Understanding price movements in financial markets is all about sentiment. On the face of it that might be an odd statement to make because surely prices defend on more fundamental factors than mere sentiment?
That’s true at one level. Whether a company is making a profit, how much revenue it is generating, how much free cash flow it has, what its earning per share are, winning a big new order, making new partnerships, take-off bids, and so on, are all important considerations to be assessed by investors when making buying and selling decision on stocks and shares.
When markets are functioning as they should, much of that information is available to all as required by law when it comes to public markets, so all investors should be on a roughly level playing field. Although professional analysts at the large investment management companies and banks may have an advantage because of the direct line their analysts will often have to the companies they cover.
How investors react to company announcements will influence prices, often dramatically, which is where sentiment analysis comes in.
If a company increases its profits you might expect its share price to rise, but if earnings per share don’t meet market expectations as set by analysts across the industry, the share price could still fall. Anticipating the reactions of professional investors and the ordinary retail investor is critical for traders to get ahead in the market. It is why Wall Street has been taking Big Data and sentiment analysis so seriously, with computer-driven “quant trading” having been around for a few years now.
And it is not just the headline-grabbing news flow that matters. In the many weeks, days and hours between quarterly market-moving earnings reports, investors and traders are making decisions all the time, often by taking into account both the fundamental analysis from business performance and the technical analysis of price movements.
All of the millions of decisions made by traders, taken together, creates the price movements reflected in technical data and the profit and loss showing up in individual investor portfolios. Sentient analysis is a way to get underneath the macro picture by making sense of the millions – if not billions – of pieces of information that are generated by investors that reflects their opinions and stance towards different companies, sectors and geographical regions that may be considering investing in or getting out of.
Sentiment Analysis for All
Today, for the first time, the attempt to analyze this mass of sentiment data represents has moved beyond the deep-pocketed investment banks and is now, with the help of blockchain, Big Data and artificial intelligence (AI), accessible by private investors.
Global data provider Thomson Reuters recently added to its MarketPsych Indices a bitcoin sentiment data feed that employs AI to analyze 400 data sources to provide subscribers with predictions upon which they can base their decisions.
By capturing what market participants are posting on social media, the news and analysis in the financial and wider mainstream media, and even the contributions of attendees at industry conferences if there’s a consumable live audio feed, all this data can be plugged into a sentiment analysis engine powered by AI.
There are a host of blockchain-based projects focusing on trading solutions using sentiment analysis. Capitalise has a beta running that makes it easy to set up triggers to execute trades, powered by real-time sentiment analysis data. To keep down costs Capitalise has partnered with Senno, a blockchain sentiment analysis platform with an open API.
Senno is interesting because its offering is aimed at providing a lower-cost solution that can be employed by smaller companies and individuals. The software development kit (SDK) is downloadable for free and the application programming interface (API) is public, so both corporates and private citizens can build applications with it to connect real-time sentiment analysis and business intelligence analytics to their ecosystem.
Focusing on algorithmic trading platforms and advertising/marketing firms, Senno has cleverly positioned its product so that it has a relatively shallow learning curve for adoption and much more manageable integration expenses. Behind the software, Senno deploys a distributed hardware solution that allows it to deliver a relatively low-cost solution to its clients…