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Nowadays, various financial reports are widely available on the Internet. While this kind of information was previously published in official journals and department statistics, contemporary media sources allow economic news to be rapidly spread among a large number of people. Twitter is one of the most popular social networks for this kind of purpose. Many traders use it to report on the stock market news or to let others know about various deals they have made. Personal thoughts regarding the future of a certain stock are also common for this group of users. Scientists try to take advantage of such posts by using models of computational linguistics. Statistical analysis and computer programs are used to analyze financial reports that are posted on the Internet and to predict the future of the stock market. Three articles use different frameworks to extract information about post expertise, risk prediction, and future stock prices.
The article by Bar-Haim et al. features in-depth work done to distinguish between expert authors and novice creators of posts. The primary aim was to select those people who were able to provide accurate information regarding the stock market. This would give others an opportunity to better predict any movements in this field, which is a very critical feature, as many people wish to have some background knowledge before making a trading decision.
The researchers first focused on classifying the types of messages found on Twitter regarding the stock market. The article contains a note that many of those posts are usually written with the help of a language that can be understood only in a professional circle. Bar-Haim et al. divided all the analyzed posts on those containing facts, opinions, or questions, with a category of irrelevant information also making up a certain portion of tweets. A computer program was developed afterward that aimed to analyze the posts and determine whether a user was an expert or not. Overall, the general idea behind the program was to measure whether the bullish tweets of a particular user were followed by a stock rise. If it was determined that a post’s author is an expert, a number of statistical tests like the Chi-square were performed to calculate the accuracy of this assumption. Since the language of tweets was usually far from grammatically correct, the researchers created a program that would recognize the most common signs and transform them into proper words and phrases.
However, this approach did not provide the most accurate results since the program analyzed only a small portion of tweets that mentioned deals of either buying or selling stock. Thus, the researchers decided to include more posts by focusing on the price mentioning. After decoding this information, several models were applied. They included the joint-all, the transaction, the per-user, and the joint-experts model. While the transaction approach showed results just a little above the average baseline, those frameworks that focused on studying users by unsupervised learning determined that they are far more precise. The authors conclude that there is still more work to do regarding the improvement of the classifier accuracy and inclusion. However, distinguishing between expert and non-expert users is determined as an essential requirement for accurate predictions of the stock market movements in the future.
Another article by Kogan et al. focuses on predicting economic risks by analyzing the information regularly submitted via financial reports of different kinds. The quantity chosen as the primary study characteristic is the stock return volatility as a measure of risk. It is taken instead of the returns quantity since the main objective is to predict the stability of the price instead of measuring how well the stock will perform in the future.
The computational linguistics approach was taken in this case as it was in the previous article described. A program analyzed the massive of text that could be distinguished by a human. The parameter of weight was determined by solving the optimization problem that featured the minimum and maximum values of function quantities. Datasets that were used consisted of the annual reports that were compulsory for submission under the American financial regulations, thus guaranteeing that the general structure of this information would be similar. Sections including a discussion and analysis of the financial situation presented the particular interest to the researchers.
The theory implied that volatility could be measured by regression formulas. While this quantity was not constant, it could predict the future volatility upon the regression calculations. The mean squared error method was used throughout a series of experiments. It included such calculations as the feature representation, the training data effects, the effects of Sarbanes-Oxley, the qualitative evaluation, and the delisting. The series of experiments showed that not all terms had a similar weight for predicting the volatility. In general, the authors concluded that text regression is a useful method for predicting financial outcomes in the real world. Nevertheless, financial reporting can be accurately analyzed only in case there is a set structure of text determined by legislation.
The third article also uses the methods of text analysis to predict the movements in the financial sector. Deshmukh et al. study the issue of predicting the stock prices by using information available, incorporate blogs and personal posts in social networks made by industry experts. Three methods were used, including Natural Language Processing, Sentiment Analysis, and the actions associated with machine learning. The results of the study show that posts made through Twitter give a 70 percent chance of predicting a stock market price change. Moreover, this number can become more precise if combined with the analysis of commodity prices, increasing the level of accuracy by 15 percent. The article also shows that expert opinion also helps to understand the stock market price changes.
All three articles prove that computational linguistics is valuable for making predictions for the stock market. While financial reports often contain a lot of irrelevant information, expert opinions help to shape the model of accurate trends, whether they describe price fluctuations or risk levels. All three studies determine the importance of paying attention only to the pieces of information that were proven to correspond with the real situation in the stock market. One of the difficulties associated with the computational linguistics method is that many posts do not follow the structure of grammatically correct language and have to be decoded by a human first before this data can be transferred further for being used in a formula. One of the most important conclusions coming from this fact is that machines are still not able to process information, including texts with financial data, without people’s engagement, and the human factor still plays a significant role in the stock market analysis.
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