An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges
Forecasting the behavior of the stock market is a classic but difficult topic, one that has
attracted the interest of both economists and computer scientists. Over the course of the last …
attracted the interest of both economists and computer scientists. Over the course of the last …
[HTML][HTML] Data-driven stock forecasting models based on neural networks: A review
As a core branch of financial forecasting, stock forecasting plays a crucial role for financial
analysts, investors, and policymakers in managing risks and optimizing investment …
analysts, investors, and policymakers in managing risks and optimizing investment …
Multi-source aggregated classification for stock price movement prediction
Predicting stock price movements is a challenging task. Previous studies mostly used
numerical features and news sentiments of target stocks to predict stock price movements …
numerical features and news sentiments of target stocks to predict stock price movements …
Temporal relational ranking for stock prediction
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 …
good investment decisions. Traditional solutions for stock prediction are based on time …
Enhancing stock movement prediction with adversarial training
This paper contributes a new machine learning solution for stock movement prediction,
which aims to predict whether the price of a stock will be up or down in the near future. The …
which aims to predict whether the price of a stock will be up or down in the near future. The …
Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection
Recent stock market studies adopting machine learning and deep learning techniques have
achieved remarkable performances with convenient accessibility. However, machine …
achieved remarkable performances with convenient accessibility. However, machine …
A multimodal event-driven LSTM model for stock prediction using online news
In finance, it is believed that market information, namely, fundamentals and news
information, affects stock movements. Such media-aware stock movements essentially …
information, affects stock movements. Such media-aware stock movements essentially …
Using Twitter trust network for stock market analysis
Online social networks are now attracting a lot of attention not only from their users but also
from researchers in various fields. Many researchers believe that the public mood or …
from researchers in various fields. Many researchers believe that the public mood or …
Investigating the informativeness of technical indicators and news sentiment in financial market price prediction
Real-time market prediction tool tracking public opinion in specialized newsgroups and
informative market data persuades investors of financial markets. Previous works mainly …
informative market data persuades investors of financial markets. Previous works mainly …
Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks
R Cheng, Q Li - Proceedings of the AAAI Conference on artificial …, 2021 - ojs.aaai.org
In finance, the momentum spillovers of listed firms is well acknowledged. Only few studies
predicted the trend of one firm in terms of its relevant firms. A common strategy of the pilot …
predicted the trend of one firm in terms of its relevant firms. A common strategy of the pilot …