Predictive Trading Analytics: Supercharging the Quantitative Hedge Fund

An advisor monitors stocks at the computer using predictive trading analytics

 

Over the last decade, predictive trading analytics has become more of a mainstay in the world of modern investing. Predictive trading analytics refers to a subset of Al that recognizes patterns in human behavior, which it then uses to make predictions and set rules. Although the technology was pioneered as early as the 1970s, it made its way into finance only recently as more consumer data has become widely available due to the rise of social media and online access. 

 

As a result, many investors have lingering questions about what predictive trading analytics actually is and how it has garnered such success in the stock market. This article aims to answer precisely these questions: A) how predictive trading analytics works and B) why the process is used in the financial market.

 

Predictive Trading Analytics Defined

Predictive Analytics hinges on the development of what is known as an algorithm. Simply put, algorithms are patterns or equations which quantitative analysts (also known as “quants”) use to solve problems. Quants first design complex learning equations. 

 

These rules become very complex in nature, but an example might be something along the lines of “buy when stock goes up.” They then feed the machine years’ worth of trading data, from which the machine begins to develop a working knowledge of patterns in the market.

 

This is, of course, how the process works when the learning program is initially designed, but as it becomes more experienced and consumes more data, it becomes capable of reading these market trends itself. This makes it more adept at reading market trends and even interpreting new data than many seasoned investors. For this reason, most hedge funds and investment firms are increasingly turning to predictive trading analytics as a primary data tool when formulating their investment strategy.

 

Predictive Trading Analytics in Trading

Predictive trading analytics contributes to what is known as a quantitative investment strategy using large dumps of data to recognize patterns in the market and using that information to make informed trading decisions. Hedge funds have increasingly turned to quantitative strategies with the rise of AI in investing, which has led to several benefits:

 

  • Efficient, speedy processing: AI programs are capable of much faster computation than the human mind when accounting for market data. This allows it to consider many more factors when forming predictions about long or short-term investment strategies. 
  • Improved maneuverability in shifting markets: Predictive trading analytics monitors market data in real-time, giving investors access to up-to-the-minute predictions. This allows hedge funds to pivot when stocks go up or down, maximizing returns and hedging losses. 
  • Removal of human emotions: When the market makes an unexpected turn, humans can often get overwhelmed and make the wrong investment call (backing out of trades when staying the course is the right call, etc.). Using predictive trade analytics offers a more long-term vision of market predictions without the fear or doubt that comes from a human investor. 
  • Better rate of returns: Because of the benefits listed above, predictive trading analytics often boasts significantly higher returns than without them. On average, most AI-based algorithms generate average returns between 2.5%-4% monthly (a notably good rate, by any standard).

 

These tools were initially reserved for exclusive hedge funds that catered to only accredited investors for a very high buy-in. At a minimum, their services start at $100,000 but typically cost much more, starting in the millions. 

 

By this extension, the average investor did not and does not have access to tools of this nature. Modern robo-advisors provide predictive trading analytics (albeit a less powered version) for a minimal fee, opening the market to a larger demographic that was previously unable to trade.

 

The Best Predictive Trading Analytics Option

While more straightforward robo-traders are easily more accessible than something exclusive to the hedge funds, the tools provided by these traders often do not match up to what that larger buy-in provides you. In many cases, investors are left with nothing more than the data provided by the AI. While this can be a helpful investment tool in the hands of a seasoned professional, many newcomers struggle with this information.

For this reason, it may be a good idea to consider a seasoned firm to generate the proper returns for your money. Companies like RIMAR offer the consulting services you would receive at a larger hedge fund but with an initial buy-in of $1,000. Additionally, we don’t take any initial or annual fee, ensuring that we only make money when your investment grows. If you want more information, contact us today.

 

Ryan Gordon

Ryan Gordon

Ryan is a qualified chartered accountant of South Africa. Ryan is an avid sportsman who also enjoys reading and spending time with friends and family in his down time. Ryan joined the RIMAR Capital team in 2019 as a business development manager.

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