The succeeding quote is taken from Niderhoffer's Practical Speculation. Looking for your opinion.
The one thing that everyone, from the most uninformed layperson to the most erudite professor knows about the stock market is that earnings determine returns. Chapter 1 of every finance text book, lecture 1 of every investment class and every news dispatch all agree:
1) Earnings and markets move up and down together
2) The greater the earnings increases, the higher the market return.
3) When earnings are up, it is time to buy stocks, and when earnings are down, it is time to sell stocks
4) When the markets price/earnings ratio is high, it is time to sell.
Analysis of each of these hypothesis would invalidate each and every one of them as false. Contradictorily, each is completely opposite to the actual empirical relations. Almost everything investors are taught about in relation to the earnings and stock markets returns, whether in business schools or on the stock market pages of the newspaper is wrong.
Shrewd articles know that they will not get far in life by relying on the conventional explanation of market moves. But the great majority of investors are deceived on both the big and the small aspects of this relation.
Firstly, it is necessary to examine the actual relations between earnings and the market returns. There are many different leads, lags and ratios to be tested. The best way to reveal these relationships is with a scatter diagram. If these data tend to life close to the line of best fit, then there is a strong relation. If the data tends to appear to lie In a shapeless mass around the line, then there is little or no relation. Each of the charts uses earnings data from S&P securities price index record, updated with current figures and returns data from London Business Schools comprehensive database of global markets.
According to the induction, stocks are supposed to go down when earnings are down and go up when earnings are up. In fact, they dont and the opposite relation holds good.
1) If reported S&P 500 earnings rise in a year, the S&P 500 is likely to perform worse than average that year.
2) If the reported earnings fall in a year, the S&P 500 is likely to perform better than average that year.
The inverse relationship is shown in the above figure. Notice how the line of best fit slopes gently from the northwest quadrant to the south east. This is the hall mark of what statisticians call negative correlation. Regression analysis is used to calculate an equation that expresses this slightly inverse relation between earnings and next years market return as a line of best fit: S&P 500 return = 9.6 percent minus one fifth of the annual percentage change in the S&P earnings. The equation explains some 5% of the total variation in stock prices from normal. Another way of saying this is that if a person new nothing about earnings change in a year, the best forecast of S&P 500 returns in 2002 would be 9.6%. Knowledge of the earnings would increase the predictive ability of the analyst by only 5%
1) Earnings and markets move up and down together
2) The greater the earnings increases, the higher the market return.
3) When earnings are up, it is time to buy stocks, and when earnings are down, it is time to sell stocks
4) When the markets price/earnings ratio is high, it is time to sell.
Analysis of each of these hypothesis would invalidate each and every one of them as false. Contradictorily, each is completely opposite to the actual empirical relations. Almost everything investors are taught about in relation to the earnings and stock markets returns, whether in business schools or on the stock market pages of the newspaper is wrong.
Shrewd articles know that they will not get far in life by relying on the conventional explanation of market moves. But the great majority of investors are deceived on both the big and the small aspects of this relation.
Firstly, it is necessary to examine the actual relations between earnings and the market returns. There are many different leads, lags and ratios to be tested. The best way to reveal these relationships is with a scatter diagram. If these data tend to life close to the line of best fit, then there is a strong relation. If the data tends to appear to lie In a shapeless mass around the line, then there is little or no relation. Each of the charts uses earnings data from S&P securities price index record, updated with current figures and returns data from London Business Schools comprehensive database of global markets.
According to the induction, stocks are supposed to go down when earnings are down and go up when earnings are up. In fact, they dont and the opposite relation holds good.
1) If reported S&P 500 earnings rise in a year, the S&P 500 is likely to perform worse than average that year.
2) If the reported earnings fall in a year, the S&P 500 is likely to perform better than average that year.
The inverse relationship is shown in the above figure. Notice how the line of best fit slopes gently from the northwest quadrant to the south east. This is the hall mark of what statisticians call negative correlation. Regression analysis is used to calculate an equation that expresses this slightly inverse relation between earnings and next years market return as a line of best fit: S&P 500 return = 9.6 percent minus one fifth of the annual percentage change in the S&P earnings. The equation explains some 5% of the total variation in stock prices from normal. Another way of saying this is that if a person new nothing about earnings change in a year, the best forecast of S&P 500 returns in 2002 would be 9.6%. Knowledge of the earnings would increase the predictive ability of the analyst by only 5%
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