Pattern Classification of Stock Price Moving

Wang, Chenyu

doi:10.54097/fcis.v2i2.3754

Cited by 1 publication

(1 citation statement)

References 51 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess and manage the performance of ATSs, risk management strategies and safety measures based on human judgment are necessary. Creating an ATS incorporates several factors, including the trading strategy to be used, complex mathematical functions reflecting the status of a particular stock, deep learning algorithms for predicting the stock's future value, and specific news on the stock being analyzed [3].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Karachi Stock Price index with an Enhanced multi-layered Sequential Stacked Long-Short-Term Memory Model

Mahboob,

Shahbaz,

Ali1

et al. 2023

VFAST trans. softw. eng.

View full text Add to dashboard Cite

The study proposes the use of a stacked Long-Short-Term Memory (LSTM) model to predict the KSE-100 stock exchange trend and provides a comprehensive review of the literature on deep learning models and time series forecasting in the stock market. The study's findings suggest that the stacked LSTM model outperforms other models in terms of prediction accuracy. The study's contribution lies in its approach to improving the accuracy of stock price prediction using deep learning models. The stacked LSTM model architecture is a novel approach that provides better results than other traditional time series forecasting models. Furthermore, the study's use of hyper-parameter optimization techniques demonstrates the importance of model tuning for improving performance intended for accurate time series forecasting in the financial market. The study's results have practical implications for investors, who can use the stacked LSTM model to make informed decisions about buying or selling stocks in the KSE-100. The model's ability to predict stock prices accurately can help investors maximize their profits and minimize their losses. Hence, the proposed stacked LSTM model can effectively predict stock prices in the KSE-100 and can assist investors in making informed decisions in the stock market.

show abstract

Section: Introductionmentioning

confidence: 99%