What question did this study set out to answer?

Evaluate the predictive performance of recurrent neural networks and long short-term memory models in forecasting equity market prices and financial trends from historical time-series data.

November 22, 2023ICST Transactions on Scalable Information SystemsOpen Access

Stock Market Analysis using Long Short-Term Model

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Evaluate the predictive performance of recurrent neural networks and long short-term memory models in forecasting equity market prices and financial trends from historical time-series data.
Implemented recurrent neural network architectures with long short-term memory (LSTM) cells using historical market data obtained from Yahoo Finance.
Processed financial time-series inputs and evaluated predictive modeling pipelines using Python packages Pandas and Matplotlib.
Conducted statistical correlation assessments evaluating daily return values against closing prices across four randomly selected companies.
LSTM networks accurately predicted stock market price trends and trajectories utilizing historical portfolio time series.
Correlation analyses demonstrated a statistically significant relationship between daily returns and equity closing prices.

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Implication

Machine learning analysis demonstrates accurate price trend forecasting in corporate equities, indicating substantial utility of long short-term memory networks for investment decisions.

Cite This Study

Gupta et al. (2023) studied this question.

synapsesocial.com/papers/6a8255290ebbaf848246c63d https://doi.org/10.4108/eetsis.4446

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Implication

Machine learning analysis demonstrates accurate price trend forecasting in corporate equities, indicating substantial utility of long short-term memory networks for investment decisions.

Stock Market Analysis using Long Short-Term Model

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Discussion

Implication

Cite This Study

Stock Market Analysis using Long Short-Term Model

Key Points

Discussion

Implication

Cite This Study

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