Predicting the Stock Market Index Using Stochastic Time Series Arima Modelling: The Sample of BSE and NSE

2019 ◽  
Author(s):  
Viswanatha Reedy C
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Market Index ◽  
Market Index ◽  
Stochastic Time Series ◽  
Stochastic Time

scholarly journals Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory

2021 ◽  
Vol 5 (3) ◽  
pp. 456-465
Author(s):  
Harya Widiputra ◽  
Adele Mailangkay ◽  
Elliana Gautama
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
Term Memory ◽  
Market Index ◽  
Long Short Term Memory

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.  

Effects of Intraday Patterns on a Time Series Analysis of the Korean Stock Market Index

2011 ◽  
Vol 58 (2) ◽  
pp. 396-399 ◽  
Author(s):  
Doo Hwan Kim ◽  
Seong Eun Maeng ◽  
Yu Sik Bang ◽  
Hyung Wooc Choi ◽  
Moon-Yong Cha ◽  
...  
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Stock Market Index ◽  
Series Analysis ◽  
Market Index ◽  
Korean Stock Market ◽  
Intraday Patterns

scholarly journals EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices

2018 ◽  
Vol 13 (2) ◽  
pp. 268-279
Author(s):  
Li Tang ◽  
Ping He Pan ◽  
Yong Yi Yao
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prediction Model ◽  
Computational Intelligence ◽  
Financial Time Series ◽  
Extraction Procedure ◽  
Stock Index ◽  
Stock Market Index ◽  
Financial Time ◽  
Market Index

This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clustering for prediction via regression. The EPAK model is then used as a kernel for predicting each of all the sector indices of the stock market. The sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. The EPAK model and the complex prediction model for stock index are tested on real historical financial time series in Chinese stock index including CSI 300 and ten sector indices, with results confirming the effectiveness of the proposed models.

scholarly journals Econophysical bourse volatility – Global Evidence

2020 ◽  
Vol 9 (2) ◽  
pp. 87-107
Author(s):  
Bikramaditya Ghosh ◽  
Krishna MC
Keyword(s):  
Time Series ◽  
Reynolds Number ◽  
Stock Market ◽  
Information Asymmetry ◽  
Financial Literacy ◽  
Stock Markets ◽  
Herd Behaviour ◽  
Stochastic Time Series ◽  
Stochastic Time ◽  
Global Evidence

AbstractFinancial Reynolds number (Re) has been proven to have the capacity to predict volatility, herd behaviour and nascent bubble in any stock market (bourse) across the geographical boundaries. This study examines forty two bourses (representing same number of countries) for the evidence of the same. This study finds specific clusters of stock markets based on embedded volatility, herd behaviour and nascent bubble. Overall the volatility distribution has been found to be Gaussian in nature. Information asymmetry hinted towards a well-discussed parameter of ‘financial literacy’ as well. More than eighty percent of indices under consideration showed traces of mild herd as well as bubble. The same indices were all found to be predictable, despite being stochastic time series. In the end, financial Reynolds number (Re) has been proved to be universal in nature, as far as volatility, herd behaviour and nascent bubble are concerned.

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