FIN 405 Week 2 Heuristics and Cognitive Biases Example

A Portfolio Review That Found Five Biases: Anchoring, the Disposition Effect, Overconfidence, Home Bias and Availability in One Engineer's Brokerage Account

[Student Name]

University of Phoenix

FIN/405: Behavioral Finance

Week 2 Assignment

[Instructor Name]

[Date]

The client and his account are composites written for a model paper; the heuristics, biases and research findings come from the sources listed.

Daniel Kowalski, a composite 52-year-old process engineer at a chemical company in Texas, manages a $640,000 brokerage account himself. He reads company reports, follows financial news and trades two or three times a week. His returns have lagged a simple index portfolio for six of the past eight years, and he asked an advisor to review his decisions. He was confident the review would find bad luck rather than bad habits. The review found habits, and each one matched a pattern that researchers had measured decades earlier. This paper explains those patterns and how they appeared in his account.

Shortcuts and Their Errors

Tversky and Kahneman (1974) showed that people judging uncertain events rely on a few heuristics, mental shortcuts that save effort and often work. Representativeness judges probability by resemblance, so a company that looks like past winners seems likely to win. Availability judges frequency by how easily examples come to mind, so vivid recent events seem common. Anchoring starts from an initial number and adjusts too little. These shortcuts produce systematic biases when applied to markets, where resemblance, vividness and starting points carry little information about future returns.

Bias One: The Disposition Effect

The account showed a clear pattern: Daniel sold stocks soon after they rose and held stocks that had fallen. Over two years, he realized gains on 41 sales and losses on 9, while the account held 14 positions showing paper losses, several more than 30 percent below cost. Shefrin and Statman (1985) named this the disposition effect, and Odean (1998), studying 10,000 accounts at a discount brokerage, found that investors realized gains at a much higher rate than losses, even though the winners they sold went on to outperform the losers they kept. The pattern also cost Daniel tax savings, since selling losers would have offset gains.

Bias Two: Anchoring on Purchase Prices

When asked why he held a regional bank stock bought at $58 and now trading at $36, Daniel said he would sell once it got back to $58. The purchase price had become an anchor with no bearing on the bank's prospects. The relevant question, whether $36 of capital would earn more in the bank or elsewhere, did not depend on what he once paid. Anchoring and the disposition effect reinforce each other, since the anchor defines a loss that loss aversion makes painful to accept.

Bias Three: Overconfidence

Daniel's account turned over about 140 percent a year, meaning he replaced more than his entire portfolio annually. He believed his engineering background gave him an edge in chemical and energy stocks. Overconfidence leads investors to overestimate the precision of their information and to trade too much. Barber and Odean (2000) found that the households trading most actively earned the lowest net returns of any group they studied. Daniel's trading costs and the short-term gains taxed at ordinary income rates took an estimated 1.6 percentage points a year from his return.

Bias Four: Home Bias and Familiarity

About 38 percent of the account sat in energy and chemical companies, including 12 percent in his own employer's stock, and nearly all holdings were U.S. companies, half of them based in Texas. Investors favor what is familiar, which feels less risky. Yet his salary, bonus and pension already depended on the same industry and region. A downturn in chemicals could cut his income and his portfolio at the same time, a concentration of risk he had not considered.

Bias Five: Availability

In 2022, after a vivid news story about a plant explosion at a competitor, Daniel sold his chemical holdings except his employer's stock and bought them back two months later at higher prices. The event was memorable but said little about long-term industry profits. Availability made a rare event seem to signal a trend.

What the Biases Cost

Comparing the account with a diversified index portfolio of the same stock and bond mix, the gap over eight years averaged about 2.3 percentage points a year. Trading costs and taxes explain about 1.6 points; the rest came from holding losers, concentration and mistimed trades. On a $640,000 account, 2.3 points is about $14,700 a year.

Mental Accounting in the Same Account

A sixth pattern appeared once the five were mapped. Daniel kept a separate sub-account he called his play money, funded with a bonus, and traded it far more aggressively than the rest, including options on energy stocks. He described losses there as not really counting. Thaler's work on mental accounting explains the habit: people sort money into categories with different rules, even though a dollar lost in one category is worth the same as a dollar lost in another. The play account had lost 41 percent over three years, about $19,000, which Daniel had never added to his overall results. Treating it as part of one portfolio, with the same position limits, was the hardest change for him to accept and probably the most valuable.

Steps Daniel Agreed To

Debiasing rarely works by telling people to be objective. It works by changing the decision structure. Daniel agreed to five rules. He will review each losing position once a year by asking whether he would buy it today at its current price, ignoring cost. He will harvest losses each December. He will cap any single stock at 5 percent and his employer at 8 percent, reducing the employer position over two years to spread taxes. He will move 70 percent of the account into broad index funds, keeping 30 percent for his own selections, where his skill can be measured against an index. He will wait 48 hours before acting on any news story.

Why Rules Help

Rules made in calm conditions are not exposed to the emotions of the moment. They also make outcomes measurable: after two years, Daniel will see whether his 30 percent of personal selections outperformed the index, a test his previous approach never allowed.

Conclusion

Daniel's account showed the disposition effect, anchoring, overconfidence, home bias and availability, each documented by researchers and each traceable to trades. Together they cost about 2.3 percentage points a year. Simple rules that change the structure of his decisions offer a better remedy than resolving to think more clearly.

References

Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The Journal of Finance, 55(2), 773-806. https://doi.org/10.1111/0022-1082.00226

Odean, T. (1998). Are investors reluctant to realize their losses? The Journal of Finance, 53(5), 1775-1798. https://doi.org/10.1111/0022-1082.00072

Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. The Journal of Finance, 40(3), 777-790. https://doi.org/10.1111/j.1540-6261.1985.tb05002.x

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131. https://doi.org/10.1126/science.185.4157.1124