APPLICATION OF ARIMA MODEL FOR TESTING
“SERIAL INDEPENDENCE” OF STOCK PRICES AT THE HSEC
Cao Hao Thi – Pham Phu – Pham Ngoc Thuy
School of Industrial Management
HoChiMinh City University of Technology
The paper is an attempt to test the “serial independence” of stock prices at HoChiMinh City Stock Exchange Center (HSEC) in Vietnam by applying the ARIMA model for preliminary assessment in terms of its market efficiency. From findings derived, it appears to be that: (a) ARIMA model could be applied for testing the serial independence of stock prices at the HSEC; (b) It is failed to prove that the HSEC market is not a weak-form efficient one; and (c) the ...view middle of the document...
However, in investment domain, this is probably the most controversial issue of “market efficiency” which has been and is likely to continue to be a topic of intensive debate in the investment community. Furthermore, the problem is much more difficult in the emerging markets like the case of the HSEC.
A market is efficient with respect to some particular information if that information is not useful in earning a positive excess return. “With respect to what information”, it could be defined three forms of market efficiency: weak, semistrong, and strong. For the case of the HESC, it seems to be to suspect that the market is a weak-form efficient one. In that case, the hypothesis states that “stock prices reflect historical price information and, therefore, an investor cannot “beat the market” by studying historical prices. There are many types of empirical tests for the above hypothesis and the first one is tests for “serial independence”. In other words, test of weak forms market efficiency would be failed if knowing how stock prices moved in the past cannot be translated into accurate predictions of future stock prices.
The available ARIMA model (Auto Regressive Integrated Moving Average) is an one using for forecasting future values of time series and then forecasted values depend on the own past values and the weighted success of current and lagged random disturbances. Therefore, it appears to be that the model could be seen as a tool for testing the serial independence in the issue of market efficiency.
From the above assessments, the problem of this study is that: (a) Would it be possible using the ARIMA model as a one among the tools for testing weakly efficient market hypothesis? And (b) Would it be possible providing an evidence (among others) on the weak-form efficiency of the HSEC’s market through applying ARIMA model for its historical records of stock prices?
• Stochastic process and its stationary
Any time series data can be thought of as being generated by a stochastic or random process. Broadly speaking, a stochastic process is said to be stationary if its mean and variance are constant over time and the value of covariance between two time periods depend only on the distance or lag between the two time periods and not on the actual time at which covariance is computed.
Using the graph of time series Yt = f(t), graph of autocorrelation function (Correlogram) or Dickey-Fuller test can identify the stationary of a time series.
If the time series is not stationary, difference it one or more times to achieve stationarity. If the original time series is Yt , then the first order difference is Wt = Yt – Yt-1 and the second order difference is Vt = Wt – Wt-1.
Seasonality is just a cyclical behavior that occurs on a regular calendar basis. Often seasonal peaks and troughs are easy to spot by direct observation of the time series. However, if the time series...