Research
Prediction of Stock Price using Autoregressive Integrated Moving Average Filter ((ARIMA (p,d,q))
Abstract - The financial system of any economy is seen to be divided between the financial intermediaries (banks, insurance companies and pension funds) and the markets (bond and stock markets). This study was designed to look at the behavior of stock price of Nigerian Breweries Plc with passage of time and to fit Autoregressive Integrated Moving Average Filter for the prediction of stock price of the Nigerian Breweries Plc. The data were collected from Nigerian Stock exchange and Central Securities Clearing System (CSCS).Time plot was used to detect the presence of time series components in the daily stock prices of Nigerian breweries from 2008 to 2012 and to check if the series is stationary. The structure of dependency was measured by using autoovariance, the auto-correlation and partial autocorrelation. An autoregressive model and moving average model were fitted to stationary series to predict the future stock prices. Alkaike Information Criteria (AIC) was used to determine the order of the fitted autoregressive model. Diagnostic checks were carried out to assess the fit of the fitted autoregressive model. The time plot showed an irregular upward trend. A first difference of the non stationary series made the series stationary. The plots of the Autocorrelation and Partial Autocorrelation showed that stationary has been introduced into the original non-stationary series in which most of the Plotted points decaying to zero sharply. The plot of Akaike Information Criterion showed that the order of the fitted autoregressive model was 8. The ARIMA model diagnostic check showed that the fitted ARIMA model had a reasonable fit for the original series. Predicted stock price ranges from 138.66 to 141.49.
Modelling Optimum Response in a Longitudinal Survey
Non-response rates in surveys have been recognized as important indicators of data quality since they introduce bias in the estimates which increases the mean square error. In order to reduce this error, previous studies have examined the effects of response predictors on response rates. There is dearth of information about models which focus on the interaction effects of response predictors on response rates. The study was therefore designed to develop and validate a model which would reduce non-response and achieve optimum response by the introduction of interaction effects of the response predictors that have been broken down into levels. A two-stage stratified random sampling scheme was used in selecting 750 households in Oyo town. Households were interviewed in five waves. An interviewer-administered questionnaire was used to collect data on demographic characteristics and response predictors including age, gender, educational qualification, religion, employment status, family size, and duration of interview. Demographic characteristics were analyzed using summary statistics. Incidence Rate Ratio was used to examine the response rate at various levels of response predictors. Odd ratio was used to examine the relationship between response rate and each of the response predictors. A model was developed by breaking the predictors of response into levels and their interaction effects were introduced into Denise and Lan model. The respondents’ mean age and modal family size were 51.8 6.9 and 3 respectively, 64.8% were females, 52.8% were muslims and majority (88.9%) were employed. The family size, duration of interview, education, number of visit, Language of interview, familiarity, gender, house ownership, Nationality and duration of residence in a community are positively related to the response rate. Age is negatively related to the response rate and there is no association between employment status and response rate. The developed model showed that family size
