Durability Bias

Durability bias is a cognitive bias based on the assumption that past trends will continue. Investors commonly use it in finance and the stock market, forecasting repetitive sales orders or anticipating continuous business growth based on the recent past.

The bias substantially influences the decision-making ability of people, businesses, and investors in financial markets as they tend to believe that a particular event is highly probable just because it has happened in the past. This bias can be forecasted for positive and negative events, but it is advised to avoid it.

• Durability bias is the tendency of people and businesses to forecast a future event based on past trends.
• It is a common term used in behavioral finance and forecasting and it is advised to avoid it as it impacts rational thinking and decision-making ability.
• Businesses that successfully evade this bias often keep their growth rate in check and try to improve their operations and decisions.
• It can be observed in science, psychology, businesses, day-to-day lives, finance, and investing.

Durability bias is the simple assumption that something that has happened in the past will repeat itself in the future. It is one of the most common forms of misbeliefs and notions that is part of human behavior and can be seen in life and business-related situations. People tend to overestimate the results that are based on focalism. It is part of finance, business, and real-world scenarios.

Typically, durability bias is observed in forecasting a future event that has only happened once or twice, and there is no guarantee or evidence that it will occur or reoccur. When investors believe that a particular stock price will increase or decrease based on past trends, they imply durability bias. In such scenarios, the past experiences, data, or information is not to be blamed, but how a person perceives it to take present action and forecast future events.

It is not limited to business and behavioral finance but can be commonly observed in the real world and in people's day-to-day lives. People generally gauge the future based on past events, experiences, feelings, and emotions. Additionally, people tend to judge others based on how they treated them in the past, even if it was a one-time interaction. Similarly, people tend to measure things, social events, occurrences, and actions based on them. Durability bias is considered a lousy catalyst in decision-making as it inclines the subconscious to think in a particular direction without analyzing every component and aspect of the situation.

Below are two examples to explain its impact in different situations -

Example #1

Henry is an investor with a small portfolio. Still, for the past two years, he has been following a sugar stock that increases in market price just after the government announces the annual budget and invests in it just two weeks before the budget. Henry does not try to understand the company's fundamentals, assess different technical indicators, or look for the company's financial statements.

Henry assumes that the recent past trend will reoccur. In this case, he did not apply any rational thinking or try to research the stock. There is no guarantee that the sugar company stock will showcase the same result based on some evident proof or data. However, Henry is operating on overestimation and gut feeling. In that case, he may profit or incur a loss if the sugar stock's price declines.

Example #2

Tulip owns a company that manufactures furniture; she has been running the business for the last nine years and has a good business in the market. In 2018 and 2019, Tulip made approximately $9 million in annual sales. This year, she forecasts annual sales of up to $11 million. Just because the last two years have been good doesn't mean the growth will continue.

In 2020, a financial crisis came, and Tulip had to incur a loss for the first time since she started the company. It shows how durability bias leads to overestimating positive and negative feelings. When companies make the mistake of letting this bias influence their decision-making, not considering the various internal and external factors, it impacts their sales and revenue, affects the company's reputation, and catches companies off guard and unprepared for critical scenarios.

Effective ways to avoid this bias are as follows -

• People and businesses must not overestimate the situation and predict the future based on past data and trends.
• Every component and aspect must be checked to check and opt for a reasonable extrapolation.
• Pay attention to industry indicators and deeply understand data interpretation and insights.
• To avoid this bias, one must seek rational thinking and imply logic and research to make wise decisions and be prepared.
• Keep emotions and feelings aside while making a decision, irrespective of whether it is professional or personal.
• Acknowledge and identify the trait of this bias and eliminate it from the decision-making process.

Durability and impact bias are two distinct cognitive biases that shape our perception of events and outcomes. These biases, with their unique perspectives on prediction and emotional experience, significantly influence our decision-making processes and shape our expectations. 

Let's understand the differences between them:

• Durability bias refers to forecasting future events based on the recent past. In comparison, impact bias is the lasting intensity of a particular outcome, event, or feeling, which the former is part of.
• It revolves around feelings and predictions. In contrast, impact bias is focused on the time duration of an event and its lasting effects.
• Assuming that, based on previous vacations, the family will continue to go on a trip every year is durability bias, and impact bias is overestimating the intensity of time duration for the feeling of going on a trip will last.

This has been a guide to What Is Durability Bias. Here, we explain it in detail with its examples, how to avoid it, and its comparison with impact bias. You can learn more about it from the following articles –

• Overconfidence Bias
• Backtesting Bias
• Data-Mining Bias