactive approach among traders, who rely on forecasting and predictive models to stay ahead of traditional signal-generation techniques.
█ Implications for Market Participants
The findings suggest that traders who have relied heavily on MA strategies should reassess their trading approaches. While MA strategies may not need to be completely discarded, they should be used with a grain of salt alongside other comprehensive tools for analysis.
The decreased predictability of returns using MA rules supports the Efficient Market Hypothesis (EMH). This confirms the hypothesis that markets may efficiently reflect all known information, including known trading strategies like MA, thus negating their effectiveness over time.
On the other hand, the study strongly supports the Adaptive Market Hypothesis (AMH), emphasizing that market efficiency is not a static state but varies over time with the actions of market participants.
The AMH's view that trading strategies can ebb and flow in effectiveness depending on market conditions is corroborated by the varying success rates of MA strategies over different periods and markets.
In the context of moving averages, which are often used to identify trends by smoothing out price data over a specified period, their effectiveness can change. For instance, in a highly volatile market, MA strategies might generate many false signals, leading to poor performance. Conversely, in a trending market with less volatility, MA strategies could be quite successful. This variation in success rates across different times and market environments supports the AMH view that the profitability of trading strategies can fluctuate as market dynamics evolve.
Trend
Consolidation
█ Study Limitations
While the study provides insightful findings, it has certain limitations that should be noted.
Firstly, focusing on only three developed markets—DJIA, FT30, and TOPIX—may not fully represent global market dynamics. The behaviors and trends in these markets might not be universally applicable, especially in less developed or emerging markets.
Additionally, the study's methodology does not account for transaction costs, which could significantly impact the profitability and practical application of MA strategies in a real-world trading environment.
█ Reference
Urquhart, A., Gebka, B., & Hudson, R. (2015). How exactly do markets adapt? Evidence from the moving average rule in three developed markets. Journal of International Financial Markets, Institutions & Money, 38, 127-147. doi:10.1016/j.intfin.2015.05.019
-----------------
Disclaimer
This is an educational study for entertainment purposes only.
The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
The Efficient Market Hypothesis (EMH) has long been an important theory in finance.
Brought forth by Fama in the 1960s, the EMH suggests that it is impossible to consistently achieve returns over the average market on a risk-adjusted basis, given that price changes should only arise due to new information entering the market.
According to the weak form of EMH, this information includes historical price movements. That, by extension, renders technical trading strategies based on past price data theoretically ineffective. However, the dynamic nature of financial markets has given rise to an alternative perspective known as the Adaptive Market Hypothesis (AMH), proposed by Andrew Lo in 2004.
The AMH posits that the degree of market efficiency can vary over time due to the interactions of market participants, each adapting to changes within the market environment. This hypothesis allows for the potential profitability of trading rules during periods when markets are less efficient.
The moving average (MA) rule serves as a litmus test for the validity of both EMH and AMH. Historically, this rule has enjoyed periods of significant predictive power, famously demonstrated by Brock, Lakonishok, and LeBaron in 1992.
The primary objective of this study was to investigate the ongoing effectiveness of the moving average (MA) rule in predicting stock market prices post-1986. Andrew et al. focused on three developed markets: the DJIA in the United States, the FT30 in the United Kingdom, and the TOPIX in Japan.
█ Conclusion: The study concluded that the predictive power of the MA rule has significantly diminished in all three markets examined since 1986. This decline in effectiveness aligns with the Adaptive Market Hypothesis (AMH), which posits that market efficiency is not a static condition but evolves as market participants adapt to exploiting profitable opportunities.
The findings indicated that while the MA rule was once highly predictive, market participants' increased awareness and adaptation to these trading strategies likely eroded their profitability.
█ Methodology
⚪Data Set and Timeframe
- The study analyzed the period from 1987 to 2013, carefully selecting data from three major stock indices: the DJIA (US), the FT30 (UK), and the TOPIX (Japan).
- This timeframe follows the period studied in the original BLL research, allowing for a fresh evaluation of the MA rule in a contemporary market context.
⚪Analytical Techniques Used
The study used a comparative analysis of the MA rule against a traditional buy-and-hold strategy. It serves as a benchmark for market performance over time. By evaluating the returns generated by following the MA signals versus simply holding stocks, it aimed to determine the rule's effectiveness in generating excess returns.
Additionally, the analysis included a detailed examination of market reactions to buy and sell signals generated by the MA rule. This approach assessed the immediate impact of these signals on stock prices and looked at how quickly and efficiently the markets absorbed this information.
█ Key Findings
Across all three markets studied—DJIA, FT30, and TOPIX—the findings consistently showed a decline in the predictive power of the MA rule post-1986. This trend was evident in the reduced profitability of strategies based on this rule.
⚪Market Adaptation to Trading Signals
The study revealed significant insights into how markets have adapted to trading signals. It appears that as market participants have become more sophisticated, the ability of traditional trading rules like the MA to outperform simpler strategies has decreased.
This adaptation may be partly due to the increased predictability of market reactions to known trading signals, leading to quicker adjustments in stock prices.
⚪Anticipation of MA Signals and Shift in Strategy
One of the more novel findings from the study was the shift in how traders anticipate MA signals. Traders, aware of the historical profitability of these signals, have begun to preemptively act on expected signals rather than waiting for the signals to be formally generated.
This anticipation leads to a scenario where actual trading on the anticipated signals the day before their formal generation often yielded superior profits compared to following the signals post-generation.
This shift in strategy underscores a more proactive approach among traders, who rely on forecasting and predictive models to stay ahead of traditional signal-generation techniques.
█ Implications for Market Participants
The findings suggest that traders who have relied heavily on MA strategies should reassess their trading approaches. While MA strategies may not need to be completely discarded, they should be used with a grain of salt alongside other comprehensive tools for analysis.
The decreased predictability of returns using MA rules supports the Efficient Market Hypothesis (EMH). This confirms the hypothesis that markets may efficiently reflect all known information, including known trading strategies like MA, thus negating their effectiveness over time.
On the other hand, the study strongly supports the Adaptive Market Hypothesis (AMH), emphasizing that market efficiency is not a static state but varies over time with the actions of market participants.
The AMH's view that trading strategies can ebb and flow in effectiveness depending on market conditions is corroborated by the varying success rates of MA strategies over different periods and markets.
In the context of moving averages, which are often used to identify trends by smoothing out price data over a specified period, their effectiveness can change. For instance, in a highly volatile market, MA strategies might generate many false signals, leading to poor performance. Conversely, in a trending market with less volatility, MA strategies could be quite successful. This variation in success rates across different times and market environments supports the AMH view that the profitability of trading strategies can fluctuate as market dynamics evolve.
Trend
Consolidation
█ Study Limitations
While the study provides insightful findings, it has certain limitations that should be noted.
Firstly, focusing on only three developed markets—DJIA, FT30, and TOPIX—may not fully represent global market dynamics. The behaviors and trends in these markets might not be universally applicable, especially in less developed or emerging markets.
Additionally, the study's methodology does not account for transaction costs, which could significantly impact the profitability and practical application of MA strategies in a real-world trading environment.
█ Reference
Urquhart, A., Gebka, B., & Hudson, R. (2015). How exactly do markets adapt? Evidence from the moving average rule in three developed markets. Journal of International Financial Markets, Institutions & Money, 38, 127-147. doi:10.1016/j.intfin.2015.05.019
-----------------
Disclaimer
This is an educational study for entertainment purposes only.
The information in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell securities. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on evaluating their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
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