Stock price prediction via discovering multi-frequency trading patterns
Stock prices are formed based on short and/or long-term commercial and trading activities
that reflect different frequencies of trading patterns. However, these patterns are often …
that reflect different frequencies of trading patterns. However, these patterns are often …
Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction
Stock trend prediction plays a critical role in seeking maximized profit from the stock
investment. However, precise trend prediction is very difficult since the highly volatile and …
investment. However, precise trend prediction is very difficult since the highly volatile and …
Recurrent neural network and a hybrid model for prediction of stock returns
In this paper, we propose a robust and novel hybrid model for prediction of stock returns.
The proposed model is constituted of two linear models: autoregressive moving average …
The proposed model is constituted of two linear models: autoregressive moving average …
Sailing through the COVID‐19 Crisis by Using AI for Financial Market Predictions
The outbreak of COVID‐19 has brought the world to an unprecedented position where
financial and mental resources are drying up. Livelihoods are being lost, and it is becoming …
financial and mental resources are drying up. Livelihoods are being lost, and it is becoming …
The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning
SB Jabeur, R Khalfaoui, WB Arfi - Journal of Environmental Management, 2021 - Elsevier
This study aims to predict oil prices during the 2019 novel coronavirus (COVID-19)
pandemic by looking into green energy resources, global environmental indexes (ESG), and …
pandemic by looking into green energy resources, global environmental indexes (ESG), and …
Stock market prediction and Portfolio selection models: a survey
Stock data is known to be chaotic in nature and it is a challenging task to predict the non-
linear patterns of such data. Forming an optimal portfolio of stocks is yet another challenging …
linear patterns of such data. Forming an optimal portfolio of stocks is yet another challenging …
Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm
Developing an accurate forecasting model for long-term gold price fluctuations plays a vital
role in future investments and decisions for mining projects and related companies. Viewed …
role in future investments and decisions for mining projects and related companies. Viewed …
A survey on deep learning for time-series forecasting
A Mahmoud, A Mohammed - Machine learning and big data analytics …, 2021 - Springer
Deep learning, one of the most remarkable techniques of machine learning, has been a
major success in many fields, including image processing, speech recognition, and text …
major success in many fields, including image processing, speech recognition, and text …
Real-time forecasting of time series in financial markets using sequentially trained dual-LSTMs
Financial markets are highly complex and volatile; thus, accurate forecasting of such
markets is vital to make early alerts about crashes and subsequent recoveries. People have …
markets is vital to make early alerts about crashes and subsequent recoveries. People have …
Deep belief network for gold price forecasting
P Zhang, B Ci - Resources Policy, 2020 - Elsevier
Fluctuations in gold price have historically attracted the attention of governments, institutions
and individuals alike. Accurate forecasting of gold price can effectively capture the price …
and individuals alike. Accurate forecasting of gold price can effectively capture the price …