Applications of deep learning in financial market prediction has attracted
huge attention from investors and researchers. In particular, intra-day
prediction at the minute scale, the dramatically fluctuating volume and stock
prices within short time periods have posed a great challenge for the
convergence of networks result. Informer is a more novel network, improved on
Transformer with smaller computational complexity, longer prediction length and
global time stamp features. We have designed three experiments to compare
Informer with the commonly used networks LSTM, Transformer and BERT on 1-minute
and 5-minute frequencies for four different stocks/ market indices. The
prediction results are measured by three evaluation criteria: MAE, RMSE and
MAPE. Informer has obtained best performance among all the networks on every
dataset. Network without the global time stamp mechanism has significantly
lower prediction effect compared to the complete Informer; it is evident that
this mechanism grants the time series to the characteristics and substantially
improves the prediction accuracy of the networks. Finally, transfer learning
capability experiment is conducted, Informer also achieves a good performance.
Informer has good robustness and improved performance in market prediction,
which can be exactly adapted to real trading.

本文研究了使用 Informer 网络进行金融市场预测的应用，Informer 网络在预测长度、全局时间戳、计算复杂度等方面得到了改进，在 1 分钟和 5 分钟频率的四个不同股票 / 市场指数上进行了三个实验，结果表明 Informer 网络在各项评价指标方面表现最佳，证明这种时间戳机制能够提高网络的预测准确性，同时也表现出良好的鲁棒性和转移学习能力。

使用 Informer 网络进行股票和市场指数预测

Stock and market index prediction using Informer network

We explore the applicability of the causal analysis based on temporally
shifted (lagged) Pearson correlation applied to diverse time series of
different natures in context of the problem of financial market prediction.
Theoretical discussion is followed by description of the practical approach for
specific environment of time series data with diverse nature and sparsity, as
applied for environments of financial markets. The data involves various
financial metrics computable from raw market data such as real-time trades and
snapshots of the limit order book as well as metrics determined upon social
media news streams such as sentiment and different cognitive distortions. The
approach is backed up with presentation of algorithmic framework for data
acquisition and analysis, concluded with experimental results, and summary
pointing out at the possibility to discriminate causal connections between
different sorts of real field market data with further discussion on present
issues and possible directions of the following work.

本文探究了基于时间延迟的因果分析和皮尔逊相关性在金融市场预测中的应用。作者提出了一种适用于金融市场的时间序列数据的实用方法，并使用不同类型的金融和社交媒体数据进行了实验测试。结果显示该方法可以判别不同实时市场数据之间的因果关系，同时讨论了目前工作中存在的问题和可能的方向。

应用于市场预测的通用时间序列数据的因果分析

Causal Analysis of Generic Time Series Data Applied for Market Prediction

In this paper, we explore the usability of different natural language
processing models for the sentiment analysis of social media applied to
financial market prediction, using the cryptocurrency domain as a reference. We
study how the different sentiment metrics are correlated with the price
movements of Bitcoin. For this purpose, we explore different methods to
calculate the sentiment metrics from a text finding most of them not very
accurate for this prediction task. We find that one of the models outperforms
more than 20 other public ones and makes it possible to fine-tune it
efficiently given its interpretable nature. Thus we confirm that interpretable
artificial intelligence and natural language processing methods might be more
valuable practically than non-explainable and non-interpretable ones. In the
end, we analyse potential causal connections between the different sentiment
metrics and the price movements.

本研究探讨了不同的自然语言处理模型在社交媒体情感分析中的可用性，应用于金融市场预测，以加密货币领域为参考。我们研究了不同情感度量与比特币价格波动之间的相关性。通过实验我们发现有一个模型优于其他 20 多个公开模型，且通过 fine-tune 可以得到更好的性能，其解释性更强。因此我们证实可解释的人工智能和自然语言处理方法可能比不可解释的方法更有实际价值。最后，我们分析了不同情感度量与价格波动之间的潜在因果关系。