Forecasting Stock Market Returns Via Monte Carlo Simulation: The Case of Amman Stock Exchange

Dima Waleed Hanna Alrabadi, Nada Ibrahim Abu Aljarayesh


This study investigates the ability of Monte Carlo simulation (MCs) to predict stock market returns in Amman Stock Exchange (ASE). Specifically, we compare the in-sample forecasting ability of MCs with the Simple and Exponential Moving average techniques. The data of the study consists of the daily general float index of ASE over the period (2003-2012). Forecasting accuracy is measured by four proxies: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil Inequality Coefficient (U). The results indicate that MCs is the most accurate forecasting technique among the others investigated. Moreover, ASE seems to be inefficient at the weak level, given that technical analysis approaches enable investors to predict stock market returns.


Efficient Market Hypothesis, Random variables, In-Sample Forecasting, Monte Carlo Simulation, Simple Moving Average, Exponential Moving Average, Geometric Brownian Motion, Amman Stock Exchange

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