Stock market forecasting with financial micro-blog based on sentiment and time series analysis

During the past few decades, time series analysis has become one popular method for solving stock forecasting problem. However, depending only on stock index series makes the performance of the forecast not good enough, because many external factors which may be involved are not taken into consideration. As a way to deal with it, sentiment analysis on online textual data of stock market can generate a lot of valuable information as a complement which can be named as external indicators. In this paper, a new method which combines the time series of external indicators and the time series of stock index is provided. A special text processing algorithm is proposed to obtain a weighted sentiment time series. In the experiment, we obtain financial micro-blogs from some famous portal websites in China. After that, each micro-blog is segmented and preprocessed, and then the sentiment value is calculated for each day. Finally, an NARX time series model combined with the weighted sentiment series is created to forecast the future value of Shanghai Stock Exchange Composite Index (SSECI). The experiment shows that the new model makes an improvement in terms of the accuracy.     

Rigorous Running Time Analysis of a Simple Immune-Based Multi-Objective Optimizer for Bi-Objective Pseudo-Boolean Functions

A simple immune-based multi-objective optimizer (IBMO) is proposed, and a rigorous running time analysis of IBMO on three proposed bi-objective pseudo-Boolean functions (Bi-Trap, Bi-Plateau and Bi-Jump) is presented. The running time of a global simple evolutionary multi-objective optimizer (GSEMO) using standard bit mutation operator with IBMO using somatic contiguous hypermutation (CHM) operator is compared with these three functions. The results show that the immune-based hypermutation can significantly beat standard bit mutation on some well-known multi-objective pseudo-Boolean functions. The proofs allow us to understand the relationship between the characteristics of the problems and the features of the algorithms more deeply. These analysis results also give us a good inspiration to analyze and design a bio-inspired search heuristics.     

Fine-Grained Opinion Extraction from Chinese Car Reviews with an Integrated Strategy

With rapid development of E-commerce, a large amount of data including reviews about different types of products can be accessed within short time. On top of this, opinion mining is becoming increasingly effective to extract valuable information for product design, improvement and brand marketing, especially with fine-grained opinion mining. However, limited by the unstructured and causal expression of opinions, one cannot extract valuable information conveniently. In this paper, we propose an integrated strategy to automatically extract feature-based information, with which one can easily acquire detailed opinion about certain products. For adaptation to the reviews’ characteristics, our strategy is made up of a multi-label classification (MLC) for reviews, a binary classification (BC) for sentences and a sentence-level sequence labelling with a deep learning method. During experiment, our approach achieves 82% accuracy in the final sequence labelling task under the setting of a 20-fold cross validation. In addition, the strategy can be expediently employed in other reviews as long as there is an according amount of labelled data for startup.