Capital Market Assumptions 2024

Capital Market Assumptions 2024

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Our long-term return expectations for capital markets serve as key inputs into our strategic asset allocation process for multi-asset portfolios and provide context for shorter-term forecasting.


Each year, the Voya Multi-Asset Strategies and Solutions team formulates capital market forecasts for the coming decade. This exercise is an opportunity for us to step back from the day-to-day noise within the markets and consider longer-term trends in economic and financial factors that are likely to drive asset class return and risk. We rely on these assumptions to set our strategic asset allocations for our multi-asset portfolios.

In conducting our analysis, we look at numerous macroeconomic and financial data series to generate our forecasts. For example, how do labor force participation rate expectations impact potential GDP? Are profit margins likely to mean-revert? What are the long-run trends in productivity?

The next decade will likely be characterized by returns below historical averages across all major asset classes.

To avoid the pitfalls of single-point estimates, we incorporate an alternative macroeconomic scenario into our forecasts. We produce blended estimates for our base and alternative scenarios that prevent us from becoming too upbeat or downbeat in our forecasting techniques or extrapolating from the recent past. Our uncertainty measures of our forecasts similarly blend two estimates of risk, leading to more resilient portfolios. For any fiduciary or person managing assets with a long investment time horizon, this exercise combines both judgment and quantitative inputs, which are reflected in the forecasts.

Given higher valuations and lower risk premiums in equity markets, our analysis for the 2024–2033 period paints a picture of relatively low expected returns for equities. In contrast, our outlook for bonds has improved, owing to higher starting bond yields compared with the depressed levels of 2022. Return forecasts are generally above inflation and, if our expectations prove broadly correct, asset allocators will find numerous opportunities to generate alpha across and within asset classes after 2022’s losses and high asset class correlations.

We hope this report provides a helpful reference for your own decision-making process, and we wish you a successful 2024.


1 S&P Global is an independent research firm that provides a comprehensive global macroeconomic model, linking 68 individual country models with key global drivers of performance. The model accounts for 95% of global GDP, covering 250–500 time series per country.

2 “Understanding Glide Path Design: Distribution of Labor Income among Participant Populations,” Sinha, A. and Yuen, R., Voya Investment Management, 2Q18.

3 Mahalanobis, P., “On the Generalized Distance in Statistics,” Proceedings of the National Institute of Sciences of India vol. 2 no. 1 (1936): 49–55.

4 Kritzman, M. and Y. Li, “Skulls, Financial Turbulence, and Risk Management,” vol. 66 no. 5 (2010): 30–41.

5 Hall, R., “Stochastic Implications of the Life-Cycle-Permanent Income Hypothesis: Theory and Evidence,” Journal of Political Economy 86 (1978): 971–988.

6 Poterba, J. and Summers, L., “Mean Reversion in Stock Prices: Evidence and Implications,” Journal of Financial Economics 22 (1988): 27–60.

7 Ljung, G.M. and Box, G.E.P., “On a Measure of Lack of Fit in Time Series Models,” Biometrika, 65, (1978): 297–303.

8 The p-value is the probability of rejecting the null hypothesis of no serial correlation when it is true (i.e., concluding that there is serial correlation in the data when in fact serial correlation does not exist). We set critical values at 10% and thus reject the null hypothesis of no serial correlation for p-values <10%.

9 Khandani, A.E. and Lo, A., “Illiquidity Premia in Asset Returns: An Empirical Analysis of Hedge Funds, Mutual Funds, and US Equity Portfolios,” Quarterly Journal of Finance 1 (2011): 205–264.

10 International Monetary Fund,, accessed 10/31/22.

11 Climate change risks can be divided into two categories: 1) physical risks, which result from climatic events such as wildfires, storms and floods; and 2) transition risks, which result from policy actions taken to shift the economy away from fossil fuels.


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