CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction
Authors: Shuqi Li
Yuebo Sun
Yuxin Lin
Xin Gao
Shuo Shang
Rui Yan
Venue: NeurIPS 2024 poster (2024)
Keywords: Causal discovery
Stock movement prediction
Text mining
Primary area: natural_language_processing
TL;DR: We propose a novel news-driven multi-stock movement prediction framework called CausalStock.

Abstract: There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part when leveraging the price information of other stocks to achieve accurate stock movement prediction. Given that stock relations are often unidirectional, such as the "supplier-consumer" relationship, causal relations are more appropriate to capture the impact between stocks. On the other hand, there is substantial noise existing in the news data leading to extracting effective information with difficulty. With these two issues in mind, we propose a novel framework called CausalStock for news-driven multi-stock movement prediction, which discovers the temporal causal relations between stocks. We design a lag-dependent temporal causal discovery mechanism to model the temporal causal graph distribution. Then a Functional Causal Model is employed to encapsulate the discovered causal relations and predict the stock movements. Additionally, we propose a Denoised News Encoder by taking advantage of the excellent text evaluation ability of large language models (LLMs) to extract useful information from massive news data. The experiment results show that CausalStock outperforms the strong baselines for both news-driven multi-stock movement prediction and multi-stock movement prediction tasks on six real-world datasets collected from the US, China, Japan, and UK markets. Moreover, getting benefit from the causal relations, CausalStock could offer a clear prediction mechanism with good explainability.

CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction
Shuqi Li Yuebo Sun' Yuxin Lin? Xin Gao Shuo Shang' Rui Yan
Gaoling School of Artificial Intelligence, Renmin University of China 2 Peking University King Abdullah University of Science and Technology University of Electronic Science and Technology of China {shuqili, sunyuebo0418, ruiyan] @ruc edu. cn
linyuxinOstu, pku. edu. en, xin. wan.gaookaust. edu. sa, jedi shang@gmail... com
Abstract
There are two issues in news-driven multi-stock movement prediction tasks that not well solved in the existing works. On the one hand, "relation discovery" a pivotal part when leveraging the price information of other stocks to achieve accu- rate stock movement prediction. Given that stock relations are often unidirectional, such as the "supplier-consumer"" relationship, causal relations more appropriate to capture the impact between stocks. On the other hand, there is substantial noise existing the news data leading to extracting effective information with difficulty. With these two issues in mind, we propose a novel framework called CausalStock for news-driven multi-stock movement prediction, which discovers the temporal causal relations between stocks. We design lag-dependent tempo- ral causal discovery mechanism to model the temporal causal graph distribution. Then a Functional Causal Model is employed to encapsulate the discovered causal relations and predict the stock movements. Additionally, we propose a Denoised News Encoder by taking advantage of the excellent text evaluation ability of large language models (LLMs) to extract useful information from massive news data. The experiment results show that CausalStock outperforms the strong baselines for both news-driven multi-stock movement prediction and multi-stock movement prediction tasks six real-world datasets collected from the US, China, Japan, and UK markets. Moreover, getting benefit from the causal relations, CausalStock could offer clear prediction mechanism with good explainability.
Introduction
The financial services industry has maintained leading position in embracing data science method- ologies to inform investment determinations. Within this domain, quantitative trading has garnered substantial attention from both academia and industry. Researchers have consistently worked ploring different approaches to predict the stock movement (rise or fall of stock price) for many years, such uni-stock movement prediction (21) multi-stock movement prediction 44 23 news-driven
stock movement prediction [4] [9 and so on, which have shown significant success. These methods usually model the stock movement prediction task as a time series classification problem.
In this paper, we focus on the news-driven multi-stock movement prediction task. A prevalent model paradigm for this task often takes the historical price features and the stock-related news of multiple stocks as inputs and then leverages the well-designed neural networks to make stock movement predictions. There are two key modeling points for tackling this task: modeling the stock
'The first two authors contributed equally. t Corresponding author: Rui Yan (ruiyan www...uu.cnee...e
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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relations to enhance the prediction accuracy, and building the text mining module to extract effective information from news data that benefits stock movement prediction. Although previous work has made significant progress, there are still some issues that require further attention. We will elaborate on them in the following.
19 23 or graph-based [3 23 These methods aim model the correlation relation between stocks. However, the company relations are often unidirectional, such as the "investing" and "member of." leading to the unidirectional relations of their stocks. Thus, causal relations are more appropriate for depicting the impact between stocks, as they identify the direction of information flow and are more informative than correlations. With the development of causal science, many researchers have started use deep end-to-end networks for causal relations discovery of panel data or temporal data [4], in which the causal relations are defined as directed acyelic graphs, i.e., causal graphs, and the Functional Causal Models (FCMs) are often utilized to optimize the causal graph by simulating the data generation mechanism. This provides a solid theoretical foundation for causal discovery for stocks.
For stock relation modeling, many existing works are commonly attention-based 15
In recent years, an extrinsic text mining module has emerged as a plausible avenue through the alignment of financial news and social media posts, thereby elucidating intricate market insights that extend well beyond mere considerations of price dynamics, trading volumes, or financial indica-
tors 17 35 33). Conventional text representations obtained by using GRU 42 or LSTM [T5] exhibit many limitations. Specifically. news text data are often characterized by substantial noise because of the presence of irrelevant or ambiguous information 38 37 The effective information
for stock movement prediction gets intertwined with this noise, presenting considerable challenge for these modules to discern meaningful signals accurately. (LLMs) have unique advantages in this situation due to their advanced knowledge and reasoning abilities. Besides, LLMs can identify meaningful information within noisy environments (2 4
contrast, Large Language Models
Motivated by these requirements, we propose an innovative news-driven multi-stock movement prediction model named CausalStock. In CausalStock, we design a Denoised News Encoder, which leverages LLMs to score every news text from multiple perspectives. Then the evaluation scores are taken as denoised text representations. To discover the causal relations between stocks, we propose a Lag-de bug-dependent temporal causal discovery module, from which we obtain the causal graph distribution. Based on the input market information and learned causal graph distribution, CausalStock employs an FCM [14] to make predictions. We summarize the contributions of our paper as follows:
We propose a novel news-driven multi-stock movement prediction method named Causal- Stock, which could discover the causal relations among stocks and make accurate movement predictions simultaneously.
Different from the past ingiindependent causal discovery method [] CausalStock involves a lag-dependent temporal causal discovery module, which intuitively links the temporal causal relations according to the time lag, making it more suitable for temporal stock data.
To extract useful information from the massive noisy news text data, an LLM-based Denoised News Encoder is proposed by taking advantage of the evaluation ability of LLM, which outputs the denoised news representation for better information utilization.
Experiments on 6 public benchmarks show the performance of CausalStock as a news-driven multi- stock movement prediction method. Moreover, we conduct extensive analytical experiments to show the explainability of our key modules.
2 Related work
Stock prices prediction In traditional trading practices, there are two analysis paradigms commonly used to make stock movement predictions: technical analysis and fundamental analysis. With technical analysis, investors and traders tend to forecast stock prices relying on historical price patterns. Fundamental analysis aims to assess the intrinsic value of a stock by considering other factors besides historical prices, such as financial statements, industry trends, and economic conditions.
Since stock movement prediction involves sequential data, RNN-based networks are applied in many works. AL.STM (28 integrated a dual-stage attention mechanism with L.TTM. Adv-ALSTM [8] further employed adversarial training by adding perturbations to simulate the stochastic and unstable

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nature of the price variable. In recent years, researchers also exploited attention based mechanisms to model complex interactions. DTML - is proposed to predict by using a transformer and LSTM to capture the asymmetric and dynamic correlations between stocks. With the development and prosperity of NI.P technology, text from social media and online news has become a new popular source of fundamental analysis. HAN [1]] designed two attention networks to recognize both the
influential time periods of sequence and the important news at a given time. Stocknet [42] proposed
deep generative model with recurrent, continuous latent variables. MSHAN
13 exploited
multi-stage TCN-LSTM hybrid model. PEN 21 proposed a Shared Representation Learning
module to capture interactions between price data and text data. Additionally, many works modeled
the correlation between stocks to enhance stock price prediction. MAN-SF [32 constructed a graph attention network with price features, social media, and inter-stock relationships based on the interrelationship between price and tweets. CMIN [23] was proposed to model the asymmetric correlations between stocks by computing transfer entropy. In addition, Co-CPC 40 modeled the dependence between a certain stock industry and relevant macroeconomis variables. All the aforementioned methods aim discover the correlation relations among stocks, as elaborated before, the causal relations are more appropriate to depict the information flow of stocks. In this work, we aim model the causal relations for better stock movement prediction performance.
Causal discovery The conventional approach discovering causal relations typically involves conducting randomized experiments [27] 12]. However, conducting randomized experiments can often be excessively expensive, overly time-consuming, or impossible execute. Consequently, causal discovery, which aims to infer causal relationships from purely observational data, has attracted considerable attention within the machine learning community over the last decade [5] 12 Causal
discovery can be classified into three groups: constraint based [10, 30 31]. score-based 26 46)
and functional causal models (FCMs) [12 26). FCMs define the causal relations by directed acyclic graphs (DAGs) and identify causal links through nonlinear functions, such as neural networks [14] g
18) Specifically, DECI [ a deep end-to-end framework to discover causal relations based on additive noise FCM. After that, Rhino [14 was proposed to tackle the temporal causal discovery problem, which incorporates non- non-linear relations, instantaneous effects, and flexible history-dependent noise. In this work, we focus on utilizing the FCM to discover stock relations.
3. Preliminary & problem formulation
3.1 Preliminary
In CausalStock, we integrate the model inputs with causal relations into FCM for prediction. In this section, we introduce the fundamental concepts of FCM and the temporal causal graph.
Temporal causal graph Consider a multivariate time series {x;), with D variables, the temporal causal graph G [46] is commonly defined as a series of directed acyclic graph G = [G,, G2, . Gr [G E RL with maximum time lag L.. Each G E RDD specifies the lagged causal relationships between X,- and X,, the element Grzz 1 if there exists a causal link X + X3 and Gr,a otherwise.
Functional causal model (FCM) FCM represents set of generative functions that incorporate the input features based on causal knowledge (structured as a causal graph) to produce a final prediction. Optimizing the prediction accuracy concurrently refines the underlying causal graph. The theoretical demonstration presented in [14, 2 indicates that if the prediction is accurate, the causal graph can be considered a reliable approximation of real causal relations. Given the temporal causal graph G defined as before, a temporal FCM is defined as follows:
x; Fi Paac (< 1))
(I)
where Pal (< t) indicates the time-lagged parent nodes of variable X; following the temporal causal graph G and 2 represents mutually and serially independent exogenous noise. Here F, is function which implies how variable X; depends on its parents and the noise 2/. Given the distribution of
noises for different variables {=))_= /2 and causal graph, this FCM induces a joint distribution of the multivariate time series process {X
3

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9g(G)
3.2 Problem formulation
G
Po 0)
In this paper, we focus on tackling the news-driven multi-stock movement prediction task. For the target trading day T, we denote the model imputs as the past time lag information [C<T, P<r] where Cj and Pi represent the news
Xer
yr
of D stocks as X = [X;] (=T--T--T--- LT-1
G2
GL
f'Ci, P corpora representation and the historical price features rep- resentation of i-th stock at time step respectively. The ob- jective is to predict the movement of adjusted close prices yr (2 RDx1 T-th trading day of all stocks si- multaneously, where y {0, representing the i-th stock price will fall or rise at trading day T' i.c., stock movement. In theoretical way, this task could be trained by maximiz- ing the log-likelihowe of conditional probability distribution p (yr X so that the most likely yr generated.
Figure I: Illustration of the process of stock movement yr forecasting. The forecasting process is denoted by solid lines with parameters 0 and the causal discovery process is de- noted by dashed lines with varia- tional approximation parameters ø, le is the posterior distribution of the causal graph.
4 CausalStock
4.1
Model overview
The conditional probability distribution could be further factorized follows:
( Xxr) (yr,, G Xcr) (yr G)r,G) X
(2)
The overall process is taken as two joint training parts: temporal causal graph discovery p (G X
and the prediction process given the causal relations p (yr X <T, G). The probabilistic graphic representation of this modeling process is shown Figure T In CausalStock, we develop a lag- dependent causal discovery module, according which could take another step by modeling (G X x)) as lag-dependent format:
(G X xx))= P(i XT- (G, Gr-i.
1=2
For the prediction part p (yr X G), design an FCM shown in Equation to predict the future movement based on the past information Xet and the discovered temporal causal graph G.
In nutshell, CausalStock comprises three primary components as shown in Figure 2
1. Market Information Encoder (MIE) encodes the news text and price features. this part, an LLM-based Denoised News Encoder is proposed;
2. Lag-dependent Temporal Causal Discovery (lag-dependent TCD) module leverages varia- tional inference to mine the causal relationship based on the given market information of stocks, i.e., modeling p (G' Xer);
3. Functional Causal Model (FCM) generates the prediction of future price movements accord- ing to the discovered causal graph, i.e., modeling p (yr XxT, G).
4.2 Market information encoder (MIE)
Market Information Encoder (MIE) takes news corpora and numerical stock price features as inputs. and outputs the historical market information representations X [C<T, {[C PiNeee- = {X: for D stocks with time lag L. For i-th stock, each time step representation X; is the combination of the text representation C; generated by the news encoder and the historical price features representation Pf generated by the price encoder.
Price encoder For i-th stock, we denote the raw adjusted closing, highest, lowest, open, closing prices and trading volume on trading day as p; Í pish pi. pi:o pire Vi By feeding Pi into the embedding layer, the historical prices could be represented as P E R where d, the price embedding size.

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07/06
NETA
COOG
APPLB
Functional Causal Model
Feed For word Laper
Aggreguthnn Function
Feed Forword laywr
Feod Forword Layer
News Representation
Causal Graph
Prices Representation
G2
G2
C2
07/05
UM Evaluatio
Corriattin
Prices Fonnder
Sennimerd
Denoised News Encoder
Importance
A
Anppct
07/05
Prizes Frstadter
Newn Stoda
07/04
1. Apple heoal, for lngest
2076 os ithooe sster slons
pevertur shoee
00708
CASS
07/02
42 show Frooe
11 Distibuioo of Cassal Link Exisience
dishoer
Caneil Link
Lag-dapeddet Temperal Cuasal Link
Figure 2: The structure of CausalStock. For illustration, we use market information during 07/02- 07/05 of three stocks (AAPL, GOOG, META) to predict the movements of 07/05.
LLM-based denoised news encoder (DNE) News Encoder aims to embed stock-related news text, which evolves from the small sequential module, e.g., GRU (23), to pre-trained models, e.g.. Bert and Roberta 66] [22], offering greater performance and scalability. However, news text data often contains massive noise due to the following factors. Firstly, news comes from wide range of sources with varying degrees of reliability and editorial standards. This variability contributes to inconsistencies and inaccuracies in the information presented. Secondly, the sheer volume of news content generated daily can lead to information overload, where significant information is buried under less relevant or redundant information. Thirdly, the use of complex or ambiguous language can also add noise, making it difficult to extract precise information relevant to specific needs, such stock movement prediction. Addressing these challenges requires sophisticated text mining and natural language processing techniques to filter out noise and extract useful, accurate information from news text data. With the development of large language models, current large language models can accurately capture the meaning of text and have strong capability to evaluate text. Therefore, here we propose an LLLM-based Denoised News Encoder to tackle these standing challenges.
LLM-based Denoised News Encoder an innovative textual representation approach that not only proficiently captures salient information from extensive news texts but also assimilates external knowledge derived from LLMs to enrich the representations. Specifically, we employ an LLM and devise series of prompts (see Appendix A for the whole designed prompts) to analyze the relationship between a news text and a specific stock from five dimensions: correlation between the news and the stock, sentiment polarity of the news, significance of the news event, potential impact of the news on stock prices, and duration of the news impact. Each dimension is scored, with Correlation, Importance, Impact, and Duration ranging from 0 to 10, while Sentiment varies from -1 1. Thus the i-th text at day is represented as a dinenniiniionl representation Č₁ R After the embedding layer, we obtain the final denoised news embedding C, E R This novel encoding method amalgamates information derived from the primary text, the external knowledge embedded and the evaluation ability within the LLM. Besides, this method effectively reduces the significant noise present in the original text data.
4.3 Lag-dependent temporal causal discovery (Lag-dependent TCD)
In this section, we propose Lag-dependent Temporal Causal Discovery module. Inspired by [14, our model takes Bayesian view for modeling the distribution of temporal causal graph, which aims to leam the posterior distribution p (G) Xer). Unfortunately, the exact graph posterior is intractable
5

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because of the large combination space of G. Here we adopt the variational inference 3 to get the approximator q (G), where @ indicates the parameter set of variational inference.
Graph prior The prior p (G) consists of two parts: the graph sparseness prior and the domain- specific knowledge prior. The unnormalised graph prior is as follows,
p G) x exp (-x, |.....ll - Xa G1., - Gizzlli).
(4)
where A and Aa are scalar weights of graph sparseness and domain-specific knowledge constraint; GP is an optional domain-specific knowledge graph, which allows users to incorporate pre-defined knowledge for guiding CausalStock, turning it into a knowledge and data-driven framework. Suppose sudden event affects the causal relationships between stocks, such company ending partnership. By incorporating this new pre-defined knowledge into G the causal graph is dynamically updated to reflect the latest market structure and relationship changes. II-Il denotes Frobenius norm. It should be noted that there is no need to give a DAG constraint for the temporal causal graph defined in our paper, it is DAG naturally for the irreversibility of time.
Variational approximating graph posterior According to Equation 3 we factorize the approx- imator go (G) in the same way. For each underlying causal link Gt.j in G, we let the posterior To (Guji Gy-1ji)) subject to Bernoulli distribution B. So that the probability distribution of qo (G) could be a product of Bernoulli distributions as follows,
D qo (G) = 40 (Gi) II d0 (G: GL-)) - IIIII 4s (G j=
D IIIIII 90 (Gess Gr-1)). 1=
(5)
1=2
The existence and non-existence likelihood tensors of causal links are parameterized U (Ui) {u ). E RLLxDD and V (v)
(v1,s) - RLLxxD separately, where Ufei indicates the likelihood for edge existence from X to of and vijz is the likelihood for no-edge, which are all learnable parameters. To model the dependency between G1,, with G.- we propose the following transformation:
Migs hy (urds, 1--1.)s,, vl.gs hy (Mi)i, M-11)))
(6)
where hy and hr are trainable 3-layer MIPs. After normalization, the link existence probability 1.
tensor is denoted Σ J {01
oiji exp (ui.))) / (exp (u/.s)) + exp (vi,))))
(7)
where oije represents the link probability from X²_1 to yr. Thus, we could derive the variational posterior:
(Gi,s)) ~ B (1, , (Gr.j/Cii-aa ~ B (1, qs (G)
I B (1, o1,))
(8)
In the training stage, we employ the Gumbel-softmax reparameterization (24 16 to stochastically estimate the gradients with respect to Φ. Besides, we design another parameterized learnable causal weight graph & (GiY= E LxD to measure the causal degree. The separate design of the causal existence graph and the causal weight graph allows for more comprehensive modeling of causality. Once our model is fitted, the time series causal graph G can be sampled by G ~ qs (G) to represent the relation network and information flow of the stock market.
4.4 Functional causal model (FCM)
In this section, design FCM to model pe (yr <T, G), where denotes the parameter of FCM. We focus on additive noise FCM [18 to generate yr = [yr E TR Dx1,
y = Fi Pacs (< T): +4) = fo (Pa' (< m) + +
(9)
where 2 represents mutually and serially independent dynamical noise, and f: Dx - R are general differentiable non-linear function that satisfies the relations specified by the temporal causal graph G strictly, namely, if X? (= T), then 0 0.

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We design novel FCM to aggregate market information including news and prices based on the discovered causal graph G and causal weight graph G:
fi (Pac (< T) Sigmoid (5. ΣΣ GrarG [e(Pt_)) ( (Gt-))))
(10)
where S., and & are all neural networks. and vo are shared weights across nodes and lags for efficient computation. [;:]] denotes the concatenate operation. We apply the logistic Sigmoid function
to output the movement probability of yr and use directly as the output of CausalStock.
For the exogenous noise z} modeling, we adopt Gaussian distribution, i.e., = N (0.
where ver-variable variances (ot)² i [1, DJ are trainable parameters to represent the uncertainty part. According to Change of variables formula [18]. the conditional distribution pe (v/ Pag (< t)) could be represented as:
pe (yr Palc (< (<)))= Ps,
(
0F:
ps;
24
(11)
where ps; is the aforementioned Gaussian distribution for stock i.
aF Ds
indicates the absolute value
of the Jacobian-determnnan for F,,
ar
is derived according to Equation
Now the log
B2)
likelihood log (yr
<T G) could be further represented as:
D (a T)) =
log pa (yr
XxT, G) Σ
log (vi
logps Ps, (2h)
(12)
1=1
4.5
Training objective
We train our model by maximizing the conditional log- log-likelihood log pe (yr XxT). The variational evidence lower bound (ELBO) of the model objective is derived follows:
X <T) = log [
G
log (yr
po (yr
X<T, (G)
do
(G)
/ to (G) Po (yT X G) p (G)
(ga (G))
(13)
(logg) (yr Xxr, G) + log (G) H (4x (G))
2 (G)
Σ loge: ( + log (G)
(g+ (G)).
Here, (G) represents the prior of causal graph, and H (qs (G)) is the entropy of the posterior approximator. log Pe (yr XxT, G) = log (zT) the log-likelihood of the target distribution, in which 2y is calculated by Equation
training stage.
Besides, we further adopt the binary cross entropy loss as another objective BCE (gr: yr) to improve the learning performance, where gr is the ground truth movement at target trading day T. Overall, final training loss as follows,
gr log (vr) (1 (1-92) log 1 vt
BCE (gr, yr) =
(14)
ELBO + XBCE(gT. yr))
where À is the scalar weight to balance loss terms. We note that the required assumptions and the theoretical guarantees summarized in Appendix
5
Experiments
5.1 Experimental setup
Except for the news-driven multi-stock movement prediction task, our model could also handle the multi-stock movement prediction task without news by removing the Denoised News Encoder. Thus, do the experiments for both two tasks.
7

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Table 1: Main results of CausalStock and baselines for two stock movement prediction tasks multiple datasets. Following the setting of baselines, the standard deviations are calculated across 10 runs for the news-driven task and 5 runs for the task without news.
dewsedriven Multi-stock movement prediction task
ACL18 (US)
CMIN-US (US)
CMIN-CN (CN)
Models
ACC
MCC
ACC
MCC
ACC
MCC
HAN
57.64 0.0040
0.0518
0.0050
53.72 0.0020
0.0103
0.0015
53.59 0.0037
0.0159 a: 0.0026
StockNet
58.23 + 0.0030
0,0808 + 0.0071
52.46 0.0041
0.0220 = 0.0025
54.53 = 0.0062
0.0450 = 0.0043
PEN
59.89 + 0.0090
0.1556 # 0.0018
53.20 0.0051
0.0267 = 0.0023
54.83 # 0.0086
0.0857 # 0.0065
CMIN
62.69 0.0029
0.2090 0.0016
53.43
0.0460
55.28 0.0094
+ 0.1110
CausalStock
63,42 1 0.0039
0.2172 0.0017
54.64 = 0.0083
0.0481 = 0.0057
56.19 + 0.0084
0.1417 + 0.0813
Multi-stock movement prediction task
KDD17 (US)
N1225 (JP)
FTSE100 (UK)
Models
ACC
MCC
ACC
MCC
ACC
MCC
LSTM
$1.18 a. 0.0066
0.0187 a 0.0110
50.79 = 0.0079
0.0148 = 0.0162
50.96 a 0.0065
0.0187 a: 0.0129
ALSTM
51.66 : 0.0041
0.0316 + 0.0119
0.0125 0.0139
51.06 : 0.0038
0.0231 : 0.0077
StockNet
51.93 0.0001
0.0335 0.0050
0.0050 00.15
50.36 0.0095
0.0134 0.0135
Adv-ALSTM
$1.69 + 0.0058
0.0333 + 0.0137
51.60 0.0103
0.0340 + 0.0201
50.66 + 0.0067
0.0155 * 0.0140
DTML
53.53 1 0.0075
0.0733 1 0.0195
$2.76
0.0626 + 0.0230
$2.08 1 0.0121
0.0502 1 0.0214
CausalStock
56,09 0,0069
0.1235 0.0189
53.01 0.0150
0,0640 = 0.0310
52.88 0.0009
0.0534 0.0210
Dataset (Appendix C.1 y: We train and evaluate our model and baselines on six datasets: ACL18 42
CMIN-US (23, CMIN -CN 23 KDD17 45), NI225 [4], and FTSE100 [4].. The first three of them including both historical prices and text data used for news-driven multi-stock movement prediction task evaluation, while the last three are for multi-stock movement prediction task evaluation without news data. Evaluation metries (Appendix C.2): We evaluate the prediction performance of
models by Accuracy (ACC) and Matthews Correlation Coefficients (MCC). Baselines (Appendix
C.3): HAN [15,, Stocknet (42). PEN (21], CMIN (23) for news-driven multi-stock movement
prediction task. LSTM (25), ALSTM 28 Adv-ALSTM 8 DTML (44 for multi-stock movement
prediction task. Parameter setup (Appendix C.4): Our model is implemented with Pytorch on
NVIDIA Tesla V100 and optimized by Adam
20 The parameter sensitivity study can be found in
Appendix C.4.
5.2 Results of prediction accuracy
As shown in the top half of T CausalStock outperforms all baselines on ACC as well as MCC across three datasets demonstrating robustness performance for news-driven multi-stock movement prediction task. For the multi-stock movement prediction task, the results are reported in the bottom half of Table As can be seen, CausalStock exceeds all baselines across three datasets with stable performance. Overall, the results demonstrate that the proposed CausalStock can indeed improve the performance for two stock movement prediction tasks, showing the strong capabilities in handling financial texts and discovering causal relations among stocks.
5.3 Ablation study
For the ablation study, we conduct several model variants on ACLI8, CMIN-CN and CMIN-US to explore the contributions of different settings in CausalStock. For the main framework, we have the following five variants. CausalStock w/o TCD: removing the causal discovery module from CausalStock; CausalStock w/o News: removing the news encoder from CausalStock and just taking prices data as input; CausalStock w/o link non-existence modeling: only model the causal link exis- tence likelihood and leverage Sigmoid function to obtain the link existence probability: CausalStock w/o Lag-dependent TCD: replacing the Lag-dependent Temporal Causal Discovery module with the Lag-independent Temporal Causal Discovery module; CausalStock with Variable-dependent TCD: we add a variable-dependent causal mechanism that explicitly captures the dependencies among different stock edges. Specifically, each edge's 's probability is conditioned on the states of all other edges at the same time step, and the conditional function is the same as the function in the lag-dependent mechanism (Equaatio (). Furthermore, we explore the performance of six different Traditional News Encoders by replacing the denoised news encoder, which outputs the news embed- dings representations. CausalStock with Glove + Bi-GRU: leveraging the Glove word embedding
8

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Table 2: Ablation study results different datasets.
ACL18
CMIN-US
CMIN-CN
Ablation Type
Ablation Variants
ACC
MCC
ACC
MCC
ACC
MCC
CausalStock w/o TCD
51.08
0.0102
51.48
0.0106
51.37
0.0102
CausalStock w/o news
58.10
0.1421
53.16
0.0375
54.16
0.1264
Main Framework
CausalStock w/o link non-existence
58.21
0.1652
52.32
0.024]
53.96
0.0670
CausalStock w/o Lag-dependent TCD
59.19
0.1757
52.93
0.0312
54.97
0.1298
CausalStock with Variable-dependent TCD
63.50
0.2175
54.60
0.0479
56.25
0.1419
CausalStock with Glove+Bi- BI-GRU
60.78
0.1952
53.87
0,0467
55.13
0.1326
CausalStock with Bert
61.74
0.2067
53,92
0.0472
55.43
0.1352
Traditional News Encoder
CausalStock Roberta
61.81
54.06
0.0477
55.58
0.1364
CausalStock with F FinBert
61.72
0.2062
$4.01
0.0471
55.61
0.1362
CausalStock with FinGTT
61.69
0,2066
54.00
0.0470
55.60
0,1360
CausalStock with Llama
62.20
0.2130
54.40
0.0480
55.85
0.1390
CausalStock with FinGPT CausalStock with Llama CausalStock (with GPT-3.5)
61.92
0.2105
54.30
0.0475
55.67
0.1386
Denoised News Encoder
62.82
0.2164
54.52
0.0483
55.97
0,1406
63.42
0.2172
54.64
0.0481
56.19
0.1417
and the Bi-GRU news encoder [23 CausalStock with Bert: leveraging the pre-trained Bert
(Bert-base- (Bert-bae-multiinnuull multilingual- [6]] as news encoder; CausalStock with Roberta: leveraging the pre-trained Roberta (Roberta-bod basee (22] news encoder; CausalStock with FinBert: leveraging
the pre-trained FinBert 1 as news encoder; CausalStock with FinGPT: leveraging the pre-trained
FinGPT (FinGPTT-3.3
43 news encoder; CausalStock with Llama: leveraging the pre- pre-trained
Llama ( -J-caa-7-- (39] as news encoder to output news embeddings. Moreover, we explore the performance of three different LLMs for the denoised news encoder. CausalStock with FinGPT: leveraging a financial LLM FinGPT (FinGPT-v3.3
(43) as denoised news encoder; CausalStock denoised news encoder. The ablation
with Llama: leveraging Llama (Lllam--b-ccha-hf
39
study results are summariized in Table
We have the following observations: (1) CausalStock with news encoders all perform better than CausalStock without news, suggesting news data is particularly helpful for stock movement prediction. (2) Compared to CausalStock w/o Lag-independent TCD, CausalStock with Lag-dependent TCD has a better performance, demonstrating the value of the lag-dependent mechanism. (3) By comparing the CausalStock and CausalStock with Variable-dependee a variable-dependent causal mechanism has the potential to enhance model performance. However, the improvements are not uniform and vary depending on the dataset, which emphasizes that further emphesize validation is needed. While the above results show a promising performance of the variable-dependent causal mechanism, significantly increases the computational complexity (from OLL x D²) to OLL x D*)). making it challenging to apply the model to markets with large numbers of stocks. (3) By using FinGPT and Llama as the news encoder and denoised news encoder respectively, we can observe that denoised news encoders have relatively higher ACC and MCC than their as the traditional news encoders, suggesting the value of denoised news encoders. Overall, the ablation studies show that every component contributes to CausalStock.
TCD, the results show that incorporating
5.4 Results of explainability
Here, present many cases detailing the interpretability of CausalStock from perspectives: the news representation from the Denoised News Encoder, and the causal graph discovered by the Lag-dependent TCD module.
Firstly, regarding the Denoised News Encoder module, three cases are selected as shown in Figure 3tc). A piece of news about APPL suggests potential delay in its 5G iPhone launch, with Denoised News Encoder giving a negative sentiment score of -0.7 and an impact score 9. Similarly, a news about TSLA hints at surpassing significant delivery milestone, receiving positive sentiment score of 0.7. In contrast, a news piece showing no discernible connection to GOOG is scored with negligible impact. These cases indicate the Denoised News Encoder's i efficacy in discerning and quantifying the potential influence of news on respective stock prices.
Secondly, concerning the causal graph discovered by Lag-dependent TCD, we denote the causal strength graph as the dot product of the causal graph G and the causal weight graph &. Every item of causal strength graph indicates not only the causality of two stocks but also the degree of causality. The visualized causal strength matrix for ACL18 is shown in Figure b) with a heatmap. From

---

(8)
(c)
TolMtner Cae (2ng))
Stock: MPL, Text: MMppee Likely to be 56 iPhone Launch Until December Says Analyst", Correlat lonz8, Sent inent=-6.7 Importance17, impacti9, Ouration:
1822
421
MG2
"Testa Could Stock: TSLA, Million Delivery Mark 2022 Says analyst", Correlation: Sentiment:8.7 Iaportance:7, Ispact:9, Buration:6
Stock: 600L, for Investors correlation:0, Sontinort:l Inportance:0, Irpacti0, Durationn:
80A2
Figure 3: (a) Correlation visualization between market value and causal strength for the top 20 companies of UK and Chinese markets. (b) Partial causal strength matrix visualization for ACLI8, encompassing the companies with the highest and lowest market values across various industries. Each matrix entry indicates causal strength between stocks, with darker shades signifying stronger causality. (c) Examples of denoised news encoder module output.
various industries, we select companies with the highest and lowest market value. The top half of Figure 3 ((b) represents stocks corresponding to the nine companies with the largest market value, while the bottom half illustrates stocks from companies with the smallest. The causal strength of stocks is determined based the average overall lags. In this heatmap, could observe that distinct patterns emerge according to different market values. Stocks of low-market-value companies appear to have less pronounced causal relationships. We could also observe causal connections between certain high and now-market-valus stocks. This is attributable to the dominant roles of large-value companies with their significant impact those small-value firms and the stock prices.
Based on these observations, we compute the Spearman's 's rank correlation coefficient [36] between the aforementioned company's market value and their stock's s causal strength on ACL18, CMIN-CN, NI225, and FSTE100 datasets, representing the US, Chinese, Japanese, and UK stock markets respectively. The correlation results are shown in Appendix DaaandixDDanna and we also visualize some results in Figure aa). These results show strong positive correlation between the market value and causal influence. This aligns with the intuition that only do large-value companies hold pivotal economic positions, but also play crucial roles in influencing other companies. Our findings demonstrate that CausalStock does well in uncovering the causal relations within the stock market.
5.5 Investment simulation
Following prior works 44 23), we evaluate Causal- Stock's applicability to the real world trading sce- nario. We conduct portfolio strategy by choosing the top three stocks (based on predicted probabilities) with equal weight each day of the test set and cal- culate the Accumulated Portfolio Value (APV) and Sharpe Ratio (SR) for evaluation. See Appendix
Figure Investment simulation results.
NI225
Model
SR
APV
SR
APV
SR
APV
Market Index
PEN
0.293
1.12
0.132
1.39
0.171
1.43
C.2
DTML
0.304
1.11
0.157
1.39
0.184
1.42
CMIN
1.24
0.169
1.46
0.201
1.51
for metrics details. The results on three datasets are shown in Table which indicates that CausalStock achieves higher profits, and the excellent capabilities of CausalStock to balance risk with returns.
CausalStock
0.369
1.32
0.192
1.49
0.259
1.52
Conclusion
In this paper, we propose a novel news-driven multi-stock movement prediction framework called CausalStock. We design lag-dependent temporal causal discovery mechanism to uncover the causal relations among the stocks. Then the functional causal model employed to encapsulate causal relations and predict future movements. The effectiveness of CausalStock is demonstrated by experiments on multiple real-world datasets. Moreover, CausalStock could offer clear prediction process with explainability.
10

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Acknowledgments and Disclosure of Funding
This work was supported by the National Natural Science Foundation of China (NSFC Grant No. 62122089), Beijing Outstanding Young Scientist Program NO. BJJWZYJHO1 2012210000019 1000000000098 and Intelligent Social Governance Platform, Major Innovation Planning Interdisciplinary Platform for the "Double-First Class" Initiative, Renmin University of China. Shuqi Li is supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (NO.297522615221)
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A Prompt design
This structured prompt encompasses three fundamental components:
System: This section defines the role of the AI. It acts as preliminary introduction to set the tone and context for the AI. It informs the Al that its primary role is to analyze stock-related news in various dimensions such as correlation, sentiment, importance, impact on prices, and duration of impact.
This segment provides detailed instructions to the Al how carry out its analysis. outlines the specific criteria and the scales on which the news should be evaluated. It also provides guidance how to handle ambiguous non-analyzable content and finally, it prescribes the desired output format.
Default Prompt:
The final section is where the user provides the specific details about the stock, the news content, and the time of publication. It acts as the data point based on which the Al will perform its analysis as instructed in the Default Prompt.
Input:
[System]
(As a stock trading news analyst, you are a helpful and precise assistant. Your task to analyze the correlation between and the given stock, sentiment polarity of the news, importance of the news, the impact of the news on stock prices, and the duration of the news impact.)
[Default Prompt]
I need you to analyze the provided stock-related news from five dimensions:
Correlation between the news and the given stock: Rate the correlation on a scale of 0 to 10, where higher score indicates stronger correlation between the news and the given stock.
Sentiment polarity of the news: Rate the sentiment polarity on scale of -1 to 1, where a value closer to -1 indicates stronger negative sentiment and a value closer to 1 indicates stronger positive sentiment.
3. Importance of the news event: Rate the importance on a scale of 0 to 10, where a higher score indicates higher importance of the news event.
Impact of the news on stock prices: Rate the impact on a scale of 0 to 10, where a higher score indicates greater impact of the news stock prices.
Duration of the news impact: Rate the duration on scale of 0 10, where higher score indicates longer potential duration of the news impact.
(When you encounter situation where analysis is not possible, please try avoid assigning all-zero scores and instead make an effort to analyze the text content and derive scores accordingly. Only when analysis is truly impossible should you assign
of 0 to all factors.)
(Please refrain from providing analysis and simply provide the answer according to the following format.)
Output format:
Correlation: <Correlation score between the news and the stock>>
Sentiment: <Sentiment polarity score of the news>>
Importance: <Importance score of the news event>>
Impact: IImpact score the news on stock prices>
Duration: <Duration score of the news impact>> [Input]
[Stock Name]: (stock name)
INews Content]: (news content)
[Publish Time]: (publish time)
14

---

B Assumptions and theoretical guarantees
There are some common assumptions in causal discovery. In this paper, we assume our model satisfies the Causal Markov Property, Minimality and Structural Identifability, Correct Specification, Causal Sufficiency and Regularity of log likelihood. A detailed explanation can be found in 19, which explains how our model satisfies these assumptions. These assumptions guarantee the validity of the causal relations discovered by CausalStock. Considering the instability of news data, we only leverage price data P<r to discover causal graph G meet the Causal Stationary assumption. Then we use the discovered causal graph G' for aggregating both news information and price information. Technically, this could be realized by detaching the gradient from C'r to G.
C Datasets & metries & baselines & parameter setting
C.1 Dataset
Six datasets from various countries' stock markets are employed for conducting the experiments. The first three are used for models of fundamental analysis, which include both historical prices and text data. ACL18 42 is a collection of data from 88 stocks in 9 industries in the US market over two years. Specifically, the price vectors after preprocessing are made up of 7 entries: date, movement percent, open price, high price, low price, close price, and volume, and the text data from Twitter are treated with tokenization and cleaning. Two CMIN datasets (3 are published subsequently following similar format ACL18. CMIN-US is collected from the US market, whereas CMIN-CN comes from 300 CS1300 stocks in the Chinese market.
The other three datasets contain historical prices only and are applied to methods of technical analysis. KDD17 45 collects prices of US stocks. [ proposed to transfer the raw price vectors KDD17 into 11 temporal features for normalizing prices and capturing the interaction between different raw price entries. With this transfer calculation, NI225, and FTSE100 [4 record -f-fatuer stock prices from the US, China, Japan, and UK market respectively over the different time periods. For all datasets, the train-test set split is chronological. More detailed statistics about the datasets are presented in Table 3 below.
Table 3: Dataset Description
Data Range
Data Resource
Dataset
Country
Stock
Train
Valid
Test
Price
Text
Price Dim
ACLIS
88
2014/01AX2-
201 5108112
501500003-200 5/09/30
2015/10012201601..
Yahoo Finance
Twimer
CMIN-US
110
2018/01001 2002 (4430
2021/9901-20211/1
Yahoo Finunce
Yahoo
CMIN-CN
CN
300
2018/01/01
2021 1/4/30
202 1/25/05/0110
(00/5/11-22021000 2/21/008/11
2021/09/01-2022 2021//233
Yahoo Finance
Wind
7
KDDI7
US
50
2007/01A03-20
5/01/00
2015011002-2016000
2016s01/04-22
7/01/01
Yahoo Finance
NI225
S1
2016A77001-001 NAX3K01
2013803902-2019900000
201900107-20099//
Finunce
FTSE100
UK
24
201-001/06-2017000000
2017701004-21228700
2017007001-211/06333
11
C.2 Metries
Given the confusion matrix ( tp In ), where tp, fp, tn, fn represent the true positives, false fp in positives, true negatives and false negatives, we calculate ACC and MCC as follows:
tp ACC
(15)
tp + in + fp gn'
Ip x In Sp x fu
MCC
(16)
Vitp + fpn n (f) tn)(fp tn)
'htps::/gitthub.. hatps://gghuub.oo//ymmuuuuuuuu com//ymoxxu/tttooeeeedaassssss
Phutp://github. cww/BigRaadyyyCCMM---D Dataset
htttp://gghaaa.ooottliiengttttt com/fulffeng/Ads-AA..ST
'htts://datalab.a
15

---

Accumulated investment portfolio value (APV) shows the accumulation of wealth over time in an intuitive form and the Sharpe Ratio (SR) is probably the most widely used metric to measure a trading strategy's s return compared to its risk. The Sharpe ratio is calculated as follows,
APV II (1 8=1
(17)
APV R2 SR = APP -Ry]
(18)
where p² the daily return ratio on i-th trading day and Ry is the risk-free return.
C.3
Baselines
For multi-stock movement prediction
LSTM [25] is LSTM-based network that is trained with rolling window of the last 10 months. 175 technical indicators on the characteristic of stocks and 5 features on normalized historical prices jointly form the input layer and fed into the model.
ALSTM 28 uses attentive LSTM in both encoder and decoder. The input attention mechanism in the encoder could extract the relevant features of stock price, whereas the temporal attention mechanism in the decoder could help learn the long-tern dependencies.
Ady-LSTM g tries to improve ALSTM through adversarial training to capture the stochas- tic nature of stock price and ameliorate the over-fitting. During the training process, adver- sarial examples are generated from latent representation and integrated with clean samples to serve as input.
DTML 44
exploits the correlations between stocks in three parts: compressing the mul- tivariate historical prices of a stock into a context vector with attentive LSTM, generating multi-level context vectors by aggregating local and global context, and capturing the correlations between stocks via transformer encoder and self-attention.
For news-driven multi-stock movement prediction
uses attention mechanism to select useful news for stock movement prediction from chaotic online resources. The framework first applies news-level attention find out more sigmificant news in a date and encodes the output corpus vectors with Bi-GRU. Then, another temporal attention is applied to focus
HAN
[5
impactful time periods.
predicts stock trend based on text and price with recurrent, continuous latent variables. The model has 3 modules, which are Market Information Encoder (MIE), Varia- tional Movement Decoder (VMD), and Attentive Temporal Auxiliary (ATA) in sequence.
Stocknet [22:
PEN 21 a model that fuses the Bi-GRU text embedding and price inputs into Shared Representation Learning (SRL) study their interaction. SRL also yields Vector of Salient (VOS) that could display the importance of a piece of news and display the explainability of the model.
stock correlations and text for stock movement prediction. The approach aims to cover not only the asymmetric correlations between stocks via newly proposed causal attention mechanism but also the multi-directional interactions between text and stock correlations. In addition, two memory networks are used for selecting the relevant information in text and stock correlation.
CMIN [23]: integrates
C.4 Parameter setting
Our model is implemented with Pytorch on 4 NVIDIA Tesla V100 and optimized by Adam 20) All
parameters of our model are initialized with Xavier Initialization I1. To better explore the model's performance, we use grid search to decide on many key hyper-parameters. The learning rate is set as le 5 selected from [le 3, le 4, 1e 5, le 6]. The time lag L is set as 5 selected from
16

---

(,5, 9). We select the price encoder hidden size from [4, 8, 16] and get the best performance with size 4. The batch size
32. The scalar weight is set to 0.01. For the traditional news encoder, the maximum word number in one piece of news and news number in one day set to # = 20, I= 10, respectively. 'he embedding size of word and news are set to du 50, dmm 64, respectively. For the Lag-dependent temporal causal discovery module, X₂ 1, h2 and ha are all 1-layer MLPs. For the FCM part, the neural modules Çi, and d are 3-layer MLPs with hidden size 332.
Hyper-parameter sensitivity study We take a further step to analyze the main parameter sensitivity of CausalStock. We tune the key hyper- parameters learning rate ir, maximum time lag L and loss weight by grid search from this combination = le 5, L = 5, = 0.01 while controlling other parameters. Table 4 the results of metric ACC with different parameter settings on two tasks.
Table 4: Syper-parameter sensitivity study results.
Learning rate Ir
Time lag L.
Loss weight À
Parameters
1e:3
le-4
1e-5
1e-6
5
9
0.1
0.01
0.001
ACL18 (with news)
62.56
62,34
63.42
61,58
61.04
63.42
63.29
63.15
58.26
62.35
63.42
63.45
KDD17 (w/o news)
55.45
55.69
$6.09
55.13
54.94
56.09
55.95
55.94
53.19
55.57
56.09
55.45
D The correlation results
Table 5: The correlation of the causal strength and the market value of companies on four datasets.
Statistics
ACL 18
NI225
CMIN-CN
FTSE100
Spearman Corr.
0.7939
0.7212 0.0185
0.6491 0.0036
0.8909
P-Value
0.006
0.0005
E
Limitations and future works
This paper explores method that discovers causal relations based theoretical considerations. In the future, we could try to adopt meta-learning or incremental learning training methods update the causal graph iteratively, i.e. explore the time-varied causal graph. While the Bernoulli distribution is suitable for determining whether a causal link exists, if want further explore the multi-level nature of causal relationships, more complex distributions might be needed. In the future, we could improve the model in this way.
Broader impacts and safety issues
In this paper, designed LLM-based Denoised News Encoder to evaluate the news from multiple perspectives by LLMs. There exists a risk that the evaluation results of LLMs may violate human values. This safety issue needs careful consideration.
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Reviews:
[{"review_id": "3nex7cCoJv", "reviewer": "Reviewer_iTYx", "summary": "This paper proposes a stock price prediction system based on the history of stock price features and news features, with a model which models the causal relationships between different stacks throughout window of time. The model is based on a temporal causal graph that determines whether the features of stock $i$ (including news and price features) at time $t$ can affect stock $j$ at a future time point (e.g., $t + \\ell$, $\\ell \\in [L]$, where $L$ is the maximum time lag). Causal discovery is performed by modeling the posterior of the lag-dependent causal graph given the data, $p(\\textbf{G} | \\textbf{X}_{<T})$--- this is done by optimizing a variational inference objective. The features of a stock $X_t^i$ capture a combination of its price $P_t^i$ and news $C_t^i$. The system is evaluated on several benchmarks and compared against previous stock prediction systems.", "questions": "- Can you elaborate on the choice of variational approximating class of distributions and discuss any possible limitations? It seems that this is product distribution, making all edges independent (though a particular edge is not independent of that edge at a previous point in time). Is that correct? For example, this wouldn't be able to capture phenomena like \"sectors\" whose stocks are closely tied to each other. It's fine to make such an assumption for tractability, but it warrants discussion.\n- Can the model capture a causal graph that varies with time? I.e., perhaps in 2010 stock X is not really causally connected to stock Y, but after 2020 they become causally connected. E.g., this might capture a company entering a new sector, or starting a partnership with another company (e.g., Open AI and Microsoft).\n- How are news articles selected over time? For example, what if there are multiple articles for a stock within a time period or there are none?\n- Why do you need variables for both the likelihood of an edge $u_{\\ell, ji}$ and the likelihood of no edge $v_{\\ell, ji}$? Since this is a binary variable, can't you have a single variable and let $\\sigma_{\\ell, ji} = \\mathrm{sigmoid}(u_{\\ell, ji}')$?\n\nA minor thing: the use of the word \"remarkably\" in a few places in the paper is grammatically incorrect (e.g., \"Remarkably, we put the Related work in Appendix E\"). You are using it to mean something like \"we note that\", but the word \"remarkably\" means something more like \"suprisingly\".", "limitations": "There is a short section describing limitations & future work in the appendix, but I would encourage the authors to expand on it and incorporate it into the main paper.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The proposed method seems to be a reasonable solution to the problem and incorporates several components that address the multi-faceted nature of such a real-world problem (e.g., temporal causal discovery, encoding different types of features)\n- The reported results show modest but consistent improvement over previous stock prediction systems.\n- There is a nice ablation study on the effect of different components of the system (e.g., w/ vs w/o news, different news encoders, w/ vs w/o lag-dependence in the causal graph).", "weaknesses": "- The procedure for extracting news features is somewhat limited. I.e., it is based on simply prompting an LLM to evaluate a news text across 5 dimensions, obtaining a 5-dimensional feature vector. It may be possible to obtain more information from the news text by more directly accessing an embedding representation, perhaps via a fine-tuned model. However, the ablation results in this paper do demonstrate the usefulness of the news component.\n- Although reasonable, the temporal causal graph model can likely be extended to better capture more complex aspects of the evolution of stock prices. For example, it appears to model different stock edges as independent of each other.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "---\nThank you very much for your valuable suggestions! We will try our best to tackle the concerns one by one.\n\n(1) About the choice reason of Bernoulli distribution\n\n- Thank you for the question. The essence of causal relationships is determining whether a change in one variable directly causes a change in another variable. This binary decision process can naturally be modeled using the Bernoulli distribution. For each causal link, we need to determine whether it exists, which is a typical binary problem—either the causal link exists (represented by 1) or it does not exist (represented by 0). The Bernoulli distribution is used to describe this type of binary random variable, making it very suitable for our needs. \n\n---\n\n(2) About the independence assumption of causal edges\n\n- While each causal link is not completely independent, in our proposed Lag-dependent temporal causal discovery, the causal links are conditionally dependent on temporal lags, as shown in Equation 5 of our paper. However, for different stocks, the causal links are indeed assumed to be independent, which simplifies computational complexity, making the model more efficient to train and infer by parallel computation, especially for large-scale stock sets. We also consider the dependent relationships among stocks, such as sector-specific relationships like you mentioned. We included a plug-and-play option in the prior, as shown in Equation 4, allowing the model to incorporate trusted prior knowledge, such as industry knowledge or company collaboration information. This flexibility enables the model to capture more complex dependencies when such information is available.\n\n---\n\n(3) About the time-varied causal graph\n\n- Thanks for your constructive comment. The ground truth causal graph of the stock market is unknown, so we need certain theoretical guarantees to approximate the true causal relation. Under the current setup, We can theoretically prove that this time-invaried causal learning mechanism converges to the true causal graph (see Appendix B).\n\n- The point you raised is highly valuable in practical applications. In the future, we can consider adopting meta-learning or incremental learning training methods to update the causal graph iteratively. This way, the model can continue to learn and update based on new data in practical applications, thereby reflecting the dynamic causal relation changes in the market.\n\n---\n\n(4) About the varied number of news\n\n- The number and distribution of news articles are related to the dataset. Below are the statistics of our news datasets:\n\n| Dataset  | #news |   |   |   | #words |   |   |\n|----------|-------|---|---|---|--------|---|---|\n|          | Mean  |   | Std |   | Mean   |   | Std |\n| ACL18    | 4.14  |   | 9.92|   | 43.59  |   | 50.28 |\n| CMIN_CN  | 5.21  |   | 4.99|   | 84.22  |   | 62.22 |\n| CMIN_US  | 6.23  |   | 6.16|   | 42.97  |   | 39.81 |\n\n\n- For the absence of news data, CausalStock leverages only the price data to construct the causal graph (see Appendix B for details) and subsequently integrates the discovered causal relations and price data for predictions. Even without news data, our model can effectively perform causal inference and make predictions using price data alone.\n\n- When there are multiple news articles for a particular stock within a given look-back window, we employ LLMs to filter and score the articles. From this process, we retain up to a maximum number of 30 most relevant articles per day to ensure that the model receives focused and informative input. These selected articles are then encoded and fed into the network, maintaining the model's effectiveness and accuracy in handling multiple news sources.\n\n---\n\n(5) About the simultaneous modeling of the existence and non-existence likelihood\n\n- We model the existence and non-existence likelihood simultaneously, which ensures greater flexibility and could avoid the constrained optimization. Besides, we also perform the idea the reviewer mentioned: only model the causal link existence logits and operate the Sigmoid function to obtain the link probability. We compare these two methods and the results are as follows: \n\n|       | ACL18       |           | CMIN_US      |           | CMIN_CN      |           |\n|-------|--------------|-----------|--------------|-----------|--------------|-----------|\n|       | ACC          | MCC       | ACC          | MCC       | ACC          | MCC       |\n| only-existence  | 58.21        | 0.1652    | 52.32        | 0.0241    | 53.96        | 0.0670    |\n| both existence & non-existence| **63.42**   | **0.2172** | **54.64**   | **0.0481** | **56.19**   | **0.1417** |\n\n---\n\n(6) About limitations and future work\n\nThanks for your constructive suggestions! We have deeply considered it and come up with new thinking results of the limitations.\n\n- This paper explores a method that discovers causal relations based on theoretical considerations. In the future, we could try to adopt meta-learning or incremental learning training methods to update the causal graph iteratively, i.e. explore the time-varied causal graph. \n\n- While the Bernoulli distribution is suitable for determining whether a causal link exists, if we want to further explore the multi-level nature of causal relationships, more complex distributions might be needed. In the future, we could improve the model in this way.\n\n---\n\nWe would appreciate it very much if you could raise the score if the concerns are solved. Thank you!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kxWk2i9L0Q", "reviewer": "Reviewer_omfr", "summary": "This paper proposes a news-driven multi-stock movement prediction model called CausalStock. The paper introduces a Denoised News Encoder, which utilizes LLMs to evaluate news text and obtain denoised representations. It also presents a Lag-dependent temporal causal discovery module to discover causal relations between stocks. Based on the input market information and learned causal graph distribution, CausalStock employs a FCM to make stock movement predictions. The contributions of the paper include the design of the Denoised News Encoder, the Lag-dependent temporal causal discovery module, and the application of FCM for stock movement prediction.", "questions": "1. Are the sparseness constraints used in the loss function?  It doesn’t appear in the Equation 14.\n2. How to calculate the correlation in Section 4.4 and Appendix D?  How to obtain the causal strength of a stock in Figure 3(a)?\n3. In Appendix B, the author assume their model satisfies the Causal Markov Property. Does it mean the current state is only correlated with the last state? However, the proposed method is based on lag-dependent as in Equation 3. Does the proposed model really satisfy the Causal Markov Property?\n4. it’s better for the authors give a case study of the method and provide a visualization of the learned causal graphs, which can show their method can indeed obtain information in news to help the prediction of stocks.", "limitations": "Yes", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written with a complete and coherent structure.\n2. The idea of using MIE and Lag-dependent TCD to improve the performance of stock prediction is novel and the effective.", "weaknesses": "1. There exist some typos in the paper:\na. In line 199, the “or” should be “and”.\nb. In Figure 2, the abbr of stock APPLE is “AAPL” while in Figure 3 is “APPL”.\nc. In line 594, the hidden size should be 32 rather than 332.\nd. In line 603, “it’s” should be “its”.\n2. You’d better place the Related Work section into the main manuscript.\n3. In Appendix B, the explainations of the assumptions are not very sufficient.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "---\nThank you very much for your valuable suggestions and your encouraging comments! We will try our best to tackle the concerns.\n\n(1) If sparseness constraints used in the loss function?\n\n- Thank you for the question. We use sparseness constraints in the loss function. These constraints are included in the Evidence Lower Bound (ELBO) term, as shown in Equation 13. The final loss function (Equation 14) includes the ELBO term (Equation 13) and the BCE loss term (Equation 13). \n\n---\n\n(2) About the correlation calculation in Section 4.4\n\n- The causal strength graph $\\hat{G}$ is designed to evaluate the causal strength. It has the same size as the causal graph $G$, with each position being a learnable parameter. After averaging the causal strength graph across all time lags, we obtain a matrix that indicates the magnitude of the causal influence of each company on other companies. Figure 3(a) is an example of such a matrix. Then the correlation in Section 4.4 and Appendix D is calculated by Spearman’s rank correlation coefficient between this matrix and the market values of the corresponding company to explore the explainability.\n\n---\n\n(3) About the Causal Markov Property\n\n- The definition of Causal Markov Property [1] is as follows: Given a directed acyclic graph (DAG) $G$ and a joint distribution $p$, this distribution is said to satisfy Causal Markov Property w.r.t. the DAG $G$ if each variable is independent of its non-descendants given its parents. It is different from the traditional temporal Markov Property. \n\n- Under our setup, all the historical nodes we considered are served as the parent nodes of the target nodes. Specifically, in our lag-dependent temporal causal mechanism, even though we set the causal nodes are lag-dependent, once all of them are given, the target nodes are independent of the other unmodeled factors. This is a common assumption in the field of causal discovery, and our model satisfies this assumption.\n\n[1] Jonas Peters, Dominik Janzing, and Bernhard Scho ̈lkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017.\n\n---\n\n(4) About the visualization of the learned causal graphs and news cases\n\n- We have provided a visualization of the learned causal graph and some news cases in Figure 3. \n- For a more detailed illustration, if negative news is detected, then the causal relationships in the model can identify which stocks might be affected and react accordingly by passing the information extracted from the news, which is empowered by the integration of news and causal discovery.\n\n---\n\nWe would appreciate it very much if you could raise the score if the concerns are solved. Thank you!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vGU0WBDugb", "reviewer": "Reviewer_Xs58", "summary": "This work predicts stock movements by inferring the causal relation between stocks and news. News are encoded to structured representation through LLMs to filter out noises. Causal relation is modeled as directional graph and is inferred through variational approach.", "questions": "1. Since graph inference is challenging and can be erroneous, I am curious about the performance without graph module, that is, simply using the news representation and historical price as input features for predictions. \n\n2.  Table 2 shows that \"CausalStock w/o lag-dependent TCD\" outperforms the proposed method. How do you reconcile the results?", "limitations": "None", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The news denoising module is inspiring and might be extent to other applications. The graph inference module shows promise with interesting design feature such as sparseness and knowledge prior. \n2. The paper present comprehensive experiments by evaluating the propose approaches on various datasets and comparing with multiple baselines. The results are promising. \n3. Interpretability is more valued in finance applications and the explainability analysis hits on the pain point of many DL based stock prediction system.", "weaknesses": "1. The size of graph inference module $O(n^2)$ where $n$ is the number of stocks. The graph is soon becoming unlearnable as the $n$ grows due to limited historical data. The benchmarks used here are small, it is unclear whether the performance can persist on a larger stock set. \n\n2. I think the general framework is not sufficiently novel, neither the graph inference module. I believe the news denoising module is inspiring and worths further investigation. The paper unfortunately doesn't provide such more analysis on the Denoised News Encoder.\n\n3. There are several typos in the paper such as line 42, 44, etc.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "---\nThank you very much for the insightful and constructive comments! We sincerely appreciate the suggestions to improve our submission. We will address all raised concerns one by one.\n\n\n(1) About the performance without graph module\n- Following your suggestion, we ablate the graph module and compare it with our method to explore the value of our causal discovery module. Besides, we compare our causal discovery module with other traditional causal discovery methods, i.e. PC and GES, and the Spearman correlation matrix. The performance results are shown as follows, which indicates that our causal discovery module has a significant advantage over others.\n\n|                           | ACL18        |          | CMIN_US      |          | CMIN_CN      |          |\n|---------------------------|--------------|----------|--------------|----------|--------------|----------|\n|                           | ACC          | MCC      | ACC          | MCC      | ACC          | MCC      |\n| Correlation Matrix        | 52.24         | 0.0220   | 52.85         | 0.0230   | 52.52         | 0.0210   |\n| PC Algorithm              | 51.51        | 0.0138   | 52.04         | 0.0184   | 51.90         | 0.0166   |\n| GES Algorithm             | 51.41         | 0.0135   | 51.92         | 0.0152   | 51.83         | 0.0145   |\n| No Causal Graph           | 51.08         | 0.0102   | 51.48         | 0.0106   | 51.37         | 0.0102   |\n|CausalStock                | **63.42**   | **0.2172** | **54.64**   | **0.0481** | **56.19**   | **0.1417** |\n---\n\n(2) About the ablation results\n\n- Thanks for pointing this out! We have noticed that two ablation results of two datasets were mistakenly swapped in our presentation of the results. We sincerely apologize for this oversight, and we have now corrected it in the following table. \n\n| Ablation Type            | Ablation Variants                        | ACL18       |           | CMIN-CN     |           | CMIN-US     |           |\n|--------------------------|------------------------------------------|-------------|-----------|-------------|-----------|-------------|-----------|\n|                          |                                          | ACC         | MCC       | ACC         | MCC       | ACC         | MCC       |\n| Main Framework           | CausalStock w/o lag-dependent TCD        | 59.19       | 0.1757    | 52.93       | 0.0312    |54.97       | 0.1298    | \n|                          | CausalStock w/o news                     | 58.10       | 0.1421    | 53.16       | 0.0375    |54.16       | 0.1264    | \n| Traditional News Encoder | CausalStock with Glove+Bi-GRU            | 60.78       | 0.1952    | 53.87       | 0.0467    | 55.13       | 0.1326    |\n|                          | CausalStock with Bert                    | 61.74       | 0.2067    | 53.92       | 0.0472    | 55.43       | 0.1352    |\n|                          | CausalStock with Roberta                 | 61.81       | 0.2071    | 54.06       | 0.0477    | 55.58       | 0.1364    |\n| Denoised News Encoder    | CausalStock with FinGPT                  | 61.92       | 0.2105    | 54.30       | 0.0475    | 55.67       | 0.1386    |\n|                          | CausalStock with Llama                   | 62.82       | 0.2164    | 54.52       | 0.0483    | 55.97       | 0.1406    |\n|                          | CausalStock (with GPT-3.5)               | **63.42**   | **0.2172**| **54.64**   | **0.0481**| **56.19**   | **0.1417**|\n\nThank you for your thorough review again. We will be more diligent in our checks to prevent similar errors in the future. We would appreciate it very much if you could raise the score if the concerns are solved. Thank you!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xKSSgpLreA", "reviewer": "Reviewer_99F3", "summary": "The paper presents a novel framework, termed CausalStock, for news-driven multi-stock movement prediction. The authors address two key issues in existing methods: the unidirectional nature of stock relations and the substantial noise in news data. CausalStock introduces a lag-dependent temporal causal discovery module and an LLM-based Denoised News Encoder. The experimental results show that CausalStock outperforms strong baselines on six datasets and provides good explainability.", "questions": "1.\tHow to make sure the learned G is exactly the causal graphs? It seems that the final loss only considers the prediction accuracy. \n2.\tHow would the model handle sudden market shifts or unprecedented events that significantly impact stock prices?\n\n### After rebuttal\nMost of my concerns have been addressed. I raise my score to 5.", "limitations": "The authors discussed the limitations of the proposed method.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The combination of causal discovery and news denoising for stock movement prediction is novel. The authors emphasize the explainability of their model, which is a significant advantage in financial applications. In addition, the paper also includes detailed ablation studies that highlight the contributions of different components of the model.", "weaknesses": "1.\tRegarding the Lag-dependent Temporal Causal Discovery module, since there are already many traditional causal discovery methods such as PC [1] and GES [2], there is a lack of ablation studies comparing this module with traditional causal discovery methods in terms of performance and time efficiency.\n2.\tRegarding the learned causal graph G, I have concerns about the difference between it with a learned correlation matrix. Though it is derived from the causal discovery and Bayesian perspective, in terms of implementation, what is the difference between the correlation matrix? Incorporating a comparison with a correlation matrix in Figure 3b will be convincing.\n3.\tLack of complexity analysis and cost time comparison.\n4.\tMinor comments: The MIE and the Lag-dependent TCD are not explicitly shown in Figure 2. The detailed structure of each module (DNE, Price Encoder, and FCM) is also lacking.\n\nReference:\n\n[1] Estimating high-dimensional directed acyclic graphs with the PC-algorithm (2005)\n\n[2] Optimal Structure Identification With Greedy Search (2002)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "---\nThank you very much for your valuable suggestions! We will try our best to tackle the concerns one by one.\n\n(1) Comparision with the traditional causal discovery method and the correlation matrix\n- We greatly appreciate your insightful comments! Following your suggestion, we compare our discovered causal matrix with the matrix calculated by the traditional causal discovery methods and the correlation matrix. The performance results are shown as follows, which indicates that our causal discovery module has a significant advantage over others.\n\n|  Dataset  | ACL18  |          | CMIN_US      |          | CMIN_CN      |          |\n|--|-|-|-|-|--|-|\n|                           | ACC          | MCC      | ACC          | MCC      | ACC          | MCC      |\n| Correlation Matrix | 52.24         | 0.0220   | 52.85         | 0.0230   | 52.52         | 0.0210   |\n| PC Algorithm  | 51.51        | 0.0138   | 52.04         | 0.0184   | 51.90         | 0.0166   |\n| GES Algorithm | 51.41         | 0.0135   | 51.92         | 0.0152   | 51.83         | 0.0145   |\n|CausalStock| **63.42**   | **0.2172** | **54.64**   | **0.0481** | **56.19**   | **0.1417** |\n\n---\n\n- Compared to traditional causal methods, our integrated causal discovery and prediction modules form an end-to-end deep learning framework, which can constrain the discovered causal graph by simulating the data generation process. In comparison to correlation-based methods, causal relationships are more suitable for representing stock relationships.\n---\n\n(2) Complexity analysis and cost time comparison.\n\n- Thanks for pointing this out. We made a rough estimate of the baselines' complexity on the multi-stock movement prediction task from two perspectives: running time and training cost FLOPs corresponding to the time complexity and the number of parameters corresponding to the space complexity. \n- We leverage an NVIDIA GeForce RTX 3090 and all the parameter settings of baselines follow the original papers. \n\nThe complexity and prediction accuracy results of our method and baselines are shown in the following table. \n\n| Model       | FLOPs  | Parameters  | Running Time|Acc on KDD17|Acc on NI225|Acc on FTSE100|\n|--|--|-|--|--|-|--|\n| LSTM     | $2.3 × 10^8$   | $5797$| $5m58s$|$51.18$ | $50.79$| $50.96$|\n| ALSTM       | $3 × 10^8$   | $6917$ | $6m32s$ |$51.66$| $50.60$| $51.06$|\n| Adv-ALSTM   | $3 × 10^8$   | $6917$ | $7m02s$ |$51.69$| $50.60$| $50.66$|\n| StockNet   | $5.0 × 10^{10}$   | $4.4 × 10^6 $ | $112m$ |$51.93$|$50.15$ |$50.36$ |\n| CausalStock | $1.4 × 10^9$   | $5 × 10^5 $| $7m58s$  |$56.09$| $53.01$| $52.88$|\n\n- From the table above, it can be seen that we can achieve good performance with comparable complexity.\n\n---\n\n(3) About Figure 2\n\n- We have shown the main structure and the most contributive part details of our model in the submitted version. \n- Thank you for your reminder. We have made a more comprehensive model structure in the attached pdf file.\n\n---\n\n(4) About the learning of causal graph\n\n- The ground truth causal graph of the stock market is unknown, so we need certain theoretical guarantees to approximate the true causal relation. Under the current setup, we can theoretically prove that this learning process can converge to the true causal graph.\n\n- **Validity of variational objective:** \n  - Our model holds the following assumptions: Causal Markov Property, Minimality and Structural Identifiability, Correct Specification, Causal Sufficiency, and Regularity of log-likelihood (see Appendix B for a detailed description). \n  - If we further assume that there is no model misspecification, then the Maximum Likelihood Estimation solution $\\theta'$ and the variational posterior distribution of $G$ satisfies $q'_\\phi(G)=\\sigma(G=G')$ by optimizing the ELBO term with infinite data, where $G'$ is a unique graph. \n  - In particular, $G'=G^*$ and $p_\\theta'(X;G')=p(X;G^*)$, where $G^*$ is the ground truth graph and $p(X;G^*)$ is the true data generating distribution. Please find reference [1] for a more detailed proof.\n\n[1] Wenbo Gong, Joel Jennings, Cheng Zhang, and Nick Pawlowski. Rhino: Deep causal temporal relationship learning with history-dependent noise. arXiv preprint arXiv:2210.14706, 2022.\n\n---\n\n(5) About the sudden market shifts or unprecedented events\n\n- Thanks for this constructive comment! Our model could handle sudden market shifts or unprecedented events from the following perspectives:\n\n  - Incorporating News to Capture Market Dynamics: One of the primary purposes of incorporating news into our model is to capture real-time market dynamics. News data can reflect market changes and sudden events promptly. By processing and analyzing news information quickly, our model can rapidly adjust its predictions based on new information. This allows the model to react swiftly and accurately to sudden market shifts.\n\n  - Causal Relationships to Respond to Sudden Events: Our model's ability to learn causal relationships helps it respond effectively to sudden events. For example, if negative news is detected, the causal relationships in the model can identify which stocks might be affected and react accordingly. This mechanism ensures that the model not only considers the directly impacted stocks but also identifies related stocks that might be influenced, providing a comprehensive view of market dynamics.\n\n  - Adjusting Priors to Reflect Changes in Causal Relationships: If a sudden event affects the causal relationships between stocks, such as a company ending a partnership, our model can adjust by incorporating the new pre-defined knowledge into the priors. This allows the causal graph to be dynamically updated to reflect the latest market structure and relationship changes, maintaining the accuracy and relevance of the predictions.\n\n- Thus, our model could effectively handle sudden market shifts and unprecedented events. \n\n---\n\nWe would appreciate it very much if you could raise the score if the concerns are solved. Thank you!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
The more detailed model structure.

Meta-review: This study introduces CausalStock, a model designed to predict movements in multiple stocks leveraging news data. A key feature of the model is the Denoised News Encoder, which leverages Large Language Models (LLMs) to process news text and extract important information. Besides, the model incorporates a Lag-dependent temporal causal discovery module that models causal links between different stocks. Experiments show the improvement of the proposed approach over baselines. 

During the discussion, reviewers actively interacted with authors and concerns raised by reviewers were properly addressed. I’d encourage authors to incorporate these updates into the next version.

Overall, this submission technically sounds with strong empirical results. I’d like to recommend its acceptance.
Decision: Accept (poster)