Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 381

The DOI matches its Crossref record.

OPTIMAL INVESTMENT STRATEGIES FOR CONTROLLING DRAWDOWNS

Grossman, Sanford J., Zhou, Zhongquan·1993·Mathematical Finance

Abstract

We analyze the optimal risky investment policy for an investor who, at each point in time, wants to lose no more than a fixed percentage of the maximum value his wealth has achieved up to that time. In particular, if Mt is the maximum level of wealth W attained on or before time t, then the constraint imposed on his portfolio choice is that W t α Mt, where α is an exogenous number betweenα O and 1. We show that, for constant relative risk aversion utility functions, the optimal policy involves an investment in risky assets at time t in proportion to the “surplus”Wt ‐ α Mt. the optimal policy may appear similar to the constant‐proportion portfolio insurance policy analyzed in Black and Perold (1987) and Grossman and Vila (1989). However, in those papers, the investor keeps his wealth above a nonstochastic floor F instead of a stochastic floor α Mt. the stochastic character of the floor studied here has interesting effects on the investment policy in states of nature when wealth is at an all‐time high; i.e., when Wt =Mt. It can be shown that at Wt=Mt, α Mt is expected to grow at a faster rate than Wt, and therefore the investment in the risky asset can be expected to fall. We also show that the investment in the risky asset can be expected to rise when Wt is close to α Mt. We conjecture that in an equilibrium model the stochastic character of the floor creates “resistance” levels as the market approaches an all‐time high (because of the reluctance of investors to take more risk when Wt=Mt).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.1467-9965.1993.tb00044.x

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

Metadata

Authors
Grossman, S. J., & Zhou, Z.
Venue
Mathematical Finance
Publisher
Wiley
Published
1993-07
Volume
3
Issue
3
Pages
241-276
ISSN
  • 0960-1627 (ISSN-L)
  • 1467-9965
OpenAlex
W2046913100
Cited by
381
References
0
FWCI
3.23
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.