Predicting Financial Time Series for Value Investment

Abstract

Value investment is an attractive paradigm for individual investors. It involves different steps including evaluating past performance that could be challenging. We propose a representation for financial time series in a form appropriate for both human interpretation and automatic processing. We design a model for predicting sequence of values as opposed to point values. Combined with application of encoder-decoder type of neural network model architecture this allows interpretation of model parameters and intermediate activations by domain experts. We show that predictions better than the trivial last observed value are possible. Therefore, informed investment decisions can be supported by neural network models and the proposed representation and model interpretation.