Innovations in Finance with Machine Learning and Big Data

Innovations in Finance with Machine Learning and Big Data

Innovations in Finance with Machine Learning and Big Data 1200 900 AltReturns
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Investment Possibilities with Artificial Intelligence

In practice, Artificial Intelligence is a group of technologies that help facilitate the discovery and analysis of information for the purpose of making predictions and recommendations, support decision making, facilitate interactions, and automate certain responses. Since AI applications are continually transforming business models, the scope of traditional technology applications will scale up towards a multi-channel world with recommendation systems, virtual assistants, chatbots, and AI-managed marketing platforms.

Essentially a model that helps identify patterns and associations from large amounts of data, Artificial Intelligence enhances quality control and improves operational effectiveness through digitized information assets. This allows businesses to focus time and resources on identifying new opportunities and customers, as well as different channels to market.

Apart from large technology firms, the Banking and Securities sector is one of the leading industry verticals in Artificial Intelligence adoption. The business has relatively high digital maturity, access to troves of data, a desire to glean patterns from historical events as a guide for future decisions, and certainly has tasks that are amenable to automation.

Thus far, AI has made its way into Financial Services with automated trading and investment discovery, trading strategies, robo-advisors, voice-based commerce, customer behavior analysis, and chatbots for customer services, identity verification and fraud detection.

Connecting Dates with Trading Decisions Using Machine Learning in Interest Rate Markets

machine learningMachine learning in trading is entering a new era. While previous algorithms were hard-coded with rules, J.P. Morgan is exploring the next generation of programming, which allows machine learning to independently discover high-performance trading strategies from raw data.

In a recent initiative focused on interest rate markets, a team fed in some 1,250 raw input features from a wide variety of sources, such as daily close levels of U.S. Treasuries, dates of Federal Reserve meetings and international interest rates. The model was built on data from 2000 to 2016, with the intention of then determining whether it could be applied to timing and sizing contemporary trades in 2017.

After testing a variety of machine learning methods, a technique that created an interlinked collection of decision trees emerged as the most effective. Known as “random forest,” the method’s results for U.S Treasuries are shown below.

The third and sixth bars indicate the return on simply buying bonds through conventional methods, which act as a “control.” In terms of machine learning, the first and fourth bars indicate the returns from short selling and the second and fifth bars from both buying and selling.

Natural Language Processing in Equity Investing

Applying machine learning to words, rather than to numbers, is an exciting and rapidly developing field of study. Natural Language Processing creates the potential for a machine to digest hundreds of thousands of written reports and classify the language as sentiment to create a broad investment picture.

In a case study, J.P. Morgan Research built an algorithm based on some 250,000 analyst reports that provided the source material for learning the implication of financial terms such as “overweight,” “neutral” and “underweight.” The team then tested the model on 100,000 news articles that focused on global equity markets with a view to informing future equity investment decisions.

Machine Learning in Value Investing

While there are a number of valuation metrics to account for when calculating the “fair value” of stocks, machine learning has proven to offer a new perspective when assessing value strategies.

In this study, J.P. Morgan implemented machine learning algorithms to assemble a valuation-based strategy to predict the “fair value” of stocks. This is formed around a large number of equity characteristics and the connection between profitability and the quantification of a “mispricing” signal. To further enhance the valuation strategy, RavenPack’s news sentiment data was introduced as a useful overlay to the mispricing signal, along with measuring the impact of investor sentiment. The advanced valuation strategy showed that a combination of ML models can help improve predictions, as opposed to using one model.

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