Predicting stock price movements is a high-stakes task that demands explainability for human decision-makers. A key shortcoming in current methods is treating sub-predictions independently, without learning from accumulated experiences. We propose a novel triplet network for contrastive learning to enhance the explainability of stock movement prediction by considering instances of "integrated textual information and quantitative indicators". We refer to the target past-l-day tweet-price time series as the "anchor instance". Each anchor instance is paired with a "positive instance" characterized by highly correlated return trends yet significant differences across the entire feature space, and a "negative instance" that exhibits similar return trends along with high proximity in the feature space. The model is designed with the objective of (1) minimizing the cross entropy loss between input logits and target, (2) minimizing the distance between the anchor instances and positive instances, and (3) maximizing the distance between the anchor instances and negative instances. Our framework's effectiveness is demonstrated through extensive testing, showing superior performance on stock prediction benchmarks.

The prediction of stock price movements is challenging due to the inherently dynamic and complex characteristics of financial markets. A current research gap is the lack of exploration into the complex interrelationships inherent in stock price dynamics, often analyzing predictions in isolation with an implicit presumption that solely the historical data of a given stock influences its future trend. However, stock prices are impacted by a diverse array of driving factors that extend beyond the traditionally examined historical prices, encompassing influences such as inter-stock correlations. In this paper, we present a predictive approach using a dynamic dual-graph neural network. The network combines textual data and quantitative metrics to capture multiple dynamic relationships. Specifically, We have developed a price relationship graph (PRG) and a semantic relationship graph (SRG), which are later integrated using a graph attention neural network. The effectiveness of our neural architecture is validated through extensive testing on two benchmark datasets for stock movement prediction, illustrating its superior performance compared to other graph-based networks for stock market prediction.

Metaphors frequently appear in financial news headlines due to their ability to effectively convey complex financial concepts and market trends in a concise and memorable manner. Cognitive scientists have found that metaphors serve as the reflections of human cognition by means of concept mappings. In this work, we aim to analyze the metaphorical expressions and associated cognitive patterns employed by financial analysts in the headlines of financial analysis reports. Such an examination would enhance our comprehension of the cognitive state of financial analysts regarding various financial trends. We employ the latest computational metaphor processing tool, MetaPro to achieve this target by mining metaphors and cognitive patterns from 1,407,328 financial analyst report headlines, spanning the period from 14 February 2009 to 11 June 2020. We analyze the mined concept mappings by different time periods, and market movements, and deliver novel findings in these two dimensions.