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Certainly, you’ll agree with me that the hedge fund investment industry has become synonymous with the popular machine vs. Man debate. More and more questions are being asked about the suitability of the two regarding investments. For instance, do we allow AI and ML algorithms to make investment decisions for us? Or do we use our human expertise? And which of the two choices can lead to maximum profitability?
Those advocating for AI and ML suppose the algorithms are less affected by emotions. Therefore, they are more effective than their human counterparts. Besides, they’re optimistic that ML will soon take over the entire investment industry since more hedge fund organizations are adopting them.
On the contrary, those vouching for financial experts opine that AI and Ml algorithms can’t predict the ever dynamic markets despite their advancements. In their opinion, investment markets and rules constantly change. As a result, past historical data patterns become useless.
So, who is right?
And can we really trust a machine learning-driven hedge fund to beat the market?
Let’s look at why AI and ML-based hedge funds will most likely dominate the market shortly. And the role data scientists, and software engineers, can play. Lastly, how hedge fund managers, traders, and financial officers can contribute to the course.
Here we go!
First off, there is an increase in hedge fund organizations integrating AI and Ml algorithms into their daily operations.
One main reason for their adoption is the increasing evidence that they actually work. For instance, a study conducted by Cerulli in 2020 shows that the net new flows and assets under management of Europe-domiciled AI-led funds experienced significant growth between 2016 and 2019. Their cumulative returns during that period were almost 3X more than that of the overall hedge fund universe. 33.9% against 12.1%, to be specific.
Another reason for the increased adoption of AI and ML among hedge funds is to effectively use data from different sources to get a competitive advantage. For instance, from social media platforms, point of sale systems, global capital flows, the internet, and the satellite.
Here, the organizations use the algorithms to evaluate the enormous amounts of data to predict market movements to enable strategic asset allocation. As well as use them to anticipate adjustments in supply and demand disparities in the market. This enables the organizations to trade automatically.
Importantly, the algorithms allow chief investment officers to combine various tactics to tailor allocations to beat the market.
Examples of hedge fund organizations that have embraced AI and ML include Rebellion Research, Renaissance technologies, and Two Sigma. Similarly, we have others like Sentient, Aidyia, etc., that are learning on AI spread across thousands of machines.
“If we all die, it would keep trading.” This is according to Aidyia’s chief scientist, Ben Goertzel.
Another reason ML-driven hedge funds can beat the market is the advancement in AI and ML algorithms. According to www.nature, AI and ML algorithms have significantly improved in recent years because of the availability of large datasets. As a result, computing power is exponentially growing, creating more effective codes, and promoting deep learning.
At this juncture, hedge fund organizations are more likely to take advantage of this progress to better their results.
In particular, to acquire:
- More diversified alpha streams
- More persistent alpha
- Added value at different phases of the investment process
This way, they can increase their chances of beating the market.
This leads us to the next issue.
According to K-1- digital, beating the investment market with ML is challenging because of the complex unknowns and variables. Namely, the ever-changing market ratios and the different data that make it difficult for hedge funds to identify the most suitable ones.
However, data scientists and software engineers can develop new ways to outsmart the markets.
As an example, they can:
- Use helpful platforms such as Accern to get insights, sentiments, and themes they can code around. They can also use reinforcement learning to test their models against different extremities, including those that have never happened in the past. Instead of trying out their algorithms with historical data, that can lead to errors as situations. And variables change with time.
- Use robust algorithmic languages, such as MQL5, SQL, Python, and Java, to develop utility applications, trading robots, and trading indicators. This is critical for quant funds as they assist in proper risk management and bet sizing.
- Improve the current AI and ML algorithms by combining them with evolutionary computation (Neuroevolution) for better results.
Similarly:
Use ML in different ways
Apart from using ML to determine stock prices and future risk, you can use it for financial modeling as a trader. For example, you can combine the AutoARIMA model with your simple stock trading method to improve performance.
Additionally, forward test your strategy to determine its actual performance.
Optimize your middle and back-office operations using ML
As a hedge fund executive, you can use robust AI and ML-based software, such as the MetaTrader 5 for hedge funds to maximize your organization’s activities. For example, to make quick decisions, administer your team and investors. Furthermore, perform advanced Algo trading and backtests, among other functions.
Other alternatives include Dynamosoftware, Archewaytechnology, and Hedgeguard software platforms.
Using them can give you a similar experience.
Indeed, an ML-driven hedge fund can beat the market. Nonetheless, it requires a combined effort from data scientists, software engineers, and a hedge fund’s management. Here, the algorithm developers can use the best programming languages in the market, such as MQL5, SQL, Python, and Java.
On a similar note, individual hedge funds can use any of the best commercial software platforms to run their businesses. Such includes MetaTrader 5 for hedge funds, Dynamo software, Archewaytechnology, and Hedgeguard.
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References
[1] C. Francesca, AI And Machine Learning Can Take Fund Managers To The Next Level (2021),
https://www.alpha-week.com/ai-and-machine-learning-can-take-fund-managers-next-level
[2] Cerulli, COVID-19 Strengthens the Case for Artificial Intelligence in Fund Management (2020),
[3] F. Boris, The Rise of the Machines: AI Funds Are Outperforming the Hedge Fund Benchmark (2019),
[4] Geeksforgeeks, Reinforcement learning (2020),
https://www.geeksforgeeks.org/what-is-reinforcement-learning/
[5] K. Jeremy, Can an A.I. hedge fund beat the market? (2020),
https://fortune.com/2020/08/25/can-an-a-i-hedge-fund-beat-the-market/
[6] L. Hamlin, Battle of the Quants (2013),
https://thehedgefundjournal.com/battle-of-the-quants/
[7] Mql5, MQL5 Reference (2021),
[8] P. Neo, How Renaissance beat the markets with Machine Learning (2020),
https://towardsdatascience.com/how-renaissance-beat-the-markets-with-machine-learning-606b17577797
[9] Programiz, Java programming,
https://www.programiz.com/java-programming/algorithms
[10] S. Peter, Artificial Intelligence Sweeps Hedge Funds (2021),
[11] Scholarpedia, Neuroevolution (2013),
http://www.scholarpedia.org/article/Neuroevolution
[12] Tutorialspoint, Python Algorithm Design (2021),
https://www.tutorialspoint.com/python_data_structure/python_algorithm_design.htm
[13] Twitter, New #AI hedge fund: “If we all die, it would keep trading,” says fund CTO (2016),
https://mobile.twitter.com/kdnuggets/status/692411251314528257
[14] W. Karlijin, SQL Tutorial: How To Write Better Queries (2019),
https://www.datacamp.com/community/tutorials/sql-tutorial-query
[15] Wired, The Rise of the Artificially Intelligent Hedge Fund (2016),
https://www.wired.com/2016/01/the-rise-of-the-artificially-intelligent-hedge-fund/
