News is a prime factor affecting the price, volume, and volatility of financial assets. Investor Daniel Calugar says that savvy traders are able to pay attention to breaking news, quickly interpret it and make trading decisions to take advantage of prime opportunities.
This is called news-based trading. Through this strategy, traders try to take advantage of temporary mispricing of security in the market. The challenge to this, of course, is that there is so much news that hits the airwaves that it can be difficult to track it all quickly enough to jump on the opportunities.
Today, algorithmic news trading is being used to overcome these challenges that humans would face. Computer algorithms are created to interpret the news as it breaks and quickly makes trading decisions based on it.
The algorithms work in a variety of ways. For example, they can scan news articles based on keywords or sources, process the data included, identify the underlying meaning of the news, assess whether the news is important, and finally execute a trade based on it — all automatically without the need for human intervention.
Below, Dan Calugar will explore the limitations and benefits of algorithmic news-based trading.
What is News-Based Trading?
News-based trading is fairly self-explanatory. It’s a trading strategy that looks to identify pertinent investment-related news and then make savvy trading decisions based on it.
The most common news stories utilized in this strategy are breaking news snippets, general economic reports, quarterly company performance reports, and other information that could impact financial assets in the short term.
The general strategy is to attempt to gain a profit by taking advantage of public sentiment that leads up to an anticipated news release or trades on the response that the market is likely to have after the news is released.
What Does News-Based Trading Focus On?
One of the biggest focuses of news-based trading is the planned events likely to impact certain financial assets’ short-term value. For instance, this could be the scheduled release of quarter company earnings or the Department of Labor’s next release of the Consumer Price Index.
In both instances, the news-based trader already has actions in mind that they will take depending on the news that is announced. For example, if they anticipate that the CPI will show a decrease in inflation, they might purchase certain stocks that benefit the most before the announcement is made.
Then, if they are correct, they’ll look to quickly sell the stocks they just purchased after many other traders drive the price up.
News-based trading can also be flexible enough, though, that it can be used for unplanned news events, according to Daniel Calugar. Of course, traders need to constantly pay attention to financial and economic news in order to be able to do this successfully. But, by creating processes for collecting breaking news and quickly analyzing it, news-based traders can act fast right after the breaking news has been announced — and before the rest of the trading public jumps on board.
Benefits of News-Based Trading
The most significant benefit of news-based trading is it takes advantage of market sentiment and not necessarily the actual performance of an individual financial asset. This provides another outlet for traders to make a profit in the markets, in addition to their in-depth analysis of specific assets or asset classes.
In essence, news-based trading is taking advantage of public perception of where financial markets may go. This can often be a very profitable undertaking, as the general investing public can be quite predictable.
News-based trading can provide a huge boon to an investor’s returns in a very short amount of time. It doesn’t require traders to invest a lot of money for an extended period to realize their returns since the turnaround from trade to return is quick.
Drawbacks of News-Based Trading
There are some drawbacks to news-based trading. For one, there aren’t always great opportunities to profit from the news. Not every piece of news will result in huge swings in the value of a financial asset one way or the other. This means that traders may purchase or sell a large number of shares, for example, and then not have it pay off because the news didn’t reveal itself in the way they had anticipated — or hoped.
Even when news-based trading does work, the fact is that it’s typically only going to produce short-term returns. The strategy can be a complementary piece to an overall investment plan, but it’s hard to be a consistently successful focal point of that strategy.
But Dan Calugar says the biggest drawback to news-based trading is that it takes a lot of manual work. Traders need to be very organized if they want to employ this strategy — knowing when certain big news announcements will be made and then having a plan for how they will react based on what the news actually is.
They also need to constantly be tuned to financial news stations and/or be plugged in with real-time financial news updates sent to their email and mobile devices.
It’s this fact — that news-based trading requires so much manual work — that algorithmic news-based trading seeks to solve.
What is Algorithmic Trading?
Algorithms are computer code written to train high-powered computers to analyze thousands of pieces of data each second and make split-second decisions based on that analysis. The programmer builds parameters for what the algorithm should tell the computer to do based on the inputted data.
In algorithmic trading, this code is used to have computers quickly process and analyze a wealth of data in a matter of seconds and then make automatic trades based on the parameters the coder has set. For instance, the algorithm may tell the computer to buy 500 shares of a particular stock if its price dips below its average price over a specific time period.
Of course, this is a very simplistic example, but it does explain the general principles of algorithmic trading. Of course, those who use algo trading will go much deeper than that to identify the best buy and sell opportunities in financial markets.
Essentially, algorithmic trading accomplishes two main things: It removes a lot of manual work for traders, and it removes the human emotional element from trading. At its core, it is a strategy that’s as objective as possible.
What is Algorithmic News-Based Trading?
Algorithmic news-based training uses the same principles outlined above but focuses specifically on interpreting the news. Algorithms are written to interpret news items and then make trading decisions based on the various parameters that are set — all without the direct involvement of a human.
There are many ways that this is done, Daniel Calugar explains. But, generally speaking, the algorithms will scan news items based on specific keywords. They will then process all the data included therein so they can identify the underlying meaning and assess its importance.
Once the algorithm has done all of that, it will compare its conclusions to the parameters that are set and make specific trades if the analysis meets the parameters.
To be able to accomplish this, the algorithm has to be able to quantify the news. In most cases, this involves assigning a particular score to every piece of news that is inputted. The score is then used to interpret whether the news is relevant to the parameters set up.
The best algorithms will also assess the quality of the news source. As most people know, not every news source is reputable. For example, a breaking news item from Bloomberg is likely assigned a higher reputation score than breaking news from an independent blog.
This is why drilling down and ranking the reputation and trust of the news source is essential to set up a successful algorithmic news-based trading strategy. It can’t just rely on keywords without taking into account the source.
How Algorithmic News-Trading is ‘Fed’ the News
There are many ways that algorithms will be “fed” the news to interpret. One of the most common ways, Dan Calugar explains, is through APIs. These are open sources of code that news sites and feeds make available for others to use to automatically pull in all of their items to another program.
APIs are used in a number of different applications around the world. As it relates to algorithmic trading, the program will pull in the news from various sources using their API. Think of this essentially as an RSS feed that the computer program will use to pull in all the data
Similar coding to APIs can be used for search engines as well. By pulling in Bing News and Google News searches based on certain keywords, the coder doesn’t have to individually input every individual news sources they want to include in the algorithm. This same approach can be applied to financial news aggregators such as Yahoo Finance, BloombergMarket, and Financial News.
All of this can be done by a competent and experienced coder. There are also algorithmic news-trading platforms that can be purchased outright or subscribed to for a monthly fee. However, many people who aren’t tech savvy, don’t have the time to dedicate to coding, or want to stick with other trustworthy sources will typically purchase the service from an experienced firm.
Benefits of Algorithmic News-Based Trading
There are many benefits of algorithmic news-based trading, which we’ll discuss below.
Speed is often cited as the most significant benefit of any type of algorithmic trading strategy, but that’s a particularly huge benefit of a news-based trading strategy. That’s because, as Daniel Calugar says, there is so much news that could impact financial markets that it can be very challenging for a single person — or even a large group of people — to consume all the pertinent news let alone actually analyze it effectively.
High-powered computers, by contrast, specialize in this type of data collection and analysis. Once the feeds are set up correctly, it’s no issue for a solid algorithm to collect every possible news item and process it instantly.
It’s not just the speed of the data collection and analysis that’s a benefit of algorithmic news-based trading, though. It’s also the speed by which the computer will execute the trade. Humans simply don’t have the capacity to complete trades as quickly as computers do, even if they have the same information at the same time.
Accuracy is another benefit of algorithmic news-based trading. Using computers to execute trades automatically removes the possibility of human error when completing the trade. In addition, once the algorithm has been written and verified correctly, there’s no possibility that the computer will incorrectly execute a trade.
On the other hand, humans can be prone to mistakes at times. For example, even the most seasoned traders can buy instead of sell and vice versa. Or they could type in 1,000 instead of 100. Any of those examples would be honest mistakes, of course, but they could be incredibly costly mistakes.
Computers won’t make those mistakes. Instead, they’ll only execute the exact trades based on the algorithm’s parameters.
Mistakes can also be made in the information and analysis stage of news-based trading. For instance, a human might hear or read a number wrong in a breaking news article. They might then use that incorrect information as the basis for a trade, which could end up being disastrous.
Like the earlier example, computers won’t make those mistakes. So, for example, they can’t “misread” or “mishear” words or phrases. And as such, they won’t make faulty decisions based on incorrect information.
A final benefit of algorithmic news-based trading is the objectivity it brings. Dan Calugar says that even very determined, focused, and restrained individuals can sometimes make emotional decisions.
They may understand the fundamentals behind news items and be able to accurately analyze the future effects. However, when it comes time to execute the trade, they hesitate or go “off-script” based on an emotional leaning.
Human emotions can cloud our judgment at times. News-based trading, in general, seeks to take advantage of temporary market inaccuracies driven largely by human emotion.
Algorithms remove the emotion from trading and bring a high level of objectivity to the strategy. The computers will analyze the news items from a completely objective, data-based perspective and then make accurate trading decisions based on it. You’ll never have to worry about a computer being swayed in a certain direction because of their love of, or disdain for, a particular company, for instance.
Limitations of Algorithmic News-Based Trading
While algorithmic news-based trading has a lot of advantages, there are some limitations, too, which Daniel Calugar will discuss below.
Algorithms are only as good as the code that creates them. In other words, if the computer isn’t told everything it needs to do — or if it is told incorrect information — then it’s possible that the algorithm won’t perform as expected.
There are many ways to back-test algorithms before they’re put into play with real money, so you don’t have to worry too much about that if you’re using a reputable program and/or coder. However, one major limitation is that it takes a lot of upfront work to perfect.
Setting aside the actual computer coding work, the algorithm needs to be fed all relevant news items from all relevant news sources. If it isn’t told to scan a particular news site or search engine or input a certain set of keywords, then it won’t be able to analyze it.
So, while the entire point of algorithmic news-based trading is to remove manual work, humans still have to do a lot of upfront manual work to set it up properly.
Along these same lines, another limitation of algorithmic news-based trading is that the computer will only analyze what it’s being told to analyze. If the code mistakenly left out a major news source or a keyword search, then you could be missing out on major trading opportunities as news breaks.
This could be of particular concern as trends start to develop in the financial markets. You may find that new or novel search terms that were never relevant before suddenly become relevant in terms of financial news. If your algorithm doesn’t include these search terms, then it won’t be able to analyze them.
The same thing applies if you mistakenly left out the news feed of a major source of financial news.
Weighting of Sources
One great thing that humans bring to the table is the ability to determine whether a piece of news is coming from a worthy source. Sometimes, this doesn’t just apply to the actual news outlet but to the specific people who are either commenting or writing about financial information.
Algorithms won’t have this same ability. While you certainly can assign a weight to each news source based on whether it’s reputable, it would be hard for a computer to discern reputable information from non-reputable information if it comes from the same news outlet, for instance.
What’s more, there are so many sources of financial news today that the algorithm may be analyzing keywords from sources you never assigned a weight to.
This could result in the algorithm making trading decisions based on news items that are not coming from a reputable source. As Dan Calugar has mentioned before, this may lead to incorrect decisions that could have potentially disastrous outcomes.