  Stock prices forecasting has always been a challenging task. Although many
research projects adopt machine learning and deep learning algorithms to
address the problem, few of them pay attention to the varying degrees of
dependencies between stock prices. In this paper we introduce a hybrid model
that improves stock price prediction by emphasizing the dependencies between
adjacent stock prices. The proposed model, ResNLS, is mainly composed of two
neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to
identify dependencies between stock prices across time windows, while LSTM
analyses the initial time-series data with the combination of dependencies
which considered as residuals. In predicting the SSE Composite Index, our
experiment reveals that when the closing price data for the previous 5
consecutive trading days is used as the input, the performance of the model
(ResNLS-5) is optimal compared to those with other inputs. Furthermore,
ResNLS-5 outperforms vanilla CNN, RNN, LSTM, and BiLSTM models in terms of
prediction accuracy. It also demonstrates at least a 20% improvement over the
current state-of-the-art baselines. To verify whether ResNLS-5 can help clients
effectively avoid risks and earn profits in the stock market, we construct a
quantitative trading framework for back testing. The experimental results show
that the trading strategy based on predictions from ResNLS-5 can successfully
mitigate losses during declining stock prices and generate profits in the
periods of rising stock prices.

Machine Learning;Computational Engineering, Finance, and Science