Print ISSN: 2204-1990

Online ISSN: 1323-6903

Keywords : ARIMA


FORECASTING USING NEURAL NETWORKS AND STOCHASTIC MODELS ON DAY OF THE WEEK EFFECT: A CASE STUDY OF KSE 100 INDEX

Azhar Ali Marri, Mir G.H.Talpur

Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 5, Pages 2385-2395
DOI: 10.47750/cibg.2021.27.05.116

The main objective of this research was to determine the day of the week effect in Karachi stock exchange (KSE) 100 Index. The problems of financial market structure is analyzed and forecasted by many statistical models. For example Auto regressive integrated moving average (ARIMA) model and artificial neural network (ANN) models. In this research day of the week effect was investigated Wednesday found significant and Monday was noted not significant. 15 step ahead prediction of Wednesday was observed through ARIMA model and ANN models. The coefficient of correlation for actual and forecasted values was perceived 0.8871 by ARIMA model and 0.924 by ANN model. Power of accuracy was displayed 88.765% by ARIMA model and 97.18% by ANN model. Keywords; 

Suzuki Swift Marketing Data Comparative Study of Different Forecasting Methods

DR.PRIYANKA RAWAL

Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 3, Pages 1067-1073
DOI: 10.47750/cibg.2021.27.03.144

Sincere approaches to practical forecasts in organisations have been accomplished through operative research (OR) since its inception. Scientists have affected forecasts in other disciplines. Forecasting has an enormous social, economic and environmental impact and has a very important aspect of every business. Several prediction models have been developed to help people decide correctly against future uncertainties. However, there are distinct advantages and limitations for every prediction model. It is important to succeed in selecting correct forecasting methods from other alternatives. This paper aims to analyse predictive techniques to forecast car sales results, Ford Mustang. Companies depend on precise projected data to make the right decisions and to predict the business results over a long and short time. Predictions are usually based on historical results, industry comparisons and developments in the sector. Different model time series foresees were used in this phase, for example the moving average, exponential smoothing, the Holt double exponential smoothing, winter’s three times exponential smoothing and ARIMA. The predictions were made on the basis of the annual (non-seasonal) data and the cumulative annual data (seasonal) in the ARIMA model. For both the threefold exponential smoothing system of winter and the ARIMA model, Minitab was used to produce forecast. In addition, the best prediction method for this given set of data was found to be the double-exponential smoothing process used by Holt when calculating the mean absolute deviation.

Time-series Models - Forecasting Performance in the Stock Market

Prof. Tinni Chaudhuri; Dr. Abhijit Pandit

Journal of Contemporary Issues in Business and Government, 2021, Volume 27, Issue 2, Pages 3758-3766
DOI: 10.47750/cibg.2021.27.02.387

Contradicting evidence on time-series and financial analysts’ forecasting performance calls for further research in financial markets. Motivation to use time-series models rather than analysts’ forecasts stems from recent research that reports time-series predictions to be superior to analysts’ forecasts in predicting earnings for longer periods and for small firms that are hardly followed by financial analysts. The paper aims to explore performance of time series models in forecasting earnings for six firms considering historical data of 11 years from January, 2010 to December, 2020. Monthly average stock data of last 11 years for five firms namely HCL, TCS, Infosys, Reliance, Tech Mahindra and Wipro was considered from NSE site. Every company had 132 values whose graphical plotting and stationarity check was performed. Data series for each of the five companies was found to be non-stationary. After differencing each of them, the series became stationary and graphical plotting was again done. Then best suited ARIMA Model for each stationary time series was determined upon comparison of goodness of fit statistics. After choosing the best suited ARIMA model, residuals were extracted and were found to be random with no external influence whatsoever. Hence forecasting was done based on chosen model for the monthly average stock price of these top six companies of India in 2020. The paper finds that premier ARIMA family models outperform naive time-series models in terms of mean percentage errors, AIC and average ranks. The findings suggest that investors use the selected ARIMA model to form their expectations