Evaluating Sentiment Analysis in the Context of Securities Trading

Abstract

There are numerous studies suggesting that published news stories have an important effect on the direction of the stock market, its volatility, the volume of trades, and the value of individual stocks mentioned in the news. There is even some published research suggesting that automated sentiment analysis of news documents, quarterly reports, blogs and/or twitter data can be productively used as part of a trading strategy. This paper presents just such a family of trading strategies, and then uses this application to reexamine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application. This discrepancy comes at a cost.

ESG Investing: A Sentiment Analysis Approach

“…And finally, we also have the work of Fronzetti Colladon and Elshendy (2017) and Tilly et al (2021) where they analyzed news data from the GDELT project to forecast the macroeconomic indices. Several other studies have tried to apply sentimental analysis to the field of trading such as the work of Kazemian et al (2016) and Mudinas et al (2019).…”

Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention

“…[Nguyen and Shirai, 2015] predicted stock price movement by simultaneously analyzing topics and sentiments of social media. [Kazemian et al, 2016] made some in-depth analysis on how to evaluate specific tasks of financial sentiment analysis. Their conclusion is consistent with our motivation.…”

“…Sentiment analysis has been widely applied in financial applications since Robert Engle [Engle and Ng, 1993] suggested the asymmetric and affective impact of news on volatility. In recent years, researchers exploited various text resources e.g., news, microblogs, reviews, disclosures of companies to analyze the effects on markets in multifarious manners: impacting on price trends [Kazemian et al, 2016], volume of trade [Engelberg andParsons, 2011], volatilities [Rekabsaz et al, 2017] and even potential risks [Nopp and Hanbury, 2015].…”

Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines

“…There is a growing amount of research being carried out related to sentiment analysis within the financial domain. This work ranges from domainspecific lexicons (Loughran and McDonald, 2011) and lexicon creation (Moore et al, 2016) to stock market prediction models (Peng and Jiang, 2016;Kazemian et al, 2016). Peng and Jiang (2016) used a multi layer neural network to predict the stock market and found that incorporating textual features from financial news can improve the accuracy of prediction.…”

“…Peng and Jiang (2016) used a multi layer neural network to predict the stock market and found that incorporating textual features from financial news can improve the accuracy of prediction. Kazemian et al (2016) showed the importance of tuning sentiment analysis to the task of stock market prediction. However, much of the previous work was based on numerical financial stock market data rather than on aspect level financial textual data.…”