The year ahead has a lot in store for the dedicated investor and trader who is trying to gain an edge anywhere possible.
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One of the most talked about and promising areas for improving the probability of success is the implementation of artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time., or . The technology is advancing rapidly, holding Moore’s Law true to form as the capacity of is doubling every two years, if not faster.
Adoption of artificial intelligence in many industries will take hold and dominate how certain things are made. Adapting to the financial markets will require more time and sophistication than something like Siri finding information on your iPhone or your Amazon finding you a ride from Lyft.
With that said, the hedge fund community is investing vast sums of capital into to help generate consistent performance with lower volatility that has real potential of outpacing the benchmark indexes year after year. For professionally managed funds, there are two interconnected factors that drive the adoption of .
The first key factor spurring is the exponential advancement in computing power as data moves from on-site to off-site cloud data centers that are filled with servers loaded with cutting-edge processors priced at a fraction of the cost of just five years ago. The second primary catalyst behind ’s phenomenal growth is the sheer availability of data.
Information as fuel
If processing power is ’s engine, then information is its fuel. Artificial intelligence allows engineers to teach algorithmsAn algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars. to adapt and learn skills without human interaction. This is called “machine ” and is exactly what I deploy with every trade I recommend. Other code names for this science are “neural ” and “.” Training a machine to ferret out information where statistical data and content is relatively fixed is much easier than trying to coach a machine to adapt to a highly fluid environment.
Finance is perhaps ’s most challenging space to achieve a high degree of accuracy. Training a computer to break down how a stock behaves against a multi-faceted trading landscape is highly sophisticated. Markets are influenced by economics, regulations, politics, news events and human judgement. To say the ground is always shifting under Wall Street is an understatement and all these variables impact how a computer spews out data such that it can lead to nowhere if its algorithms are not constructed right. […]