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 five 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.

中文翻译：

---

ResNLS：一种改进的股价预测模型

股票价格预测一直是一项具有挑战性的任务。尽管许多研究项目采用机器学习和深度学习算法来解决该问题，但很少有研究项目关注股票价格之间不同程度的依赖关系。在本文中，我们介绍了一种混合模型，该模型通过强调相邻股票价格之间的依赖性来改进股票价格预测。所提出的模型 ResNLS 主要由两种神经架构组成：ResNet 和 LSTM。ResNet 作为特征提取器来识别跨时间窗口的股票价格之间的依赖性，而 LSTM 通过被视为残差的依赖性组合来分析初始时间序列数据。在预测上证综指时，我们的实验表明，当使用前连续五个交易日的收盘价数据作为输入时，与其他输入相比，模型（ResNLS-5）的性能是最优的。此外，ResNLS-5 在预测精度方面优于普通 CNN、RNN、LSTM 和 BiLSTM 模型。它还表明比当前最先进的基线至少提高了 20%。为了验证ResNLS-5是否能够帮助客户在股市中有效规避风险并赚取利润，我们构建了一个量化交易框架进行回测。实验结果表明，基于ResNLS-5预测的交易策略可以成功地减轻股价下跌期间的损失，并在股价上涨期间产生利润。