Can Data Science Predict the Stock Market?

CAN DATA SCIENCE PREDICT THE STOCK MARKET?

Over the years, data science has proven to be a highly valuable tool for many different pursuits, from securing sensitive data to processing huge amounts of information more quickly to helping improve data-driven decision making.

But what about using data science to predict the stock market?

It’s an interesting question, and one you might be asking yourself if you’re interested in a career in data science or finance.

For as long as it has existed, people have been looking for a way to predict the activity of the stock market so they know when to sell for a profit and when to buy at a discount. Being able to harness the power of data science to predict the behavior of the stock market could, quite literally, help you and others become quite wealthy. While money certainly isn’t everything, being financially stable reduces stress, improves your ability to provide for your family, and gives you the opportunity to experience things like extensive travel that you might not otherwise be able to afford.

Let’s consider this question of “Can data science predict the stock market” in more detail.

What Data Science Can Do

What Data Science Can DoData science uses algorithms to predict future events. This is something that people already experience in their everyday lives.

For example, Spotify and Netflix make recommendations for media based on what a person has already seen and indicated that they liked. Data science can also use algorithms in order to identify unusual patterns of behavior based on past behaviors. For example, data science is used in the detection of credit card fraud. As another example, facial recognition features allow Facebook to tag people on images and make it possible for a phone to recognize its owner.

There are plenty of other examples of what data science can do as well.

In healthcare, data science can be used to track – in real-time – viral outbreaks like the flu. It can also use machine learning to devise optimal treatment programs for cancer patients.

In transportation, data science can be used to map routes for drivers that save gas. This can be a huge financial savings for transportation companies, but it can also help everyday drivers use less fuel and keep more money in their pockets. For delivery companies, data science can help them reroute packages around weather events to ensure timely delivery to their customers.

Other uses include predicting crime, maximizing the performance of sports athletes, and improving e-commerce.

In short, the usefulness of data science is virtually unlimited. This has been proven time and time again in various industries around the globe. But what impact might it have on the stock market?

Predicting the Stock Market

Predicting the Stock MarketWhen we consider the different uses for data science discussed above, many of them are much more simplistic than applying data science to something as complex as the stock market.

For example, liking a song on spotify enables data science to make recommendations to you for similar songs you might like. That’s pretty easy.

What’s much more difficult is for Spotify to pick the exact song at the exact time that maximizes the emotional effect the song might have on you at that very moment. This is kind of what it would be like to pick the right stock in which to invest.

So why is it so complex?

There are a lot of reasons for its complexity. First, the stock market is inherently volatile and unpredictable. Even when using machine learning to analyze past performance of a stock’s price, it isn’t a given that what the model will predict in the future will actually happen.

Second, there are a large number of variables that influence a stock’s price. Interest rates, weather events, company scandals, government oversight, and even the behavior of company executives can cause a stock’s price to rise or plummet.

Third, making predictions is different from long-range forecasting. It’s far easier to predict what a stock’s price will do tomorrow based on what it’s done over the course of the last week. What’s much more difficult is to forecast what a stock’s price will do a year from now or five years from now.

In other words, there are simply too many factors at play to make pinpoint predictions or long-term forecasts for the stock market. It’s not for a lack of trying, either! People have been trying to win at the stock market game for generations, including those that have been using data science to try to make better predictions since the 1980s.

Types of Data Science Tools That Might Be Used for Stock Market Predictions

Types of Data Science Tools That Might Be Used for Stock Market PredictionsSetting aside the difficulties of predicting the stock market’s behavior, let’s examine some data science tools that might be useful in trying to make such predictions.

For starters, data scientists use a lot of algorithms to help gauge what the stock market may or may not do. Algorithmic trading identifies when buying or selling a stock is ideal – such as buying after a stock only after it has decreased in value by a certain percentage in a certain timeframe, like 2.5 percent in a four-hour period. Likewise, algorithms can be used to help make more informed selling decisions, such as selling a stock after it has increased in value by at least 10 percent.

As Bloomberg noted, though, data science cannot be used to predict the stock market quite yet.

Choosing a good investment is much harder for a machine to do than it is for a machine to pick a product a person might like on Amazon. For decades, computer algorithms have been built and tweaked in order to try to predict the stock market and make the right investment at the right time.

Overall, these algorithms usually do not fare much better than the average. That is to say, a person could get the same results by flipping a coin.

Another way that data science can be used in the stock market is to use models. This involves exploring data related to past stock market behaviors and using that to forecast what might happen in the future.

Usually, data scientists use time-series models for this, such as the price of a stock that’s ordered by a set amount of time, like hourly, daily, or monthly. By evaluating how a stock’s price has performed over the course of the last week, traders can predict what might occur with the stock’s price in the upcoming week.

Data scientists might also use what’s called training to try to predict what the stock market will do.

Training involves using certain data to teach machine learning what predictions to make on past data. So, a data set would be split in two, typically with 80 percent of the data being used for training and 20 percent used for testing. 

The 80 percent of the data that’s used for training would be comprised of past data on a particular stock, such as the historical trend of a stock’s price over the last year. Then, machine learning would use that information to predict what the price of the stock might do over the course of the next month, six months, year, and so on.

To validate what the machine learning has predicted, data scientists would compare the predictions with the testing set of data.

For example, if machine learning is trained on 12 months of a stock’s price data, the data from the first ten months would be used for training and the data from the final two months would be used for testing. Then, using what happened in the first ten months, machine learning would predict what would happen in the final two months. The model’s predictions would then be compared to what actually happened.

The goal in doing so is to see how accurate the model is in predicting stock market behavior. The goal here would be to reduce the error between the real data and the predictions in order to create a reliable model for predicting stock market behavior.

Data scientists can even turn to less traditional data in an effort to forecast what the stock market might do.

For years, investors have used data like sales figures and financial statements to determine how worthy a company is of investment. But today, investors look at alternative data like consumer reviews and social media activity to try to predict what a stock price may or may not do. 

We see this principle playing out to a degree with certain cryptocurrencies, like Dogecoin and Shiba Inu. The prices of these currencies are very heavily influenced by popular opinion, so when billionaire Elon Musk tweeted about Shiba Inu, its price soared. The application to the stock market, then, would be to use data science to monitor “chatter” on social media about certain stocks, with the assumption that as chatter increases, so too will the price of the stock.

Of course, the operative word in all this is “predict.” Making a prediction does not mean that what’s predicted will occur. It might be somewhat likely to occur, or it might even be highly likely to occur, but at the end of the day, it isn’t a guarantee.

This is an important point because data science is not a magic tool that will enable you to know the future. There is still plenty of uncertainty and risk involved when using data science to examine the stock market.

Why Data Science Cannot Yet Predict the Stock Market

There are several reasons why machine learning is not yet consistently making better-than-average predictions about the stock market.

One reason is that the data related to good investments is always changing. Algorithms do better with stationary data than data that is constantly in flux. This limits the ability of algorithms to make predictions about what stocks will do in terms of their future price.

Another reason is that there is more noise than signal in the data that is collected. Stocks move up and down a little for no discernible reason, and machines cannot figure out what the noise is versus what the signal is when a stock moves in either direction.

The available data is also rather small. Stock market data only dates back around 125 years, and most companies on the exchange haven’t been on the market for that length of time. As a result, there’s only so much data that’s available for things like training and testing.

Having said that, data science is making gains in its application to the stock market. A study by MIT discusses the potential of using traditional and alternative data to predict stock market outcomes. The study found that machine models were able to outperform their human counterparts by 57 percent.

While 57 percent might not seem all that great, when trillions of dollars are involved, even a percentage point improvement in selecting the right stocks to buy or sell can result in a significant increase in stock-related income.

The Difference Is Small

A good time to sell a stock may only be a small difference in a stock’s price – pennies, even. Machines might not pick up on such a small difference. Often, machines need clearer results and more identifiable patterns for their algorithms to work.

Our brains, on the other hand, are much more adept at picking up on very small cues that a stock’s price is just right for buying or selling. Call it intuition or a gut feeling or whatever you like, but many stock traders rely on their gut to make predictions about the market that beat the average.

Data science is advancing at a rapid pace, and it is propelling machine learning and artificial intelligence to new heights. It is also true that a lot of stock market activity takes place on machines, with algorithms in place to trigger buying and selling.

And while data science might not be there yet in terms of making accurate predictions about stock market behavior, perhaps someday in the near future it will. Until then, just know that there are plenty of options for you to study data science and many ways that you can use your data science training in business, economics, finance, and other careers.

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