Only if the value of the data outweighs its cost can a company buy it.

Companies also sell data, but in order to charge for it, they must first determine its value.

When valuing a business, investors need to take into account not only its physical assets, but also the company’s intangible assets such as its people, technology, and data as well.

The question of how to value data is the same in every situation, regardless of the circumstances. Researchers at MIT Sloan have devised a strategy to help answer that question.

Farboodi, a co-author of the recent paper “Valuing Financial Data,” said that “the past couple of decades have witnessed a huge rise in the use of data in various aspects of the economy, but we don’t know how to value this asset.”

In the current market, some of the largest companies in the United States are highly valued because of the data they possess, such as consumer information or production data. ‘ Farboodi explained that “accounting rules do not allow book value to include data, unless that data was purchased,” which has resulted in “a huge dispersion” between their book value and stock market value.

Companies and investors alike benefit from data because it reduces uncertainty and gives them more information with which to make informed decisions. She argues that not knowing how to value data has significant economic ramifications because it leads to erroneous estimations of both the value of individual companies and the overall GDP of the United States.

The rational expectations equilibrium model was used by Farboodi and her co-authors to put a monetary value on financial data.

From 1985 to 2015, the Institutional Brokers Estimate System annual earnings forecasts of 5,506 companies were used to examine how investors’ valuations of standard data vary based on investor characteristics. Investors can purchase this information and use it to form their own predictions or beliefs about the potential returns.

The model was estimated using a sufficient statistics approach. They used regression analysis and the data series they wanted to value to construct the expected return and volatility of various portfolios. It was then that investors’ monetary value was calculated using these sufficient statistics.

When markets are highly volatile and illiquid, the value of financial data decreases because it becomes more difficult and expensive to execute profitable trades, and the value of financial data that informs these trades decreases as a result.

If the markets are too illiquid, “data helps a financial firm execute profitable trades that others might not know about, but it moves the price against the firm, and the firm cannot use the data very effectively,” Farboodi stated. A lack of market illiquidity has a negative impact on the value of data for all investors, regardless of asset class, investment style, or wealth level.”

Wealthier investors’ losses are “orders of magnitude greater,” as Farboodi put it. When the price goes against them, “larger investors need to put larger trades,” she said. “The value of the data diminishes significantly for them.”

“That can lead to self-fulfilling cycles and financial fragility,” said Farboodi if data is an asset whose value is highly dependent on liquidity.

With large data assets, “the firm will become less valuable” if the market becomes illiquid due to an adverse shock, no matter how small. When a company’s value decreases, investors who own it will sell it because they no longer see it as a valuable company, and the cycle continues: the market becomes vulnerable.

Policymakers must understand how to value financial data in order to design policies that regulate data and determine if and how much consumers should be compensated for their data, which is critical for policymakers.

A framework for measuring data, Farboodi said, “is to be able to think about the magnitude of these forces,” he explained. Policymakers must have access to the forces at play, and this is something that is sadly lacking in the data literature.

Ongoing work on Farboodi’s part will include looking at how valuable data produced by businesses is for society and the economy as a whole, rather than just the company’s bottom line. Data brokers who sell anonymized customer data to companies for their own use will be examined in a second concept.