How AI Changes Investment Decisions We Make

Ai Investors

AI Investment Decisions

Artificial intelligence and machine learning are changing the way investors make decisions both at corporate and individual level. Institutional investors are using computing machines to make smarter investment decisions for years. Proliferation of predictive analytics, machine learning, and AI tech, however, make it possible for individual investors to benefit from enterprise-level solutions. Which in turn reshapes the world of financial markets, as we know it.

Algorithms Manage Assets Worth Billions of Dollars

Robo-advisors, for Instance, are forecast to manage assets worth over $250 billion by 2020. This is the single most popular AI solution available to individual investors willing to invest on the financial markets. Robo-advisors can make passive investment decisions based on a set of rules but the progress in the field of AI and Machine Learning turns them into active platforms that are able to invest in accordance with experience and market patterns.

(Image Source: Statista)

Machine learning algorithms already power every single investing platform used by an institutional investor. Deep learning is also becoming widespread across the investing world, allowing fund managers and large investors to benefit from pattern recognition. In result, AI algorithms that are able to recognize previously unknown patterns on the financial markets are replacing traditional technical analysis.

AI Replaces Traditional Investment Analysis Methods

Specific algorithms now track social media and spot messages revealing the overall market sentiment or investors’ expectations about the performance of a specific financial asset. This is a promising technology that is not actually new – market rumors have influenced the financial market for centuries. What is new is that machine learning and AI is more accurate in following trends in the market sentiment and can harness the power of Big Data to analyse investors’ mood at scale.

Speaking of Big Data you should be aware that no one could really cope with the amounts of data available on today’s financial markets. Thousands of large institutional investors are intertwined with millions of small and individual traders in networks where terabytes of data are exchanged on a daily basis. So you need algorithms i.e. AI that can deal with Big Data, processing and analyzing data which contain market patters unrecognizable by a human or traditional computing, machines.

What is Quant Investor?

Advancements in Big Data and machine learning algorithms give birth to a new sort of investors, the so-called “quant investors”. They actively look for the biggest available data sets and perform analysis that reveal trading signals which are unavailable to traders without access to cutting edge predictive analytics. The platforms they use also incorporate speech and image recognition capabilities while borrow form these technologies to conduct a very sophisticated market analysis.

Vast amounts of unstructured data are out there for anyone possessing the required tech to grab these data and find patterns mostly unavailable to the average investor. You need enormous computing power and quite sophisticated algorithms to deal with unstructured data but all the required technology is already in place. So quant investors are doing just that using AI and machine learning to beat the market.

Current investing models used by quant investors and their more conservative peers are far from perfect, though. The very application of AI and machine learning in the financial markets creates new patterns and obscures the investing models known to investors. The strange mix of algorithmic trading and human-made decisions opens the gates for both unexplored investment opportunities and errors that could potentially lead to massive losses. A very reliable AI investing algorithm would face other similar algorithms and should incorporate capabilities far beyond the limited world of pure stock or currency trading. As deep learning tech grows, investors should pursue development of AI investing platforms able to cope with the world in its entirety if they are to replace humans as investing gurus.

By Kiril V. Kirilov

Isc2

Episode 2: Coronavirus Phishing Emails and Work-from-Home Meetings

Coronavirus Phishing Emails What to watch out for as scammers exploit pandemic panic, and tips on how to attend meetings while working from home. Working from home this week? There are a few challenges and ...
Patrick Joggerst

Living on the Edge: The New Real-Time Communications Security Risks

Real-time communications Security Risks As more and more people have been forced to work remotely due to the global public health crisis, collaboration platforms have unexpectedly saved the day for millions of businesses and allowed ...
Ramanan GV

Establishing a Unified Governance Model for the Digital Workforce

Increase visual control and reduce OPEX by 30% The Digital Service Providers (DSPs) are riding an automation wave. Painful manual tasks, which burdened staffs for ages, can now be easily handled by the software bots ...
Meta Data

Data-Driven PPC and The Benefits Of Drilling Down On The Data

Drilling Down On Big Data Running a pay per click campaign for your business, which isn’t driven by detailed metrics, offers no more than the hit-and-hope approach which a billboard in the 80’s may have ...
Lauren Brunson

The Growing Need to Consolidate Multi-Tenant Environments

Consolidate Multi-Tenant Environments Over the past four months, countless businesses and universities have scrambled to the cloud to enable their employees and students to work remotely during the global coronavirus pandemic. Managed service providers (MSPs) ...
Staeadfast

Episode 5: How the Pandemic is Changing Business and the Cloud

An Interview with Ed Dryer of Steadfast With the global pandemic wreaking havoc on business and society, everything is changing. Ed Dryer, Senior Technology Strategist at Steadfast Networks, which specializes in Colocation, Managed Infrastructure as ...