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

by   Jieyun Huang, et al.

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 multi-sourced 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, multi-sourced 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.



There are no comments yet.


page 1

page 2

page 3

page 4


A new approach for trading based on Long Short Term Memory technique

The stock market prediction has always been crucial for stakeholders, tr...

A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model...

Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model

Predictive model design for accurately predicting future stock prices ha...

Stock Prediction: a method based on extraction of news features and recurrent neural networks

This paper proposed a method for stock prediction. In terms of feature e...

A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models

Long-term investors, different from short-term traders, focus on examini...

Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

Spatiotemporal data is very common in many applications, such as manufac...

Ising formulations for two-dimensional cutting stock problem with setup cost

We proposed the method that translates the 2-D CSP for minimizing the nu...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.