Machine learning techniques, particularly Support Vector Machines (SVM), have emerged as valuable tools for aiding trading and investment decisions. SVM, known for its effectiveness in binary classification problems, has seen significant application in finance and investment. However, prior research has seldom explored the correlation between macroeconomic factors and stock market movements employing the SVM model. Notably, the Australian stock market has been relatively less explored compared to other major stock markets. This paper aims to fill that gap by providing a comprehensive examination of short-term movement forecasts tailored to the Australian stock market and investigating the semi-strong-form market efficiency in Australia. With the Efficient Market Hypothesis (EMH) serving as the theoretical foundation, this research is structured into three key phases to provide an effective forecasting analysis, specifically targeting the daily movement direction of the S&P/ASX 200 index. First, the study investigates predictive indicators, with a particular focus on macroeconomic features derived from the Toda-Yamamoto causality test. Second, an SVM model is developed to forecast stock index movements, concentrating exclusively on insights from the Australian market. Finally, the research rigorously evaluates the forecasting capabilities of the SVM model while assessing the efficiency level of the Australian market. This study sheds crucial light on the application of SVM in daily stock movement direction prediction and provides implications of the Australian market's efficiency level on investors, policymakers and financial institutions.