Not only will advanced AI get better at social media sentiment analysis, but it will also open doors to how much weight to give sentiment and how to balance that with other factors, many of which play a far greater role than sentiment. Dr. Anna Becker, CEO and co-founder of EndoTech.io, discusses how advanced AI will transform financial trading and make it more secure for users.
Can Twitter – and other social media platforms – predict the direction of specific stocks or the market in general? Research shows that AI-based social media sentiment analysis can indeed be a useful tool for market or stock predictions – but it’s only useful if you’re a day trader or are trading news-sensitive stocks or assets. If you want to invest long-term, the best options are the tried and true investment methods – blue chips, the Dow, a 60/40 mix, etc.Â
But that’s set to change. As advanced AI – known as artificial general intelligence (AGI) – becomes more prominent, AI systems will be able to develop multiple models that can incorporate events that may yet happen and factor those possible results into investment advice and strategy – updating investment strategies as warranted, and enabling investors to make better choices based on their own, specific needs.
How Does Sentiment Analysis for Investing Work?
Current sentiment analysis for investing achieves its goal by parsing posts and applying its findings to models based on sentiment analysis of historical posts. The system’s algorithm evaluates new posts based on what is considered “positive†or “negative†for a specific stock or market in light of the model; if enough negative sentiment is discovered based on the evaluation, the system will rate the asset negatively, generating a sell/don’t buy signal. Applying machine learning to the algorithm, systems refine their evaluation methods, so each application of the algorithm makes future predictions even more accurate. Thus, investment algorithms using sentiment analysis utilize the mechanics of current AI systems to make investment predictions – quite accurately, according to the studies.
But the sentiment is fleeting – and social media responses are largely based on reactions to very current events, with a cycle that winds down after a limited period, usually limited to stocks whose performance is news driven. Under some circumstances, sentiment analysis may be useful for predicting daily or even weekly fluctuations in valuations in such stocks – but it can’t account for the many other factors that can influence markets. And there are also many stocks whose performance is not necessarily driven by news cycles; the impact of social media sentiment analysis on those stocks is likely to be nil.
Sentiment Analysis Is Not Enough: How Can AI Help Put it in Context?
So is sentiment analysis a non-starter for investment purposes? It appears that relying on sentiment analysis for investment advice alone in its current form would not seem to be such a good idea. But there is actually a great deal of potential in sentiment analysis systems for investors, such as with systems that could look beyond current social media posts and build models based on current data–in other words, those that can both predict and put the sentiment in perspective along with other data and factors.
Using those models, AI systems could take into account both sentiment and the potential impact of events that haven’t even happened yet. These systems would understand the interplay of the relative value of sentiment vs. other market elements, going beyond just mirroring sentiment to understanding its impact on stocks based on the many other factors that need to be considered.
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Thinking – not Just Mirroring – on the Fly
While that sounds futuristic – if not impossible – that is the kind of advance artificial general intelligence systems can bring to investments. Among the important advances of AGI over current iterations of AI is its ability to build multiple models that can change or be adjusted over time and reinterpret data in light of those changes.
Thus, an algorithm based on AGI could develop different investment scenarios for users based on their needs, using multiple “what if†scenarios. When addressing a client that wanted to ensure solid stability of their assets far into the future, for example, the system could develop models based on potential events – wars, shortages, pandemics, advances in medical research, etc. – and provide advice on how a specific stock or market will do based on those possibilities.Â
The client could then choose the recommendation based on the scenario with the events most likely to foster investment stability (i.e., advances in medical research). That is likely to be the least risky investment for that client and the one that would retain its value over time. That system could be applied to any kind of investment preference – growth, risk, concentrating on a specific industry, etc.
These AGI systems would build the scenarios for their models using historical data – events that in the past influenced stock and market prices, including forex, commodities and digital currencies – and would take into account all events that in the past were cited as triggers for market price movements, from world events to skirt lengths. The data would be collected by parsing the enormous databases – including news reports, market analyses, and societal trends – both public and proprietary.
We (and AGI) Are Not There Yet
Obviously, there is no way humans could develop a system like this without the help of advanced algorithms and advanced data collection and analysis; there are just too many possibilities and too much data. But advanced AGI systems are capable of doing all this and much more; AGI is being considered for a large number of applications, such as advancing smart cities and ensuring national defense.
Armed with huge amounts of data and advanced, flexible analysis systems designed to adjust predictive models as required, AGI-based systems would be a much better bet for investment predictions than current AI-based sentiment analysis systems, with the technology providing a basis for actual decision-making.
While we are not quite there yet, data scientists are working on developing AGI using various strategies, from robotics to neuroscience, and the coming years should see significant advances in the field. Until then, investors will have to take on traditional risks and use their traditional strategies to make investment decisions – but the future could bring a more secure and accurate investment landscape for everyone.
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