Artificial intelligence for most people is still rocket science.
The recent times there have been breakthroughs and an ocean full of opportunities in the financial arena.
The dawn of Big Data provided the raw materials; graphical processing units (GPUs) opened up the platform for a series of hardware innovations and a wide range of experimented sets of algorithms further stretched the scope of Artificial Intelligence and its application touching the maximum number of fields which is still expanding as you read this article.
Artificial Intelligence has made its way into finance starting from algorithmic trading and Robo-advisors and now it has come a long way to Artificial Intelligence.
The financial sector is now leveraging artificial intelligence to support everything right from automating savings to managing risk for both individual consumers and institutional investors.
This blog article let us understand artificial intelligence from the portfolio management perspective.
Also, through such implications let us have a look at what other baskets full of opportunities Artificial Intelligence has to offer.
Let’s first have a look at the complete definition of Artificial Intelligence?
Artificial intelligence is simply an artificially wired human brain.
Of course, this is not in the literal sense, this humanoid intelligence is achieved with advanced technologies and language processing.
One of the best examples to consider is Apple’s Siri and Amazon’s Alexa, which use natural language processing (NLP) algorithms to interpret language whereas AlphaGo defeated a world champion Go player in a highly complex game.
Given below is a list of some of the most important AI techniques including:
- Natural Language Processing
- Artificial Neural Networks
- Decision Trees
- Support Vector Machines
- Cluster Analysis
- Genetic Algorithms
Without denying the facts, Machine learning is the most popular branch of artificial intelligence and throws special importance on developing algorithms that automatically improve through experience.
What are the implications for Portfolio Management?
Artificial intelligence can fundamentally transform asset allocation, trading processes, and risk management and touch other areas of portfolio management.
Most Robo-advisors already practice these technologies to deliver portfolios with better performance for investors while rebalancing and automatically managing risks with minimal transaction costs.
To know more about Portfolio Rebalancing click here.
Note that some of the most popular portfolio management applications include:
Fundamental Analysis:
AI techniques are used to conduct analysis based on annual reports, economic reports and other meaningful financial information.
Such techniques help extract hidden correlations between asset classes and then pinpoint stocks that could outperform or underperform based on those correlations, which is quite insightful.
Portfolio Optimization:
With the help of Artificial Intelligence, we can predict expected returns, variances and covariances to determine optimal asset weights, note that this determining cycle is accurate.
With the help of genetic algorithms, the AI solves complex problems in the given time constraints, which is done by adjusting various parameters.
Note that if one is a seasoned investor and still wants to experiment with a portfolio, the investor can optimize their portfolios accordingly!
To know the importance of risk management for your portfolio, click here.
Risk Management:
AI techniques can incorporate qualitative data (e.g., news reports or social media), forecast risk variables and validate existing risk models to minimize risk and ensure that total risk falls within acceptable risk tolerance levels for clients.
Trading Activities:
From FUndamental analysis to technical analysis to algorithmic trading, all the traders have come a long way now.
In addition to predictions with technical analysis, these techniques can assist with analysing transaction costs and executing large trades that must often be broken up into smaller chunks to achieve the best price.
It’s just a matter of time for these possibilities to be witnessed until artificial intelligence techniques get noticed in the other areas.
Investors nowadays must keep an eye on the new evolving investing trends which would help them secure their future and enjoy the investing journey.
To know all about algorithmic trading, click here.
Risks & Drawbacks to Keep in Mind
In Artificial intelligence, decisions are made based on some of the complex neural networks, genetic algorithms and other techniques.
These strategies tend to produce unforeseen insightful results, their complexity makes it difficult to understand what’s happening under the hood but can be completely trusted.
One of the breath-taking features of inculcating Artificial Intelligence in the Portfolio Management arena is that the “black box” algorithms may include inherent biases or be unprepared to cope with “black swan” events.
There are several data-related challenges with AI, which could be reconsidered and fixed based on the technological advancement:
Training Data:
To run the AI-powered algorithms, one needs to feed loads of gigantic chunks of data to calibrate their models.
Failure of feeding the required amount of data might be upsetting as the required accurate predictions cannot be gauged.
Therefore, it is challenging to produce data that is too high-quality once!
Bias:
Biases are global, this could be another problem tampering with the prediction accuracy of the system.
Financial Markets are all about trends, therefore these trends need to be documented.
But when we check the roots of trends, most of the time they are based on fragile rumours or insane market sentiments etc.
Black Swans:
Historical data is a diamond mine for the artificial intelligence algorithm to predict the possibility of an upcoming event, especially in times of market crash and recession.
Also, on the contrary, it could lead to inadequate experiences which goes by the name “black swan” event.
Therefore, the system setup needs to be tested thoroughly and multiple simulations of such rare events must be recorded.
The results of such simulations can assist us by being prepared for future adverse consequences.
Since Artificial intelligence technology is quite new in the market, it might be expensive for some.
To read more about Black Swan events click here.
Conclusion
Artificial intelligence is quite a broad term of which machine learning is just a part, other concepts are yet to be discovered and practised.
Portfolio management and Artificial Intelligence are buzzing hot in the market, if you want to start investing why wait! Jarvis Invest is for you!
