In this research report we analyze the performance of sentiment indicators of the components of the NASDAQ-100. The signals were selected from our Pulse Picks (trading signals) category, which runs multiple optimizations on the base message data on a daily basis. The results are buy and sell signals with hit ratios and significance levels based […]
Signal or noise? This is the classical question that guides a considerable amount of work in computational finance and in data mining. This research paper evaluates competing sentiment indicators for EUR/USD trading and identifies those with the highest predictive value for future exchange rate developments. Sentiment indicators are based on different types of sources, e.g. traditional […]
Text Analysis and Sentiment Detection Algorithms Extended for Chinese Language Processing. Over the course of the last months we have worked intensively on adding a whole new region and language to our Natural Language Processing (NLP) engine. After already doing NLP for English and German, we can now proudly announce that we have extended our […]
In recent years, sentiments accumulated on social media have received considerable attention as predictive indicators for asset markets. Sentiment indicators are often appealing to investors since financial markets are known to be influenced not only by economic fundamentals but also by emotions. Twitter has drawn most attention as a source to derive research insights from […]
For this research study we use messages collected in social media about the S&P 500 index. Based on our buzz indicator (communication intensity) we conducted an analysis on the effect of message volume on price movement of the S&P 500 future index. The period of our analysis ranges from January 1st, 2012 until February 2017. […]
Sentiment indicators are often appealing to investors since financial markets are known to be influenced not only by economic fundamentals but also by emotions (Shiller & Akerlof, 2009). In current studies, however, a range of alternative sentiment indices is used each of which purports to have predictive value for the movements in financial markets. Consequently, […]
This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor’s 500 index (S&P 500) during a period from June […]