Quantitative analysts must consume and process large volumes of data through statistics, information models, and computer simulations, to study the behavior of stocks and create trading signals. The goal of quants is to observe historical quantitative relationships and incorporate the relationships into complex mathematical models and find alpha, or excess returns, in investment opportunities.
One of the major obstacles that quants face is filtering out the noise in data that is constantly generated.
Let’s take a look at the unstructured data challenge for quants.
The Problem of Unstructured Data for Quants
Quantitative hedge funds must search through both historical and real-time data to create accurate predictions on stock movement. By studying a company’s past data up to the present, quants have a better chance of creating accurate investment signals and finding alpha. However data is being generated by the second and the IDC estimates that 90 percent of all digital data is unstructured content, which creates a barrier in identifying stock movement trends within an industry or for a company.
While structured data like credit card transactions, geolocations, and satellite imaging data is organized in tables and columns making it easier to digest, unstructured data such as earnings , transcripts, conference audios, images, and raw content from the internet are difficult to analyze.
Quants must use both structured and unstructured content to reveal trends, patterns, and associations within a company’s stock performance. Due to the text-heavy, undefined information in unstructured data, critical information is hidden and often overlooked.
As unstructured data continues to grow at 55-65 percent each year, quants will continue to face challenges in researching large sets of unstructured data and extracting relevant information to generate alpha.
Using AI/ML/NLP to Structure Unstructured Text
As per a J.P. Morgan study, processing power is “estimated to double every two years, while global data, including alternative data sources, is projected to grow five-fold from 2018 to 2024.”
Predictive accuracy for Machine Learning (ML) will become ever more pronounced over time, and quants can use these tools to enhance workflows by relieving the time-consuming processes of procuring, licensing, cleaning, normalizing, and integrating various unstructured data sources.
Quants can use AI/ML/NLP to automate the processes of researching, identifying, and extracting insights with intuitive workflows, which can help them identify investment and risk decisions quickly with more accuracy.
Quantitative management firms spend significant amounts of time and money trying to process large amounts of data and predict stock movement. Globalization, technological innovation, and new data creates noise and overcrowding when trying to identify alpha. Machine Learning can be a vital tool for any quant looking for quick and accurate insights without all of the manual-heavy and time consuming processes of research.
Natural Language Processing
Natural Language Processing (NLP) is a subset of AI that enables computers to read text, hear speech, analyze it, measure sentiment and determine which elements are important. With the traditional methods of research, it is increasingly becoming impossible to create accurate, actionable signals for quantitative investment strategies and thus solving the unstructured data challenge.
Additionally, without being able to consume and quantify the impact of it quickly and at scale, quant managers will be left at a competitive disadvantage. However, monitoring corporate news and events with NLP can positively impact quants’ strategies and systematic processes in creating alternative data sets and building accurate investment signals.
Using AI/ML/NLP to Find Alpha
The market for AI/ML/NLP in quantitative management continues to grow as quants turn to alternative data, which is data used to obtain insights into the investment process, to find and maximize alpha returns.
Quants can use AI/ML/NLP to analyze large amounts of information and reveal trends, patterns, and associations within stock movement. They can enhance systematic strategies and processes in building:
Quants can create alternative datasets with custom indicators such as sentiment, relevance and impact scores.
By searching within a large collection of sources such as news, blogs, research reports, transcripts and corporate filings, and internal sources, quants can build investment signals and alpha indicators.
Investors can then use the analyses for more accurate, faster, and specific insights and metrics into company performance than with traditional data sources.
Quants develop algorithms to support traders in their day to day trading activities. Algorithms guide traders to make certain decisions (e.g., when x happens, do y).
While traditional algorithms require programming and mathematics, the if/then rules must constantly be updated as they cannot learn on their own. However, machine learning takes out the complexity of coding, math, and science by identifying patterns and behaviors in historical data and learning from it.
AL can also schedule algorithm execution by identifying trends to maximize on alpha.
Once algorithms are trained and ready to be deployed, then next step is to test the algorithm on live market data and generate real investment or trading signals. Quants will feed the algorithm as much data needed, and the algorithm will send a signal that gives insight into when a trader should buy or sell, an entry price, a stop loss price, target price and more.
Traders can become overwhelmed by stock volatility. Volatility causes inconsistencies in buy and sell signals which can cause turnover, high commissions, and taxable events. AI/ML/NLP insights allow quant managers to navigate through the noise in structured and unstructured text, automate algorithm decisions, and build trading signals.
Accern NoCodeNLP Platform for Quants
The Accern NoCodeNLP Platform solves the unstructured data challenge for quants and quantitative management firms can now implement NLP models easily and quickly to enhance efficiency and productivity across the quantitative value chain.
Traditional code-heavy, manual work for quants can be challenging. Once a quant passes the phase of building algorithms, the process of constantly updating the algo with new data can also be overwhelming. The Accern Platfom supports quants in building alternative data sets, algorithms, and trading signals without the complex coding, mathematics, and statistics strategies.
These quantitative trading strategies are gaining momentum, making it difficult for others in the industry to ignore. Although challenges and risks remain, investors who can combine the power of no-code NLP tools with quant and strong portfolio construction strategies can significantly improve portfolio outcomes.
To learn more about how you can use AI to find alpha, download an informative e-book on AI for Quantitative Analysts.Share this Post!