There is a consensus about the good sensing characteristics of Twitter to
mine and uncover knowledge in financial markets, being considered a relevant
feeder for taking decisions about buying or holding stock shares and even for
detecting stock manipulation. Although Twitter hashtags allow to aggregate
topic-related content, a specific mechanism for financial information also
exists: Cashtag. However, the irruption of cryptocurrencies has resulted in a
significant degradation on the cashtag-based aggregation of posts.
Unfortunately, Twitter' users may use homonym tickers to refer to
cryptocurrencies and to companies in stock markets, which means that filtering
by cashtag may result on both posts referring to stock companies and
cryptocurrencies. This research proposes automated classifiers to distinguish
conflicting cashtags and, so, their container tweets by analyzing the
distinctive features of tweets referring to stock companies and
cryptocurrencies. As experiment, this paper analyses the interference between
cryptocurrencies and company tickers in the London Stock Exchange (LSE),
specifically, companies in the main and alternative market indices FTSE-100 and
AIM-100. Heuristic-based as well as supervised classifiers are proposed and
their advantages and drawbacks, including their ability to self-adapt to
Twitter usage changes, are discussed. The experiment confirms a significant
distortion in collected data when colliding or homonym cashtags exist, i.e.,
the same \$ acronym to refer to company tickers and cryptocurrencies. According
to our results, the distinctive features of posts including cryptocurrencies or
company tickers support accurate classification of colliding tweets (homonym
cashtags) and Independent Models, as the most detached classifiers from
training data, have the potential to be trans-applicability (in different stock
markets) while retaining performance.

本研究提出了自动分类器来区分冲突的 $ 标签并通过分析涉及股票公司和加密货币的推文的独特特征来区分它们的容器推文。实验结果确认了当存在冲突或同音异义的 $ 标签时，所收集数据的显著失真。根据我们的结果，包括加密货币和股票公司标签的推文的独特特征支持冲突推文（同音异义的 $ 标签）的准确分类，独立模型作为最脱离训练数据的分类器具有在不同股票市场中适用的潜力，同时保持性能。

加密货币涌入推特货币标签：一种分类解决方案

The irruption of cryptocurrencies into Twitter cashtags: a classifying  solution

We propose a time series forecasting method named Quantum Gramian Angular
Field (QGAF). This approach merges the advantages of quantum computing
technology with deep learning, aiming to enhance the precision of time series
classification and forecasting. We successfully transformed stock return time
series data into two-dimensional images suitable for Convolutional Neural
Network (CNN) training by designing specific quantum circuits. Distinct from
the classical Gramian Angular Field (GAF) approach, QGAF's uniqueness lies in
eliminating the need for data normalization and inverse cosine calculations,
simplifying the transformation process from time series data to two-dimensional
images. To validate the effectiveness of this method, we conducted experiments
on datasets from three major stock markets: the China A-share market, the Hong
Kong stock market, and the US stock market. Experimental results revealed that
compared to the classical GAF method, the QGAF approach significantly improved
time series prediction accuracy, reducing prediction errors by an average of
25\% for Mean Absolute Error (MAE) and 48\% for Mean Squared Error (MSE). This
research confirms the potential and promising prospects of integrating quantum
computing with deep learning techniques in financial time series forecasting.

我们提出了一种名为量子格拉米安角场的时间序列预测方法（QGAF）。该方法将量子计算技术与深度学习相结合，旨在提高时间序列分类和预测的精度。通过设计特定的量子电路，我们成功地将股票回报时间序列数据转化成适合卷积神经网络（CNN）训练的二维图像。与经典的格拉米安角场（GAF）方法不同，QGAF 的独特之处在于消除了数据归一化和反余弦计算的需求，简化了从时间序列数据到二维图像的转化过程。通过在中国 A 股市场、香港股市和美国股市的数据集上进行实验证明了该方法的有效性。实验结果表明，与经典的 GAF 方法相比，QGAF 方法在时间序列预测准确性方面具有显著改进，平均减少了 25％的平均绝对误差（MAE）和 48％的均方误差（MSE）。这项研究证实了在金融时间序列预测中，将量子计算与深度学习技术相结合的潜力和有前景的前景。

量子增强预测：利用量子 Gramian 角场和 CNN 进行股票收益预测

Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field  and CNNs for Stock Return Predictions

The structure of return spillovers is examined by constructing Granger
causality networks using daily closing prices of 20 developed markets from 2nd
January 2006 to 31st December 2013. The data is properly aligned to take into
account non-synchronous trading effects. The study of the resulting networks of
over 94 sub-samples revealed three significant findings. First, after the
recent financial crisis the impact of the US stock market has declined. Second,
spatial probit models confirmed the role of the temporal proximity between
market closing times for return spillovers, i.e. the time distance between
national stock markets matters. Third, preferential attachment between stock
markets exists, i.e. spillover from market j to market i is more likely if A)
market j influences other markets other than i, or when B) market i is
influenced by other markets other than j.

该研究通过构建格兰杰因果网络，使用 20 个发达市场的日收盘价，研究了回报溢出的结构，发现了三个重要结果：一是美国股市的影响在最近的金融危机后下降；二是时间接近性对于回报溢出有重要作用；三是股票市场之间存在着优先附着效应。