Market prices : Do non-economic factors affect market prices? There are a couple very large examples in the last 100 years that prove the human emotion and in particular fear or ambiguity aversion can affect the market and be very economically meaningful. The first is the stock market crash of 1929. Though driven by economic factors, the stock market was inflated exceeding fundamental values, debt was high among investors, many were invested in the market and using debt/margin to pay for their investments. :Business and Finance Homework Help
Then as investors became fearful and began to pull out of the market, it caused the crash of 1929, (History.com Editors).
Two non-economic factors helped precipitate the crash and those were over-confidence and ambiguity aversion. Over-confidence in the economy and the market drove investors to exceed their debt limits which drove volume into the market and inflated prices. While some of this may not have been driven by self-attribution, it was the role of many unsophisticated investors which helped drive prices above cash-flow values. The after effect of the run on banks and massive sell-off of shares was driven by the uncertainty or ambiguity aversion. If we look at A Survey of Behavioral Finance, Barberis and Thaler explain that “people do not like situations where they are uncertain about the probability of distribution of a gamble.” Here, many unsophisticated investors were in the market and I would speculate that this was a gamble for most. They essentially followed the herd which lead to an emotion driven market and uncertainty of the future which caused everyone to try to get out as soon as they could.
The second example is the market crash in March 2020. Here again, as the COVID virus spread around the globe, investors became nervous as factories and stores were closing temporarily and workers were being sent home to quarantine. These events set up a shutdown of economic activity which triggered panic due to the potential consequences. This led to the three worst point drops in US History, (Frazier, L. 2021).
Again, fear driven selling helped drive the market lower as “…speculation about how bad it could get created even more fear among investors, (Frazier, L., 2021). As the government intervened, in April investors began to reinvest and by year end, the market had grown. Frazier again highlights the subject topic best as she says, “While there was (and still continues to be) a very real economic, financial and health crisis globally, market fluctuations aren’t based solely on economic factors. The economy is a major factor, but panic plays just as equal a role in stock market volatility.” Fear, regret, and aversion of the unknown have a major impact on the market just as optimism, and confidence do. These two events are two among many where non-economic factors have very meaningful economic results.
It’s hard to say that these two events were driven by “human mistakes” as I believe the other obvious circumstances drove investor emotion. But in both cases, you can see where “human mistakes” or human engagement in the market aggravated both situations. Had the human element been eliminated in both situations, I don’t believe the bias could still exist. In the case of the 1920’s, if we remove human bias and involvement in the market, then prices would stay at fundamental values. In other words, the market wouldn’t have been overbought. Similarly, in 2020 the virus drove fear which created a major sell-off and then government intervention created a rebound buying opportunity. In 2020, I think values would drop without human intervention because of the economic slowdown, but the peaks and valleys would not have been as pronounced if we removed the human emotion around this event.
Non-economic factors are interesting to think about in terms of there effects on market price. From historic research one may be influenced to believe that there is sufficient information to prove that human biases can affect the market and be economically meaningful. Let’s take into example local biases. Investors seem to invest less in companies that are in their home state when their home state is undergoing or portraying some recession indicators, such as high unemployment or weakened housing collateral. During times of local recession, consumption smoothing becomes more difficult and risk sharing levels decline, leading to regional variation in risk aversion and predictable patters in return. Since investors are now less likely to invest in their local companies the local companies become undervalued leading to the ability to purchase shares at a significant discount and the opportunity to have a significant gain. Proving one bias that directly affects market prices.
I think human mistakes aggregate and influence financial markets. Humans can be predictable and unless managed and made aware of their own biases, will not change their behaviors leading to an arbitrage opportunity for sophisticated investors. Overconfidence biases make investors overbuy or oversell investments driving high volatility in the markets, one can be able to predict when overconfidence biases will be present, such as when there is local economic instability or when there is a new political party coming into office. If one can learn when these biases will be present one can exploit the opportunities and figure out patterns that influence financial markets. In today’s time one also has to factor in computerized trading or high frequency trading (HFT). These types of trading strategies help exploit these human mistakes in matters or milliseconds. If the whole financial market were to turn to computerized trading, I don’t believe it will eliminate the importance of biases in the world. These algorithms are created to exploit small opportunities that arise, without biases in the market the computerized traders would not be able to have as significant gains as they would when human mistake is present. Not only that but one could figure out these algorithms and exploit the weaknesses that they portray. I think that just like with human mistake, computers can also exhibit patterns of mistakes. Yes, computer trading would eliminate some biases such as local biases or overconfidence, but it would just arise a new set of biases that can be studied and exploited. Finding exploitable opportunities bases on biases is all about finding patterns in the market that correlate to a specific combination. Local biases, presidential puzzles, and investor sophistication are all studied patterns from humans that continue to arise over time. The same things will be done if one has a market of just computerized trading. Computers work on algorithms, which are basically just patters coded into the computerized program. Although it can be adjusted and quickly changed, patterns will be developed, and people will be able to figure them out to formulate new biases.
In summary, I think that yes computerized trading would eliminate human biases in the market, but they would just be the beginning of new studies and new forms of biases will arise.
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