Machines Learning
Machine Learning
What Is Machine Learning?
Machine learning (or ML) is the discipline of creating computational algorithms or systems to build “intelligent machines,” or machines that can complete tasks strategically in ways that humans do, often better.
As a subset of artificial intelligence, ML is often confused with AI. AI, however, is a wide-ranging discipline covering several subdisciplines like machine learning, robotics, visual computation, and others. Machine learning effectively serves as the “brain,” or foundation of AI.
Types Of Machine Learning
Supervised Learning
An ML system is provided with a data set along with a set of expected inputs and outputs.
Unsupervised Learning
The machine learning system is given a set of unstructured data with no intended outputs.
Reinforcement Learning
The system operates in a virtual environment where a cumulative reward is provided for strategically advantageous actions toward a goal or set of goals.
Benefit of Machine Learning
01
Forecasting Real-world Data
Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
02
Finding Patterns
Trading about Identifying Localised Patterns
Which are often limited in time and space – and then guessing how to exploit them. The process of finding patterns is arduous and time consuming. But AI algorithms are basically pattern-finding machines. If an analyst suspects irregularities in a particular dataset, they can save time by using AI to find them. So AI can find useful patterns, as long as it’s guided by an experienced analyst who knows what to look for.
03
Turning High-frequency Trading Machine
In high-frequency trading – as the name suggests – machines execute thousands or millions of trades per day, trying to take advantage of inefficiencies that only exist over very short time spans. Human beings can’t make these trades – there are simply too many – but humans define the rules by which these machines operate. However, because the market keeps changing, these machines need to be adjusted constantly. That takes a lot of time and effort.
The 3Ds of
Why We Apply Machine Learning
Deep Data
Data silos need to be connected, data relationships evolve from N to N², forming a network effect which in turn produces greater correlation value.
Deep Learning
The requirements for processing complex nonlinear data relationships cannot be handled by statistical modeling. This calls for machine learning techniques for deep mining of data.
Deep Discovery
Uncover relationships, signals, anomalies, and insights to increase alpha, predict trend, and reduce risk exposure by combining fundamentals and alternative data with the help of new tools.
Aetixa
With Aetixa, you can:
Accurate Data
Access complete, accurate data
Management
Manage operational risk
Productivity
Improve productivity
Reduce Error
Reduce human error using spreadsheets
Improve Analytics
Improve analytics capabilities with AI and machine learning
Enable Automation
Enable automation and digitization
Establish and Maintain
Establish and maintain a framework to create consistency
Create Source
Create a single source of truth for operations and investment management teams