The Illusion of Precision: How markets trade on uncertain data

Why GDP, inflation, and FX numbers increasingly reflect narrative, not measurement

Global markets depend on economic indicators that purport to be precise but often fall short. GDP growth expressed to the decimal, inflation to the basis point, and FX reserves to the dollar all offer a sense of certainty underpinning pricing and forecasting. Yet this perceived precision often masks ambiguity arising from geopolitical factors, sanctions, tariffs, and controls. Economic data is shaped by narrative, method, and institutional incentive rather than just measurement. This uncertainty forces investors to evaluate how reliable these data signals are for strategic decisions.

Muhammad Sukri Bin Ramli’s research demonstrates that economic data visibility is diminished not only by indicators that are withdrawn, rebased, frequently revised, or selectively published, but also by illicit activities such as trade misclassification and extreme price markups, which may signal trade-based money laundering (Ramli, 2025). These distortions obscure actual economic activity. Markets, models, and balance sheets persist but depend on incomplete, delayed, or curated signals. The main risk lies not in falsehood, but in treating data as fact rather than probabilistic information. As confidence intervals widen and alternative indicators gain prominence, investors, insurers, and policymakers must rigorously assess the trustworthiness of available data.

Country classifications are based on a qualitative synthesis of IMF, World Bank, UN statistical capacity assessments, market transparency indicators, conflict and sanctions data, and standard macro-financial analyst practice. Categories reflect relative macroeconomic data reliability risk rather than economic performance; Mosope Arubayi

To better understand how these risks manifest globally, countries can be grouped along a spectrum of data opacity. The drivers of opacity may include conflict, sanctions, government control over information, weak institutions, or structural informality. Regardless of the cause, the result is increased macroeconomic data risk and less reliable forecasts.

• Statistical black holes: The IMF’s 2024 External Sector Report notes that, in extreme cases, standard economic measurements break down, forcing reliance on indirect indicators and external sources. For example, North Korea discloses almost no official data—GDP, inflation, FX, and trade are inferred from satellite imagery and reports (Kyoochul, 2022). Venezuela followed a similar path during hyperinflation and CPI suspension, with official stats rendered unreliable (Xie, 2024). Myanmar’s post-coup reporting declined, so inflation, FX, and trade are now reconstructed from market prices, border flows, and satellite data (2025). Somalia and South Sudan, due to conflict and weak states, also lack reliable reporting, forcing dependency on proxies (Fund, n.d.). Investors face greater data risk, as valuations and risk assessments depend on indirect information. Decisions in these markets require greater uncertainty management and the use of alternative data for portfolio risk.
• Countries with significant statistical uncertainty: In some places, published data is undermined by sanctions, conflict, or politics, diminishing its usefulness. This aligns with the thesis that markets often trade on context-driven rather than accurate data. In Russia for instance, key statistics are restricted after the Ukraine invasion, complicating verification amid sanctions and secrecy (Brooks & Harris, 2024). Iran’s sanctions, exchange rates, and reporting issues erode trust in its data (Alikhani et al., 2025). Ethiopia’s conflict and unreliable numbers cause gaps and errors (Fund, n.d.). Sudan’s instability and weak institutional capacity impede data collection (Sudan Crisis Information Landscape, n.d.). Libya’s intermittent data and secrecy further undermine trust (IMF Executive Board Concludes…, 2025). Zimbabwe’s currency and policy changes, plus inflation, reduce reliability even with continued releases (IMF Staff Completes…, 2025).
• Countries with structural sources of statistical uncertainty: In other countries, statistical uncertainty is primarily a consequence of economic structure, policy frameworks, and incentive-driven reporting rather than a complete collapse of data systems. China, for instance, maintains a sophisticated reporting system. Yet, the withdrawal of key data series, limited transparency in property markets and capital flows, and controlled dissemination of information have heightened uncertainty regarding growth and consumption (Fund, 2024). As a result, analysts increasingly rely on electricity demand, freight volumes, and satellite imagery to triangulate economic activity. Nigeria faces a large informal sector, segmented foreign-exchange markets, inconsistent oil production reporting, and infrequent data rebasing (Fund, n.d.). In Pakistan, frequent data revisions, political pressure on statistical agencies, foreign exchange instability, and International Monetary Fund-driven methodological changes complicate trend analysis (Fund, n.d.). Turkey’s high inflation, diminished institutional independence, and skepticism regarding the consumer price index and the credibility of its foreign exchange policy further contribute to uncertainty (Fund, 2024). Egypt’s opacity is rooted in foreign exchange controls, capital restrictions, and substantial off-the-books economic activity (Fund, n.d.). Kenya and Ghana, despite relatively robust institutions, face challenges in fiscal transparency, off-balance-sheet liabilities, arrears, and data revisions, which complicate sovereign risk assessment (Fund, 2021). Algeria remains constrained by its reliance on hydrocarbons and limited fiscal disclosure. (IMF Country Report No. 25/270, n.d.)
• Countries with manageable data uncertainty: At the lower end of the data opacity spectrum, some countries experience data distortions, yet their statistics remain suitable for directional analysis when interpreted with caution. For instance, Argentina’s consumer price index and multiple foreign exchange rates face credibility issues, but independent analysis and external benchmarks help provide stability, making Argentina’s parallel data ecosystem a partial success (Fund, 2025). In contrast, Angola faces volatility in the oil sector, though transparency has improved with IMF-supported reforms (Fund, 2025). Meanwhile, South Africa’s robust statistical systems face uncertainty from energy constraints, SOE liabilities, and data revisions (South Africa Economic Update, Edition 15, n.d.). Elsewhere, Morocco, Senegal, Botswana, and Namibia offer generally reliable macroeconomic data, though they remain prone to commodity and fiscal risks (IMF Executive Board Concludes 2025 Article IV Consultation with Namibia, 2025). Finally, Sri Lanka and Bangladesh maintain statistical systems allowing for cautious interpretation, despite foreign exchange and debt pressures (Nawshin et al., 2024).

Investors should reassess portfolios, recognizing that market data reflects narratives and methodology as much as measurement. Effective risk pricing now explicitly considers data uncertainty, since opacity alters global risk assessment. The key issue is distinguishing market value based on verifiable metrics from constructed belief, helping identify assets most exposed to unreliable data for informed exposure adjustments.

Economic data serve strategic purposes and should be seen as signals, not definitive reality. Where transparency is absent, credible assessments require wider uncertainty bands and the integration of official, external, and high-frequency proxies. For example, satellite imagery and electricity use help monitor activity. Alternative indicators like freight volumes, light emissions, and mobile usage are increasingly used to assess economies.

Elevated data risk necessitates a transition from point forecasts to probabilistic analysis that incorporates alternative and market-based indicators. Furthermore, integrating geopolitical context and policy incentives into macroeconomic models is essential, as these factors increasingly shape data production and disclosure.