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
Every single row lists the research along with its publication date. It is beneficial to distinguish international bias from prejudices present in one’s native 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 part of the literature argues it is not exactly a mistake.
1. 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 best documented case: since the share of US equity in world capitalisation was 56 % in 2016, US investors should have allocated 56 % of their equity portfolio to domestic assets and the remaining 44 % to foreign equity. Foreign equity accounted for only 23 % of the US portfolio in 2016.
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) | 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 |
2. 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 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 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.
3. Who has the most home bias
Anders Karlsson and Lars Nordén used the Swedish pension system, where each member chooses among funds, to do in Journal of Banking and Finance something that did not exist: analyse home bias at the individual investor level, controlling for transaction costs and risk level.
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 demographic most prone to exhibiting home bias tends to be an older man of modest wealth, employed within the public sector, and possessing zero alternative risky investments.
4. 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.
5. The reading that qualifies it
Eduard Gaar, David Scherer, and Dirk Schiereck analyzed the complete body of research regarding home bias and local bias within Management Review Quarterly, ultimately arriving at a conclusion that diverges from the conventional 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.
• Distrust familiarity as a criterion. Labelling an asset as one’s own raises its weight by 25 % with nothing about its return changing.
• Exercise caution when data is limited. The labeling phenomenon surfaces strictly during significant ambiguity, precisely when errors carry the highest price.
- 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 standard benchmark makes quantification possible. Because American stocks represented 56 percent of global market capitalization back in 2016, the market portfolio should theoretically allocate 44 percent to international equities, whereas the actual proportion found in American portfolios sat at only 23 percent. This skew proves more pronounced across emerging markets, drops to its minimum in Europe, reaches its maximum peaks throughout Central and South America, and has gradually shrank over the years while still persisting.
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 significant is it when something simply resembles 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 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 provided for informational purposes only and should not be considered financial advice or an investment recommendation. The referenced research examines diverse nations, timeframes, and investor profiles that lack direct comparability, and none of these studies can establish the proper asset distribution for any specific portfolio.