Contents
In 2016 the United States accounted for 56 % of world stock market capitalisation, so a US investor holding the market portfolio would have 44 % in foreign equity. The actual share was 23 %. Home bias is the most persistent phenomenon in international investing, and it has survived three decades of attempts to explain it. But there is a less quoted reading: it may not be entirely a mistake. This article goes through what has been measured, including the part suggesting that investing close to home sometimes pays.
Summary: the seven findings that matter
Each row names the study and the year. It is worth separating bias across countries from bias within one’s own country.
| Finding | Result | Study and year |
|---|---|---|
| Foreign equity in the US portfolio | 23 % against a 44 % benchmark | Hnatkovska, Oxford Res. Encycl. (2019) |
| Where the bias is largest | In developing economies; Europe the lowest | Hnatkovska (2019) |
| Bias within one’s own country | Preference for firms headquartered nearby | Coval and Moskowitz, Journal of Finance (1999) |
| Which firms are preferred | Small, leveraged producers of non-tradable goods | Coval and Moskowitz (1999) |
| Who has the most home bias | Older man, not very wealthy, public employee | Karlsson and Nordén, J. Banking and Finance (2007) |
| Effect of labelling an asset as one’s own | +25 % portfolio weight, with nothing else changed | Dlugosch et al., J. Economics and Business (2023) |
| The reading that qualifies the error | May reflect the local investor’s superior information | Gaar et al., Management Review Quarterly (2020) |
What you will find here
- The scale of the bias, accompanied by benchmark statistics that are virtually never provided.
- The discovery that this bias operates within a single country as well.
- The exact profile of the investor with the most home bias.
• The experiment that measured how much the label “ours” weighs on its own.
- Why a segment of the literature contends that it is not precisely an error.
The size of the phenomenon
Viktoria Hnatkovska’s review for the Oxford Research Encyclopedia of Economics and Finance gives the benchmark that makes the magnitude intelligible. Under the international asset pricing model with homogeneous investors, every investor should hold the world market portfolio: the share of domestic assets equal to the share of those assets in world capitalisation.
Applied to the premier documented instance: as US equities constituted 56 % of global capitalization back in 2016, domestic investors ought to have dedicated 56 % of their stock holdings to local assets, leaving the remaining 44 % for international shares. Overseas equities comprised merely 23 % of the US portfolio during that same year.
And the global trend indicates: domestic equity preference is generally more pronounced across emerging markets than throughout advanced nations, with European territories exhibiting the smallest domestic bias and Central and South American territories recording the largest. The writer points out that, notwithstanding the long-term erosion of this tendency across every nation, it persists as a resilient characteristic within the empirical record.
A clarification about what counts as a puzzle
One point the review itself stresses is worth keeping: whether home bias is a puzzle depends on the benchmark allocation used. It is a puzzle against a model of homogeneous investors. It is not necessarily one against models that incorporate the costs of trading in international markets, or against theories stressing that local equity can hedge real exchange rate risk and non-tradable income risk, such as labour income.
| Study | What it analysed | What it found | What it does not support |
|---|---|---|---|
| Hnatkovska (2019) | An overview of macroeconomic data | 23 percent foreign versus a 44 percent benchmark | Being purely a literature review, the benchmark relies on the chosen model |
| Coval and Moskowitz (1999) | The portfolios of United States managers alongside corporate headquarters | A pronounced tendency toward geographical proximity | Institutional professionals rather than retail investors |
| Karlsson and Nordén (2007) | A Swedish pension scheme using a multinomial logit framework | A distinct demographic profile characterizing the biased investor | Generalizability beyond a single pension arrangement |
| Dlugosch et al. (2023) | An experimental setup featuring ambiguous urns and distinct labels | The presence of a label increases weighting by 25 percent solely under conditions of high ambiguity | Simulated choices rather than actual investment portfolios |
| Gaar et al. (2020) | An evaluation of home and local biases | Such patterns might indicate superior insights and thus prove rational | As an analytical review, it lacks a precise quantification of that proportion |
The bias also happens at home
Joshua Coval and Tobias Moskowitz published in Journal of Finance the discovery that deepened the issue: the inclination to invest near home extends to domestic equity portfolios as well.
Their specific result: US investment managers show a strong preference for locally headquartered firms, particularly small firms that are highly leveraged and produce non-tradable goods.
The selection of those three characteristics is not accidental, and the authors interpret it: they suggest that informational asymmetries between local and non-local investors may drive the preference for geographically proximate investments. These are precisely the firms about which public information is scarcest and where the knowledge of being there is worth most.
They also add a consequence for pricing theory: the relationship between investment proximity and firm size and leverage may shed light on several documented asset pricing anomalies.
Who has the most home bias
Anders Karlsson and Lars Nordén leveraged the Swedish pension system—where every participant selects their own funds—to accomplish something unprecedented in the Journal of Banking and Finance: they examined home bias among retail investors while controlling for risk levels and transaction expenses.
By employing a multinomial logit model that incorporates demographic traits, statistically and economically significant relationships between individual characteristics and the probability of home bias are uncovered by them. Specifically, age, gender, net wealth, occupation, and familiarity with risky investments play a crucial role.
And the resulting profile is unusually specific: the type of individual most likely to display home bias is an older man, not very wealthy, who works for the public administration and holds no other risky assets.
What the label weighs by itself
Dennis Dlugosch, Kristian Horn alongside Mei Wang outlined an experiment within the Journal of Economics and Business to isolate the strictly symbolic element. Subjects distributed their portfolio across assets featuring returns tied to an ambiguous urn draw, while every other variable remained constant and the ambiguity degree was regulated.
The manipulation was only the name: labelling assets as familiar or as coming from the subjects’ home country increases portfolio weight by around 25 %, even though the return-generating process is unaffected.
| Experimental condition | What happens to portfolio weight | Reading |
|---|---|---|
| Asset labelled as familiar | +25 % approximately | Only the name changes, not the return |
| Asset labelled as from the home country | +25 % approximately | Effect equivalent to that of familiarity |
| Under low ambiguity | No effect | The label only operates under uncertainty |
| Under high ambiguity | Effect present | Where decisions are made with least information |
| Consequence for the portfolio | More high-ambiguity assets | Greater downside loss risk |
The authors’ practical conclusion is the one worth keeping: familiarity and affinity for one’s own can lead to portfolios containing more high-ambiguity assets, that is, with greater downside risk. The bias does not only reduce diversification: it tilts the portfolio towards the worst-measured assets.
The reading that qualifies it
Eduard Gaar, David Scherer and Dirk Schiereck reviewed the whole literature on home bias and local bias in Management Review Quarterly, and their conclusion departs from the usual narrative.
First they place the phenomenon: although there is little doubt about its existence, its magnitude varies across countries and investor groups, and it must be regarded as a multifactorial phenomenon, a combination of numerous causes contributing synergistically.
And then they introduce the qualification: unlike other biases, home bias can be explained at least partly by reasons unrelated to irrational investor behaviour. While institutional restrictions play a minor role, information asymmetries and the superior information of domestic investors are important factors.
Hence the uncomfortable conclusion for anyone who treats it as a plain mistake: investment performance may well benefit from a home bias, and the bias would then cease to be a puzzle and become rational behaviour, given that a lower level of diversification can lead to higher returns.
This reading should not be taken alone either. Critics concur that no isolated explanation accounts for the full scale of the bias, suggesting it likely stems from a blend of rational and behavioural drivers. What the available data forbids is dismissing it as a mere mistake or viewing it as a calculated tactic: it functions as both simultaneously, in proportions that remain unquantified.
What to do with this in practice
Ordered by the strength of the evidence behind each point:
- Work out your benchmark before judging your portfolio. Your own country’s market share is the starting point; for most countries it is a small fraction of world capitalisation.
• Watch the bias inside the country too. The preference for the close operates between regions and sectors as well, not only between countries.
- Never equate comfort with quality. Attaching ownership to an investment artificially inflates its perceived worth by 25 %, despite its fundamental yields remaining completely unchanged.
• Be careful when information is scarce. The label effect appears only under high ambiguity, which is when being wrong costs most.
- Do not assume all home bias is error. Part of it may reflect the local investor’s superior information, although nobody has quantified how much.
Frequently asked questions
To what extent does home bias exist?
The usual benchmark allows it to be quantified. Since US equity accounted for 56 % of world capitalisation in 2016, the market portfolio would imply 44 % in foreign equity; the actual share in the US portfolio was 23 %. The bias is larger in developing countries, lowest in Europe and highest in Central and South America, and it has declined over time without disappearing.
Is it exclusively a cross-border occurrence?
No, it happens within each country too. The study published in Journal of Finance documents that US investment managers show a strong preference for locally headquartered firms, particularly small, highly leveraged producers of non-tradable goods, which the authors interpret as evidence of informational asymmetry between local and non-local investors.
Who has it most?
There is a profile identified with individual data. In the analysis of the Swedish pension plan with a multinomial logit model, age, gender, net wealth, occupation and familiarity with risky investments are significant, and the type of individual most likely to display home bias is an older man, not very wealthy, who works for the public administration and holds no other risky assets.
How much does it matter that something merely sounds like one’s own?
Quite a large amount, and it has been isolated through experimentation. During the study utilizing ambiguous urns, characterizing holdings as familiar or originating from the participants’ native nation boosts their portfolio allocation by roughly 25 % despite the underlying return-generating mechanism remaining identical, while this outcome emerges exclusively when returns exhibit high ambiguity, meaning familiarity ultimately burdens the portfolio with instruments bearing elevated downside exposure.
Is investing close to home always a mistake?
Not necessarily, and that is precisely the least cited aspect. The paper featured in Management Review Quarterly concludes that, unlike alternative cognitive errors, home bias can be justified at least partially through rational motives, that informational gaps together with the advantage held by local investors represent key elements, and that financial returns might actually gain from a domestic preference. Nonetheless, the evaluations concur that no isolated factor fully explains the entire scale of the phenomenon.
What we do not know
• What share is information and what share is bias. It is the central question of the field and no review resolves it.
• How much it costs in returns. There are estimates of the cost of under-diversification, but they depend entirely on the chosen benchmark model.
• Whether the secular decline continues. The bias has fallen with financial globalisation; whether it stabilises or reverses with geopolitical fragmentation remains to be seen.
• How it operates through funds. Much of an individual’s international exposure is indirect, and the classic measurements do not always capture it.
References
1. Hnatkovska, V. V. Home Bias in International Macroeconomics. Oxford Research Encyclopedia of Economics and Finance, 2019. DOI: 10.1093/acrefore/9780190625979.013.311
2. Coval, J. D. and Moskowitz, T. J. Home Bias at Home: Local Equity Preference in Domestic Portfolios. Journal of Finance, 1999. DOI: 10.1111/0022-1082.00181
3. Karlsson, A. and Nordén, L. Home Sweet Home: Home Bias and International Diversification Among Individual Investors. Journal of Banking and Finance, 2007. DOI: 10.1016/j.jbankfin.2006.04.005
4. Dlugosch, D., Horn, K. and Wang, M. New experimental evidence on the relationship between home bias, ambiguity aversion and familiarity heuristics. Journal of Economics and Business, 2023. DOI: 10.1016/j.jeconbus.2023.106131
5. Gaar, E., Scherer, D. and Schiereck, D. The home bias and the local bias: A survey. Management Review Quarterly, 2020. DOI: 10.1007/s11301-020-00203-8
This content is for information only and does not constitute financial advice or an investment recommendation. The studies cited analyse countries, periods and investor types that are not comparable with one another, and none of them allows the appropriate allocation for any particular portfolio to be determined.
