Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models

Volatility prediction--an essential concept in financial markets--has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the mainstream approach to forecast market risk. We therefore study different fusion methods to combine text and market data resources. Our word embedding-based approach significantly outperforms state-of-the-art methods. In addition, we investigate the characteristics of the reports of the companies in different financial sectors

Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

Many state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that can’t be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocks’ news sentiment, the focal stock’s news sentiment and the focal stock’s quantitative features to predict the focal stock’s price movement. Results show that our method has an improvement of 8.1% compared with the traditional method

WallStreetBets Beyond GameStop, YOLOs, and the Moon: The Unique Traits of Reddit’s Finance Communities

While the effect of established social media on stock markets has been thoroughly investigated, the recent surge in retail investing and the emergence of different finance-related Reddit communities with unique new traits have led to new research questions. In this work, we aim to understand the linguistic and thematic characteristics and differences of the largest financial Reddit communities, r/WallStreetBets, r/stocks, and r/investing. Using different techniques for the analysis of linguistic features and topic modeling, we identify keywords and phrases that are most prominent in each community and determine each community’s thematic focus and risk affinity. An analysis of users that post on all of these communities confirm these findings, as they appear to adapt to the respective target audience when posting. The stock returns for each community prove consistent with their respective risk profile. Overall, we conclude that understanding these communities can help investors in making more informed investment decisions

How did Markets and Public Sentiment React During Demonetization? Study of a Significant Event in the Indian Economy

The present study aims to determine the impact of shock of demonetization which happened in November 2016 in India. It has been observed in literature that while the market moves due to unforeseen events, market movements are largely affected by news reports on such events. Considering these two threads and the association between them, the study follows mixed method research methodology and assesses the impact of demonetization on stock market movement through time series analysis and text analytics of news items generated during the period. This study examines, through time series analysis, the impact of demonetization as an unexpected event on stock market movement. Time series analysis evaluates the impact on overall stock market movements and on sectoral indices, liquidity shocks in the emerging Indian economy due to demonetization. This study integrates time series analysis with robustness tests and follows text analytics, news analytics and sentiment analytics to gauge public sentiment (influenced by media coverage) during the event. These evaluations validate negative movements in the market and most of the sectors due to the negative sentiment of people about demonetization

Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.Comment: Full version of the paper published at IEEE International Conference on Data Mining (ICDM), 201