Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning

Y Hao, Q Gao - Applied Sciences, 2020 - mdpi.com
In the stock market, predicting the trend of price series is one of the most widely investigated
and challenging problems for investors and researchers. There are multiple time scale …

Utilizing historical data for corporate credit rating assessment

M Wang, H Ku - Expert Systems with Applications, 2021 - Elsevier
Corporate credit rating assessment is one of the crucial problems of credit risk management;
it will help the financial institutions and government decide whether to issue debts. Recent …

[PDF][PDF] Performance forecasting of share market using machine learning techniques: A review

S Kamley, S Jaloree, RS Thakur - International Journal of Electrical …, 2016 - academia.edu
Forecasting share performance becomes more challenging issue due to the enormous
amount of valuable trading data stored in the stock database. Currently, existing forecasting …

Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction

S Kumar Chandar - Journal of Ambient Intelligence and Humanized …, 2019 - Springer
Stock market prediction is one of the most important financial subjects that have drawn
researchers' attention for many years. Several factors affecting the stock market make stock …

[HTML][HTML] Quantifying StockTwits semantic terms' trading behavior in financial markets: An effective application of decision tree algorithms

A Al Nasseri, A Tucker, S De Cesare - Expert systems with applications, 2015 - Elsevier
Growing evidence is suggesting that postings on online stock forums affect stock prices, and
alter investment decisions in capital markets, either because the postings contain new …

Predicting stock market trends using random forests: A sample of the Zagreb stock exchange

T Manojlović, I Štajduhar - 2015 38th International Convention …, 2015 - ieeexplore.ieee.org
Stock market prediction is considered to be a challenging task for both investors and
researchers, due to its profitability and intricate complexity. Highly accurate stock market …

Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach

SK Chandar - Cluster Computing, 2019 - Springer
Stock market prediction is the challenging area for the investors to yield profits in the
financial markets. The investors need to understand the financial markets which are more …

[PDF][PDF] Stock market prediction using feed-forward artificial neural network

S Jabin - International Journal of Computer Applications, 2014 - researchgate.net
This paper presents computational approach for stock market prediction. Artificial Neural
Network (ANN) forms a useful tool in predicting price movement of a particular stock. In the …

Machine Intelligence in Africa: a survey

AA Tapo, A Traoré, S Danioko, H Tembine - arXiv preprint arXiv …, 2024 - arxiv.org
In the last 5 years, the availability of large audio datasets in African countries has opened
unlimited opportunities to build machine intelligence (MI) technologies that are closer to the …

An efficient gan-based multi-classification approach for financial time series volatility trend prediction

L Liu, Z Pei, P Chen, H Luo, Z Gao, K Feng… - International Journal of …, 2023 - Springer
Deep learning has achieved tremendous success in various applications owing to its robust
feature representations of complex high-dimensional nonlinear data. Financial time-series …