A stateless deep learning framework to predict net asset value

Recurrent neural networks (RNN) such as Long Short-Term Memory and Gated Recurrent Unit have recently emerged as a state-of-art neural network architectures to process sequential data efficiently. Thereby, they can be used to model prediction of time series data, since time series values are also a sequence of discrete time data. However, due to various existing RNN architectures, their functioning modes, the growth of hyper parameters and some other bottlenecks arise when it comes to train them. Thus, it becomes perplex to find out the most suited model for a given task. To address these matters, we propose a step-wise approach to predict the time series data, especially net asset value in this paper. We have started the study with the memory size of RNN to set the optimal memory size based on the prediction accuracy. Then, the study follows by analyzing existing data preparation methods and proposing a new one. The proposed data preparation methods prove their effectiveness in both stateless and stateful mode with single RNN layer. Finally, we confront the single RNN layer to stacked and bidirectional RNN to sort out the best performing models based on their prediction accuracy in various time horizons.