Optimizing Technical Parameters and Stock Price Prediction using: Linear Regressive MapReduce and Quasi-Newton Deep Learning

DOI

10.2991/978-94-6463-314-6_32How to use a DOI?

Keywords

Deep Learning; MapReduce; Quasi-Newton; Machine Learning; Stock Prediction

Abstract

The Stock value forecast is a significant issue to determine the future direction of the financial Markets. Many research works are carried out and design many techniques to predict stock price of Individual stocks. But, the forecast precision of the regular methods was lower when taking the enormous size of the dataset. To address this downside, a Novel Deep learning based Broken-Stick Linear Regressive MapReduce Based on Quasi-Newton Deep Neural Learning (BLRM-QNDNL) method is proposed right now. BLRM-QNDNL technique targeting optimizing the parameters engaged with stock profit prediction. Quasi-Newton based Deep Neural Network Learning (Q-NDNL) is applied in the BLRM-QNDNL procedure for precisely foreseeing the stock prices dependent on the news with higher accuracy. Q-NDNL used several hidden layers in order to thoroughly examine the effects of emotions conveyed in news items on stocks with the least amount of time complexity. The BLRM-QNDNL method expands the stock price forecast performance with lower time complexity when compared with cutting edge works. The BLRM-QNDNL technique has been evaluated exploratory on metrics such as prediction accuracy, prediction time, and false positive rate with regard to different numbers of data. When compared to state-of-the-art studies, the experimental results show that the BLRM-QNDNL technique can reduce the time complexity of stock price prediction while simultaneously increasing its accuracy. The suggested BLRM-QNDNL technique reduces the false positive stock price prediction rate by 41% and 57%, respectively and decreases market price prediction time complexity by 9 percent and 29 percent relative to traditional DeepClue [1] and MFNN [2], respectively.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kalva Sudhakar
AU  - Satuluri Naganjaneyulu
PY  - 2023
DA  - 2023/12/21
TI  - Optimizing Technical Parameters and Stock Price Prediction using: Linear Regressive MapReduce and Quasi-Newton Deep Learning
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 320
EP  - 333
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_32
DO  - 10.2991/978-94-6463-314-6_32
ID  - Sudhakar2023
ER  -