Mutual Fund Forecast Analysis. Case of Indonesia ETF (Exchange Traded Fund) Period February 2014 – July 2017 (Published)
Performance of mutual fund could be reflected from several factor, one of which are groth of Net Asset Value (NAV) and price of said mutual fund. The NAV growth of Indonesia Exchange Traded Fund (ETF) are considered one of the most significant, reaching 286% growth value in 2016. Besides, ETF industry in Indonesia itself are still at initial stage of growth. Therefore, information regarding this investment instrument are fairly unknown to public. This research aim to analyse price forecasting of Indonesia ETF using ARIMA method. Weekly price data collected and analysed within period of February 2014 to July 2017, and then forecasted for the next ten weeks. Sample of this ETF are taken from actively traded mutual fund in Indonesia Stock Exchange. Results shows that forecasting model of ARIMA (0,1,1) are the best model to forecast Premier ETF price on ETF IDX30, Sharia Premier ETF JII, and ETF Premier LQ45. Meanwhile, ETF Premier Indonesia Consumer, the best forecasting model are by using ARIMA MA(1) MA(4). This results are also supported by accuracy analysis, which shows that each of ETF Premier reached over 80% of accuracy.
Autoregressive Integrated Moving Average (Arima) Model for the Major Airline Disasters in the World from 1960 Through 2013 (Published)
This research fit a univariate time series model to the major Airline Disasters in the world from 1960 through 2013. The Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model was estimated and the best fitting ARIMA model was used to obtain the post-sample forecasts for five years. The fitted model was ARIMA (0,1,1) with Akaike Information Criteria (AIC) of 323.14, Normalized Bayesian Information Criteria (BIC) of 327.04, Stationary R2 of 0.348.This model was further validated by Ljung-Box test with no significant Autocorrelation between the residuals at different lag times and subsequently by white noise of residuals from the diagnostic checks performed which clearly portray randomness of the standard error of the residuals, no significant spike in the residual plots of ACF and PACF. The forecasts value indicates that Airline Disasters will increase slightly with almost equal number of cases for the next five years (2014-2018).
Modeling and Forecasting of Armed Robbery Cases in Nigeria using Auto Regressive Integrated Moving Average (ARIMA) Models. (Published)
We have utilized a twenty-nine year crime data in Nigeria pertaining to Armed Robbery, the study proposes crime modeling and forecasting using Autoregressive Integrated Moving Average Models, the best model were selected based on the minimum Akaike information criteria (AIC), Bayesian information criteria(BIC), and Hannan-Quinn criteria (HQC) values and was used to make forecast. Forecasted values suggest that Armed Robbery would slightly be on the increase
Analysis of the Volatility of the Electricity Costs in Kenya Using Autoregressive Integrated Moving Average Model (Published)
Electricity has proved to be a vital input to most developing economies. As the Kenyan government aims at transforming Kenya into a newly-industrialized and globally competitive, more energy is expected to be used in the commercial sector on the road to 2030. Therefore, modelling and forecasting of electricity costs in Kenya is of vital concern. In this study, the monthly costs of electricity using Autoregressive Integrated Moving Average models (ARIMA) were used so as to determine the most efficient and adequate model for analysing the volatility of the electricity cost in Kenya. Finally, the fitted ARIMA model was used to do an out-off-sample forecasting for electricity cost for September 2013 to August 2016. The forecasting values obtained indicated that the costs will rise initially but later adapt a decreasing trend. A better understanding of electricity cost trend in the small commercial sector will enhance the producers make informed decisions about their products as electricity is a major input in the sector. Also it will assist the government in making appropriate policy measures to maintain or even lowers the electricity cost.
This study modeled the inflation rates in Nigeria using Box Jenkins’ time series approach. The data used for the work ware yearly collected data between 1961 and 2013. The empirical study revealed that the most adequate model for the inflation rates is ARIMA (0, 0, 1). The fitted Model was used to forecast the Nigerian inflation rates for a period of 12 years. Based on these results, we recommend effective fiscal policies aimed at monitoring Nigeria’s inflationary trend to avoid damaging consequences on the economy.
This paper fit a time series model to the consumer price index (CPI) in Nigeria’s Inflation rate between 1980 and 2010 and provided five years forecast for the expected CPI in Nigeria. The Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models was estimated and the best fitting ARIMA model was used to obtain the post-sample forecasts. It was discovered that the best fitted model is ARIMA (1, 2, 1), Normalized Bayesian Information Criteria (BIC) was 3.788, stationary R2 = 0.767 and Maximum likelihood estimate of 45.911. The model was further validated by Ljung-Box test (Q = 19.105 and p>.01) with no significant autocorrelation between residuals at different lag times. Finally, the five years forecast was made, which showed an average increment of about 2.4% between 2011 and 2015 with the highest CPI being estimated as 279.90 in the 4th quarter of the year 2015.
Any change in sale price may affect customers, distributers and sellers. Anticipating future prices is one of the best ways to face appropriately such these price changes in the market. Time series have wide range of application in various fields such as economy, management and marketing. Time series is a very important tool to analyze a collection of observations which are recorded as daily, weekly, monthly and annually reports. In this paper, the world price of each ounce of gold during 338 continuous months are considered (Average per month) and the target is to assess the behavior of data and to release a suitable model for this data to anticipate world price of each ounce of gold during upcoming months by means of analysis of time series. The first step to analyze time series is to draw data. Next step is to recognize effective parameters on the series (trend, cycle and seasonal) and to remove them from time series and at last to process a static model on time series. We drew autocorrelation function (ACF) and partial autocorrelation function (PACF) for data. Auto-regression model (AR), moving average model (MA) and a combination of AR and MA models (ARIMA, ARMA) were selected as the grade of recognition model and appropriate model. After all stages to analyze time series and creation of remained parameters and after consideration of fitness of represented model, anticipation of world price of gold for each ounce will take place. In this regard, the result of considering the data in this paper produces information for future to make appropriate and profitable decision based on current data. The process is done by means of MINITAB software.