There is no doubt that timely access to information that helps institutional investors, as well as individuals, form an educated picture of the markets and the economy is essential. Whether it is about decision making for public investments, private capital allocation or personal financial wellbeing, leveraging data-driven insights becomes the common denominator.
The first months of 2020 and the large gap between Wall Street and Main Street has raised concerns about the accuracy of fundamental data and increased the need for data analytics to deliver in-depth explanation and insights. Realizing that information asymmetry does not arise from incomplete fundamental information, but rather from insufficient coverage of alternative data points, people now turn to news analytics and other machine-driven insights to navigate in times of crisis fueled by market volatility.
Despite historically low-interest rates and the overall accessibility and attractiveness of e.g. loans, the ongoing market volatility still requires additional risk control measures, which can be achieved by e.g. leveraging technologies such as Natural Language Processing (NLP) as they often help increase efficiency and reduce potential human error together with effort requirements. Artificial Intelligence offers the possibility to get a 360º view on the markets, asset classes, counterparties, etc. adding value by connecting the dots to form a clearer picture of the heavily interconnected global economy.
Machine learning-driven data analytics generally enable algorithms to analyze vast data sets and give insights based on a specified set of goals. The algorithms are finetuned and improve in accuracy through a trial and error process as more data flows in. When talking about data analytics in finance, it is important to bring out the essence it should play. It is the speed and efficiency in bringing insights, which makes data analytics particularly adaptable throughout the majority of sub-fields of finance. Combined with automated ongoing monitoring and near real-time insights, this combination enables both finance professionals as well as individuals to shift their focus on more challenging tasks.
The ball, however, does not stop with public markets. News and data analytics are also experiencing rapid growth in private markets where information is more difficult to come by. Whether we look at untransparent markets or non-listed entities, where language barriers are tackled by application of NLP, or early growth companies with limited financials due to their early business stage, investors turn to alternative indicators to confirm their predictions or monitor their peers.
With increased scrutiny from board members and individual investors participating in larger funds, the role of advanced data analytics sees potential already in the fundraising period. Demonstrating a company’s strategy with the support of objectivized data-driven tooling often gives firms a competitive edge and assures partial elimination of potential human error.
With more data generated in the last two years than in the entire history before that calls for consolidation and new angles of looking at the finance landscape. Data analytics is thus at the forefront of an industry-wide transformation, joining a great number of market participants, who want to retain a competitive edge, find hidden opportunities, or minimize risk, which would otherwise be only possible through increased headcounts.