Contents
Back in 2016, the United States represented 56 % of global stock market capitalization, meaning a domestic investor owning the market portfolio should have allocated 44 % to international equities. Yet, the real figure sat at just 23 %. Home bias remains the most stubborn anomaly within cross-border finance, having outlasted thirty years of academic efforts to decode it. Nonetheless, an overlooked interpretation suggests this tendency might not be a total miscalculation. This piece examines the empirical findings, even touching upon the evidence indicating that keeping investments local can occasionally be advantageous.
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 precise 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 scale of the phenomenon
Viktoria Hnatkovska’s assessment of the Oxford Research Encyclopedia of Economics and Finance establishes the reference point that clarifies the scale of the phenomenon. Within the framework of the international asset pricing model featuring homogeneous investors, all participants ought to maintain the global market portfolio, meaning the proportion of domestic holdings must match the weight of those securities within worldwide capitalization.
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 international pattern: bias towards domestic assets tends to be larger in developing countries than in developed economies, with Europe as the region with the lowest equity bias and Central and South America as the highest. The author adds that, despite the secular decline of the bias in all countries over time, it remains a robust feature of the data.
An explanation regarding what constitutes 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) | Review of the macroeconomic evidence | 23 % foreign against a 44 % benchmark | It is a review; the benchmark depends on the model |
| Coval and Moskowitz (1999) | US managers’ portfolios and firm headquarters | Strong preference for the geographically close | Professional managers, not individual investors |
| Karlsson and Nordén (2007) | Swedish pension plan, multinomial logit model | Clear demographic profile of the biased investor | One particular pension system |
| Dlugosch et al. (2023) | Experiment with ambiguous urns and labels | The label raises weight by 25 %, only under high ambiguity | Experimental decision, not a real portfolio |
| Gaar et al. (2020) | Review of home bias and local bias | May reflect superior information and be rational | It is a review; it does not quantify that share |
The bias also happens at home
Joshua Coval and Tobias Moskowitz published in Journal of Finance the finding that widened the problem: the preference for investing close to home also applies to domestic equity portfolios.
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 choice of those three traits is intentional, and the researchers explain it by proposing that information gaps between domestic and external backers could fuel the bias toward nearby business ventures. These happen to be the exact enterprises regarding which publicly available data is most limited and where local familiarity carries the highest value.
They also introduce an implication for asset pricing models: the connection linking investment proximity with corporate scale and financial leverage could potentially clarify multiple established financial market 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. The reviews agree that no single explanation accounts for the whole magnitude of the bias, and that it probably reflects a combination of rational and behavioural factors. What the evidence does not allow is treating it as pure error or as an informed strategy: it is both at once, in proportions that have not been measured.
How to apply this practically
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.
• Distrust familiarity as a criterion. Labelling an asset as one’s own raises its weight by 25 % with nothing about its return changing.
• 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
How much home bias is there?
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 holds the 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 lot, and it has been isolated experimentally. In the experiment with ambiguous urns, labelling assets as familiar or as from the subjects’ home country increases their portfolio weight by around 25 % even though the return-generating process does not change, and the effect appears only when returns are highly ambiguous, so familiarity ends up loading the portfolio with assets carrying greater downside risk.
Is investing close to home always a mistake?
Not necessarily, and that is the least quoted part. The review published in Management Review Quarterly concludes that, unlike other biases, home bias can be explained at least partly by reasons unrelated to irrationality, that information asymmetries and the superior information of domestic investors are important factors, and that performance may benefit from a home bias. The reviews do agree, however, that no single explanation accounts for the whole magnitude.
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. Financial globalisation has dragged down the bias, yet it remains uncertain if geopolitical fragmentation will trigger a stabilization or a reversal.
• 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.
