Why Financial Data Isn't Always Accurate

Sometimes financial, and economic data don't seem to make sense, but in reality it does as most things happen for reasons, even if those reasons are no reason at all. In other words, just like the people, events and computers data represents, financial and economic data is a monetary reflection of a medley of fact, opinion, motive, and analysis.

To rely solely on authoritative data is like blindly worshiping a financial god that's measurements are often only angles of a statistical sample. Since no humans or computers are gods i.e. figuratively perfect, it follows that data emerging from such is subject to error, fallacy, manipulation or any number of influences. Statistical data supports this claim!

To illustrate with example, according to the U.S. Bureau of Labor Statistics there are six alternative measures of "labor utilization" including the 'official' unemployment rate. So what exactly does this mean sans le langage locales? It means that the BLS is officially acknowledging there are five other ways of looking at the same thing, but that only one way is endorsed as official data, and not that the other ways are wrong.

Another example of inaccurate financial data is historical in context. According to a publication by the Herald Tribute Media Group, Moodys, Standard & Poor's and Fitch all over rated mortgage backed securities that they were 1. Paid millions to rate the MBS' by Goldman Sachs, and 2. Were later acknowledged to contain bad loans by Goldman Sachs, the MBS packager. If that's not convincing enough, how about Bernie Madoff's client financial statements; they were on paper and on company letter head so had to be authentic right?

Some may contend, financial data is transparent, but what good is transparency if all you clearly see is false, partially false or occasionally true? Conflicting financial data might not even conflict until it's viewed in hindsight such as with the above examples. Even if financial data is accurate, which it is sometimes, it often only represents a fraction or variable in the total equation of what is really going on with a sector of the economy, financial market, business etc.

So what does work then? How can conflicting financial data be assessed accurately? That is the big question, that the answer to is probably priceless if it can be applied to any situation. For starters though, as with scientific verification, conditions must be repeated to be true of causal and/or correlated relationships. For example, and there are many, if two of four major companies in an oligopolistic industry report layoffs and a quarterly loss does this implicitly necessitate the industry is in decline or that the other two businesses are also experiencing financial challenges? Not always.

There are many ways to financially slice and dice the above scenario. For example, what stage in the business cycle are they in? did the CFO's report that revenue increased do justice to the increased cost of goods sold? Do the other companies have better technology, marketing and business development? Is their cost of capital too high?

There is simply often lots of data to analyze in any given financial scenario, and the more data there is, the greater the probability some will conflict as size yields complexity and hence, differentiation among attributable financial data. In such case accurately and efficiently compiling and interpreting financial data becomes important. Hence, the answer to the question, 'how can conflicting financial data be assessed' is, with effective data management.

Sources:

1. http://bit.ly/bhaiHm (D. Dremen: Forbes)
2. http://bit.ly/cStGam (Bureau of Labor Statistics)
3. http://bit.ly/cdUjyN (J. McCabe: Herald-Tribune Group)