Data lies at the heart of intelligence. With the world at the brink of a digital overhaul, not only has information moved to a digital platform, but it has also become accessible within a globally connected network. In less than a decade, the world has moved from gathering and organizing digital data, to analyzing data, to making data-driven decisions, to having machines analyze real-time data and drive real-time solutions, and then onward to Artificial Intelligence (AI). Investment into AI is expected to grow from $12.4 billion in 2018 to about $232 billion by 2025, a KPMG report indicates.
Rather than making reactive decisions after hours of crunching numbers and analyzing data, AI learns from its own from experience, analyzes immense volumes of data at real-time speeds, makes pro-active predictive decisions, and implements real-time solutions based on deep learning. While "narrow AI" solutions have already been implemented in the fields of healthcare, logistics, and e-commerce, among others, it's interesting to take cognizance of the impact it is already having on the stock market and what potential it bears for the future.
According to a PwC report, AI could contribute up to $15.7 trillion to the global economy in 2030, which is more than the combined output of China and India. To put this figure into perspective, the current annual global technology spend is estimated at $3 trillion. This means that within a decade, AI in itself will drive more than five times the current global tech spend.
Stock market impact
AI-based tools have already begun to play an important role in predicting stock market trends, which is evident in a number of use-cases. From using AI for trade executions, as done by companies such as Sigmoidal; or the use of AI-based tools for advisory as handled by Kavout; or its use in discretionary trading by companies such as Trade Ideas, the use of artificial intelligence is no longer an unrealistic tale. Tools such as the Trade Scheduler, for instance, are already being used in Asian markets to give portfolio managers deep insights into when and how to sell and buy specific stocks. Artificial Intelligence not only analyzes data on the stock market, but can also learn based on stock market trends, and predict the trading patterns of investors, stock brokers and the market, in order to then sell or buy the stock at scale while communicating its action through thousands of emails in real-time.
Well-recognized players on Wall Street such as Morgan Stanley and Goldman Sachs have already begun looking at narrow AI solutions through data mining, using self-learning algorithms, and natural language processing (NLP) tools, which are capable of much more complex interactions than more well-known applications such as Apple's Siri, Amazon's Alexa or the Google Assistant. For wealth management companies or others keeping a close eye on the stock market, artificial intelligence offers interesting possibilities – such as rebalancing portfolios. While AI, in its current form, does not completely replace the human workforce, it does augment multiple roles by reducing lower-order work and offering real-time advice.
However, there are a few entities such as a Hong Kong-based AI company called Aidiya that has created a hedge fund capable of making all stock trades using AI without any human intervention. While stock markets have been using algorithms, automation, and elements of machine learning for many years now, there has always been the need for high-skilled human involvement due to certain unmeasurable factors such as emotion and sentiment.
Advanced AI and deep learning are also being used to identify, and prevent "rogue transactions".
"Algorithmic trading supported by advanced analytics has gone mainstream today, given that traders wish to continuously detect rogue transactions or be aware of trends up to the moment. In fact, AI/ML platforms are increasingly of interest in the capital markets to ensure operational risk elements are considered especially when stakeholders would want to validate current exposure, liquidity or forex rates among other factors prior to committing a transaction," said Chiradeep Bhattacharya, Tibco AI and Analytics expert, and Alliance Business leader.
AI solutions that use artificial neural networks are getting increasingly "smart" at predicting financial and technical trading trends but still have not reached a point of accurately predicting sentiment. Humans are still better at locating market signals, understanding emotion, as AI still hasn't reached a point of using signals and strategies to solve for causality, a Bloomberg report confirms.
This hasn't stopped big stock market players from attempting to recruit the top machine-learning and AI-based experts from companies such as Google, Microsoft, and Apple. Deep-learning AI is still being viewed as a breakthrough technology that can create better predictive models to estimate trading, and drive automated investment decisions based on data, even from unstructured data sets such as social media, news websites, blogs and so on. Robo advisers could soon integrate behavioral patterns within their financial objectives, thus, creating a much more targeted trades and investments.