ABSTRACT is also described as the process of estimation in unknown future situations. In a more general term it is

commonly known as prediction which refers to estimation of time series or longitudinal type data.

Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago,

but is now once again accepted as a potential currency. The demand for this commodity is on the rise.

Approach: Objective of this study was to develop a forecasting model for predicting gold prices based

on economic factors such as inflation, currency price movements and others. Following the melt-down

of US dollars, investors are putting their money into gold because gold plays an important role as a

stabilizing influence for investment portfolios. Due to the increase in demand for gold in Malaysian

and other parts of the world, it is necessary to develop a model that reflects the structure and pattern of

gold market and forecast movement of gold price. The most appropriate approach to the understanding

of gold prices is the Multiple Linear Regression (MLR) model. MLR is a study on the relationship

between a single dependent variable and one or more independent variables, as this case with gold

price as the single dependent variable. The fitted model of MLR will be used to predict the future gold

prices. A naive model known as forecast-1? was considered to be a benchmark model in order to

evaluate the performance of the model. Results: Many factors determine the price of gold and based

on a hunch of experts. several economic factors had been identified to have influence on the gold

prices. Variables such as Commodity Research Bureau future index (CRB); USD/Euro Foreign

Exchange Rate (EUROUSD); Inflation rate (INF); Money Supply (M1); New York Stock Exchange

(NYSE); Standard and Poor 500 (SPX); Treasury Bill (T-BILL) and US Dollar index (USDX) were

considered to have influence on the prices. Parameter estimations for the MLR were carried out using

Statistical Packages for Social Science package (SPSS) with Mean Square Error (MSE) as the fitness

function to determine the forecast accuracy. Conclusion: Two models were considered. The first

model considered all possible independent variables. The model appeared to be useful for predicting

the price of gold with 85.2% of sample variations in monthly gold prices explained by the model. The

second model considered the following four independent variables the (CRB lagged one), (EUROUSD

lagged one), (INF lagged two) and (M1 lagged two) to be significant. In terms of prediction, the

second model achieved high level of predictive accuracy. The amount of variance explained was about

70% and the regression coefficients also provide a means of assessing the relative importance of

individual variables in the overall prediction of gold price.

Key words: Gold prices, forecasting, forecast accuracy and multiple linear regressio