Predicting the Trends in Stock Movement Based on Neural Networks

Abstract

Forecasting results for stock markets exhibit variabilities in forecasting accuracy as the tenure of prediction is varied. Typically stocks are predicted for long, short as well as mid term tenures based on the tenure of prediction. While longer tenures have relatively much larger data to be trained as training data, divergences are also potentially large as the forecasting period may render higher randomness due to unprecedented events. The short term forecasting is relatively less prone to unprecedented events due to the tenure of forecasting. However, the lesser amount of training data may result in less accurate pattern recognition. A common ground is typically found in terms of mid term forecasting. This paper presents an experimental evaluation of all three formats of forecasting based on the training, testing split. The deep neural network model is used for the forecasting purpose and the forecasting MAPE and accuracy has been tabulated for a multitude of stocks. The comparative values of the MAPE for prediction have been tabulated to estimate prediction performance of the proposed approach.

Introduction

Stock movement forecasting is vital for navigating the volatile financial markets and making informed investment decisions. It helps investors manage risk, optimize portfolios, and time buy/sell actions effectively. Forecasting can be short-, mid-, or long-term, and increasingly incorporates global factors via opinion mining and sentiment analysis from financial news and social media to capture investor sentiment.

The proposed forecasting model uses a combination of data preprocessing with Discrete Wavelet Transform (DWT) to filter noise from stock price signals and a deep neural network (DNN) for pattern recognition. A moving window technique captures recent trends, improving prediction accuracy over varying time horizons. The training algorithm includes momentum-based weight updates to ensure convergence.

Experimental results on Tesla stock data over 10 years demonstrate the model’s strong performance, with mean absolute percentage errors (MAPE) of 2.64% for long-term (1 year), 5.14% for mid-term (100 days), and 4.46% for short-term (10 days) forecasts, achieving overall accuracy around 97%. Compared to other models and previous studies, the proposed method yields lower prediction errors across multiple stocks, indicating its effectiveness in stock market forecasting.

Conclusion

This paper presents a DWT-Neural Network based approach for forecasting stock trends over a varied interval period. The categorization of the forecasting has been done based on the number of samples ahead in forecasting. A 365, 100 and 10 day split has been chosen for the long, mid and short term forecasts respectively. An illustration analysis has been made for the Tesla stocks over the variable forecasting period. The prediction MAPE analysis shows that the accuracy achieved is 97.36%, 94.86% and 95.54% for long, mid and short term forecasts respectively, with a mean MAPE of 3% across all intervals of forecast. The model also attains an MAPE of around 3% across various stocks for benchmark S&P 500 dataset obtained from Yahoo Finance. Low values of the MAPE ascertains accurate prediction of the stock trends.

References

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Copyright

Copyright © 2025 Yukti Dewangan, Priyanka Bande. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.