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Robo-Advisors - Equatorial Guinea

Equatorial Guinea

The market segment of Robo-Advisors market is poised for significant growth in Equatorial Guinea.
According to projections, the assets under management in this market are expected to reach US$98.24m by 2024.
Furthermore, it is anticipated that these assets will experience an annual growth rate of 10.76% (CAGR 2024-2027), resulting in a projected total of US$133.50m by 2027.
Additionally, the number of users in the Robo-Advisors market is expected to reach 2.670k users by 2027.
In terms of average assets under management per user, it is projected to be US$40.77k by 2024.
It is worth noting that United States currently boasts the highest assets under management in the Robo-Advisors market, reaching a staggering US$1,459,000.00m in 2024.
Equatorial Guinea's emerging market and growing middle class are driving the demand for robo-advisors as a convenient and accessible investment option.

Key regions: Asia, Canada, Hong Kong, Singapore, Germany

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Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on transaction values / revenues / assets under management and user data of relevant services and products offered within the FinTech market.

Modeling approach / Market size:

Market sizes are determined through a combined top-down and bottom-up approach, building on a specific rationale for each market segment. As a basis for evaluating markets, we use annual financial reports of key players, industry reports, third-party reports, publicly available databases, and survey results from primary research (e.g., the Statista Global Consumer Survey). In addition, we use relevant key market indicators and data from country-specific associations, such as GDP, consumer spending, population, internet penetration, smartphone penetration, credit card penetration, and online banking penetration. This data helps us estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited for forecasting digital products and services due to the non-linear growth of technology adoption.

Additional notes:

The market is updated twice a year in case market dynamics change. The impact of the COVID-19 pandemic and the Russia-Ukraine war is considered at a country-specific level.