What Is A Quant Hedge Fund?
A quant hedge fund is a non-traditional and passive investment fund, with securities chosen with the help of numerical data collected from quantitative analysis. Investments are determined with customized models using software programs.
Advocates of quant funds believe that choosing investments with the help of computer programs helps companies cut down on the losses and risk associated with management done by a human fund manager.
How A Quant Fund Works
The investment decisions made by a quant fund are based on the use of quantitative analysis and advanced mathematical models. Managers use custom-built computer models and algorithms to pick investments.
That means quant funds don’t use the judgments, opinions or experience of human managers to make these decisions. The use of quantitative analysis rather than fundamental analysis is what gives their name, quant funds. The greater access to large market data and the growing number of solutions regarding the use of big data have fueled the growth of quant funds. The increasing innovation of automation software and the developments in financial technology have drastically increased the data sets that quant fund managers can work with. Robust data feeds allow for a broader analysis of time horizons and scenarios.
As fund managers have problems beating market benchmarks over time, large asset managers have increased their investment in quant funds; smaller hedge fund managers have increased their quant fund offerings as well.
Quantitative algorithms and quant fund programming have thousands of trading signals they can use, ranging from trending global asset values to economic data points. Quant funds are also known for building complicated models around quality, value, momentum and financial strength with the help of algorithms developed in advanced software programs.
Quant funds are managed with the help of algorithms that are made based on hard facts and cold logic. The algorithm will execute the trades exactly as programmed, with no room for human biases or emotions.
The main advantages of a quant fund are:
- As mathematical models are responsible for transactions and investments, there is less scope for human error.
- The decisions and strategies are consistent with the fund’s objectives.
- Quant funds are not subject to human emotions, which can cloud judgment and affect the fund’s profitability. That means there is no room for biases such as loss aversion or preference for certain stocks.
- The investment speed is increased and the investment process is smooth.
- The investments are done in a disciplined and systematic way. There won’t be cases of random reactionary decisions or impulse buying due to market conditions, volatility in the market, political decisions or sentiments.
The Technology Used By Quant Hedge Funds To Achieve Bigger Goals Faster
Because of its access to capital, the financial sector has been the sector that embraced technological advancements before other industries. So when new software technology with algorithmic programs arrived in the 20th century, it was natural that the financial sector would harness this power first.
The main advantage of using automated trading with the help of software was to lower the operational cost. Index funds didn’t have to pay for human resources for making selection and allocation decisions. To this day, quant funds have steadily moved upward to hold the biggest share of institutional trading on US stock exchanges.
David Siegel, the co-founder of quant fund Two Sigma, said in 2015 “The time will come when no human investment manager will be able to beat the computer”.
Automated trading is superior because it’s emotionless trading. Bots don’t get gut feelings, unlike humans. They are not subject to FOMO (fear of missing out) and they don’t suffer from FUD (fear, uncertainty and doubt). They are unbiased, cold and trade on hard data, something that can’t be replicated by humans.
A trading bot sticks to the algorithm and it’s disciplined. On the other hand, it’s tough to layout and stick to a strategy manually. Great opportunities present themselves too often and we will not hesitate to divert from our plan to “sell the top” or buy the dip.
Those tops and dips might result in poor trading results which burn the investor and make them hesitant to trade the next opportunity – even if that opportunity is the one the investor should have taken at the end to turn a profit.
Bots are also much faster than humans at making orders. The bot is working 24/7, executing trades at the most precise time in a market where a few minutes can make a difference between profit and loss. In the notoriously quick-moving crypto market, speed is even more important.
Besides trading at a faster speed, bots can also trade on multiple accounts and execute multiple strategies at the same time. That means hedge funds can negate or spread the risk out over a multitude of instruments.
One last technological advantage that algorithmic trading brings is the use of backtesting. Backtesting allows an investor to analyze the risk and profitability of a strategy by simulating it using historical data. The theory is that any strategy that worked well in the past is likely to perform well in the future as well.
The Complexity Of Backtesting And How Professionals Do It
As we said, backtesting allows an investor to check the viability of a trading strategy by analyzing it retrospectively using historical data. That helps the investor analyze profitability and risk without using actual capital.
When testing a strategy with historical data, a selected time period is used for testing purposes. Testing the strategy on out-of-sample data or alternate time periods can further confirm its success rate.
A well-conducted backtest with positive results ensures the investor that the strategy is sound and will probably wield profits when applied with capital. This is even more necessary when you deal with algorithmic trading, as such strategies would be too costly to test with money.
The ideal backtest is done using sample data that reflects a relevant time period over a variety of market conditions. A well-made strategy should thrive when the market conditions are favorable and also lose as little as possible when the market conditions are unfavorable.
How To Receive The Most Correct Results From Backtesting
The historical data used for backtesting should be representative samples of stock, including companies that were liquidated or sold. If only companies that still exist today are used, the backtesting will produce artificially high returns.
Backtesting should also consider trading costs, no matter how irrelevant they might seem, as they can add up in the backtest and affect the appearance of a strategy’s viability. Investors should verify if their backtesting software includes these costs.
To get the most correct results from backtesting, the investor must be aware of the biases that affect the performance of backtesting. These biases have the tendency of inflating the performance of the strategy, therefore posing a great risk of losing capital if the strategy is not properly vetted.
While the biases cannot be removed entirely, they should be minimized as much as possible to allow the trader to make the most informed decision regarding the algorithmic strategy.
One of the hardest biases to detect, the optimization bias involves adjusting the trading strategy to the backtest data to the point where it becomes inefficient in the future. This bias is hard to eliminate because an algorithmic strategy usually involves many parameters. The problem with optimization bias is that the strategy shouldn’t be optimized for a specific set of historical data, because that will make it vulnerable for real-time trading.
The best way to avoid this bias is to use fewer parameters and keep the simulation system simple. Test your algorithm over diverse time periods and markets. Once you are done with the first backtest, run the algorithm through new and unfamiliar data to ensure its effectiveness.
Since we are dealing with an entire data set of historical information, the human mind usually overlooks that you can use future information in a backtest. When you backtest on the same data set multiple times, it’s much easier to introduce this type of error.
A look-ahead bias appears when future data is accidentally included into a backtesting system at a point in the simulation when that specific data wouldn’t have been available. The look-ahead bias can be really subtle and hard to notice.
A common example of look-ahead bias is when the investor calculates optimal parameters such as linear regressions between time series. If the entire data set is used to calculate the regression coefficients, which are then used for the optimization of a trading strategy, the future data has been introduced into the strategy.
To avoid this bias it is requisite that the investor uses exclusive data available at the time of the trade. This will help the investor avoid diminishing the accuracy of the strategy’s true performance.
This is a dangerous phenomenon that can lead to inflated performance for some strategies. It happens when algorithmic strategies are tested on datasets that only include assets that survived the whole period of time tested, therefore excluding many stocks that went bankrupt.
By testing a strategy only with equities that managed to stay afloat or thrive during that period tested creates an altered image of the performance of the algorithm. This survivorship bias is a specific case of look-ahead bias because only future information can make the distinction between successful and unsuccessful equities, and therefore we incorporate future information if we only use stocks that survived.
To avoid survivorship bias the investor must always include all the data from a time period regardless of their future success or failure. Using different periods of time for testing and using recent data is a great way to confirm that an algorithm has been tested properly and this bias has been reduced.
Best Software And Solution For Backtesting
When you look for backtesting software for a quant hedge fund, you look for more than the ability to backtest. You also need a platform that allows you to manage your hedge fund and easily set up management, financial instruments, and separate access for your employees, administrators, and investors. MetaTrader 5 for hedge funds does all of this, being an all-in-one platform to establish and automatize the quant fund in 5 minutes.
The platform has a unique system that provides access to the investors to see the AUM performance in real-time from any device.
MetaTrader also comes with one of the largest communities of algorithmic traders in the world, a community that has grown over a decade. There’s a wealth of information on algorithmic trading and plenty of professional groups ready to assist you.
You can test your algorithmic strategy for your quant fund easily with the Strategy Tester, using historical or real market data. Analyze your strategy with different settings and see which settings provide the best results before applying the strategy to capital.
The backtesting tool is easy to use and comprehensive in reports. You can test the robots you build or the ones you bought from the MQL5 market with a few clicks.
One problem that comes with backtesting is the limitation of the computing power, as regular backtesting tools rely on the user’s computer to perform the algorithmic testing. MetaTrader 5 uses the MQL5 Cloud Network to give you access to thousands of agents around the world to increase your computation power, allowing you to perform tests that take 15 minutes instead of 3 hours.
The powerful backtesting tool combined with the hedge fund tools the platform provides makes MetaTrader 5 one of the best choices for managing and growing your fund using algo trading.