Financial Social Networks Research Articles

An Intelligent Financial Fraud Detection Model Using Knowledge Graph-Integrated Deep Neural Network

Financial fraud detection has been an urgent technical demand in cyberspace. It highly relies on clear extraction and deep representation toward complex relationships inside financial social networks. As consequence, this study combines both knowledge graph and deep learning to deal with such issue. Thus, an intelligent financial fraud detection model based on knowledge graph guidance and deep neural network is proposed in this paper. First, a new knowledge graph based on financially related systems is constructed, which includes multiple entities and relationships. Then, an adversarial learning-based neural network structure is formulated to extract financial attributes. Finally, the detection results can be output according to the extracted factors. Empirically, the proposal is implemented on a real-world dataset for performance evaluation. The experimental results show that it has higher accuracy and effectiveness compared to traditional fraud detection methods. The proposed detection model can not only identify known fraudulent behaviors, but also predict potential fraud patterns based on consumer habits, thereby improving the security and reliability of financial transactions. It can also update the knowledge graph in real-time, enabling it to cope with emerging fraud methods and variants.

Research on Deep Learning-Based Financial Risk Prediction

Financial text-based risk prediction is an important subset for financial analysis. Through automatic analysis of public financial comments, fundamentals on current financial expectations can be evaluated. A deep learning method for financial risk prediction based on sentiment classification is proposed in this paper. The proposed method consists of two steps. Firstly, the abstract of the financial message is extracted according to the seq2seq model. During the extraction process, the seq2seq model can cope with the situation of different input message lengths. After the abstraction, invalid information in the financial messages can be effectively filtered, thus accelerating the subsequent sentiment classification step. The sentiment classification step is performed through the GRU model according to the abstracted texts. The proposed method has the following advantages: (1) it can handle financial messages of different lengths; (2) it can filter out the invalid information of financial messages; (3) because the extracted abstract is more refined, it can speed up the subsequent sentiment classification step; and (4) it has better sentiment classification accuracy. The proposed method in this paper is then verified through financial message dataset from the financial social network StockTwits. By comparing the classification performances, it can be seen that compared with the classical SVM and LSTM methods, the proposed method in this paper can improve the accuracy of sentiment classification by 5.57% and 2.58%, respectively.

Fear and hope in financial social networks: Evidence from COVID-19

This paper examines the diffusion of COVID-19 news on social media using a large sample of approximately 45 million tweets. Using textual analysis, I identify tweets containing COVID-19 news, and construct an index representing the intensity of Twitter discussions. Moreover, I use retweets and favorites as additional measures of investor attention to COVID-19. The results show that the intensity of Twitter discussions about COVID-19 (and about the treatment program) correspond to market returns. This suggests a role for financial social networks in transmitting information related to crises, such as COVID-19, and the resolution of crises.

Big Data: Deep Learning for financial sentiment analysis

Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important for organizations that need to collect a huge amount of data like a social network and one of the greatest assets to use Deep Learning is analyzing a massive amount of data (Big Data). This advantage makes Deep Learning as a valuable tool for Big Data. Deep Learning can be used to extract incredible information that buried in a Big Data. The modern stock market is an example of these social networks. They are a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisors, but what is the best resource to support the decisions these people make? Investment banks such as Goldman Sachs, Lehman Brothers, and Salomon Brothers dominated the world of financial advice for more than a decade. However, via the popularity of the Internet and financial social networks such as StockTwits and SeekingAlpha, investors around the world have new opportunity to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with the reasonable accuracy, but what is the sentiment of a mass group of these expert authors towards various stocks? In this paper, we seek to determine if Deep Learning models can be adapted to improve the performance of sentiment analysis for StockTwits. We applied several neural network models such as long short-term memory, doc2vec, and convolutional neural networks, to stock market opinions posted in StockTwits. Our results show that Deep Learning model can be used effectively for financial sentiment analysis and a convolutional neural network is the best model to predict sentiment of authors in StockTwits dataset.

Group Recommendation Based on Financial Social Network for Robo-Advisor

Robo-advisor is a financial advisor that can get help from machine-learning algorithms to automatically analyze financial product risk levels and provide portfolio recommendations. In the previous work, robo-advisor mainly focused on the basic information and investment preferences of individual users and often ignored the relationship between groups and the individual's risk preference. In the actual environment, the individual investment behavior and the group's social relations are inseparable. In order to solve this challenge, this paper proposes a group recommendation model based on financial social networks and collaborative filtering algorithms. Compared with the latest personalized recommendation system, it not only considers the asset status and risk level of individual investors but also considers social relationships and risk levels among groups. With experiments on benchmark and real-world datasets, we demonstrate that the proposed algorithm achieves the superior performance on both the tasks compared to the state-of-the-art methods.