Predicting the Stock Markets Using Neural Network with Auxiliary Input

2021 ◽  
pp. 743-752
Author(s):  
Sakshi ◽  
Sreyan Ghosh
Keyword(s):  
Neural Network ◽  
Stock Markets ◽  
Auxiliary Input

Selective presentation learning for neural network forecasting of stock markets

1996 ◽  
Vol 4 (3) ◽  
pp. 143-148 ◽  
Author(s):  
Kazuhiro Kohara ◽  
Yoshimi Fukuhara ◽  
Yukihiro Nakamura
Keyword(s):  
Neural Network ◽  
Stock Markets

STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS

2021 ◽  
pp. 1-19
Author(s):  
GÖRKEM ATAMAN ◽  
SERPIL KAHRAMAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Stock Market ◽  
Stock Markets ◽  
Hybrid Models ◽  
Stock Market Prediction ◽  
Macroeconomic Variables ◽  
Brics Countries ◽  
Artificial Neural

The BRICS (Brazil, Russia, India, China and South Africa) acronym was created by the International Monetary Foundation (IMF)–Group of Seven (G7) to represent the bloc of developing economies which crucially impact on the global economy by their potential economic growth. Most of the foreign direct investment are considering the stock markets of BRICS as the most attractive destination for foreign portfolio investment. This study aims to identify the relationship between macroeconomic variables and the stock market index values of BRICS and generate accurate predictions for index values by performing linear regression and artificial neural network hybrid models. Monthly data from January 2003 to December 2019 are used for the empirical study. The results indicate that a strong correlation exists between the stock market and macroeconomic variables in BRICS over time. The hybrid model is observed very accurate for index value prediction where the mean absolute percentage error (MAPE) value is 0.714% for the whole data set covering all BRICS countries data during the study period. Additionally, MAPE values for each of the BRICS countries are, respectively, obtained as 0.083%, 2.316%, 0.116%, 0.962% and 0.092%. Thus, the main findings of this study show that while neural network-integrated models have high performances for volatile stock market prediction, macroeconomic stabilization should be the priority of monetary policy to prevent the high volatility of stock markets.

scholarly journals The The Impact of the Exchange Rate Volatility on Stock Markets Dynamics in Tunisia and Turkey: An Artificial Neural Network Analysis

2021 ◽  
pp. 1-21
Author(s):  
Nesrine Mechri ◽  
Christian De Peretti ◽  
Salah BEN HAMAD
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Stock Market ◽  
Multiple Regression ◽  
Stock Markets ◽  
Exchange Rate Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Impact

The present research provides an overview of links between exchange rate volatility and the dynamics of stock market returns in order to identify the influence of several macroeconomic variables on the volatility of stock markets, useful for political decision makers as well as investors to better control the portfolio risk level. More precisely, this research aims to identify the impact of exchange rate volatility on the fluctuations of stock market returns, considering two countries that belong to the Middle East and North Africa (MENA) zone: Tunisia and Turkey. Previous works in the literature used very specified and short periods of study, many important variables were neglected, and most of the earlier research was concentrated on the developed countries. In this research, we integrate several control variables of stock market returns that have not been simultaneously studied before. In addition, we spread out our research period up to 15 years including many events and dynamics. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and multiple regression models are first employed. Then, an Artificial Neural Network (ANN) is used and compared with the results of the multiple regression. Hence, the results show that for both Tunisia and Turkey, exchange rate volatility has a significant effect on stock market fluctuations.

scholarly journals A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

2021 ◽  
Vol 35 (6) ◽  
pp. 483-488
Author(s):  
Asmaa Y. Fathi ◽  
Ihab A. El-Khodary ◽  
Muhammad Saafan
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Spectrum Analysis ◽  
Stock Prices ◽  
Stock Markets ◽  
Stock Price ◽  
Singular Spectrum Analysis ◽  
Predictive Performance ◽  
Backpropagation Neural Network ◽  
Singular Spectrum

The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series.

scholarly journals Multifractal Behaviors of Stock Indices and Their Ability to Improve Forecasting in a Volatility Clustering Period

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1018
Author(s):  
Shuwen Zhang ◽  
Wen Fang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Stock Markets ◽  
Short Term Memory ◽  
Fluctuation Analysis ◽  
Test Results ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Volatility Clustering ◽  
Detrended Fluctuation ◽  
Gated Recurrent Unit

The financial market is a complex system, which has become more complicated due to the sudden impact of the COVID-19 pandemic in 2020. As a result there may be much higher degree of uncertainty and volatility clustering in stock markets. How does this “black swan” event affect the fractal behaviors of the stock market? How to improve the forecasting accuracy after that? Here we study the multifractal behaviors of 5-min time series of CSI300 and S&P500, which represents the two stock markets of China and United States. Using the Overlapped Sliding Window-based Multifractal Detrended Fluctuation Analysis (OSW-MF-DFA) method, we found that the two markets always have multifractal characteristics, and the degree of fractal intensified during the first panic period of pandemic. Based on the long and short-term memory which are described by fractal test results, we use the Gated Recurrent Unit (GRU) neural network model to forecast these indices. We found that during the large volatility clustering period, the prediction accuracy of the time series can be significantly improved by adding the time-varying Hurst index to the GRU neural network.

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