Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,527

The DOI matches its Crossref record.

By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior

Open access
Campbell, John, Cochrane, John·1995·National Bureau of Economic Research

Abstract

We present a consumption-based model that explains the procyclical variation of stock prices, the long-horizon predictability of excess stock returns, and the countercyclical variation of stock market volatility. Our model has an i.i.d. consumption growth driving process, and adds a slow-moving external habit to the standard power utility function. The latter feature produces cyclical variation in risk aversion, and hence in the prices of risky assets. Our model also predicts many of the difficulties that beset the standard power utility model, including Euler equation rejections, no correlation between mean consumption growth and interest rates, very high estimates of risk aversion, and pricing errors that are larger than those of the static CAPM. Our model captures much of the history of stock prices, given only consumption data. Since our model captures the equity premium, it implies that fluctuations have important welfare costs. Unlike many habit-persistence models, our model does not necessarily produce cyclical variation in the risk free interest rate, nor does it produce an extremely skewed distribution or negative realizations of the marginal rate of substitution.

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 · 10.3386/w4995

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

See all 1,527

Metadata

Authors
Campbell, J., & Cochrane, J.
Venue
National Bureau of Economic Research
Published
1995-01
OpenAlex
W2163683534
Cited by
1,527
References
0
Type
report
Language
en
Open access
green

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior. https://app.cochat.ai/papers/10.3386/w4995
The paper
Campbell, J., & Cochrane, J. (1995). By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior. National Bureau of Economic Research. https://doi.org/10.3386/w4995

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.