AIM Program Continues to Embrace Use of Big Data Analytics
Students in Marquette’s AIM program learn that investment management is a
process that involves evaluating various opportunities, analyzing large financial
datasets, and making informed decisions about where to invest money. They are presently starting
a section of the course that focuses on a growing part of financial technology
(FinTech) - the use of big data analytics. Although large-scale data analysis has been around for over a decade,
it's still growing.
As more investment analysts use FinTech tools the financial markets will become more efficient, transparent, and even more accessible to all participants. The use of big data and artificial intelligence in this process has helped to improve the efficiency and accuracy of investment decisions.
The use of automation within in investment management is not new. It has
been around for a while; however, major advancements in technology are allows
analysts to utilize huge datasets and artificial intelligence (AI) to conduct more
thorough research. The use of AI in investment management can be traced back to
the 1950s when computers were first used to analyze large datasets.
The advances in financial technology (FinTech) are disrupting the industry making it more accessible and efficient. Analytical tools are used by hedge funds and trading firms to predict future trends in stocks, commodities, currency, and digital assets, such as cryptocurrencies. Automated trading platforms are being used to execute trades at lightning speed with just a click of a button. Robo-investing and the use of automated financial advice platforms have become popular among millennials and those in Generation Z who don't want to spend time managing their finances themselves.
Automated trading software can help investors make better decisions by
analyzing data and finding patterns in real time, while automated advice allows
investors to automate their portfolio management process and receive
personalized recommendations from an algorithm. The use of analytical tools can
help financial record keepers stay up-to-date with all regulations and
requirements, while natural language processing (NLP) helps them analyze
contracts or understand legal documents more quickly and easier than before.
Financial record keeping is now being done using NLP which makes it easier
for humans to read large amounts of data without having to do much of the
traditional manual work. The financial industry is one of the most regulated industries
in the world. Investment managers and other financial professionals are able to
use these technologies to make their operations more efficient in maintaining
compliance with the growing amount of financial regulations.
Since the Financial Crisis of 2007-2008, increased regulations combined with
the high cost of back office operations has made it difficult for legacy
financial companies to adopt new technologies. However, startup FinTechs have
emerged and been able to exploit the advantages offered by this dynamic.
In the AIM program the goal of data analysis is to help determine the future
cash flows of a company based on sentiment analysis and trend analysis. Sentiment
analysis is the process of analyzing tones and emotions as expressed by a firm’s
management in their interviews, financial filings, speeches, public comments,
social media posts, and other such sources. It has been used to help predict trends
in a firm’s revenue and earnings over time.
Predictive analytics is a type of statistical analysis that uses machine
learning and data mining to produce predictions about future events or trends.
It can be used for many purposes besides equity investment analysis including
financial forecasting, risk management, and strategy development. These are
some of the topics covered over the next two weeks in Marquette’s Applied
Investment Management program.
Dr. David Krause is the director of the AIM program and he also teaches
undergraduate and graduate FinTech courses at Marquette University.
