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 …

Accurately predicting the prices of financial time series is essential and challenging for the
financial sector. Owing to recent advancements in deep learning techniques, deep learning …

The prediction of stock price movement direction is significant in financial studies. In recent
years, a number of deep learning models have gradually been applied for stock predictions …

With the increasing use of deep neural network (DNN) in time series classification (TSC),
recent work reveals the threat of adversarial attack, where the adversary can construct …

Using hierarchical CNN, the company's multiple news is characterized as three levels:
sentence vectors, chapter vectors, and enterprise sentiment vectors. By combining the stock …

Bitcoin's volatile nature has made its price prediction a sought-after mathematical model in
the FinTech industry. Existing studies, however, need to look into the critical aspect of time …

As is known, the financial market prediction and high investing value is receiving more
increasing attentions nowadays. But affected by many complex factors, it is difficult to …

As a fundamental task in modern data mining, time series classification is powering mission-
critical tasks including stock price prediction and network traffic analysis. Due to the non …

This paper aims to analyze influencing factors of stock market trend prediction and propose
an innovative neural network approach to achieve stock market trend prediction. With the …

Stock performance prediction plays an important role in determining the appropriate timing
of buying or selling a stock in the development of a trading system. However, precise stock …