Network-based direction of movement prediction in financial markets

Market prediction has been an important research problem for decades. Having better predictive models that are both more accurate and faster has been attractive for both researchers and traders. Among many approaches, semi-supervised graph-based prediction has been used as a solution in recent researches. Based on this approach, we present two prediction models. In the first model, a new network structure is introduced that can capture more information about markets’ direction of movements compared to the previous state of the art methods. Based on this novel network, a new algorithm for semi-supervised label propagation is designed that is able to prediction the direction of movement faster and more accurately. The second model is a mixture of experts system that decides between supervised or semi-supervised approaches. Besides this, the model gives us the ability to identify the markets that their data are helpful in constructing the network. Our models are shown to be both faster regarding computational complexity and running time and more accurate in prediction comparing to best rival models in literature of graph-based semi-supervised prediction. The results are also tested to be statistically significant.