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October 17, 2024
PI Global Investments
Hedge Funds

Is it Time for Hedge Funds to Take New Measures in Managing Their Market Impact?


Large-scale trading can carry a wholly uneven impact on financial markets and provoke significant levels of volatility when left unmanaged. Is it time for hedge funds to take more care when making a splash with their trades?

Usage of the term ‘whales’ has steadily increased over time on Wall Street and throughout a variety of markets like cryptocurrency. It’s a term that signifies the impact of the movement large institutional investors can make in markets that have relatively low liquidity or are simply vulnerable to large-scale trades.

The total assets under management (AUM) for hedge funds has grown from around $0.2 trillion in Q1 2000, to $5.149 trillion by Q1 2024.

With around 35% of hedge funds focused on equities markets, this significant presence can make hefty waves when it comes to leveraging trades or reallocating investments.

Acclimatizing to Volatility

While both hedge funds and more traditional traders operate on the same playing field, the resources at the disposal of institutions can provide far more options when leveraging trades.

This means that the ability of hedge funds to identify and act on trading opportunities using low-latency tools offers a significant advantage, but the level of volatility left in their wake can be challenging for the assets traded.

Though the sheer scale of resources at the disposal of hedge funds can open the door to market manipulation tactics, many institutions would prefer to minimize its market impact to avoid short term volatility in asset prices among their long-term holds.

Volatility is becoming a growing challenge for hedge funds to manage. Recent data from the top 60 companies in Europe’s broad STOXX 600 Index (STOXX) shows that average stock price swings on earnings days over the past 12 months have reached their highest levels since at least 2016.

The role that hedge funds have played in this increased volatility is clear. With multi-strategy hedge funds controlling around a third of equities markets, their movements can carry a significant impact on price volatility.

Market structure changes have further increased these rates of volatility while long-term asset managers buying into low-cost funds that track indices have impacted liquidity in less common markets, resulting in the vulnerabilities we’ve seen in the STOXX of late.

Finding Answers in Iceberg Trading

One functional solution to limit volatility emerging from these ‘whale’ movements is for hedge funds to utilize iceberg ordering when making trades. Iceberg orders refer to large single orders that have been broken down into smaller limit orders.

Automated programs have helped hedge funds to manage iceberg orders while mitigating instances of slippage. The term itself stems from the term ‘tip of the iceberg’, and refers to the high volume of limit orders that will occur over time to manage market impact.

Alternatively referred to as reserve orders, iceberg trades have emerged to become a key function of hedge fund tier 1 services among prime brokers, and are an essential consideration for institutions operating in illiquid markets.

Under the Cover of Darkness

While iceberg ordering is an excellent resource for hedge funds seeking to mitigate their market impact, other market participants will be intent on identifying these clandestine orders as a means of tracking the intentions of some of Wall Street’s biggest players.

For hedge funds seeking to make these movements in a more discreet way, it’s possible to utilize dark pools to keep iceberg trades hidden from view. Dark pools are private exchanges that can empower insitutions to execute large orders without revealing their intentions to the market.

Because trades can be leveraged in this manner, hedge funds can avoid slippage more effectively while reducing their overall market impact.

Timing Trades

Artificial intelligence and machine learning tools can also work on behalf of hedge funds to accurately time market trades to minimize their impact.

With the sheer amount of data hedge funds generate, algorithmic platforms can quickly learn and understand the most efficient times to execute trades and how best to break them down to avoid slippage and maximize opportunity cost.

By monitoring the historical performance of an asset, machine learning can work alongside AI algorithms to highlight the best time to make a trade at a scale that’s unlikely to cause a splash throughout the market.

We’re already seeing platforms in forex trading like MetaTrader 5 offering in-depth insights into market liquidity while displaying bid and ask levels beyond a currency’s current price. This helps to pave the way for algorithmic trading solutions to leverage high volume trades with greater efficiency.

By getting the timing right for trades, more hedge funds can execute iceberg trades at prices they want by using predictive tools to understand the times in which asset values are at their most opportune. This process can continuously break purchases down at a scale that suits the buyer without running the risk of losing out on completing a trade because of costly slippage implications.

Mitigating Market Impact

Hedge funds may thrive on volatility, but it’s in their best interest to minimize the size of the waves these whale movements cause when a major asset purchase is made in illiquid markets.

Technologies like AI and machine learning are helping to provide solutions that can help hedge funds to manage their impact, and through intelligent iceberg orders, it’s possible for institutions to manage their trades at a scale that suits their needs.

While hedge funds have been drivers of volatility in the past, algorithmic trading opens the door to more intelligent purchases within different markets. This can help to provide more control over market impact and a better level of management when it comes to keeping on top of slippage.

Read Also: Embracing the Future of Finance: Analyzing the Opportunities and Risks Posed by Blockchain in Fintech



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