Asset allocation

About: Asset allocation is a research topic. Over the lifetime, 6963 publications have been published within this topic receiving 126809 citations.

International Asset Allocation With Regime Shifts

Abstract: Correlations between international equity market returns tend to increase in highly volatile bear markets, which has led some to doubt the benefits of international diversification. This article solves the dynamic portfolio choice problem of a U.S. investor faced with a time-varying investment opportunity set modeled using a regime-switching process which may be characterized by correlations and volatilities that increase in bad times. International diversification is still valuable with regime changes and currency hedging imparts further benefit. The costs of ignoring the regimes are small for all-equity portfolios but increase when a conditionally risk-free asset can be held. In standard international portfolio choice models such as Sercu (1980) and Solnik (1974a), agents optimally hold the world market portfolio and a series of hedge portfolios to hedge against real exchange rate risk. From the perspective of these models, investors across the world display strongly homebiased asset choices. One popular argument often heard to rationalize the “home bias puzzle” relies on the asymmetric correlation behavior of international equity returns. A number of empirical studies document that correlations between international equity returns are higher during bear markets than during bull markets. 1 If the diversification benefits from international investing are not forthcoming at the time that investors need them the most (when their home market experiences a downturn), the strong case for international investing may have to be reconsidered. Our goal is to formally evaluate this claim. To quantify the effect of these asymmetric correlations on optimal portfolio choice, we need a dynamic asset allocation model that accommodates time-varying correlations and volatilities. In the standard portfolio choice models and their empirical applications [French and Poterba (1991), Tesar and Werner (1995)], correlations

Asset allocation: management style and performance measurement

Abstract: is widely agreed that asset allocation accounts for a large part of the variability in the return on a typical investor’s portfolio. This is especially true if the portfolio is invested in multiple funds, each including a number of securities. Asset allocation is generally defined as the allocation of an investor’s portfolio across a number of ”major” asset classes. Clearly such a generalization cannot be made operational without defining such classes. Once a set of asset classes has been defined, it is important to determine the exposures of each component of an investor’s overall portfolio to movements in their returns. Such information can be aggregated to determine the investor’s overall effective asset mix. If it does not conform to the desired mix, appropriate alterations can then be made. Once a procedure for measuring exposure to variations in returns of major asset classes is in place, it is possible to determine how effectively individual fund managers have performed their functions and the extent (if any) to which value has been added through active management. Finally, the effectiveness of the investor’s overall asset allocation can be compared with that of one or more benchmark, asset mixes. An effective way to accomplish all these tasks is to use an asset class factor model. After describing 7 5 w 8

Portfolio Choice and Asset Prices: The Importance of Entrepreneurial Risk

Abstract: Using cross-sectional data from the SCF and Tax Model, we show that entrepreneurial income risk has a significant inf luence on portfolio choice and asset prices. We find that households with high and variable business income hold less wealth in stocks than other similarly wealthy households, although they constitute a significant fraction of the stockholding population. Similarly for nonentrepreneurs, holding stock in the firm where one works reduces the portfolio share of other common stocks. Finally, we show that adding proprietary income to a linear asset pricing model improves its performance over a similar model that includes only wage income. IN CONSTRUCTING INVESTMENT PORTFOLIOS, it appears that many if not most households fail to behave in a manner consistent with simple economic theory. Even among relatively wealthy households, the share of financial assets held in different asset classes varies widely, and there is evidence that among those who hold common stock, there is often little diversification ~e.g., King and Leape ~1987!, Blume and Zeldes ~1994!!. We begin this paper with an empirical investigation into some of the risk factors and demographic variables that might explain these cross-sectional differences in portfolio composition. A number of previous studies focus on the level and variability of wage income growth as one of the largest sources of undiversifiable income risk. Here we present evidence that, for the subset of the population that has significant stockholdings, income from entrepreneurial ventures ~which we refer to as proprietary business income) represents a large source of undiversifiable risk that is more highly correlated with common stock returns. These findings motivate the investigation in the second part of the paper of a linear asset pricing model that incorporates proprietary income from pri

Financial advisory system

Abstract: A financial advisory system is provided. According to one aspect of the present invention, return scenarios for optimized portfolio allocations are simulated interactively to facilitate financial product selection. Return scenarios for each asset class of a plurality of asset classes are generated based upon estimated future scenarios of one or more economic factors. A mapping from each financial product of an available set of financial products onto one or more asset classes of the plurality of asset classes is created by determining exposures of the available set of financial products to each asset class of the plurality of asset classes. In this way, the expected returns and correlations of a plurality of financial products are generated and used to produce optimized portfolios of financial products. Return scenarios are simulated for one or more portfolios including combinations of financial products from the available set of financial products based upon the mapping.