Application of ensemble of recurrent neural networks for forecasting of stock market sentiments

Abstract

Research background: Research and measurement of sentiments, and the integration of methods for sentiment analysis in forecasting models or trading strategies for financial markets are gaining increasing attention at present. The theories that claim it is difficult to predict the individual investor’s decision also claim that individual investors cause market instability due to their irrationality. The existing instability increases the need for scientific research. 
 Purpose of the article: This paper is dedicated to establishing a link between the individual investors’ behavior, which is expressed as sentiments, and the market dynamic, and is evaluated in the stock market. This article hypothesizes that the dynamics in the market is unequivocally related to the individual investor’s sentiments, and that this relationship occurs when the sentiments are expressed strongly and are unlimited.
 Methods: The research was carried out invoking the method of Evolino RNN-based prediction model. The data for the research from AAII (American Association of Individual Investors), an investor sentiment survey, were used. Stock indices and sentiments are forecasted separately before being combined as a single composition of distributions.
 Findings & Value added: The novelty of this paper is the prediction of sentiments of individual investors using an Evolino RNN-based prediction model. The results of this paper should be seen not only as the prediction of the connection and composition of investors’ sentiments and stock indices, but also as the research of the dynamic of individual investors’ sentiments and indices.

A comprehensive evaluation of ensemble learning for stock-market prediction

“…However, most of these studies [12,19,21,22,[24][25][26][27][28][29][30] were based on boosting (BOT) or bagging (BAG) combination method. Only a few [4,18,20,37] examined ensemble classifiers or regressors based on stacking or blending combinational technique.…”

“…Despite numerous works revealing the dominance of ensemble classifier over single classifier, most of these studies only ensemble a specific type of classifier or regressor for stock-market prediction, such as NN [18][19][20], DT [21,22] and SVM [12,23]. Also, most previous studies [12,19,21,22,[24][25][26][27][28][29][30], on ensemble methods for stock-market predictions adopted the decrease variance approach (boosting or bagging) and experimented with data from one country.…”

A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

“…The effects of sentiments on stock market volatility have received recent attention in the literature [27][28][29][30][31][32]. One core source of information for sentiment analysis is the news articles [27,28] and the other commonly used data source is the social media [33][34][35][36]. Using a Support Vector Machine (SVM) and Particle Swarm Optimisation (PSO), Chiong et al [31] proposed a stock market predictive model based on sentiments analysis.…”

“…The tweets used in this study were downloaded from Twitter, using the Twitter API Tweepy [55]. Moreover, like many works in literature [33][34][35][36], we used the dollar ($) sign as a means to obtained 1,101 stock market-related tweets and all other tweets concerning our selected companies. Business news, financial news and events headlines concerning our selected companies we downloaded from three popular news sites in Ghana, namely, ghanaweb.com, myjoyonline.com and graphic.com.gh using the BeautifulSoup API.…”

The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach

“…Recently, several academic studies have attempted to examine the predictability and influence of these factors on the stock market volatility. The following studies (4,(6)(7)(8)(9) examined the effect of public sentiment in tweets from Twitter on the stock market volatility. The results from these studies show a higher association between tweets and the stock market.…”