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Predicting long-term returns is essential for getting a full view of market efficiency. It is, additionally, more challenging for both human and algorithms, especially at the level of individual stocks, and competent solutions are still missing in previous effort. Considering the profound connections between stock prices and consumer opinions, asking experienced buyers can be a promising direction. In this paper, which is based exclusively on online reviews, a framework for predicting long-term returns of individual stocks is established. Specifically, 6,246 features of 13 categories inferred from more than 18 million product reviews are elaborated to build the prediction model. A satisfactory increase in accuracy, 13.94%, was achieved compared to the cutting-edge solution with 10 technical indicators being features. Additionally, we conduct a trading simulation on real-world stocks and our model obtains the highest return 13.12% among all baselines in half year. The robustness of the model is further evaluated and testified. Our approaches, in both feature inference and prediction model, will offer insightful advices to investors in long-term investments on individual stocks. [ABSTRACT FROM AUTHOR]

Advanced deep learning methods have been widely adopted in stock movement prediction with technical analysis (TA), while researchers prefer technical indicators to technical charts due to the divergence in quantification difficulty. In traditional TA, researchers usually utilize chart similarity to solve the quantifying problem, while chart similarity is often limited to specific charts as the templates for comparison, resulting in massive inadequate use of information. Accordingly, we propose a novel similarity framework to overcome the limitation of chart similarity to specific charts. Specifically, after extracting the key point sequence (i.e., the draft chart) from the stock price series, we transform it into a graph and ultimately employ an arbitrary graph kernel such as the Weisfeiler-Lehman graph kernel and graph convolutional network (GCN) to sufficiently mine the information in the chart for stock movement prediction. Our similarity framework is more robust than the chart similarity measures commonly used in traditional TA. Additionally, we further evaluate the effectiveness of our framework on real-world stock data and show that our framework achieves the best performance compared to several state-of-the-art baselines in stock movement prediction and obtains the highest average net values in a trading simulation. Our results complement the existing application of the chart similarity method in deep learning and provide support for the investing application of financial market decisions. [ABSTRACT FROM AUTHOR]

We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. We first performed statistical measurements for the crossover event sequence, introducing the distribution of the inter-event times of dual moving average crossovers and the correlations of local variation (LV), which is often used in spike train analysis. It was demonstrated that the crossover dynamics in most stock sectors are generally more regular than a standard Poisson process, and the correlation between variations is ubiquitous. In this sense, the proposed model allowed us to identify some asymmetric cross-excitations, and a mutually exciting structure of stock sectors could be characterized by mutual excitation correlations obtained from the kernel matrix of our model. Using simulations, we were able to substantiate that a burst of the dual moving average crossovers in one sector increases the intensity of burst both in the same sector (self-excitation) as well as in other sectors (cross-excitation), generating episodes of highly clustered burst across the market. Furthermore, based on our finding, an algorithmic pair trading strategy was developed and backtesting results on real market data showed that the mutual excitation mechanism might be profitable for stock trading. [ABSTRACT FROM AUTHOR]