A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction

Abstract

The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion.Our proposed model first utilizes tensor to integrate the multisourced data, including financial Web news, investors' sentiments extracted from the social network and some quantitative data on stocks. In this way, the intrinsic relationships among different information sources can be captured, and meanwhile, multisourced information can be complemented to solve the data sparsity problem. Secondly, we propose an improved sub-mode coordinate algorithm (SMC). SMC is based on the stock similarity, aiming to reduce the variance of their subspace in each dimension produced by the tensor decomposition. The algorithm is able to improve the quality of the input features, and thus improves the prediction accuracy. And the paper utilizes the Long Short-Term Memory (LSTM) neural network model to predict the stock fluctuation trends. Finally, the experiments on 78 A-share stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are conducted. The results demonstrate the improvement on the prediction accuracy and the effectiveness of the proposed model.

A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

“…This outcome confirms two opinions in literature. Thus, (1) the difference in accuracy (21.09%) between structured dataset (95.78%) and unstructured dataset (74.69), affirms that the unstructured stock dataset from social media and the Internet are best for argumentation of historical or structured stock dataset to enhance prediction [2,5]. (2) also, an increase in accuracy of combine dataset compared with the individual (structured and unstructured), supports that a combination of stock-related information has the propensity of improving stock prediction accuracy as pointed out in [2,10,46,47].…”

“…A total of 221 records were obtained from Google Trends, thus, 221 × 1 matrix, and we normalised the dataset in the range of [− 1,1] as defined in Eq. (5). The trend search for this study was restricted to only the two companies of focus.…”

“…However, stock market data's ubiquitousness makes effective information fusion in market analysis a challenging task [1,5]. Notwithstanding, stock market information is multi-layered and interconnected [5]; hence, the ability to make intelligence of data, by fusing them into new knowledge would offer distinct advantages in predicting the stock market [6]. Therefore, Multi-source Data-Fusion (MDF) has become a key area of interest in recent studies in this field [7].…”

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

“…The correlations enhanced the shared knowledge-based learning and hence, improved the prediction performance. Based on the collected data from various sources, another fusion approach was proposed to predict the fluctuation trend of the given stock [33] ; using a tensor-based sub-mode coordinate (SMC) algorithm, the subspace dimensions were reduced according to the stock similarity. Authors proposed to fuse data collected from financial Web news, sentiments of the investors extracted from social media platform, and quantitative data; the enhanced features were further given to long short-term memory (LSTM) model for the prediction.…”

LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

“…In the research method of Huicheng Liu et al [10], an Attention-Based RNN was used to accomplish this task and a Bidirectional-LSTM is used to capture the characteristics of the information in the news text. Based on factors such as online news and investor sentiment to prediction, Jieyun Huang et al [11] designed tensors to capture the intrinsic connections of different sources of information and then used this method to solve the problem of data sparsity and finally proposed an improved submode coordinate algorithm (SMC) to match the use of tensors for improving the prediction accuracy. Marcelo Sardelich et al [12] also studied the relationship between news and stock prices and then predicted the volatility of the day.…”