no external features

the performance of the deep network models are not as good as that was seen in the case of single-step forecasting. However, notwithstanding the above limitation of the models for the multi-step forecasting

As the forecast window size is increased (such as 28), the performance drops significantly.

the models performed quite well for the smallest window size of 3. The predictions for the window size 7 were also good for all the models. However, the results for the other window sizes varied irregularly and did not perform as well.

MLP outperforms all others.

The JSWSTEEL stock dataset contains a very high number of structural breaks and is highly volatile.

One unique aspect of these models can be observed in the forecasting graphs (refer Appendix A): all the models failed to predict the sudden increases in the prices to the actual extent

However, as we increase the window sizes, the predictions of all the models go further away from the true values increasing the error rate.

TABLE 1. Indian stock price data: 10 companies. The period is fixed for all the stocks: 1 January 2002 to 15 January 2019 (over 17 years)

Indian stock market data.

The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.

The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.

Precision is defined as the ratio of accurately predicted positive cases to the total predicted positives [146], while Recall assesses the ratio of correctly predicted positives to all actual positive instances [15]. The F-measure harmonizes Precision and Recall, emphasizing a balanced consideration of false positives and false negatives

Table 7. Evaluation Metrics for Stock Market Prediction.

examined stock market prediction using ML algorithms, focusing on technical and fundamental analyses. Linear regression showed low error in predicting closing prices, while SVM achieved 76% accuracy for sentiment analysis. Despite these results, the models lack precision for long-term investments or reliable decision-making. Hybrid approaches combining both analyses show potential for improved accuracy, offering a promising area for future research. In 202

While regression and time series models are foundational and easy to implement, their predictive power is often limited by assumptions of linearity and stationarity. Regression models perform well in stable market conditions but may fail to capture complex, nonlinear relationships without hybridization. Time series models like ARIMA are effective for short-term forecasting but are sensitive to parameter tuning and may not adapt well to sudden market changes or outlier events. Integrating these traditional methods with feature selection or preprocessing techniques can improve robustness, but their effectiveness is still largely dependent on data quality and the specific financial context.

Table 2. Summary of stock price prediction approaches.

Sentiment analysis, by incorporating unstructured data from various sources, provides a unique perspective on market behavior. Although challenges like data noise, misinformation, and variability in sentiment intensity persist, integrating sentiment analysis with machine learning and deep learning techniques has proven to significantly enhance stock market predictions.

Several promising avenues for future research exist:Contextual integration of external events such as political changes and global occurrences into prediction models can enhance their robustness.Developing models that adapt to real-time market conditions while addressing noise and unanticipated events remains a critical goal.Increasing the interpretability of prediction models could provide deeper insights into market behavior and build trust among investors.Leveraging metaheuristic algorithms to optimize NN weights and architectures is another potential research area.Expanding the scope of analysis to derivatives-based markets and hybrid approaches could yield significant advancements.

Many prediction models demonstrate promising results in controlled environments but falter in real-world,

Financial datasets are often noisy and unstructured, making feature extraction and preprocessing challenging.