Don't let models or correlations fool you.

In today's business world, regression-based models are used frequently. But what if those are wrong or not used properly at times?
Gillian Tett and Peter Larsen write "...the industry has pinned considerable faith - and business strategy - on a set of models that now seem less than fail-safe." In their article, Market faith goes out the window as the 'model monkeys' lose track of reality, they explain how models have become so important in the industry. Models are used sometime to predict situation, but what if what was predicted turned out to be the complete opposite?
These "wrong" models can impact businesses in a tremendous way. They can cause huge financial damage to the company. In this article an example is presented to us of why models have become very important. After the downfall of Ford and General Motors, banks have started trading arbitrages between debt and equity products. Also, some have gone into credit derivatives by relying on other untested models. Banks have sold their tranches of CDO's (collateralized debt obligations) to their clients; however, they've kept the most risky equity to themselves. This might hurt the banks in the long run. Also when this trading stops, it could have a very big impact on the banks since it was a big part of their investments. Some analysts said that their revenues could fall by a third. Some banks have already begun realigning their "models".
Models can mean different things and usually people decide to believe that they are right in thinking that way. We need to accept that sometimes those models can be wrong. A person before fully relying on a model should evaluate it and see if all the strategies for a model and the right range were used.
We cannot fully trust data to predict the future. In the article, Data Mining isn't a good bet for stock-market predictions, we discuss why that is. The author Jason Zweig states, "The Super Bowl market indicator holds that stocks will do well after a team from the old National Football League wins the Super Bowl. The Pittsburgh Steelers, an original NFL team, won this year, and the market is up as well. Unfortunately, the losing Arizona Cardinals also are an old NFL team." He also makes another example with the "Sell in May and go away" rule. However, the market was gone up 17% since April, so he says that rule isn't looking too good now. Jason agrees that those assumptions are completely unrealistic. However, Mr. Leinweber puts it, "they are one of the leading causes of the evaporation of money, especially in quantitative strategies." It is sad, but very real. This is what is going on in today's world. People will try to predict things that aren't real but others will believe that they are.
One example that is ridiculous is that a person can predict stock returns by tracking the number of nine-year olds in the United States. Also another example says that stocks are more likely to go up on days when smog goes down. In both examples, the things being predicted do not even have anything in common. How can people actually believe such things? The reality is that they do.
What can you do so you do not fall into this trap? In this article, Jason tells us how to do it. He says that first you need to see that the results make sense. The first rule he writes, "Correlation isn't causation, so there needs to be a logical reason why a particular factor should predict market returns.' This means that because two things correlate with one another, it does not mean that they are caused by one another. It just means that they correlate, nothing more. In some cases, yes they are caused by each other but like the examples mentioned above, that is not true for every single case.
The second rule is to break the data into other pieces. Zweig states, 'Divide the measurement period into thirds, for example, to see whether the strategy did well only part of the time. Ask to see the results only for stocks whose names begin with A through J, or R through Z, to see whether the claims hold up when you hold back some of the data." This way we can see if maybe the correlation is just happening to some parts of the data, but not to every single piece.
He then says, "Next, ask what the results would look like once trading costs, management fees and applicable taxes are subtracted. Finally, wait." Taxes and other extra costs could be impacting the results. As for waiting, we all know that time will tell. Mr. Leinwebers says, "If a strategy is worthwhile, then it will still be worthwhile in six months or a year."
So remember, correlation does not always mean causation!

The importance of how we view statistics

In the article, “The median isn’t the Message” by Stephen Jay Gould, statistics is discussed in a not so usual way. The author, Stephen Jay Gould, had been diagnosed with abdominal mesothelioma. The first thing he wanted to know was to read the usual statistics about the diagnosed disease. When he did so, he found that mesothelioma has a “median mortality of only eight months after discovery.” Because, he was an intellectual and his technical training had taught him, he understood what “median mortality” meant. To any of us, it could have meant a totally different thing. Like Gould says, “I suspect that most people, without training in statistics, would read such a statement as "I will probably be dead in eight months.” Gould on the other hand knew what this meant which was that half of the diagnosed patients could live longer than the other half. This is when he instead of panicking started to work on being a part of that “other” half of patients.

This is true in statistics. People can go very different ways. A median and mean signify and can determine very different things. Yes, they have something in common but they certainly do not mean the same thing at all. In the article, Gould jokes, “A politician in power might say with pride the mean income of our citizens is $15,000 per year. The leader of the opposition might retort, but half our citizens make less than $10,000 per year." This particular joke explains everything. The mean and median show different views of a situation.

The reason why it is important to be careful when using statistics to describe something in particular is because not everyone will understand what you mean. Therefore, people can be taking that statistic completely the wrong way. For example in the article mentioned above, a statistic could really become something very crucial to someone. It is important to be knowledgeable and informed on what those statistics mean or what they could be representing for a certain individual.

Another way that statistics can trick us is when using randomness. In the article, “The triumph of the random” by Leonard Mlodinow, he talks about the misconceptions of the random variables. People find themselves doubting whether the victory of Joe DiMaggio was either luck or probability. Leonard says, “We find false meaning in the patterns of randomness for good reason: we are animals built to do just that.” This is true. It could be dangerous to make an investment on something we do not quite understand, and it could also be devastating to see someone as a failure just because they did not succeed. Leonard cited in his article, “Extraordinary events, both good and bad, can happen without extraordinary causes, and so it is best to always remember the other factor that is always present—the factor of chance.”

These two articles talk about a main subject in our class which is statistics. The first one by Stephen Gould is about how the mean is sometimes related to the median. However, we need to understand that they are not the same thing. Extreme values can alter the mean, but will not alter the median. We have spoken about this in our class as well. Gould’s life could have been seen as an example of how someone can interpret a certain statistic in a completely different way. He chose to learn about his disease and manage it; he was able to be in the half that lived longer than 8 months. Others, however, did not understand what that statistic meant and assumed that they only had 8 months to live. This could have affected them mentally and could have been an added factor to their death.

The second article also tells us to be careful when using statistics. Randomness could mean very different things as well. It could really be that one is performing well or it could also mean that one just got lucky. Luck vs. probability. Many argue this, while others support it. Whichever really is, it should not matter to us. People can still be remembered no matter what they did or achieved. If loved, they will stand out in the memory of their loved ones.

We all view things differently which means we will decide to act on things differently as well. We are free to choose what to believe and how to do it. However, when it comes to using statistics we should be careful and look at all the possible outcomes. One should know that there are two ways an investment could go. It could either go very well and give you a big win or end up at a big loss. Whether it comes to understand the statistic well or luck vs. probability, we need to be focused and open to the worldview. This is the only way one would be able to beat our own statistics.

The effect statistics has on the world

Why do we use statistics in our lives? We can use statistics in our daily lives for pretty much anything. Statistics is usually used to collect data, organize it, interpret it, and also analyze it. In today's world, people on the internet are using statistics for many different purposes. They either just want to present and describe information, or draw conclusions about their data. Other companies are now using statistics to make forecasts in order to improve their businesses. Statistics is being used more and more each day. For whatever purpose or main focus, statistics works efficiently. A place where statistics is used every day can be the stock market. How do you think they decide on whether or not the stock price is going up or down? Easy, they put what we call statistics in action. If you think about it there are an immense number of ways in which people can even become rich by using stats.
"Airfares made easy or easier," is an article I recently read in which the author discusses about an online software that predicts how much the price of an airline ticket will rise or fall during an amount of time. Can you imagine what this would do? It would give people an opportunity to save their money and buy when the time is right. Do the airline companies mind? Does it affect their profit? Absolutely not, because right after they decide when it is the best time to buy, Farecast itself takes them to the airline website to buy their tickets. This software is called, Farecast, and it was created by a professor of computer science and engineering at the University of Washington, Oren Etzioni. His new program requires tons of data that will search for information about the seats in airplanes and with that create an algorithm that will be able to predict whether the prices of the tickets will rise or fall. The company says that its success rate was between 70 to 75 percent and that the fares would move up or down according to the range that it predicted. This is what statistics is! Can you believe what Mr. Etzioni did with it? Well, do not be too surprised with this.
While reading the article, I also found many other programs that use the same technique in which statistics is involved in order to predict the outcome of something. Mr. Etzioni himself has also been involved with these other programs as well. MetaCrawler, which is a search engine, and also NetBot which I learned is an online comparison shopping service. A really interesting program that I read about on this article was Zillow.com. This website uses tons of statistics. It searches data in certain county land records and with that guesses on the value of (in 2006) 65 homes across the United States. In times like today with the Real Estate being really low, you can use this website to see how much your house could sell for and realize that it is definitely not the time to be selling. Another program that I find really helpful for many people is Inrix. This program uses data in order to predict traffic. How does it do this? It uses statistics by measuring the speed of a sample of population of vehicles that have satellite receivers, and also takes in consideration information such as weather and schedules of schools, or concerts. This information is then sold to companies that distribute it to Internet portals, cell phones, and in car navigation systems.
Science and also medicine are two subjects that are mostly based on statistics. It is very important that in these subjects, statistics is used daily and efficiently because it can help improve a new drug or a new electronic device that can be life saving to some people but can cause major problems for someone else. Prescription drugs are always based on a certain amount of people (statistically called a sample) who are then tested and examined in order to be able to record how they react to that certain drug. For example, there is a new vaccine for the H1N1 virus and the pharmacists will need to say who is more prone to react badly to it or what the side effects of it are. How would they know this? They had to have tested and then used statistics to predict the effects on an average amount of people.

Last but certainly not least are advertisements. I can’t think of a place where statistics could be used more than in ads. Everywhere you see there is an advertisement. How do they know which advertisements should be placed where or shown to what type of people? Yes, statistics! They study who resides where, or who is more prone to go on this certain website, who buys more of a certain type of product, etc. After knowing this statistics, it all becomes real easy for them to target innocent people who are just dying to spend more money of such products. Each one of us has been a victim of these examples. It is not easy to get away from statistics. It is everywhere. But what would we do without it? Not much, in reality we all take advantage of it and we love it!