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Loss aversion

As explained in Build a Robo-Advisor with Python (From Scratch)

Loss aversion is a behavioral bias in which losses have greater psychological impact than comparable gains.

Loss aversion describes the tendency to focus more strongly on losses than comparable gains. Portfolio analysis makes this practical by displaying both outcomes in dollar terms. section 2.5

A stock–bond allocation slider

For a $1 million portfolio, an interactive slider varies the stock allocation from 0% to 100%. Each setting shows average gain and the 95% worst-case loss. Increasing stocks raises both the modeled average return and maximum loss, making the trade-off easier to evaluate. section 2.5

Figure 2.5 A slider used to visualize the effects of changing the stock–bond mix
figure

Explore how changing the stock–bond mix changes modeled gains and worst-case losses. section 2.5

The calculation emphasizes three practical choices:

  • Measure downside as the 95% worst-case loss, defined as 1.96 standard deviations from the average. section 2.5
  • Use dollar gains and losses rather than percentages when communicating portfolio risk. section 2.5

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes
Explains the behavioral motivation and implements a slider-based normal model, while deferring broader multi-asset assumptions to later analysis.
Examples
Demonstrates a $1 million portfolio, a stock-allocation slider, average gains, and 95% worst-case losses.
What it assumes
Assumes basic Python and supplied assumptions for expected returns, standard deviations, and correlations.
What you can do after
Operate an ipywidgets slider to compare modeled portfolio gains and downside across stock allocations.
How it connects
Connects loss aversion with risk tolerance, portfolio construction, stocks, bonds, normal distributions, and standard deviation.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.