Investment Brokers and Financial Advisors usually have a rating system for each publicly traded stock.  That rating is supposed to provide an indicator for you as an investor regarding which stocks to buy, which stocks to hold, and which stocks to avoid.

One common range is “buy”, “hold”, and “sell”.  Their meaning is pretty much what you would expect.  If you are interested in buying some stock, those companies with a “buy” rating are the stocks they would recommend for your consideration.  A company with a “hold” rating would be one that they are not recommending you buy, but if you already have the stock, you should hold onto it.  If you have stock with a “sell” rating, then they are recommending that you sell it and move on to happier hunting grounds.

Similarly, another range is “A”, “B”, “C”, “D”, and “E”, with stocks rated as “A” being most desirable, and “E” being least desirable.

Where do these stock ratings come from?  

Each brokerage house or investment company employs a large number of financial analysts.  There are literally thousands of publicly-traded stocks available.  That list would be subdivided into manageable groups – automobiles, hi-tech, transportation (e.g., railroads, trucking), banks, aviation, agricultural equipment, etc.  Each group would be assigned to a financial analyst for evaluation.  

A typical evaluation would include a company’s performance over the last ten years:  its rate of growth (or decline), the type of business or industry it is in, the rate that industry is growing overall and how that compares to the company being evaluated, etc.

Depending on the workload of the financial analyst, his level of experience, etc., the evaluation may for the most part be limited to these statistics.  

Is that a good idea?  What are “statistics”?  

Statistics often seem mysterious.  People use statistics when attempting to predict a stock’s future performance, to forecast the weather and climate, to anticipate Christmas sales, to predict what the economy will look like for the next year.  The cost you pay for insurance – car insurance, home insurance, life insurance, etc. – is all based on statistics.  Statistics always seem to be surrounded by very complicated mathematics that are well beyond our understanding, yet companies and governments spend a lot of money producing them, and claim to draw important conclusions from them.  Are they really that good?

What are statistics?  

Statistics are historical records, pure and simple.  Nothing more, and nothing less.

Statistics tell you what happened in the past.

Statistics – past history – as the small print will tell you, does not guarantee “similar performance” in the future.  

In other words, using statistics to predict the future is like driving your car by looking in the rear-view mirror.  Short and simple, that’s it.  

When the future unfolds the same way as the past, forecasting the future via statistics looks great.

Insurance companies can use statistics because generally thousands of people will – on average – have the same number of car accidents, the same number of home fires, die at roughly the same age, etc.  But when a really bad hurricane like Katrina hits New Orleans?  Statistics are useless. Estimating “bad” is impossible.  Insurance companies get socked with huge payouts that could easily bankrupt them (so they have something called “reinsurance”).

Using statistics to buy stocks works if companies continue to act as they have in the past, which over a ten-year period, is likely to be true for many if not most companies.  If you are satisfied with “average” performance for your stock portfolio, and the economy acts as it usually does during that time, your returns will be – well, maybe better than a savings account.

For the risk-averse, that may seem okay.  But if you are not looking for continued average performance, let’s see what can be done by the investor who has the gumption and determination to do more than just look in the review mirror.

Begin with two questions:  

First, what industries as a whole are growing at a rate generally that is out-pacing the economic growth of the country?

Focus on the various companies in that high-growth industry.  Which are out-pacing the others?  

Look at the “market capitalization” of each company – that is, the stock price multiplied by the number of shares.  

Companies worth less than about five billion are likely to be too small.  They are usually – but not always – teetering on the edge of financial stability.  

Companies worth more than hundred billion are likely to be too big to be nimble enough to have high growth rates –  although several large high-tech companies have been quite nimble lately.

Look at graphs of how the stock price has changed in the past month, in the past three months, and the past year.  All three should have a generally upward slope.

Look at the graphs for the last ten years.  If the stock price was once higher than it is now, what brought it down?  Did they fire the CEO for that fall?  What did the company do to recover?  What are the new CEO’s areas of focus and concern?

Second, learn all you can about the CEO first and foremost, and about the Executive team as a whole, for companies that interest you.  Read the company’s own statement of what it does.  What are their attitudes?  Are they clearly and sharply defined? Or are they vague and nondescript?

How are they distinguishing their company’s products from the competition?

A good CEO and Executive team can turn a mediocre product into a winner.  

A mediocre CEO and Executive team can turn a market-beater into a failed opportunity.

In 1982, IBM was in total control of the computing industry.  IBM brought out the first PC; it was an instant winner, stealing the show from Apple and all the others.  But then IBM’s Executive team decided that IBM’s focus should be on the “big iron” – the traditional “mainframes” that had always been its bread and butter.

IBM saw no value and no future in the little single-person computers.  They ignored several opportunities to build an even bigger future that included those PCs.  They failed to take into account the old adage:  “Invent what is going to replace your current best-seller, before someone else does.”

But upstart Apple and an even smaller upstart Microsoft took those PCs very seriously.  By the mid-1990s, the future was clear:  big-iron mainframes were a thing of the past.  And if that fact was not clear enough, Apple’s iPhone and iPad, and Microsoft’s Windows and Windows Server were unmistakable final nails in the mainframe’s coffin.  

The world had changed, and mainframes – and IBM – were left struggling to survive in niche markets.  IBM is now a boring, hum-drum company, with boring, hum-drum stock prices.  The stock curves go nowhere, and IBM is going nowhere.  

The same fate is true of boring old General Motors and Ford, while upstart Tesla now has a market value  almost twenty times either of them.  All three “just sell cars”.  What is Tesla selling that GM and Ford are not?

Look for the market leaders.  Look for CEOs that are over-achievers.  Find companies that are over-performers.  Buy their stocks.  Maintain a watch on their three-month performance.  Replace under-performers with over-performers.  

Study, analyze, learn, and improve your own performance.  Be a market leader yourself.■