Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023)

Abstract: Over the past few years, we’ve witnessed an enormous interest in stock price movement prediction using AI techniques. In recent literature, auxiliary data has been used to improve prediction accuracy, such as textual news. When predicting a particular stock, we assume that information from other stocks should also be utilized as auxiliary data to enhance performance. In this paper, we propose the Causality-guided Multi-memory Interaction Network (CMIN), a novel end-to-end deep neural network for stock movement prediction which, for the first time, models the multi-modality between financial text data and causality-enhanced stock correlations to achieve higher prediction accuracy. CMIN transforms the basic attention mechanism into Causal Attention by calculating transfer entropy between multivariate stocks in order to avoid attention on spurious correlations. Furthermore, we introduce a fusion mechanism to model the multi-directional interactions through which CMIN learns not only the self-influence but also the interactive influence in information flows representing the interrelationship between text and stock correlations. The effectiveness of the proposed approach is demonstrated by experiments on three real-world datasets collected from the U.S. and Chinese markets, where CMIN outperforms existing models to establish a new state-of-the-art prediction accuracy.

## introduction financial services, known for their competitiveness, have always been at the forefront of adopting data science techniques to drive investment decisions. quantitative trading, a specific field within it, has drawn immense interest from both academia and industry over the last few decades. with the rapid advancements in deep learning recently, computer scientists and quantitative researchers have * equal contribution. among various tasks, one of the most prominent is stock price movement prediction @xcite . the reason for its popularity is selfevident: once a model is able to predict future movement with considerable accuracy, numerous trading strategies can be easily built around it. recent studies have shown that deep neural networks are ideal candidates for such prediction models @xcite . supporters of the efficient-market hypothesis (emh), which posits that asset prices reflect all available information, tackle the task with price information alone @xcite @xcite . however, an alternative perspective suggests that additional insights can be gained from analyzing news articles and social media posts, which may hold valuable clues about the future @xcite @xcite . another intriguing approach analyzes the relationships between different stocks. clearly positive and negative correlations, or even non-correlations can be immensely useful in constructing a diversified stock portfolio @xcite . several studies even empirically demonstrate that exploiting correlations can improve the accuracy of stock price movement prediction @xcite . however, their correlations are often realized by acquiring industry sector and calculating correlation matrices or attention scores, which are bidirectional and symmetrical, leading to excessive attention on spurious correlations. due to the lag problem widely existed between two time series, we are more concerned about the dominance of information flow between stocks, specifically, the direction of causality. additionally, we have observed that the situation can significantly change when incorporating text information. let's consider two highly correlated companies @xcite and there is promising news specifically about company a. in such a scenario, it's fairly easy to infer that the current news might still have a substantial impact on company b, despite there being no direct connection between the two companies on paper. however, it's impossible to reach this conclusion by just examining the news about company a or the correlation between a and b alone, which highlights the limitations of relying solely on individual pieces of textual information or traditional correlations between stocks. inspired by observations above, we propose the causality-guided multi-memory interaction network (cmin), a novel end-to-end deep neural network which captures both financial news as well as the causality-enhanced correlations between stocks for better stock price movement prediction. to achieve this goal, cmin incorporates two key components: the text memory network and the stock correlation memory network. both networks utilize a recurrent neural network with nonlinear combination of memory attentions to generate a global memory abstraction. and we introduce a global causality matrix according to the transfer entropy between stock price time series to guide the abstraction process, forming a causal attention mechanism to capture the asymmetric correlations. by considering causality, cmin goes beyond traditional symmetric correlations and captures the true inter-dependencies between stocks. furthermore, we employ an attention-based fusion mechanism between the two networks, introducing multi-directional interactions through which cmin learns not only the self-influence within each network but also the interactive influence between them. it captures the interrelationship between textual information and correlations, enhancing the overall predictive power of cmin. we further demonstrate the effectiveness of cmin with experiments conducted on 3 real-world datasets collected from both the u.s. and chinese markets, where cmin achieves state-of-the-art prediction accuracy, surpassing existing models in terms of performance. to summarize, our main contributions are: ## problem formulation this paper is dedicated to predict the price movement of a target stock. to this end, we leverage both the correlations between stocks and textual information to make prediction. consider a target stock with numerical features denoted as p @xmath0, where k represents the number of time steps in the monitoring window and d represents the dimension of price features, such as the highest and the closing prices. the prices of n other relevant stocks are denoted as: besides, we have financial documents associated with the target stock, which are represented as @xmath1, m 2 , • • • , m k } ∈ r k×l×w , where l denotes the number of documents in a time step and w is the maximum number of words in a document. in cases where a specific stock has fewer than l documents at a given time step, zero padding values are added to align the lengths. similarly, if a document contains fewer than w words, zero padding is applied to ensure uniform length across all documents @xcite . we formulate the task as a binary classification problem whose goal is to predict the movement of the target stock at the next time step, denoted as ŷtarget . here, ŷtarget = 1 indicates a rise in the price while ŷtarget = 0 indicates a fall. ## experiments in this section, we empirically evaluate our cmin model with three real-world datasets collected from the u.s. and chinese stock markets. ## conclusions in this paper, we proposed cmin, a causalityguided multi-memory interaction network that simultaneously models financial documents, causality-enhanced stock correlations and the interactions between the two, and recurrently learns a global memory representation for movement prediction. this multi-modality network was designed to enable the concurrent discovery of texts and stock correlations relevant to future price change and we demonstrated, through experiments on three datasets across two distinct markets, that each component of the proposed architecture made significant contributions to the model, leading cmin to achieve state-of-the-art accuracy.