A deep learning framework for financial time series using stacked autoencoders and long-short term memory
W Bao, J Yue, Y Rao - PloS one, 2017 - journals.plos.org
The application of deep learning approaches to finance has received a great deal of
attention from both investors and researchers. This study presents a novel deep learning …
attention from both investors and researchers. This study presents a novel deep learning …
A review of stock market prediction with Artificial neural network (ANN)
Stock market is a promising financial investment that can generate great wealth. However,
the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of …
the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of …
A SEM-neural network approach for predicting antecedents of m-commerce acceptance
Higher penetration of powerful mobile devices–especially smartphones–and high-speed
mobile internet access are leading to better offer and higher levels of usage of these devices …
mobile internet access are leading to better offer and higher levels of usage of these devices …
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange
Prediction of stock price index movement is regarded as a challenging task of financial time
series prediction. An accurate prediction of stock price movement may yield profits for …
series prediction. An accurate prediction of stock price movement may yield profits for …
Assessment of dynamic line rating forecasting methods
Optimal transmission line rating use is guaranteed with dynamic line rating (DLR). It is a
smart grid technology that foresees variations in meteorological conditions affecting line …
smart grid technology that foresees variations in meteorological conditions affecting line …
Comparison of ARIMA and artificial neural networks models for stock price prediction
AA Adebiyi, AO Adewumi… - Journal of Applied …, 2014 - Wiley Online Library
This paper examines the forecasting performance of ARIMA and artificial neural networks
model with published stock data obtained from New York Stock Exchange. The empirical …
model with published stock data obtained from New York Stock Exchange. The empirical …
Forecasting daily stock market return using dimensionality reduction
In financial markets, it is both important and challenging to forecast the daily direction of the
stock market return. Among the few studies that focus on predicting daily stock market …
stock market return. Among the few studies that focus on predicting daily stock market …
Technical analysis and sentiment embeddings for market trend prediction
Stock market prediction is one of the most challenging problems which has been distressing
both researchers and financial analysts for more than half a century. To tackle this problem …
both researchers and financial analysts for more than half a century. To tackle this problem …
A survey on machine learning models for financial time series forecasting
Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
Technical analysis strategy optimization using a machine learning approach in stock market indices
Within the area of stock market prediction, forecasting price values or movements is one of
the most challenging issue. Because of this, the use of machine learning techniques in …
the most challenging issue. Because of this, the use of machine learning techniques in …