Stock Trend Prediction Algorithm Based on Deep Recurrent Neural Network

Abstract

With the return of deep learning methods to the public eye, more and more scholars and industry researchers have tried to start exploring the possibility of neural networks to solve the problem, and some progress has been made. However, although neural networks have powerful function fitting ability, they are often criticized for their lack of explanatory power. Due to the large number of parameters and complex structure of neural network models, academics are unable to explain the predictive logic of most neural networks, test the significance of model parameters, and summarize the laws that humans can understand and use. Inspired by the technical analysis theory in the field of stock investment, this paper selects neural network models with different characteristics and extracts effective feature combinations from short-term stock price fluctuation data. In addition, on the basis of ensuring that the prediction effect of the model is not lower than that of the mainstream models, this paper uses the attention mechanism to further explore the predictive K -line patterns, which summarizes usable judgment experience for human researchers on the one hand and explains the prediction logic of the hybrid neural network on the other. Experiments show that the classification effect is better using this model, and the investor sentiment is obtained more accurately, and the accuracy rate can reach 85%, which lays the foundation for the establishment of the whole stock trend prediction model. In terms of explaining the prediction logic of the model, it is experimentally demonstrated that the K -line patterns mined using the attention mechanism have more significant predictive power than the general K -line patterns, and this result explains the prediction basis of the hybrid neural network.

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

“…Their result emphasized the effectiveness of Stock2Vec as compared to Glove and Word2Vec since the sentiment value of words was taken into consideration. Similarly, the study [41] trained the Stock2Vec based on the stock news and sentiment dictionaries. Different from the study [40], they employed additional political news and stock forum speech for sentiment measure and trained the Stock2Vec on CSI 300 stock data, using the LSTM-based model.…”

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

“…Previous research on stock market prediction has shown that various indicators such as a company's price-earnings ratio, price-net ratio, and net cash flow can help predict the performance of individual stocks [6,10,16,17,19]. To obtain the numerical financial features of a stock, we select financial data (e.g., price-earnings ratio and price-net ratio), cash flow data (e.g., inflows and sales ratios), and stock information (e.g., opening and closing prices).…”

“…While econometrics-based statistical methods can rely on tentative premises, machine learning methods pose challenges due to limited interpretability, the need for manual feature selection, and the problem of overfitting. To address these issues, deep learning methods based on conventional neural networks (CNNs) and Recurrent Neural Networks (RNNs) have been used for predicting stock market trends [15][16][17]. By extracting the underlying features of highly unstructured data, such deep learning prediction techniques can be used to explore the complex inherent patterns of stock price movements based on time series data.…”

Design of Accounting Earnings Forecasting Model Based on Artificial Intelligence

“…Moreover, accounting earnings and related data are time series data. As a neural network that can display dynamic time series and use its internal memory to process the input sequence of any time series, recurrent neural network (RNN) [14][15][16][17][18][19][20] can predict earnings fluctuation and reflect the stock price behavior at this stage in combination with the influence of different time series data. Reinforcement learning can intelligently solve complex problems, get rid of the constraints of the current theoretical analysis of accounting earnings value, and bring more possibilities for the research and development of earnings information.…”

“…Reinforcement learning can intelligently solve complex problems, get rid of the constraints of the current theoretical analysis of accounting earnings value, and bring more possibilities for the research and development of earnings information. erefore, driven by artificial intelligence [12][13][14][15][16][17][18][19][20], this paper constructs an overall model of various accounting earnings value related factors, such as earnings (here refers to the specific number of earnings, which can be equivalent to profits), earnings announcement, stock price, assets and liabilities, and company cash flow, based on the neural network for time series and combined with parameter selftuning means such as reinforcement learning. Based on earnings forecasting, an enhanced RNN earnings forecasting model is proposed, which can be nonlinear and can automatically adjust the importance of factors related to earnings value through model learning.…”