How Your Personality Can Affect Your Portfolio
Neuroticism and openness, in particular, are closely linked to investors’ willingness to buy stocks
3 min
Can certain personality traits explain investors’ risk tolerance and investment decisions?
A forthcoming paper suggests it might. Specifically, the authors found that two personality traits—neuroticism and openness—significantly affect how investors perceive the economy, financial markets and their likelihood to buy stocks or stock funds, with those who are less neurotic and more open tending to have a higher allocation to equities.
While the authors primarily studied investors in the U.S., they also identified similar patterns among investors in Germany, Australia and China.
The Wall Street Journal spoke with two of the paper’s co-authors, Hongjun Yan, a professor of finance at DePaul University’s Driehaus College of Business, and Cameron Peng, an assistant professor of finance at the London School of Economics, about their findings. Zhengyang Jiang, an associate professor of finance at Northwestern University’s Kellogg School of Management, is the paper’s other co-author.
Here are edited excerpts of the conversation.
WSJ:Â How can psychology theories help to explain investor behaviour?
YAN: Investors often have very different portfolios. Traditionally, economists focus on risk aversion and market expectations, but in this paper we argue that well-known personality traits—extroversion, agreeableness, openness, conscientiousness and neuroticism—provide a new dimension to explain investors’ choices.
In the Winnie-the-Pooh stories, Tigger is always excited and optimistic while Eeyore is always down and pessimistic. You might expect their investment portfolios to look very different and reflect their overall outlook.
WSJ:How did you study this topic?
PENG:Â We collaborated with the American Association of Individual Investors, administering a survey to over 3,000 of its members. We collected information on their personality traits, market expectations, and investment decisions. The AAII sample is predominantly wealthy, white, older men. And when they make investment decisions, they are usually quite big, involving hundreds of thousands or even millions of dollars. Their actions can have a real impact on the market.
WSJ:What did you find?
YAN: We found that neuroticism and openness are correlated with investors’ beliefs about the market and their likelihood to buy equities. We were surprised that agreeableness wasn’t important when it comes to investment beliefs or decisions since other researchers have found that agreeableness tends to be correlated with other economic outcomes, like success in negotiating wages.
WSJ: How does neuroticism affect investors’ decisions?
YAN:Â Someone who is more neurotic has a very different outlook than someone who is not in terms of stock-market expectation. For example, an investor ranking in the middle of the [neurotic] scale might expect an annual stock-market premium of about 6%. But investors at the top of the scale are likely to only expect a 4% stock-market premium, while investors at the bottom of the neuroticism scale are likely to expect an 8% stock-market premium.
PENG: Neuroticism also affected how respondents invested their money in their actual accounts. More neurotic investors were less likely to own equities. Very neurotic investors invested about 56% of their portfolio in equities, while investors who weren’t neurotic invested about 64% of their portfolio in equities.
WSJ:How does openness affect investors’ decisions?
PENG: Investors ranking high for openness were more likely to entertain the possibility of extreme events, like a market crash or a run—really any scenario when the market goes up or down by more than 20%.
Investors who were very open were somewhat more likely to take risks by buying equities. Specifically, investors who were the most open were 3 percentage points more likely to own more equities than investors who weren’t. They had about 62% of their portfolio in equities, while investors who were less open had about 59% of their portfolio in equities.
WSJ:Â What did you find when you looked at data from other countries?
YAN: We find that neuroticism and openness affect market perceptions and decisions fairly consistently across different data sets. That’s quite remarkable considering the culture and investing environment in each country is very different.
WSJ:What are the study’s implications?
YAN: Personality traits may shape investors’ decisions in ways that many economists have yet to seriously consider. Our research, for instance, also suggests more extroverted and more neurotic investors’ investment choices could be highly influenced by social interactions, or what their friends or colleagues are doing. That insight goes beyond economists’ traditional framework, which focuses on risk tolerance and market expectations, and could help researchers better explain investor behaviour.
PENG: Large asset-management firms or financial planners could spend time getting to know their clients’ personalities and use those insights when they make investment recommendations. Maybe they could encourage investors who tend to be neurotic to be a little less pessimistic.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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