1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting

TL;DR

This paper introduces a hybrid 1D CapsNet-LSTM model for multi-step stock index forecasting, demonstrating superior accuracy and robustness over traditional models on real-world indices.

Contribution

The study proposes a novel integration of 1D CapsNet with LSTM for improved multi-step stock index prediction, employing a multi-input multi-output strategy for better stochastic dependency modeling.

Findings

The model outperforms baseline models in forecasting accuracy.

It shows slower error growth with longer forecast horizons.

It achieves significant error reductions on real-world stock indices.

Abstract

Multi-step stock index forecasting is vital in finance for informed decision-making. Current forecasting methods on this task frequently produce unsatisfactory results due to the inherent data randomness and instability, thereby underscoring the demand for advanced forecasting models. Given the superiority of capsule network (CapsNet) over CNN in various forecasting and classification tasks, this study investigates the potential of integrating a 1D CapsNet with an LSTM network for multi-step stock index forecasting. To this end, a hybrid 1D-CapsNet-LSTM model is introduced, which utilizes a 1D CapsNet to generate high-level capsules from sequential data and a LSTM network to capture temporal dependencies. To maintain stochastic dependencies over different forecasting horizons, a multi-input multi-output (MIMO) strategy is employed. The model's performance is evaluated on real-world stock market indices, including S&P 500, DJIA, IXIC, and NYSE, and compared to baseline models, including LSTM, RNN, and CNN-LSTM, using metrics such as RMSE, MAE, MAPE, and TIC. The proposed 1D-CapsNet-LSTM model consistently outperforms baseline models in two key aspects. It exhibits significant reductions in forecasting errors compared to baseline models. Furthermore, it displays a slower rate of error increase with lengthening forecast horizons, indicating increased robustness for multi-step forecasting tasks.