It’s true that on a factual value level a company is judged on a wide range of factors such as the amount of profit, revenue, cashflow, earnings per share, the rate of new clients, new partnerships etc. These are all important considerations to be assessed by investors when making decisions whether to buy or sell 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.
Another big factor in a company’s worth is how investors react to company announcements. This 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 earnings per share don’t meet market expectations as set by analysts across the industry, and the share price could still fall. Anticipating the reactions of professional investors and the ordinary retail investors is critical for traders to get ahead in the market. It’s why Wall Street has been taking Big Data and sentiment analysis so seriously, with computer-driven “quant trading” has been around for a few years now.
It’s not only headline-grabbing news that matters. Investors and traders are constantly making decisions, often by taking into account both the fundamental analysis of business performance and the technical analysis of price movements.
Millions of decisions made by traders made around the same time, create the price movements reflected in technical data and the profit and loss showing up in individual investor portfolios. Sentient analysis is a way to see the macro picture by making sense of the millions – if not billions – of pieces of data generated by investors reflecting their opinions and stances towards various companies, sectors and geographical regions which they may be considering to invest in, or stop investing in.
Sentiment analysis for all
Today, for the first time, attempt to analyse this mass of sentiment data has moved beyond merely the deep-pocketed investment banks and are now, with the help of blockchain, Big Data and artificial intelligence (AI), accessible by private investors and small companies.
Global data provider Thomson Reuters recently added to its MarketPsych Indices a bitcoin sentiment data feed that employs AI to analyse 400 data sources and providing subscribers with predictions on 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, all 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. CapitaliseCrypto 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 seems to be a promising project because it’s offering a low-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 positioned its product so that it has a smaller learning curve required 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. Copy-trading platform CoTrader, for example, is a Senno customer.
“Using Senno’s technology traders can identify trends and predict changes in any financial asset based on the accurate measurement of public opinion. Since financial markets are essentially driven by public sentiment, the application of a crowd wisdom technology is a perfect fit and lowers the entry threshold for newcomers while helping veteran traders make better decisions with their portfolios, which up until now remained volatile and unpredictable using traditional tools,” said Lennon Tam, Senno’s COO.
There are many other players in the field, such as is Santiment. Its Sanbase platform measures crowd moods expressed in the “sentiment wave”, where market participant attitudes can swing quickly between excitement and despair (particularly prevalent in crypto markets!) and apply insights so investors can make informed investment decisions.
Another promising project is Token AI with its proprietary Juliet sentiment analysis engine. It offers three service levels: “bottom-up” individual coin analysis, portfolio rebalancing and if you are a total novice, the token basket generator. The generator works a bit like a robo-adviser –you provide the money, answer some questions about your risk profile and provide other parameters after which a basket of tokens is selected for you.
Understanding language, as you would expect, is a crucial part of sentiment analysis. This is where natural language processing science and machine learning come in. The accuracy of interpreting the social media mentions of a cryptocurrency depend on understanding language usage.
The difficulty in doing this is when a text isn’t literal but rather ironic, symbolic or sarcastic. For example, “Verge is down 37%. Thanks for the pump John McAffee, that’s really good!”. For a machine understanding the text literally, it would seem like an endorsement of McAffee promoting the Verge (XVG) coin when it is, in fact, a sarcastic comment where the word “good” should be understood as “bad”.
Getting to really know your customers in a cost-effective way
Sentiment analysis is useful for most industries, not just financial markets.
Customer sentiment analysis is an extremely relevant use-case, in which companies can come to grips with understanding and assessing on a deep level the likes and dislikes of their consumers. Sentiment analysis in a consumer setting furnishes companies with a tool to quickly react to customer needs and address problems as they arise and before they multiply.
Being able to produce accurate predictions depends on analysing in real-time a huge amount of data, which is where the decentralised characteristics of blockchain and its ability to harness disparately located underutilised computing power comes into its own. This makes sentiment analysis much easier to come by for small and medium-sized companies or even for bigger companies that don’t have access to the requisite data sources.
Senno appears to be the first sentiment analysis platform with an open API available for third parties to use. It is built on the NEO blockchain platform so it can use the platform’s digital identity technology to determine and assign trusted data sources, pulling in feeds from social media, forums and messaging apps for a real-time crowd-sourced service.
Other interesting projects with slightly different approaches include Sether. It too is using the big three of blockchain, Big Data and AI to engineers its platform. Sether is deploying sentiment analysis to help marketers, influencers and entrepreneurs interact with their audiences more effectively by, for example, creating more engaging social media campaigns.
Emphasising social media trend intelligence is Bottlenose, although its flagship Nerve Center is not a blockchain platform. It has, however, gained a following because of its effective use of data mining and pattern recognition to provide solutions ranging from consumer insight to risk intelligence.
Sentiment analysis is already being applied profitably and is only likely to grow exponentially.
Big Data can increase operating margins by as much as 60%, according to a report by McKinsey Global Institute. Much of the benefit of the technology has up until now been confined to large corporations. That’s all about to change, with new nimble players bringing affordable solutions to smaller companies and individuals alike, the playing field will be equalised.