In modern economies, which are characterised by complexity and continuous change, information plays a vital role in resource allocation and price developments. Analysts and professionals base their perceptions and actions on analysis of economic data, especially regarding value formation, capital movements and performance in relation to risk.

Within the framework of neoclassical economics, information is a crucial factor that aligns the incentives of various economic agents and determines the market equilibrium. The efficient market hypothesis, although disputed by some analysts, highlights a key point: equal access to information defines the framework for balanced profitability, which drives economic activity. This theoretical foundation assumes rational behaviour and perfect information of economic agents, yet real-world deviations often challenge its predictive power.

At the same time, behavioural economics reveals the limits of relying on processing economic data even from trusted sources, due to biases of economic agents, such as overconfidence and loss aversion. Such behaviours create inefficiencies that experienced market participants are able to exploit. These cognitive distortions affect not only individual decision-making but also ripple through institutional strategies, amplifying volatility and mispricing.

Asymmetric information remains one of the defining distortions in markets, leading to adverse selection and moral hazard. Specifically, price formation becomes problematic when certain individuals have privileged access to data concerning future developments. This reignites discussions about the need for stricter regulatory oversight. As a result, regulators and policymakers are increasingly focused on transparency and data governance frameworks to mitigate these risks.

Central banks and statistical agencies play a critical role in smoothing out asymmetries and standardising the dissemination of economic information. The evolution of “forward guidance” by central banks is a prime example of efforts to influence markets through transparency.

Likewise, corporate earnings reports, assessments of public economic sustainability and macroeconomic forecasts serve as key sources of information. These institutional disclosures help anchor expectations and reduce uncertainty, fostering more stable market conditions.

The digital economy multiplies both the volume and speed of information dissemination, while machine learning models can process even unstructured data (news, sentiment, satellite images). This technological leap enables real-time analytics and predictive modelling, reshaping how decisions are made across sectors.

New developments, however, also carry risks. Private investors, equipped with advanced analytical tools, can significantly influence markets, disconnecting prices from fundamental economic data.

Moreover, algorithmic systems and automated investment strategies rely on information. If the data is incorrect or misinterpreted, even the most advanced models can produce flawed outcomes. The fragility of these systems underscores the importance of data integrity and robust validation mechanisms.

In conclusion, information is the architecture upon which markets are built. Its quality, distribution and interpretation affect price determination and economic policymaking. In an environment defined by the speed and depth of data, the ability to extract insight and act decisively – information dominance, not merely access – determines who possesses the competitive advantage.

Andreas Charalambous and Omiros Pissarides are economists and the views they express are personal