September 10, 2025Journal of PosthumanismOpen Access

A Machine Learning Framework for Stock Trading: Integrating Technical and Economic Indicators

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LSTM model achieved superior accuracy of 71%, significantly enhancing stock trading forecasts.
The integrated approach yielded a cumulative return of 32.4% with a Sharpe ratio of 1.45 over three years.
Backtesting confirmed that machine learning strategies outperform traditional methods like buy-and-hold.
Statistical validation through paired t-tests indicated highly significant results with p < 0.001.

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Implication

This analysis demonstrates improved predictive accuracy and profitability in stock trading using machine learning methods, suggesting new strategies for investment decision-making.

Cite This Study

Behl et al. (2025) studied this question.

synapsesocial.com/papers/68c241acb210217d647a028e https://doi.org/10.63332/joph.v5i8.3133

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Implication

This analysis demonstrates improved predictive accuracy and profitability in stock trading using machine learning methods, suggesting new strategies for investment decision-making.

A Machine Learning Framework for Stock Trading: Integrating Technical and Economic Indicators

Key Points

Discussion

Implication

Cite This Study

A Machine Learning Framework for Stock Trading: Integrating Technical and Economic Indicators

Key Points

Discussion

Implication

Cite This Study