Source: Deep Learning on Medium
Machine Learning trading has become an even more popular phenomenon, especially as traders continue to experience historical precedents with negative interest rates and an ever more globalized world.
Many investors believe markets are becoming more efficient, and opportunities for arbitrage are becoming harder to find by the minute. Therefore, large asset managers ranging from Goldman Sachs to Blackstone have begun trying to implement the latest AI-based algorithms in response.
Over the last five years, there have been enormous advances in automated trading technology. Advanced front-end solutions have introduced massive efficiencies, reduced operational risk and given traders unprecedented access to global liquidity.
In the same way that Artificial Intelligence, more specifically Machine Learning, can be used to recommend movies by Netflix or web searches by Google; so too can it be used to predict the trends in the financial markets for underlying assets.
In general, there are two different types of algorithms and algorithmic trading. There is a basic formatting type algorithm that is programmed to perform specific functions. Then there is the self-learning algorithm that takes historical data (empirical) and real-time data which it uses to adjust itself accordingly for trading. The new type of self-learning AI based algorithms are able to build trends that are often unknown even to the most intelligent analyst. The reason is because they are able to eliminate “noise” in the market.
This refers to short-term (daily or intra-day) fears, worries, and negative fueled perception regarding the price of a security or general market atmosphere. By ignoring it, the algorithms are is able to identify underlying and supporting trends in the market. The AI based algorithms, are able to adapt as a result of neural networks enabled for Deep Learning, which then allows the algorithm to adapt accordingly.
More and more institutions are utilizing automated processes, and AI and Deep Learning is currently driving some of the biggest industry changes in banking, finance and insurance. By making frequently performed duties automated, AI makes it possible to focus on higher level objectives. One sees this in tasks such as document management, where it reads through documents, including forms, contracts, etc.
The BFSI (banking, financial services & insurance) industry today is also regularly employing chatbots with AI-driven responses rather than having live customer service representatives respond to consumers, saving a great deal of both time value and resources within customer management.
AI is also currently being used to decrease friction by improving workflows and decisioning processes. It is capable of creating models for previously manual procedures, the two specific use cases of this in BFSI are creating risk management models for lending and credit risk management and fraud prevention, where AI systems identify, track and flag potential threats.
In risk management, early detection is key, and AI and Deep Learning are capable of recognizing risk patterns remarkably early. Multiple data sources are utilized to take a more comprehensive view of risk assessment. Once the risk is identified, there are significantly faster response times, and a reduced impacts of failure.
As the world searches to further improve operational functions with technology, scientists and big businesses are devoting massive amounts of resources to develop Deep Learning technology, a branch of AI. Companies such as Google and Facebook have made great strides in improving the function of their firms using Deep Learning to better analyze how to improve operations.
The financial sectors has well been moving towards a greater implementation of these types of technologies as well, in order to better capture opportunity sets regarding underlying assets in the financial markets. Tractica forecasts $7.54 billion in cumulative revenue from 2016–2025, from firms investing to improve their algorithmic trading strategy performance.