Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

With the advent of technological marvels like global digitization, the prediction of the stock
market has entered a technologically advanced era, revamping the old model of trading …

The stock market is a key pivot in every growing and thriving economy, and every investment
in the market is aimed at maximising profit and minimising associated risk. As a result …

Stock prediction aims to predict the future trends of a stock in order to help investors make
good investment decisions. Traditional solutions for stock prediction are based on time …

In recent years, machine learning and deep learning have become popular methods for
financial data analysis, including financial textual data, numerical data, and graphical data …

Stock movement prediction is a challenging problem: the market is highly stochastic, and we
make temporally-dependent predictions from chaotic data. We treat these three complexities …

Pre-trained language models have shown impressive performance on a variety of tasks and
domains. Previous research on financial language models usually employs a generic …

We propose a deep learning method for eventdriven stock market prediction. First, events
are extracted from news text, and represented as dense vectors, trained using a novel …

This paper proposes a novel application of deep learning models, Paragraph Vector, and
Long Short-Term Memory (LSTM), to financial time series forecasting. Investors make …

Financial news has been proven to be a crucial factor which causes fluctuations in stock
prices. However, previous studies heavily relied on analyzing shallow features and ignored …