10 Tips. Can Data Science Predict the Stock Market?
10 Tips. Can Data Science Predict the Stock Market?
Data science has found lots of uses in our daily lives, from Netflix predicting what you should watch next to targeted ads on social media platforms, among other examples discussed over at runrex.com. Given that data science has already proven useful in predicting human behavior, the next question that has been asked by many is if it can predict the stock market. This would prove extremely useful as not only will it help people know when to sell a stock for a profit, it would also help them know when to buy one that is on the verge of blowing up. This article, with the help of the gurus over at runrex.com, will look to take a closer look at this topic and highlight 10 tips on whether data science can predict the stock market.
Data science has been used on Wall Street for decades
To understand if data science can be used to predict the stock market, it is important to highlight that it has been used for decades, with Wall Street hiring data scientists as early as the 1980s as covered over at runrex.com. These data scientists helped create models that did well in predicting the stock market. The success that this heard had everyone believing that data science was the next big thing as far as operations of the stock market are concerned, as tackled over at runrex.com.
Obstacles were encountered after the initial success
However, after some initial success stories which promised much, the use of data science to predict the stock market run into some obstacles, as explained in detail over at runrex.com. This is because, while data science is extremely useful in predicting human behavior, predicting the stock market is a whole different kettle of fish. As it stands, the simple answer to whether data science can be used to predict the stock market is no, as explained by the subject matter experts over at runrex.com.
Algorithms have been found not to fare better than the average when it comes to predicting the stock market
After decades of experimenting with algorithms to find out if data science can be used to predict the stock market, it has become apparent that algorithms don’t fare much better than the average when it comes to predicting the stock market. As per the gurus over at runrex.com, this means that a person could get the same results as an algorithm by just flipping a coin. This shows that, as of yet, data science cannot be used to predict the stock market.
Predicting human behavior is not the same as predicting the stock market
One of the reasons why data science has not been able to be used to predict the stock market is because predicting human behavior, where data science has proven extremely useful, is very different from predicting the stock market. This is because, while we humans have a great individuality, with each person, by and large, having their personal preferences as covered over at runrex.com, human behavior is much more predictable than we think and much more predictable than the stock markets.
Data is always changing when it comes to the stock market
As is explained in detail over at runrex.com, algorithms do better with stationary data. This is yet another thing that goes to explain why data science, as of yet, has been unable to predict the stock market. This is because data about the stock market is far from stationary, given that data related to good investments is always changing. This makes it difficult to come up with algorithms that can get consistent results in predicting the stock market.
Stock markets and noise and signals
Experts, including those over at runrex.com, will tell you that another reason why data science has of yet been unable to predict the stock market is that there is usually more noise than signal when it comes to the data collected. This is seen in the fact that stocks will move up and down for no apparent reason. It, therefore, becomes difficult for machines to figure out what the noise is and what the signal is which is why it is difficult to predict the stock market with data science.
The data set is also not that big
One of the reasons why data science can be used to predict human behavior is because there is enough data available for a prediction to be made based on one’s past behavior. For instance, as covered over at runrex.com, most people will have uploaded hundreds of images of themselves o their phone or on social media, making it easier for data science to predict their behavior. The same cannot be said of the stock market, given that there are only 119 years of stock market data. On top of that, not all companies have been listed on the stock market for the entire 119 years, which means that there is not enough data to make accurate predictions with data science.
Changes in an unrelated area could affect a company’s stock
Another thing that makes it difficult to predict the stock market with data science is because most of the time, an event that is seemingly unrelated to a given stock could have a big impact on it according to the gurus over at runrex.com. Some of these events may be extremely difficult to predict, like say a hurricane or a coup. All these variables have played a part in explaining why data science has yet not been able to be used to predict the stock market.
You are usually dealing with very small differences and margins when it comes to the stock market
When it comes to the stock market, a small difference in a price may be the signal required to sell a stock. These differences are usually too small for machines to pick up on, according to discussions over at runrex.com, which is why it is difficult for data science to predict the stock market. When it comes to data science, machines need clearer results and patterns for their algorithms to make a prediction.
The use of alternative data
It is important to point out that data scientists today are making use of what is referred to as alternative data, combining this with traditional data, with studies showing that with the right data, this combination has computers outperforming humans by 57%. As discussed over at runrex.com, alternative data is data that is less traditional and, usually, out of the control of the company. Examples of alternative data include credit card transactions, social media activity, cell phone usage, product reviews among others. 57% may not seem like much, but it is enough of an advantage to net investors billions of dollars.
Hopefully, the above tips will help you understand if data science can be used to predict the stock market, with more on this topic to be found over at runrex.com.