scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

scholarly journals Will the Stock Market Index Upsurge or Deflate? Making Calculated Predictions Using the Univariate Autoregressive Integrated Moving Average Technique

2018 ◽  
Vol III (IV) ◽  
pp. 413-426
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Market Index ◽  
Short Term ◽  
Average Technique ◽  
Autoregressive Integrated Moving Average ◽  
Past Behavior ◽  
Market Index ◽  
Arima Modeling

Movements in a stock market index may safely be considered one of the mostwatched out phenomena by investors in almost every economy. One method to forecast the index is to study all those external factors that directly affect it. Another way, however, is to base ones predictions on the past behavior of the variable of interest. This paper has employed the method described latter and has, therefore, made use of the ARIMA modeling. In this connection, the daily stock market index data of the Karachi Stock Exchange 100 index was taken for twenty years from 1997 to 2017 which translated into 4940 observations. The study revealed that the model was decently efficient in forecasting the KSE 100 Index, though only for the short-range. The upshot of this study may be utilized specifically by short term investors in deciding on when, and when not, to invest in the stock market.

scholarly journals Tracing Stock Returns on Quarterly Basis: The Case of KSE-100 Index

2018 ◽  
Vol III (III) ◽  
pp. 466-476
Author(s):  
Mustafa Afeef ◽  
Nazim Ali ◽  
Adnan Khan
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
External Factors ◽  
Forecasting Model ◽  
Stock Market Index ◽  
Short Term ◽  
Market Index ◽  
Arima Modeling

The stock market index can be forecasted in two ways --- either through taking those external factors that influence movements in the index or by basing ones predictions on the previous values of the index. The current study has used the method described later by employing the Box-Jenkins methodology --- a method famously used by most researchers while conducting ARIMA modeling--- by taking past figures of KSE 100 Index. Quarterly figures of the Index were, therefore, taken for 22 years from August 1995 to October 2017 that translated into 90 observations. Results revealed that the forecasting model used in the study did well in anticipating returns in the shortrun. The findings of the study can be consumed by investors, particularly short-term, in deciding when, and when not, to risk their hard-earned funds at Pakistan Stock Exchange.

scholarly journals Autoencoder Neural Network Approaches for Anomaly Detection in IBOVESPA Stock Market Index

2021 ◽  
Author(s):  
Lucas de Azevedo Takara ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Anomaly Detection ◽  
Moving Average ◽  
Reconstruction Error ◽  
Sao Paulo ◽  
São Paulo ◽  
Stock Market Index ◽  
Short Term ◽  
Market Index

Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. Anomaly detection has been applied to many problems such as bank fraud, fault detection, noise reduction, among many others. Some approaches to detect anomalies include classical statistical econometric methods such as AutoRegressive Moving Average (ARMA) and AutoRegressive Integrated Moving Average (ARIMA) approaches. More recently, with the progress of artificial intelligence and more specifically, machine learning, new algorithms such as one-class support vector machines, isolation forest, gradient boosting, and deep neural networks were applied to such tasks. This paper focuses on propose an anomaly detection framework for the Índice da Bolsa de Valores de São Paulo (IBOVESPA). It is a major stock market index that tracks the performance of around 50 most liquid stocks traded on the São Paulo Stock Exchange in Brazil. Exploring unsupervised autoencoder neural network algorithms, we compare the long short-term autoencoder, bidirectional long short-term autoencoder, and convolutional autoencoder models, aiming to explore the performance of these architectures for anomaly detection. Due to the ability of autoencoders to learn a compressed representation of their respective input, we train these models with standard data by minimizing the mean absolute error (MAE) loss function and evaluate them with anomalous inputs. We set a reconstruction error threshold, and in case that the reconstruction error of the test data sample is beyond it, anomalies are detected. Our results show that these models perform quite well and can be applied to real stock market data.

scholarly journals Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Chieh-Fan Chen ◽  
Wen-Hsien Ho ◽  
Huei-Yin Chou ◽  
Shu-Mei Yang ◽  
I-Te Chen ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Stock Market ◽  
Minimum Temperature ◽  
Moving Average ◽  
Arima Model ◽  
Maximum Temperature ◽  
Stock Market Index ◽  
Autoregressive Integrated Moving Average ◽  
Market Index ◽  
Independent Variable

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

scholarly journals The long-Run and Short-Run Analysis between Stock Market Index and Macroeconomic Variables in Jordan: Bounds Tests Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 50
Author(s):  
Raed Walid Al-Smadi ◽  
Muthana Mohammad Omoush
Keyword(s):  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Stock Market Index ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run

This paper investigates the long-run and short-run relationship between stock market index and the macroeconomic variables in Jordan. Annual time series data for the 1978–2017 periods and the ARDL bounding test are used. The results identify long-run equilibrium relationship between stock market index and the macroeconomic variables in Jordan. Jordanian policy makers have to pay more attention to the current regulation in the Amman Stock Exchange(ASE) and manage it well, thus ultimately helping financial development.

scholarly journals GLOBALISASI PASAR MODAL DUNIA DAN PENGARUHNYA TERHADAP PASAR MODAL INDONESIA

2017 ◽  
Vol 1 (1) ◽  
pp. 10
Author(s):  
R Adisetiawan
Keyword(s):  
Stock Market ◽  
Capital Markets ◽  
Capital Market ◽  
Stock Exchange ◽  
Global Market ◽  
Stock Market Index ◽  
Causality Test ◽  
Global Capital ◽  
Market Index ◽  
Cointegration Tests

This study aims to prove causality, cointegration and the influence of global capital markets with a market capital of Indonesia for the period 2001-2016 with a Granger causality test statistics, cointegration tests and Multiple Regression testing. These results prove that the 99% confidence interval occurred a long term relationship (cointegration) and the significant influence of global market indices with the Indonesia capital market index (CSPI) in Indonesia Stock Exchange (IDX) for the period 2001 to 2016, it indicates that Indonesia's economy has been integrated with global capital markets with varying levels of integration, but is causally there is only one country that has a causal relationship with the Indonesian stock market index (CSPI), the Taiwan stock market index (TWSE).Keywords: Capital Market Integration

scholarly journals Quantitative relationship between corruption and development of the Tunisian stock market

2018 ◽  
Vol 7 (2) ◽  
pp. 39-47
Author(s):  
Ibtissem Missaoui ◽  
Mohsen Brahmi ◽  
Jaleleddine BenRajeb
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Quantitative Relationship ◽  
Stock Market Index ◽  
Private Companies ◽  
Stock Market Development ◽  
Market Index ◽  
The Government ◽  
The Impact ◽  
Positive Effect

The aim of this article is to seek especially the impact of corruption on the bond and stock market development. For the methodology/approach, the authors analyze a sample of 20 listed Tunisian firms from the Stock Exchange and Financial market, covering the period from 2006 to 2016 by using pooling cross section techniques. The results find a significant positive effect of the level of corruption on the stock market index and the logarithm of capitalization. This is consistent with the view that corruption accelerates the economic growth by speeding up transactions and allowing private companies to overcome the inefficiencies imposed by the government. Furthermore, the results find a negative association is not significant with the dependent variable of traded value as a percentage of the number of listed companies.

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.  

scholarly journals Modeling stock market volatility in Croatia

2021 ◽  
Vol 34 (2) ◽  
pp. 431-442
Author(s):  
Hrvoje Jošić ◽  
Berislav Žmuk
Keyword(s):  
Stock Market ◽  
Degrees Of Freedom ◽  
Stock Exchange ◽  
Error Distribution ◽  
Stock Market Volatility ◽  
Stock Market Index ◽  
The Past ◽  
Market Index ◽  
Generalized Error Distribution ◽  
Error Distributions

Purpose: In this paper, the volatility of the Croatian stock market index CROBEX is investigated using the GARCH(1,1) model. Methodology: The novelty provided by this paper is the estimation of the GARCH(1,1) model by using three conditional error distributions (normal (Gaussian) distribution, Student’s-distribution with fixed degrees of freedom and generalized error distribution (GED) with fixed parameters). Results: The findings obtained in the research are in the line with previous research in this field (Erjavec & Cota, 2007; Sajter & Ćorić, 2009). The volatility of CROBEX returns is positively correlated with the volume of trade on the Zagreb Stock Exchange and movements on the main European and American stock markets. The movement of S&P 500 stock market index returns is transmitted from the previous day, providing signals for the direction of change of CROBEX index returns in the present. Conclusion: Therefore, this paper provides evidence that investors in Croatia strongly rely on the past information received from the American S&P500 stock market index. Furthermore, there seems to exist the co-movement between CROBEX and main European indexes on the same trading day.

scholarly journals Forecasting Stock Market Index Based on Pattern-Driven Long Short-Term Memory

2020 ◽  
Vol 54 (3/2020) ◽  
pp. 25-41
Author(s):  
SONG DONGHWAN ◽  
BUSOGI MOISE ◽  
CHUNG BAEK ADRIAN ◽  
KIM NAMHUN
Keyword(s):  
Stock Market ◽  
Short Term Memory ◽  
Stock Market Index ◽  
Short Term ◽  
Term Memory ◽  
Market Index ◽  
Long Short Term Memory
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