With the popularity of data analytics, effective prediction approaches on a large amount of data become increasingly important. On the other hand, classification is a research area in data mining that has a long history. While many efficient classification algorithms have been developed, it can be difficult to interpret and apply their results for prediction applications especially for stock market prediction purposes. This paper investigates the benefits of classification method combining with regression approach to enhance prediction efficiency. In particular, data are first classified by hierarchical beta process based method before being projected by a local metric learning based support vector regression method. Experiments based on real stock market datasets show the effectiveness of our proposal. We also show that the prediction returns are further enhanced by considering other data sources such as news and overseas financial markets using a local metric learning based support vector regression method.