scholarly journals An Application of Hybrid Models for Weekly Stock Market Index Prediction: Empirical Evidence from SAARC Countries

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhang Peng ◽  
Farman Ullah Khan ◽  
Faridoon Khan ◽  
Parvez Ahmed Shaikh ◽  
Dai Yonghong ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Stock Market ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Hybrid Models ◽  
Machine Learning Techniques ◽  
Stock Market Index

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting.

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 Time Series Analysis on Monthly Stock Market Returns of the Nigerian Stock Exchange: An Arima Modeling Approach

2019 ◽  
pp. 1-9
Author(s):  
Eke, Charles N.
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Arima Model ◽  
Research Work ◽  
Information Criteria ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Arima Modeling ◽  
Best Fit

This research work studied the autoregressive integrated moving average (ARIMA) model that best fitted monthly stock market returns of the Nigerian Stock Exchange between January, 2008 to September, 2018. The study collected secondary data from Central Bank of Nigeria (CBN) Statistical Bulletin 2018 on monthly stock market index of NSE to compute the monthly stock market returns. The Box-Jenkins ARIMA modeling was adopted for this work. The series was tested for stationarity using Augmented Dickey Fuller test. Several ARIMA (p, d, q) models were applied to the monthly stock market returns to ascertain the best fit model for the series. The ARIMA (2, 0, 3) model was selected as the best fit for the data since it has minimum values of Akaike Information Criteria and Mean Squared Errors. The forecasted period showed a market with an unstable monthly stock market returns. Therefore, investors were advised to weigh the risks before venturing into the market to invest.

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 Financial Forecasting Model in Developed and Developing Economies

2019 ◽  
Vol 8 (3S3) ◽  
pp. 291-296
Keyword(s):  
Stock Market ◽  
Developing Economies ◽  
Moving Average ◽  
Consumer Price Index ◽  
Absolute Error ◽  
Volatility Forecasting ◽  
Stock Market Index ◽  
Monthly Data ◽  
Forecasting Models ◽  
Market Index

The study focused on the volatility forecasting in developed and developing share market. The objective of the study was to evaluate the ability of six different statistical and econometric volatility forecasting models in the context of India, Brazil, Japan and US stock market from November 1994 till February 2005 on the basis of four evaluation error measures statistics which are mean absolute error (MAE), root mean square error (RMSE), Theil’s U (TU) and MAPE. The monthly data of stock market index of India, Brazil, Japan and US were collected from January 1992 till April 2005 and also monthly data of stock market index, discount rate, consumer price index (CPI), industrial production and foreign exchange reserves of India, Brazil, Japan and US respectively were collected. Then further analysis was done using four forecasting models which were moving average, exponential weighted moving average, multiple regression, GARCH. The study found out that GARCH and MAE forecasting models are superior in developed market as well as developing market like India.

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 Trading Volume as a Predictor of Market Movement

2019 ◽  
Vol 8 (2) ◽  
pp. 57-69
Author(s):  
Edson Kambeu
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Regression Model ◽  
Trading Volume ◽  
Logistic Regression Model ◽  
Predictive Analytics ◽  
Stock Exchange ◽  
Stock Market Index ◽  
Trading Volumes ◽  
Popular Language

A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.

scholarly journals THE EFFICIENCY OF ENSEMBLE CLASSIFIERS IN PREDICTING THE JOHANNESBURG STOCK EXCHANGE ALL-SHARE INDEX DIRECTION

2019 ◽  
Vol 07 (02) ◽  
pp. 1950001
Author(s):  
THABANG MOKOALELI-MOKOTELI ◽  
SHAUN RAMSUMAR ◽  
HIMA VADAPALLI
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Prediction Models ◽  
Stock Exchange ◽  
Machine Learning Techniques ◽  
Ensemble Prediction ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
Ensemble Models ◽  
Johannesburg Stock Exchange

The success of investors in obtaining huge financial rewards from the stock market depends on their ability to predict the direction of the stock market index. The purpose of this study is to evaluate the efficacy of several ensemble prediction models (Boosted, RUS-Boosted, Subspace Disc, Bagged, and Subspace KNN) in predicting the daily direction of the Johannesburg Stock Exchange (JSE) All-Share index compared to other commonly used machine learning techniques including support vector machines (SVM), logistic regression and [Formula: see text]-nearest neighbor (KNN). The findings in this study show that, among all ensemble models, Boosted algorithm is the best performer followed by RUS-Boosted. When compared to the other techniques, ensemble technique (represented by Boosted) outperformed these techniques, followed by KNN, logistic regression and SVM, respectively. These findings suggest that investors should include ensemble models among the index prediction models if they want to make huge profits in the stock markets. However, not all investors can benefit from this as models may suffer from alpha decay as more and more investors use them, implying that the successful algorithms have limited shelf life.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

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