Technical Trading framework Outperforms Buy-and-Hold Strategies in US Stock Market
A new study reveals that a technical trading framework incorporating multiple analysis principles consistently outperformed traditional buy-and-hold investment strategies on key US stock market indexes between 2007 and 2023.
A extensive technical trading framework,moving beyond reliance on single indicators,offers a perhaps more lucrative path for investors,according to research published in the Journal of Investment Strategies. The framework leverages trend-following, conditional active trading, stop-loss mechanisms, and trading volume to formulate strategies that demonstrably exceeded the returns of a passive buy-and-hold approach.
The study focused on the SPDR S&P 500 ETF Trust (SPY) and the Invesco QQQ Trust (Series 1), two widely-tracked exchange-traded funds representing the broader US market and the Nasdaq 100, respectively. Researchers found that strategies derived from this multi-faceted framework consistently generated higher returns during the 16-year period from 2007 to 2023.
Unlike many existing analyses that concentrate on a single technical indicator, this approach integrates multiple trading principles into a cohesive technical analysis. The implementation primarily utilizes common indicators such as moving average crossovers and moving average convergence/divergence (MACD). “This framework reflects a comprehensive approach, integrating multiple trading principles into a technical analysis,” one analyst noted.
Further bolstering the framework’s effectiveness, an optimized multilayer perceptron (MLP) neural network was employed to refine parameter selection. This machine learning component leverages several volatility measures, including moving average gap volatility and downside price volatility, to identify optimal trading parameters. Strategies selected by the trained MLP consistently yielded higher returns and smaller drawdowns compared to the buy-and-hold benchmark.
Expanding the scope of the analysis, researchers also examined a large, survivorship-bias-free US stock sample spanning from 2000 to 2023.The results mirrored those observed with SPY and QQQ: machine learning-selected strategies consistently outperformed buy-and-hold, demonstrating the framework’s broader applicability.
The findings suggest a shift in viewpoint for investors. While buy-and-hold remains a popular strategy, this research provides compelling evidence that a more active, technically-driven approach, grounded in a robust framework and supported by machine learning, can deliver superior results. The study underscores the value of considering multiple technical indicators and adapting strategies based on evolving market conditions.
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Why: The study aimed to determine if a technical trading framework could outperform the traditional buy-and-hold strategy.
Who: Researchers published in the Journal of Investment Strategies conducted the study. Investors are the target audience.
What: A comprehensive technical trading framework, utilizing multiple indicators and machine learning, consistently outperformed buy-and-hold strategies on SPY, QQQ, and a broader US stock sample from 2000-2023.
How did it end?: The study concluded that a technically-driven, active approach, supported by machine learning, can deliver superior results compared to buy-and-hold, suggesting a potential shift in investment perspectives.
