Ten Interesting Facts About News and Data

Traders and investors currently suffer from a glut of information. Twenty years ago the three main newswires produced around 30,000 news items per day.

Today, 100TB of information is uploaded onto Facebook alone each day. Somehow, financial market participants have to find a way of overcoming this information obesity – to sift, analyze and react quickly to the subset that is market sensitive information. If they can’t, they end up on the wrong side of the market too often. Interestingly, there are ten facts about news and data that can help readers put their scope into context.

1. Roughly 20 million news articles a day are posted on the web

2. About 500 million tweets are sent every day

3. The big 3, i.e. Reuters, Bloomberg and Wall Street Journal/Dow Jones publish about 30,000 articles per day and falling

4. People use machines to read news, so you have no chance of manually reacting to a news article

5. News Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Read this Term (sentiment analysis) is the application of Natural Language Processing (NLP) & Machine Learning Machine Learning Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed. Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.This has many potential benefits for most industries and sectors, including the financial services industry. Machine Learning ExplainedMachine learning can be explained Read this Term to cut through ‘Infobesity’, an issue when attempting to consume too much information.

6. News analytics began with the monitoring of film reviews in the media in the 00’s, the first Hedge Fund to use news analytics was only a few years later.

7. Textual analysis using NLP is now used to decipher languages (google translate), understand hotel reviews (trip advisor), allow police forces to understand hot spots for crime (NYP), and even in the Libyan war when NATO had few assets on the ground.

8. The future will bring much more in the way of textual association, using machine learning. Imagine being able to instantly understand probable asset correlations through the study of media.

9. Soon linguistic barriers will be completely broken down, and students will no longer study foreign languages. Google will do it for you, through your Google voice box.

10. Data will become the foremost currency in buying into the future.