Fundamental Analysis based Neural Network for Stock Movement Prediction (2022)

Abstract: “Stock movements are influenced not only by historical prices, but also by information outside the market such as social media and news about the stock or related stock. In practice, news or prices of a stock in one day are normally impacted by different days with different weights, and they can influence each other. In terms of this issue, in this paper, we propose a fundamental analysis based neural network for stock movement prediction. First, we propose three new technical indicators based on raw prices according to the finance theory as the basic encode of the prices of each day. Then, we introduce a coattention mechanism to capture the sufficient context information between text and prices across every day within a time window. Based on the mutual promotion and influence of text and price at different times, we obtain more sufficient stock representation. We perform extensive experiments on the real-world StockNet dataset and the experimental results demonstrate the effectiveness of our method.”

## introduction stock movement prediction aims to predict the future price trend of a stock based on its historical price or related information. stock price prediction can help investors, ordinary users and companies to predict the stock trend in the future, which has good application value. the high randomness and volatility of the market make the task of stock movement prediction a big challenge @xcite . however, with the development of neural network technology, stock movement prediction has achieved good results in recent years @xcite @xcite @xcite @xcite . based on fundamental and technical analysis, existing methods can be roughly grouped into two categories, namely methods based on price factors only and methods based on price and other factors (e.g., news of the stock.). @xcite used the lstm @xcite network to predict future stock price trends based on historical price and technical analysis indicators. @xcite used the adversarial training as perturbations to simulate the randomness of price variables, and trained the model to work well with small but intentional perturbations. they also extracted 11 related price features to effectively help the model to predict future changes. according to the efficient market hypothesis (emh) @xcite , price signals themselves cannot capture the impact of market accidents and unexpected accidents, while social media texts such as tweets could have a huge impact on the stock market. based on this idea, different models have been proposed to model relevant news texts to improve the overall performance of stock movement prediction. @xcite proposed to use the hierarchical attention mechanism to predict the trend of stocks based on the sequence of recent related news. @xcite integrated signals from social media which reflected the opinions of general users and used variational autoencoder(vae) to capture the randomness of prices and the importance of different time steps by adding temporal attention. @xcite introduced a novel architecture for efficient mixing of chaotic temporal signals from financial data, social media, and inter stock relationships in a hierarchical temporal manner through graph attention neural network. although previous studies have achieved good results, whether it is a purely technical approach based on historical prices or a fundamental approach based on multiple factors such as prices and news, they can be improved in terms of the full integration of the two important factors of texts and prices. we found that previous works usually encode news and prices separately according to time series, and then fuse them through simple concatenation operation, similar to the work of @xcite . in fact, in practice, prices on a given day can be influenced by different news at different times (e.g., previous day or after two days). similarly, some news about a stock on a given day may be influenced by stock prices at different times. as is shown in figure 1 , if we can capture the context information of each price and text by different days, we can get more sufficient information for predicting the stock price accurately. to this end, in this paper, we propose a fundamental analysis based neural network for stock movement prediction. more specifically, we first use bi-gru to encode the original texts of each day. then, we use text-level attention to get a text representation of each day. as for the prices of each day, we use the existing 11 indicator features and 3 indicators we proposed in this paper as price representation of each day. then we use the coattention mechanism @xcite to capture more information between texts and prices across every day within a time window. finally, we incorporate a bi-gru to encode the fully integrated texts and prices representation according to the time window, so that it can obtain various prices and text-related information of the stock, and obtain the final effective representation of the stock. the contributions of this work are as follows: ## related work in this section, we will review the related work about stock movement prediction from technical analysis based approach and fundamental analysis based approach. ## conclusion to effectively fuse texts and prices to predict future stock movements, in this paper, we propose a fundamental analysis based neural network for stock movement prediction. our model introduces the coattention mechanism to capture richer implicit information between text and price as a better representation of a stock. we also introduce three new technical indicators in the financial field as price features. we perform the extensive experiments on the stocknet dataset and the experimental results show the effectiveness of our proposed method. in the future, we plan to use more data other than stock prices, such as financial reports, relationships between stock, to better capture market dynamics. in addition, extracting features that can better reflect trend changes is still a direction worth exploring.