Reference Data Tables › Portfolio Data Reference Tables

Portfolio Data Reference Tables

Portfolio data reference tables provide the historical return and risk figures that portfolio construction models require. Building a mean-variance optimized portfolio requires expected return assumptions, and the most common starting point is the long-run historical return for each asset class. These tables collect those series along with correlation data that shows how different asset classes have moved together over time.

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Direct Answer

Portfolio reference tables provide the historical data used to build and evaluate asset allocation strategies. Long-run returns for stocks, bonds, cash, real estate, and commodities show what each asset class has delivered over different economic environments. Correlation data shows how much diversification benefit different combinations actually provide. Both are inputs to any rigorous portfolio construction process.

Common questions

What is a correlation matrix and how is it used in portfolio construction?

A correlation matrix shows the pairwise correlation coefficients between different asset classes or securities, ranging from -1 (perfect negative correlation, moves in opposite directions) to +1 (perfect positive correlation, moves together). A correlation near zero means the two assets move independently. In portfolio construction, combining assets with low or negative correlations reduces overall portfolio volatility without necessarily reducing expected return, which is the quantitative basis for diversification.

How reliable are historical return assumptions for future portfolio projections?

Historical returns are the standard starting point but are not a reliable forecast. The 20th-century US equity return was exceptionally high by global historical standards, partly due to economic and geopolitical factors that cannot be assumed to repeat. Valuation-adjusted return estimates (which lower expected returns when starting valuations are high) have historically been more accurate over 10-year horizons. Using a range of return scenarios rather than a single-point historical average produces more realistic portfolio projections.

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