[HTML][HTML] Machine learning techniques and data for stock market forecasting: A literature review
In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …
stock market prediction. A focus area in this literature review is the stock markets …
CNNpred: CNN-based stock market prediction using a diverse set of variables
E Hoseinzade, S Haratizadeh - Expert Systems with Applications, 2019 - Elsevier
Feature extraction from financial data is one of the most important problems in market
prediction domain for which many approaches have been suggested. Among other modern …
prediction domain for which many approaches have been suggested. Among other modern …
Stock market's price movement prediction with LSTM neural networks
DMQ Nelson, ACM Pereira… - 2017 International joint …, 2017 - ieeexplore.ieee.org
Predictions on stock market prices are a great challenge due to the fact that it is an
immensely complex, chaotic and dynamic environment. There are many studies from …
immensely complex, chaotic and dynamic environment. There are many studies from …
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 …
Predicting the direction of stock market prices using tree-based classifiers
Predicting returns in the stock market is usually posed as a forecasting problem where
prices are predicted. Intrinsic volatility in the stock market across the globe makes the task of …
prices are predicted. Intrinsic volatility in the stock market across the globe makes the task of …
A comprehensive evaluation of ensemble learning for stock-market prediction
Stock-market prediction using machine-learning technique aims at developing effective and
efficient models that can provide a better and higher rate of prediction accuracy. Numerous …
efficient models that can provide a better and higher rate of prediction accuracy. Numerous …
Forecasting tourism demand with multisource big data
Based on internet big data from multiple sources (ie, the Baidu search engine and two
online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount …
online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount …
Prediction of cryptocurrency returns using machine learning
In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the
daily and minute level frequencies using the machine learning classification algorithms …
daily and minute level frequencies using the machine learning classification algorithms …
A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost
Many researchers attempt to accurately predict stock price trends using technologies such
as machine learning and deep learning to achieve high returns in the stock market …
as machine learning and deep learning to achieve high returns in the stock market …
St-trader: A spatial-temporal deep neural network for modeling stock market movement
Stocks that are fundamentally connected with each other tend to move together. Considering
such common trends is believed to benefit stock movement forecasting tasks. However, such …
such common trends is believed to benefit stock movement forecasting tasks. However, such …