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Financial ratios convey tremendous amount of information to an investor, however, they are no panacea. Limitations of ratio analysis can often cause you to miss good investments, and in some cases, make bad investments. Anyone who has ever tried to value a company has used some rules of thumb when conducting the financial ratio analysis. Rule of thumb a rule of thumb is a guideline that provides simplified advice regarding a particular subject. It is a general principle that gives practical instructions for accomplishing or. rules of thumb for ratio analysis can often be valuable in first cut selection for equities trading, particularly by value investors and financial managers with a value philosophy. The problem is that people look for rules of thumb that remain true in all market conditions, when in fact, different markets value different ratios. Do rule of thumb approaches to ratio analysis offer any value to the financial manager (e. , 2-1 current ratio rule or 50 debtequity rule)? Answer there is no answer at this time. In english, rule of thumb refers to an approximate method for doing something, based on practical experience rather than theory. Its earliest (1685) appearance in print comes from a posthumously published collection of sermons by scottish preacher james durham many profest christians are like to foolish builders, who build by. Anything below this level requires further analysis of receivables to understand how often the company turns them into cash. It may also indicate the company needs to establish a line of credit with a financial institution to ensure the company has access to cash when it needs to pay its obligations. But when we increase n from 10 to 25 the ratio exceeds 3 just 4 of the time. So sample size clearly plays a role in how much faith we place in this rule of thumb. Similarly, as with dcf analysis, the adr rule of thumb does not take into account an investments tax implications for different inves-tors. Previous approaches to sample size for adequate regression modeling. Green used statistical power analysis to compare the performance of different rules-of-thumb for how many subjects were required for linear regression analysis. These rules-of-thumb can be classified into two different classes.