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Hedge Funds and AI: A Viable Combination in the Future?

 4 min read / 

The era of the traditional hedge fund seems to have passed. A series of big name losses, combined with the unappealingly expensive 2 and 20 fee structure (2% of total asset value and 20% of earned profits) have seen investors move away from hedge funds with many even finding greater returns in index trackers.

Perhaps the most damning factor affecting the industry, however, is the inability of hedge funds to match the high returns for which they are famous. Traditionally, hedge funds have been seen as the foray of the high-risk investor who is willing to put their money at stake for the possibility of unmatched gain. Recent reporting, however, has shown that whilst the risk still remains, the returns do not.

In July this year, the Brevan Howard’s central fund reported losses of 5.2% in the half of 2017. This was preceded earlier in January with Odey European Inc reporting annual losses of a startling 49.5%. With seismic losses such as these affecting the industry, it would not be surprising for one to assume that the golden age of the hedge fund is long gone.

For a long time, an alternative to hedge funds was the HFT (high-frequency trading) house. This fully automated trading process allows trades to be made in milliseconds, taking advantage of market movements at the optimum times to allow for maximum returns. However, the market for HFTs has become saturated, and with many firms employing the same strategies, the returns are dying as the unique selling points of HFTs becomes diluted.

So, what’s next? What awaits for the investor with the high-risk appetite, but who still wishes for their money to be invested rather than traded. What is there to fill the gap left behind by hedge funds? The answer might well be more hedge funds.

AI

AI is the hottest topic in not just finance but technology right now, and its presence in the hedge fund industry could well and truly be a diverse turning point for the sector’s future. The fundamental concept of AI is to utilise machine learning in order to automate decisions that cannot be automated by standard computation alone.

In other words, it involves the unconscious making conscious decisions. It would not be improbable for, in the near future, AI investment and AI fund management to be a tangible concept. If AI can be taught to make informed decisions, then investment decisions should be well within the potential grasps of an AI engine.

This is particularly appealing for followers of the cyclical markets theory. Machine learning relies on the feeding of data in order for the AI to see patterns and make decisions. If AI was given market data from the past, it should therefore be able to make informed investment decisions through comparing current market trends to those that have occurred previously.

Whilst the science of this has been attempted by numerous ‘human’ fund managers with varied successes, the capacity of an AI fund manager to calculate and comprehend huge data sets could see close to “perfect” investment decisions being made.

Conclusion

Indeed, AI could well and truly be seen as not only the future of hedge funds, but also of all investment. Realistically, however, it is only hedge funds- with their higher appetite for risk- who would be placed to be the first on the scene and to utilise the full benefits of AI investment. Unfortunately, should hedge funds be successful in maximising returns through AI, they could also be responsible for their own demise.

In the meantime, however, AI is fast becoming a recognised reality in the financial services sector. The rewards for those first on the scene to utilise it innovatively could be astronomical.

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