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Cited by 1,038

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Behavioral Portfolio Theory

Shefrin, Hersh, Statman, Meir·2000·The Journal of Financial and Quantitative Analysis

Abstract

We develop a positive behavioral portfolio theory (BPT) and explore its implications for portfolio construction and security design. The optimal portfolios of BPT investors resemble combinations of bonds and lottery tickets, consistent with Friedman and Savage's (1948) observation. We compare the BPT efficient frontier with the mean-variance efficient frontier and show that, in general, the two frontiers do not coincide. Optimal BPT portfolios are also different from optimal CAPM portfolios. In particular, the CAPM twofund separation does not hold in BPT. We present BPT in a single mental account version (BPT-SA) and a multiple mental account version (BPT-MA). BPT-SA investors integrate their portfolios into a single mental account, while BPT-MA investors segregate their port? folios into several mental accounts. BPT-MA portfolios resemble layered pyramids, where layers are associated with aspirations. We explore a two-layer portfolio where the low aspiration layer is designed to avoid poverty while the high aspiration layer is designed for a shot at riches.

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Metadata

Authors
Shefrin, H., & Statman, M.
Venue
The Journal of Financial and Quantitative Analysis
Publisher
JSTOR
Published
2000-06
Volume
35
Issue
2
Pages
127
ISSN
0022-1090 (ISSN-L)
OpenAlex
W2138053063
Cited by
1,038
References
2
FWCI
10.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Behavioral Portfolio Theory. https://app.cochat.ai/papers/10.2307/2676187
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Shefrin, H., & Statman, M. (2000). Behavioral Portfolio Theory. The Journal of Financial and Quantitative Analysis. https://doi.org/10.2307/2676187

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.