ABSTRACT Technical analysis, which includes technical indicators and charts derived from specific rules, has proven effective and widely used for stock movement prediction. However, technical chart evaluation is often limited by subjectivity, arising from sparse chart types and substantial information loss due to rigid rules. While pattern recognition algorithms have been developed to address this issue, they still rely on manual chart labelling and primarily focus on closing prices, leaving much of the chart's broader information untapped. To overcome these limitations, we propose a novel framework called ChartNet, designed to extract general information from technical charts and reduce subjectivity in chart analysis. ChartNet employs a unified representation for charts across financial series with varying simplification levels and leverages a chart triplet loss function for unsupervised training, eliminating the need for labelled data. Compared with several state‐of‐the‐art baselines, our framework has reached the best prediction accuracy on CSI‐300, SZ‐50 components and Dow Jones Index in 2022: 65.91%, 63.70% and 64.96% respectively. In backtesting using actual stock data, our framework achieves the highest average return of 1.12 and 1.15. Furthermore, we highlight the interpretability of ChartNet through two case studies, some important charts and failure cases, illustrating its capability to uncover meaningful insights from charts. This research contributes to advancing the objective evaluation of technical charts and promoting a more comprehensive understanding of chart‐based stock prediction performance.

Forecasting financial data plays a crucial role in financial market. Relying solely on prices or price trends as prediction targets often leads to a vast of invalid transactions. As a result, researchers have increasingly turned their attention to turning points as the prediction target. Surprisingly, existing methods have largely overlooked the role of technical charts, despite turning points being closely related to the technical charts. Recently, several researchers have attempted to utilize chart information via converting price sequences into images for turning point forecasting, but robustness and convergence problems arise. To address these challenges and enhance the turning point predictions, this article introduces a new method known as MPCNet. Specifically, we first transform the price series into a graph structure using chart similarity to robustly extract valuable information from technical charts. Additionally, we introduce the multipersistence topology tool to accurately predict stock turning points and provide convergence guarantee. Experimental results demonstrate the significant superiority of our proposed model over existing methods. Furthermore, based on additional performance evaluations using real stock data, MPCNet consistently achieves the highest average return during the transaction backtesting period. Meanwhile, we provide empirical validation of robustness and theoretical analysis to confirm its convergence, establishing it as a superior tool for financial forecasting.

Abstract Existing studies on the selection of railway travel modes almost always compare high‐speed trains and regular‐speed trains. In this study, three high‐speed trains (Revival, Harmony, and Electric multiple unites (EMU)) on the Beijing–Shanghai line, and the first‐ and second‐class seats on these three trains are selected as research objects to analyse the travel selection behaviour of Chinese high‐speed train passengers. Three methods are selected to study this behaviour: the Support Vector Machine, Nested Logit model, and Multiple Logit model. The results of these three models are calibrated using LIBSVM software and STATA software and show that age, funding source, income level, and purpose of travel are major factors that affect Chinese railway passengers' choices on high‐speed railways. Finally, it is shown that the Support Vector Machine is the most accurate of these three methods, followed by the Nested Logit model. The results of this study can complement existing research on the travel selection behaviour of Chinese railway passengers and has important implications for enabling China's high‐speed rail operators to adjust their passenger transportation products.