This study investigates the widespread use of machine learning techniques, including recurrent neural networks (RNNs) and long short-term memory (LSTM) models to analysis stock market data. By utilizing RNN LSTM model capabilities identify temporal relationships patterns in data, it seeks overcome conventional techniques' constraints. The research provides empirical proof efficiency RNNs enhanc...

This article examines whether incorporating investors’ uncertainty, as captured by the conditional volatility of sentiment, can help forecasting stock markets. In this regard, using Markov-switching multifractal (MSM) model, we find that uncertainty substantially increase accuracy forecasts market according to forecast encompassing test. We further provide evidence MSM outperforms dynamic corre...

Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine i...

Abstract Studies have shown that Internet financial news has become an important reference for investors in investment behavior. In order to simulate trading experiments mimic the real stock market, this paper develops a volatility prediction model based on causal reasoning. It also gathers and cleans market data from Internet, such as opening price, closing change. The findings of study indica...

This paper studies volatility forecasting in the financial stock market. In general, stock market volatility is time-varying and exhibits clustering properties. Thus, this paper presents the results of using a fuzzy system method to analyze clustering in generalized autoregressive conditional heteroskedasticity (GARCH) models. It also uses the adaptive method of recursive least-squares (RLS) to...

Stock market analysis is one of the most important and hard problems in finance analysis field. Recently, the usage of intelligent systems for stock market prediction has been widely established. In this paper, a PSO based selective neural network ensemble (PSOSEN) algorithm is proposed, which is used for the Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index analysis. In...

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive mod...