In risk management and investment strategy formulation within financial markets, successfully predicting the Volatility Index (VIX) is crucial. While prediction methods leveraging social media texts have been considered promising, they predominantly rely on Twitter data, leaving the potential of online forums underexplored. However, online forums are rich sources of investor discussions and can provide valuable information for VIX prediction. In this study, we propose a deep learning architecture called ForumPFN that effectively captures the complex discussion structures and contextual dependencies unique to online forums, thereby enhancing financial market predictions. At its core, the Discussion Aggregator Module comprises two main components: a Topic Align Algorithm that classifies and reorganizes posts by topic, and a Multi-Scale 1D-Convolutional Path that integrates features at different scales. This design allows for precise modeling of the discussion flows and dynamics specific to Online Forum, maximizing the utilization of information obtained from online forums. We conduct experiments on directional prediction of the Nikkei 225 Volatility Index (Nikkei 225VI)—a representative VIX of Japanese stocks—using data from Yahoo Finance Message Boards, Japan’s largest online forum. The experimental results confirm that ForumPFN outperforms traditional baseline methods. Furthermore, through ablation studies, we demonstrate the effectiveness of each module in detail and explain the module’s operation via visualization of the attention matrix.

We developed a simulation program for trading Nikkei stock index options and verifies the validity of the volatility index (VIX) prediction model proposed by Suwa et al. (2017). We simulated two cases from 18 Nov. 2014 to 29 Jun. 2016. One case involved a benchmark of trading every day during that period and the other was in accordance with the buy/sell/hold instructions of Suwa et al.'s VIX prediction model. When using the call option butterfly spread according to their model's instructions, profit increased from -3,926 to 536 yen. When using the put option butterfly spread according to their model's instructions, profit increased from -4,818 to -799 yen. Therefore, Suwa et al.'s VIX prediction model is effective.

This study focused on Twitter, which is expected to become an increasingly influential tool of political communication, and sought to validate public opinion formation process theories such as the concentration of opinions and announcement effects. We analyzed tweets about the Osaka Metropolis Plan, which was the subject of a referendum in May 2015. We classified tweets and accounts based on support for or opposition to the plan, using a natural language process and latent Dirichlet allocation, and by comparison with newspaper articles. We analyzed changes in the number of posts and active accounts based on the results. We found that the spiral of silence theory did not apply. We also investigated retweet networks using social network analysis, and found that support and opposition were clearly divided in a network structure. Moreover, we found several accounts with extremely high degree centrality and betweenness centrality, thereby indicating possible validation of an opinion leader effect and the announcement effect.

We propose a method of predicting an increase in the Nikkei VI index by analyzing social media based on the premise that investor sentiment is posted on social media. Since the VI index expresses the fear of investors, it is a closely related index to the risk of depression. Therefore, the VI index is an important indicator as an instrument for investment judgment. To predict the increase in the VI index more accurately, we divide messages by topic models specific to social media of stock trading and predict such the increase by machine learning using those topics. As a result of leave-one-day-out cross-validation, precision of our method was 0.45. We also found that the daily fluctuation in the VI index and the number of messages are as effective as feature quantities as the topic-posting frequency.