The history of economic bubbles has indubitably shown enormous impact on certain markets and the public. The term “bubble” is often used by economists to describe how an asset price rises significantly over its fundamental or intrinsic value but then crashes with a market slowdown or contraction. Historical examples of this include the famous stock market crash of 1929; the Dot.com bubble in the U.S. information technology stocks in the late 90’s and early 2000’s; and, the more recent housing and credit bubble that has impacted U.S. and global markets. There is anything from small to large market bubbles that can have either local or even global impact. Although it is easy to see the consequences of these bubbles bursting, price bubbles still puzzle economic theory (Levine and Zajac, 2007). Although there is no clear agreement, price bubbles have been attempted to be explained by the effects of social psychological factors, how people’s thoughts, feelings, and behaviors are influenced by the actual, imagined, or implied presence of others.
From a social psychological perspective an individual’s thoughts, feelings, and behaviors drive our social interactions. Often an individual’s ability to process information from the environment can be overstimulated, so as an adaptive skill, she creates mental short cuts called heuristics in social situations. Although these mental short cuts can help process information efficiently, they can also create cognitive bias which leads to a distortion in an individual’s judgment or thinking. Cognitive bias is present in everyday life but can skew decision making, belief formation, and overall behaviors that can be detrimental to such matters such as economic and business decisions.
The Greater Fool Theory
Especially in highly competitive market landscapes, self belief can be a key adaptive skill for success. The Greater Fool Theory, however, demonstrates the potential destruction of this adaption on a collective scale. It relates to the naturally overconfident investor who buys an over-priced asset and still assumes that he can resell at even higher and inflated price. The goal of this buyer is to seek out even more gullible investors, known as the “greater fools” (David Dreman, 1993). The heart of this stubborn overconfidence is called self serving bias, where one can overlook negative feedback or external stimuli to maintain one’s own self esteem or worth. According to research, people have the tendency to assess themselves to be above average in various positive characteristics such as driving, ethics, productivity, and other desirable traits (Ola Svenson, 1981; Linda Babcock and George Loewenstein, 1997). If this were true in pertaining to a collective society participating in the rise of overpriced assets, the average participant in buying the asset will believe they could outsmart the next buyer therefore creating a chain effect until the asset plateaus and diminishes in value.
Herd mentality and behavior can also help describe how bubbles happen because herd behavior is rooted in how individuals tend to adopt to group behaviors, trends, and purchasing/consuming decisions without planned direction. This has been an exercised theory to help explain market bubbles since herd behavior explains how individuals are driven by irrationality and emotionally-charged decisions through the trend of the group. Another cognitive bias also known as the bandwagon effect, where despite the underlying evidence, people still participate with the trend of the group. From an individual outside of the group, seeing people participating in the group has an attention-grabbing effect that most likely acts as the best evidence of success. The Self Herd theory is an evolutionary theory that proposes the idea that it is instinctual to fundamentally feel safer with more people, leading individuals to gravitate towards a herd. These biological herd instincts could help explain when rationality is bypassed and individuals make decisions based on the trend of the herd.
The basis of bounded rationality is that rationality is only limited to the resources or information one can have to make a decision. For instance, in experimental designs it has been shown that bubbles abated when participants traded repeatedly within the same group (Levine and Zajac, 2007). The basis of this adaptive bias is that our minds are built to adapt the best we can given the information present in the social situation. It seems that this effect could accumulate in small groups when individual cognitive processing is limited to the knowledge of the group such as wrong pricing of the intrinsic value of the asset.
Similar to herd behavior, economic theorists have talked about the effect of how social norms could play a significant role in market bubbles. Groups often hold onto implicit rules and expectations for the participants of the group to follow, which in time become internalized into preferred behavior by the group. For instance, the simple impulse of saying “bless you” when a person sneezes demonstrates a norm. These norms differ from culture and environment, but in all societies, become an overarching influence in behavior. Therefore in an economic market, direct communication might not be necessary to enable members of groups to internalize a belief: “the mere posting of bid and asks can be sufficient to spread beliefs and sway markets away from intrinsic value” (Levine and Zajac, 2007). This might seem like the exact same thing as herding and although very similar, I refer herding as the direct impulse and instinct to gravitate towards group trends contrary to institutionalism, where the internalization of the working model of expectations or deemed a social norm creates the effect of institutionalism.
Although there has been inclinations to separate each one of these factors to explain the phenomena of market bubbles, I would propose that the complexity of market bubbles is the result of the interactions of these distinct social psychological factors. The fragility of an individual’s and groups’ reasoning and actions can be influenced and swayed by the uncertainty of real world markets. Therefore, individual cognitive bias along with social influence could create large sways in the asset prices increases, creating bubbles imminent crashes.
Levine, Sheen S.; Zajac, Edward J. (2007-06-27). The Institutional Nature of Price Bubbles.
Dreman, David. “One More for the Road?” Forbes. New York, 1993, 363.
Svenson, Ola. “Are We All Less Risky and More Skillful Than Our Fellow Drivers?” Acta Psychologica, 1981, 47, pp. 143-48.
Babcock, Linda and Loewenstein, George. “Explaining Bargaining Impasse: The Role of Self-Serving Biases.” Journal of Economic Perspectives, 1997, 11(1), pp. 109-26.
A lot of focus has been placed on pricing software and services in tech startup and entrepreneurship blogs, magazines, and forums. I had an idea for a physical product, Z Hook – a quick-drying bath towel hook, that I wanted to design, prototype and launch within 3-6 months at a minimum cost. I was going to leverage lean startup methodology to build a prototype. How would I price the resulting product?
Designing the Minimum Viable Product
My goal was to build a prototype for around $100, get feedback from potential customers, build and sell my first minimum viable quantity and then grow organically. I detailed the design process in the following Slideshare preso:
Essential Market Research
Getting out of the building is paramount. I had conversations with strangers about the concept. The consensus was that I struck a note. Drying wet towels was big problem that people initially didn’t realize they had. I did want more data. So, I set up a Google Adwords search campaign to test the interest in “quick drying bath towel hooks”. Check. There was some interest. Yet, I had more questions about who my customer was and the key features and benefits that they needed. I commissioned a market research study via Survey Monkey. I provided survey participants with a used a website with product photos and information.
In economics, there are several types of goods: Necessary, inferior, and superior. The definition of the goods is tied to the demand curve as income levels change. Luxury goods have increased demand as income increases. I wanted to know how customers perceived the product. The chart below is a snapshot of all the responses. The interesting point was that Survey Monkey enabled me to look at consumer demand across different household income levels. I did see a substantial increase in the perception of the product as a luxury for higher household incomes.
I looked at competing towel racks and hooks from Walmart, Target, and Bed, Bath, and Beyond. I noticed that metal towel hooks and racks prices ranged from $15 – $30 for products that could not do what the Z Hook did. On the higher end, there were much more expensive products $50+ (and even $200+). I started to get some boundaries on what I could charge based on customer perception and competitive price analysis. However, I had to determine my unit cost to ensure that I could produce the Z Hook and make a profit.
Determining the Minimum Viable Quantity
I worked with an Industrial Fabrication and Welding company in Falls Church, Virginia to develop and improve a working prototype for around $100. I honestly had no idea what initial demand for this product would be. I started getting more quotes from similar companies to fabricate various quantities of this product: 10, 20, 50, 100, 500. I was looking for a unit cost that could enable me to sell the Z Hook for somewhere between $20 – $50. There was another concern. Product labeling, shipping and handling materials, marketing costs, etc. Moreover, how was I going to get the product to the customer? This was a very important question. I had to think about arbitrage – there can’t be more than one “price” for the product.
If I wanted to eventually sell the Z Hook to a Big Box store I would need to do so at wholesale and then that company would sell at retail. Hence, I would need to figure out a unit cost that could work, a wholesale price for retailers, and then a retail price. Economics of scale turned out to be my friend. At a higher quantities, the unit cost of the Z Hook went down. After getting several quotes, I found that I could get 125 units of the Z Hook produced for between $16 – $23. The price range depended on the size and scale of the supplier. Moreover, the lower prices came with longer lead times and stricter payment terms. I had found my Minimum Viable Quantity (MVQ) to be 125. My wholesale price was set at $26 and the retail price is $33. I am currently applying to become a vendor at several Big Box stores. The Z Hook is being sold online directly to consumers.