Understandable Risk 111

There are only two elements of risk that anyone can truly define.  We know risk is something that could happen in the future, and we can’t see it or touch it.  So risk is a projection of possible unknowns events or occurrences.  The insurance industry has built a huge industry around the concept of spreading possible future risks.  Insurance companies use large pools of data along with subscribers where they can offer insurance to businesses and people so they can transfer the possible financial loss to an insurance company for a fee.

It is clear that risk has an inverse relationship to effort or prevention.  The slogan “failing to prepare is preparing to fail” emphasizes this.  The more effort a business or person puts into learning about the causes of possible losses that could affect them, and how to prevent risk from occurring the lesser the chance of risk.  This can be seen in many industries along with healthcare where prevention protocols are used and prevalent.  The same applies to investing where the greater the effort put into research the lesser the risk of poor outcomes.  If the  investment you are researching is too tough or complicated to research and understand, put it in the forget it pile.

The Study of Risk

Within the study of investments people such as William Sharpe, Jack Treynor, John Lintner, Jan Mossin, Michael Jensen, Karl Pearson, and Harry Markowitz have developed ways of measuring risk that are used within the investment industry today.  These statistical methods use past information to calculate historical potentials that people use to make decisions for their investment future.  As much as this information provides a potential picture of historical data predicting the future is still difficult with no guarantees of outcomes. 


When a large amount of data is available over a long period of time it becomes more reliable to use when statistically trying to estimate outcomes.  Within the stock market using over one hundred years of data can be valuable, since that data encompasses many years of life that humans and businesses have experienced and the effect that those experiences had in the past on the market.  Over that lengthy period of time the chances of the historical information repeating itself, possibly in a different form, can be good.  The world may change, but human nature stays fairly constant due to our primal instincts.  

Insurance companies perform the analysis when factoring how to make money for themselves with situations of potential loss.  When the data sample isn’t large enough then an insurance company won’t write insurance.  The same should apply when investing.  If the historical factual information isn’t large enough to present risk result patterns then an investor should act with caution.

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