Source: Emerj | January 17, 2019 Author: Raghav Bharadwaj Banks and investment organizations usually have large research teams that are tasked with investigating and monitoring events that might affect financial trading markets.
Investment research is a business function in these firms and is a fundamental part of what is required from analysts, equity managers, investors, and traders.
Individual traders not associated with the big institutions might also use self-trading platforms to manage their trading and research activities.
But in many cases, the individual investors might find sifting through mountains of data in the form of historical trading performances, news, and media reports to be very challenging.
AI is used to help traders automatically track of the most important trends and events that might affect their trading performances.
The success of most AI implementations depends on how easily companies can access the right kind of data in large volumes.
For example, Peter Norvig, Google’s director of research has stated in the past that Google doesn’t have better algorithms than other AI capability providers, but rather have access to huge amounts of data that is simply not available to any other company.
This tends to ring true for investment research and trading applications using AI as well.
Businesses and individual traders might need access to data beyond product pricing, search trends, expert insights or web traffic data.
More importantly, businesses might also use AI to make better use of data from other sources such as credit card transactions, sentiments from the news, social media, or geospatial data to provide trading insights that might be most relevant and accurate to traders.
But many financial services institutions start from a position of comparative advantage.
They have large data sets and decades of experience using analytical tools, building models, and employing large teams of software developers.
More recently, they have also begun to add several data scientists to their ranks.
It is expected that the areas where AI might help with research and trading insights the most might be in helping traders and investors make sense of and identify trends from the vast amount of financial news, better utilize structured and unstructured enterprise data, and uncover relationships in the data that might not have been apparent to humans.
We spoke with CognitiveScale‘s Robert Golladay, General Manager of their European operations.
Among its applications, CognitiveScale’s AI software can be used for gaining financial research and providing trading insights.
It is expected that the areas where AI might help with research and trading insights the most might be in helping traders and investors make sense of and identify trends from the vast amount of financial news, better utilize structured and unstructured enterprise data, and uncover relationships in the data that might not have been apparent to humans.
We spoke with CognitiveScale‘s Robert Golladay, General Manager of their European operations.
Among its applications, CognitiveScale’s AI software can be used for gaining financial research and providing trading insights.
According to Golladay, AI software might be applied in financial research and trading for the following applications: Automation of investment research: Helping both institutional and individual traders or analysts reduce the time and manual effort involved in investment research tasks, such as performing due-diligence.
Personalized Market Intelligence: Allowing banks and investment firms to provide their clients or traders with AI tools that can monitor and track financial market trends faster and at scale, while simultaneously offering them personalized insights that take into account trading history and trader risk aversion levels.
Listen to our full interview with Robert Golladay below: Go to the full article Share this:Click to share on Twitter (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Google+ (Opens in new window)MoreClick to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window).. More details