The world continues to produce amazing technological advancements. These include deep learning models. If you aren’t familiar with the technology, it’s an artificial intelligence function that can learn without human supervision.
In the financial market some people use deep learning to predict stock prices for better gains. However, it’s almost impossible to predict and take advantage of the trend. Whether you’re a trader or investor, you should know that you can only ever try to analyze the market with your own strategies and knowledge.
Deep learning methods are unreliable for predicting stock prices because the variables which affect stock price are so unpredictable that a machine cannot account for them all. Moreover, investors often behave irrationally, and a computer cannot be taught to reliably predict random behaviors.
It can work as you’re learning everything about stock prices but if you already have adequate knowledge of stocks and the market as a whole, you can’t rely on deep learning models to do all the dirty work for you.
Why isn’t deep learning a credible source to predict stock prices?
Below, we’ll address seven reasons why deep learning models are unreliable tools for people interested in playing the stock market.
1. Technology is fragile.
As mentioned earlier, even the most advanced technology can be fragile. If your sole purpose is to predict the stock market and its price action, artificial intelligence systems may do more harm than good. If you’re basing stock price prediction on an algorithm, then you’ve already lost.
It’s a much more stable method to rely on your own knowledge and expertise than to base all your decisions on artificial intelligence. As much as technology will try to imitate the human functions of the brain, it doesn’t get more real than relying on your knowledge.
2. You are risking everything.
There’s no denying the fact that stock market prices are extremely volatile, and there’s always a huge risk. You never know which way the market is going. If you’re relying on technology to determine your stock orders, you might lose everything you’ve invested.
If you rely on your knowledge and expertise, (although it takes practice and training), at least you can take complete accountability for both your profit and losses. With artificial intelligence, you can’t guarantee whether what you risk will be worth it or not.
3. It won’t suffice for anything long-term.
While certain reports can prove the effectiveness temporarily, deep learning methods can’t be useful in the long term aspect. Majority of people who invest in stocks are all about long-term goals, which is pointless if you’re using a technology-based system. The algorithm may provide you with a temporary advantage, but you can’t keep relying on that strategy for months – years, even. In the end, your advantage disappears since it only serves as a temporary fix.
4. The market doesn’t play by rules.
Other investors may argue that since artificial intelligence systems can predict aspects such as maneuvering a car engine, why can’t it possibly predict the financial market? The answer to this question is simple – the market doesn’t play by rules.
Nobody – not humans and especially deep learning methods – can predict which way the trend is going. There are only speculations and analysis, which you base on indicators, strategies, and the news. Even then, it’s not a hundred percent guaranteed prediction, and even the most successful traders and investors will tell you this. An example of the unpredictability of the stock market is how it reacts during a global pandemic.
5. It’s too complex.
If you’re not adept at technological advancements, or even if you are, you may find using deep learning methods more complex than relying on your own skillset. No matter how much convenience technology aims to provide, it’s not easy to navigate.
In terms of efficiency and convenience, nothing beats studying and learning your way through the market. Rather than relying on artificial intelligence to guide you, you can immerse yourself with strategies and tactics to make it through, and actually succeed in making a profit with your investments. There’s something so empowering about gaining profits on something you worked hard to learn, instead of relying on deep learning methods.
6. It is not the same.
If you’re a trader or investor, it’s not the same process to analyze stocks. It takes precision of checking candlestick patterns, supply and demand, price action, trend change, indicators, and even technical and fundamental analysis to accurately try your best to predict stock prices.
You can try, but you should know that technological advancements will never reach the same level of accuracy and precision when predicting the market. Again, it’s a different feeling when you profit from something you worked hard to predict, rather than relying on a machine to do this for you.
7. You lose credibility
You can’t boast that you’ve predicted the market successfully if you’ve relied on artificial intelligence the entire time. You can only do this when you’ve earned your way to the top to succeed in investing stocks. Since you’re making use of machines to predict stock prices so that it turns in your favor, you lose all credibility for this.
Unless you do it yourself, you’ll never know the rewarding satisfaction of succeeding through the volatility and unpredictability of the financial market, especially since this is stocks we’re talking about – one of the most volatile instruments in the market.
When can you rely on deep learning to predict stock prices?
If you really want to give deep learning a try, then you can only rely on it temporarily. If you’re a beginner and you want to get a feel on the financial market, then it’s an adequate option to use deep learning methods to see the trend, price action, and news to see which way the market will go.
This acts as a temporary fix while you’re gaining the expertise and skills on everything regarding stocks, including strategies, indicators, and how to analyze the overall market. Once you’ve learned everything, there’s no need to incorporate deep learning methods in predicting stock prices.