21st Century Economics

Our position, at 21st Century Economics, is that we are not addressing the professional Wall Street investor with our service. We, therefore, do not advocate investments that require minute-by-minute attention to the rises and falls of the value of the investment. Futures trading is like that.

It is for this reason that we at 21st Century Economics do not advocate anyone get into the futures market unless you have lots of excess money, lots of time to learn and follow the market and can take the stress and risks of a very volatile market.  

The Short and Simple Explanation:

You pay a fee for the “option” to buy stock at a future price so you can sell it now. In other words, you have the option to sell some stock now at a high price and then, at some time in the future, you buy it when the stock price has dropped.

The real neat trick is that you can wait until that future time to see if the price did, in fact, rise before you elect to sell the stock at today’s price. If the stock does not rise, you simple elect not to complete the transaction and all you forfeit is the cost of the option.

Typically, you buy the option and then wait to confirm that it is going to go down. If it begins, then you can elect to exercise your option and sell now at its current price of let’s say $110. Suppose you sell 100 shares for a total income of $11,000. Now you wait for the stock price to drop. When it reaches $85 per share, you buy 100 shares for $8,500. You sold the stock for $11,000 and bought it for $8,500. You made $2,500 minus the cost of the option.

Typically, the usual option buyer would buy much more than 100 shares. You can see that if you bought 10,000 shares, you would have made $250,000. The advantage is that you bought the 10,000 shares with money you made from the sale of the 10,000 shares. Sounds weird but it’s done everyday on Wall Street. You also have a low risk since if the stock goes up or does not change, you can elect to NOT exercise your option. You lose the fee you paid for the option but that’s all you are out of pocket.

Where this is most useful is when you KNOW that the stock will move down. In the case of a known political, economic or Middle East crisis, we often do KNOW that some stocks will go down and some will go up and then down. Therein lies your chance for profit.

You should read this next section but you can also skip down to the section marked Cautions.

The Longer Explanation:

As you might expect, it’s a little more complicated than what is listed above. Here’s how it really works.

You tell your broker you want to short 100 shares of ABC, Inc. at $110. This means that you are entering into an agreement with the broker to temporarily borrow 100 shares of this stock at $110/share for specified period of time. Technically, you are borrowing the 100 shares from your broker in order to sell them to someone else at the current price of $110.

The broker either has the shares in inventory or he borrowed them from a client or another brokerage firm. The sale is made and the shares are now in the hands of a third party that has paid $110 per share for them. At this point, you have not paid the broker any money but you do owe him for the 100 shares.

Now you wait. If the price of ABC, Inc. goes down, to $85, you then buy 100 shares of the stock at that price. You have now spent $8,500 for the 100 shares of stock. You now return to your broker the 100 shares of ABC, Inc. stock that you borrowed. You borrowed the stock at $110 and sold it at that price for $11,000. Then, later, you bought it back at $85. You made $25 per share in profit or $2,500. You sold the borrowed stock for $11,000 and bought it back for $8,500. Technically, you sold something before you owned it and bought it back after you sold it. Sounds crazy but that is what is called Selling Short.

Under some circumstances, it is possible to return the stock to the broker before you have to pay to buy it meaning that it is a paperwork drill until he sends you your $2,500 profit.

Cautions:

As with all stock transactions, there is a down side to this activity. Suppose the price of ABC, Inc. goes up to $125. You borrowed it from the broker and sold it at $110. Now he wants his stock back but the price has gone up. You now have to go into the market and buy 100 shares of ABC, Inc. at $125 per share or $12,500. You can then return the loaned stock to the broker. In this case, you lost $1,250.

There are ways to protect yourself from too much of a rise in price by using a ‘buy stop’ order GTC (Good Till Canceled). You decide that if the price of ABC, Inc. rises $5 you want to get out of the deal. You would place a buy stop order at $115. Then, if the price of ABC, Inc. rises to $115, you are assured that you will get out at about $115.

You may also want to get out of a short trade when you have hit a certain amount of profit. In this case, you would use a buy stop at you maximum loss level and a buy stop at your profit target level. This is called an either/or order. You are placing two orders to protect you if the stock rises and to take profit if the stock declines.

For the most part, brokerage firms do not place a time limit on the shares of stock they loan. This is because they make a commission both ways. And also, they want to keep the customer happy. There are some other rules and limits on this kind of sale but it has its rewards.

As you will see, selling short is a very useful technique when you know a stock or other investment will go down. What do you think will happen to all those defense contracting companies after this Iraq crisis all dies down? What do you think will happen to those defense contracts - like with Haliburton, if an anti-war president is put into power in 2009? What do you think will happen to GOLD after the panic passes about an oil crisis or a war with Iran? You can bet money that they will go down from their Bush Era highs. Make Money!  

This is contrary to the expectation of genetics which would seem to predict that the son’s inherited genetics would tend to be more similar to the father. Instead, what was happening was that each male person born in a family tended to contribute to the average for all males in society. That is, if one person in the family is tall then the next must be shorter to average out to the average height of the general population. Galton described this as “regression toward mediocrity” and went on to develop some very sophisticated math tools and techniques to do what he called “regression analysis”.

At first this sounds like it is a remarkable discovery but upon closer examination, it is just common sense. Let’s look at what would happen if this “regression toward mediocrity” did not happen. Let us assume that the expectation of the genetic inheritance was actually the predictor of the height of the son. This would mean that the occasional tall father would have tall sons. Unless they all grew to exactly the same height and then stopped, we can guess that the occasional son would be taller than his father. But if we follow the genetic expectation, that son would also have a tall son. If we extend this for a few dozen generations, we end of with lots of people dozens of feet tall.

This would also mean that there would not be a level average height for males in the general population but an increasingly taller trend that increases with each generation. Since that has not happened in all of history, there must be something wrong with the expectation of the genetic inheritance theory. Dr. Galton’s discovery does, in fact, apply to the general population but it has been found to apply to nearly everything that has an “average” value for some aspect of it’s description.

>>It should be noted that over the past few centuries, there has been a very slow rise in the average height in the general population - men and women - but it is due to an overall improvement in nutrition and health care rather than in genetics<<

Law of Math

Regression to the mean is a statistical phenomenon that is a fact of life in nature. It essentially occurs where the measures (for example the average heights of men) on the average regress toward the mean or average. The net effect of regression toward the mean is that the lower measurements tend to be higher, and the higher measures tend to be lower. It is important to note that regression is always toward the population mean of a group. That implies that there is a unique reference value, called the “mean”, that is an intrinsic part of every group of anything.

This intrinsic reference value, if known, allows you to define each and every individual in the group as being either above or below that value - above or below the mean or average of the group. The best example of this is in school testing of college students. Every student is tested and given a score that is relative to the overall average of the entire population that takes the tests. If you placed in the 10 “percentile” group, that means that you have a score that, on average, only 10 percent of the population gets. In this case, you are not being compared to getting a perfect score on the test but rather the comparison is against the highest scores made by anyone that took the test. This kind of test scores are called “grading on the curve”. The one student that makes the highest score is the “curve setter” and all the rest are scaled according to how many made each score so that in the end you have the familiar “bell curve” of scores for the entire population. That bell curve is the intrinsic reference value for that test and that population of students.

It has been said that regression toward the mean is a phenomenon that is similar to several everyday expressions such as “law of averages”, “things will even out” or “we are due for a good day after a string of bad ones”. And one that I would like to add is “it can’t possibly get worse (or better) than this!” Basically what all these phrases are saying is that “extreme experiences tend to be balanced by less extreme experiences”

Formal Math

Because regression can be applied to so many aspects of life and events, it is a highly developed aspect of mathematics called Regression Analysis. It uses some very sophisticated methods to look at what might otherwise be viewed as almost totally random data. I will not turn this into a mathematics textbook but I think it is important to understand that certain kinds of information can be very accurately defined with the precision of calculated numbers. Such calculations put relative quantities on the choices that we are faced with in our daily lives. When properly applied, they can be used to show us the favorable choice to make from among some very complex alternatives. For instance, in the lottery, betting, sports, politics and hundreds of other areas that require us to evaluate choices.

Below I have summarized some of the potential mathematic procedures that can be applied to the decision making processes. Don’t be confused or distressed by the complexity of these functions. You will see that, like the basic concept of regression, much of it is logical and common sense, once you know how to look at it.

Finding the Mean

If you have a lot of data and want to find the closest consistent pattern of the data, you can apply a technique called “curve fitting”. This is the basic function of regression analysis - to regress the data into a mean or average value and then be able to describe that average value in a formula. The result is a regression or prediction line or curve. It is called a prediction line because it can be used to predict the response to data you have not yet generated. For instance, the prediction line for a coin toss is 1 in 2 or 50-50. As we saw in the test runs of thousands of tosses, this prediction line could be a very accurate indicator of the response of future coin flips. In more complex regression, it is possible to determine the average response of medical studies, voter responses, accident and crime data or consumer buying patterns.

In the case of buying patterns of consumers, as the quantity of sample data (the number of products being studies) and the number of times each is recorded (number of buyers in the study), the accuracy of the prediction line improves. This gets so accurate that it become profitable for supermarkets to pay you, with discounts, so that they can get information about your buying patterns. They do this by getting you to register with their “buyer’s club” or with their “discount club” but what they actually did was get a lot of data about you and then record your every purchase so they are better equipped to market to your needs and appeal to your buying patterns.

Curve fitting or the creation of the prediction line is what the horse rasing bettor does in his head when he analyzes the past records for each horse before predicting which one will win the next race. Using regression analysis, that process can be quantified so that you have a number assigned to the chances for each horse in the next race. In each race, if you bet on the horse with the highest calculated chance of wining, you’d do better than the best racing bettors that ever lived.

Finding Multiple Independent Variables

How Good is your Prediction Line

In some cases, the real world data that you are trying to analyze appears to be very random and sporadic. What this means is that there is an average but each event or value might be very close or very far away from that average. In the stock market, this is called volatility and is the measure of how wildly the value of the stock swings from one day to the next. You can actually calculate this volatility using a technique called the Correlation Coefficient (CC). This is a value from 0 to 1 that says how close your prediction line is to the actual data. If the CC = 1 then you have an exact match and you can predict every single event with perfect accuracy. This might occur if you discover that each person in a particular store that buys diapers has a baby. An obvious conclusion but one that you can use if you are the store owner by offering everyone that buys diapers a coupon on bulk buying of baby food.

A value of CC=.5 would mean you have a 50-50 chance of prediction of the next even. This would be the case of a coin toss and it would not be that useful for betting. However, if you had a CC=.5 for data such as your chances of winning a large lottery prize, then you have a much more usable figure. The different is in the application of the prediction line to the alternatives of the response line - in other words, a 50% chance of a heads on a coin toss is not as useful as a 50% chance of winning the lottery. The Correlation Coefficient validates your ability to use the prediction capabilities of the regression analysis you have done.

Measure a Small Group - Apply the Results to a Large Group

There is a whole field of study called Statistical Inference that takes sample data and uses it to infer or predict what a larger group will do. This is the essence of the marketing analysis that is done with focus groups and public opinion polls. In fact, some very fancy math is used to determine the exact sample size in order to achieve a reasonable degree of accuracy. You can also decide on the degree of accuracy you wish to achieve (called the Confidence Interval) and then compute how many data points or samples you need to collect to achieve that degree of confidence in your resulting prediction line.

This aspect of regression and statistics is perhaps the one you have come into contact most often with and didn’t know it. Besides the focus groups and public opinion polls often used in politics, there are surveys and buyer pattern analysis that is taken on a small scale and then applied to a larger group. We sometimes call these “pilot studies” or “sample testing”. This is often the only method used in drug testing and yet it is used to “predict” the responses of everyone that will eventually take the drug.

If CC=0 then there is no more correlation between the plotted data and your prediction line than random chance would predict. There are, in fact relatively few such instances of analysis since it is now becoming more and more clear that even seemingly random events can be described by fancy formulas or sophisticated regression analysis.

In all cases, the regression must refer to some baseline or reference value. This value is held fixed or independent and then a second value is compared to it. The first value that is held fixed is called the independent or predictor variable and the second value is the dependent or response variable. In all our discussion, we have used predictor and response variables but have not called them that. For instance, in the coin toss, the 50% figure of heads or the 50% figure of tails is the predictor value. In our test flips we averaged 50.082% heads and 49.918% tails. These were the response variables. In the real world, the actual response variable may never exactly equal the predictor variable unless you spend a lifetime flipping coins. However, it should be noted that before we flipped a single coin, we could know that the RESPONSE of the flips would be VERY CLOSE to the PREDICTOR value - and it was.  

What makes any given stock go up or down almost defies logic and analysis. So many things can affect a stock’s value that the only successful investors are ones that totally involve themselves in every nuance of the business they are investing in. They know every aspect of the business, often better than the owners and managers of the business itself. There have been only a few such investors in all of the history of the stock market.

Peter Lynch, the portfolio manager of Fidelity Magellan Fund, was one such investor. He had a well-known reputation for knowing everything about a business before he invested in it. But what he was really doing is establishing a baseline or reference of performance on a company in order to more accurately predict what its future will be. This often took weeks or months of on-site study for each investment decision.

The attraction of most investors to put money into a single stock is the hope they will find the next Microsoft or Bell Telephone and their stock will skyrocket in value. Unfortunately, it can also lose everything. In fact, statistically speaking, the vast majority of initial price offerings (IPO’s) (what they call a new stock that is being introduced to the market for the first time) fail and the initial investors usually lose most or all of their investment. Although there are companies that can show very rapid gains, no one has figured out a way to precisely determine exactly which companies will fly high and which will fail.

As you can imagine, this investment decision process is very complex and requires the consideration of hundreds of parameters about the investment candidate. As a non-professional investor, you do not have the skills, time or access to do this kind of analysis and neither do most other investors.

As an alternative, they rely on investments in a broad range of stocks called mutual funds. These are groupings of individual stocks into a large collection of similar kinds of investments. For instance, they might all be from the same or related industries, such as automotive or health care; or, they may all have the same kind of investment strategy such as growth or income stocks or government bonds.

The objective is to average out the fluctuations and effects of any one stock in order to gain a more modest gain across a much larger group of stocks. It must work because that is where the vast majority of investors are now putting their money.

Peter Lynch studied and analyzed each stock that he invested in to establish a baseline or reference of past performance that he used to make investment decisions. So, too, must there be a reference for the groups of stocks that make up mutual funds. To meet this demand, Wall Street has created numerous “indexes” that plot the averages for dozens or hundreds of stocks. The most famous of these indexes is the Dow Jones Industrial Average (DJIA) which reflects the averages of only 30 industrial stocks that are suppose to be indicators of what the entire economy is doing. Another well known index is the Standard and Poor’s Stock Price Industrial Index or the S&P 500. This averages a dynamic list of the top 500 stocks on the New York Stock Exchange.

There are many others but they all have a common purpose - to supply the needed baseline or reference of performance on a group of stocks in order to more accurately predict what the investment potentials will be.
As we go from the performance of a single stock, to the performance of indexed stocks like the S&P 500 and to the mixtures contained in mutual funds, we get two characteristics:

As the number of stocks grouped together increase, the fluctuations of any one stock have less and less effect on the total value of the group. This is for two reasons:

(a) each individual stock makes up less and less of the total as the group gets larger and

(b) as one stock goes down, others in the group may go up, resulting in a more consistent and steady response to the changing economy.

As the number of stocks grouped together increases, the gains and losses tend to move into much more modest swings so that the average gains or losses are smaller than the gains and losses that you might get with a smaller group.

The swings of gains and losses is called volatility and is directly related to the risk you have of sharp and frequent changes in the value of the investment. In general, mutual funds are less volatile than an individual stock will be. The larger the mutual fund, the more likely it is to be stable and non-volatile.

As the average investor, you need a reference of performance that you can easily understand and easy to follow. Ideally, you would want to find a “megatrend” that you can invest in and be reasonably confident that it will result in profit. Use of indexes like the DJIA or the S&P 500 are a step in the right direction but if you look at a long term graphic chart of these indexes, you will see lots of fluctuations. It’s hard to base your investment decisions on a performance index that has an inconsistent trend. What we need is some other index that let’s us KNOW we will make money.

One impact of the two characteristics listed above is that although the high highs and the low lows are averaged out across the entire group of stocks, the end result is that your losses will always be less and your gains will be less than for a single stock. The larger the group you average together, the more they will tend toward a single average of gain or loss.

For instance, if you looked at investments in all of the various stocks related to railroads over the last 50 years, they would have an overall trend downward. A similar examination of all the various stocks related to the insurance business would show a general trend upward.

This kind of trend discovery also develops as you average a longer and longer period of time. For instance, if you average across one year, the S&P 500 was down by 1.54% in 1994 but up by 34.11% in 1995; however, it has shown an average gain of 11.54% over the last 15 years. These trends begin to emerge only when you average over a long enough period of time.

If we combine the idea of averaging across a large number of stocks and across a long period of time, we can discover if there are some megatrends that are inherent in the entire stock market.

Someone has done exactly that for the largest possible group of stocks and for the longest period of time possible. The result is the ultimate performance reference for the stock market investor.

Professor Jeremy J. Siegel has created an index for the entire stock market called the “real-total-return” trend. It considers all the stocks, not just the groups that make up various indexes or mutual funds. It also adjusts for inflation and considers the capital gains and dividends of the stocks. The period of time he considers is from 1801 through 1998.
That is as long as there are records to support an analysis. The resulting graphic overlooks the year-to-year fluctuations and economic turbulence and displays a remarkably stable long term total return on equities of 6.8% per year over and above inflation.

This is a remarkable discovery. It means that the overall basic trend of the stock market is increasing by that much every year for the past 197 years. The effect of that would be that if you invested just $1.00 in the market in 1801, you would have $561,264 dollars today (equal to more than $1,000,000 in 1801 adjusted dollars).

This is a useful performance index that you can use to make shorter term investment decisions, as you will see if you read the section about Regression to the Mean investment strategies.  

In commodity trading, an option is called a futures contract and it works in a very similar manner as the put and call options described in other posts to this blog but with much greater leverage and margins. For this reason, the futures contracts can have great value and there is a very active market in the buying and selling of futures contracts. However, it gets very involved and can move very fast, at times, such that this is regarded as being one of the most difficult investments to manage and follow properly. Most experienced traders will avoid the futures market entirely.

Our position, at 21st Century Economics, is that we are not addressing the professional Wall Street investor with our service. We, therefore, do not advocate investments that require minute-by-minute attention to the rises and falls of the value of the investment. Futures trading is like that.