As technology progresses, the world is becoming more and more fast-paced, and the margin of error is reducing. Machine learning is one of the best tools for this. Machine learning can be beneficial to cater to the vast amount of data related to the stock market and predict the ups and downs in stocks. It can access the data and give the required results more efficiently and accurately than an average human. So, in theory, if given the correct variables and algorithm, there is no reason why machine learning cannot predict the stock market?

Several apps today claim accurate predictions of digital currency and stocks. Most of them mislead the users with a false sense of security. Several articles claim using AI is nothing more than a lie. While the claims do have some matter to them, they are not portraying the whole truth. Using AI and learning algorithms like machine learning and deep learning for stock market predictions. However, to better understand how machine learning can predict the stock market, one must know what machine learning is and how it is applied today.

What is Machine Learning? 

Machine learning is a subfield of artificial intelligence that uses machine learning data and algorithms to make decisions. The concept revolves around teaching a machine using various algorithmic and statistical models so that it can adapt accordingly. When presented with a new data set, it can extract desirable information using the same algorithms and models while leaving the final decision in the hand of the user. Once someone understands the limits and abilities that this robust tool possesses, there is little that cannot be achieved in the name of innovation.

What everyday systems use Machine learning?

There are a lot of day-to-day examples where machine learning is being applied. But due to the lack of knowledge regarding this efficient tool, it often goes under the rug. We have all heard of famous social media platforms such as Snapchat, Facebook, Instagram, and many commuting applications such as Uber, google maps and the list. These applications use machine learning in various ways, yet it is unremarkable to the layman. Here are a few examples;

Uber

Let’s discuss how machine learning works in Uber. Ever wondered why the fares hike? How is the fare fixed even before you have arrived at your destination? Why the waiting time keeps on reducing? Or why do some people get more promotions than others? In an everyday scenario, one might think people are sitting somewhere behind computers assigning a driver to each rider, but that is not how it works. It is achieved through machine learning. 

While there still are employees on computers, their main aim is to provide customer support and minimize errors. The hike in fares is usually because more people want to travel than the available drivers in the nearby radius. Still, the timing at which it is applied is based upon the data collected through monitoring the frequency of previous trips in each time frame. 

The fares are fixed based upon an average time required for travelling from your starting point to your destination by considering traffic conditions, weather, and several other factors monitored during the earlier trips. Waiting time is referred to the duration required for a driver to pick up the client from their start-off location. The reduction of waiting time is essential because if a driver takes a lot of time to reach the client, they might cancel the ride. Ultimately Uber might lose the client to another application with a lesser waiting time. If any of these losses pile up, it will be a significant financial setback. The machine learning process also includes collecting data and extracting useful information to reduce the waiting time. 

Emails

Another good example of machine learning in our daily lives is emailing. Whenever the emailing application is updated, you might have noticed a few additional popups that make your life easier. You finally stopped getting those spam emails that offer you free world trips or a million-dollar lottery. 

These updates resulted from studying email data and how people respond when faced with such situations. Many applications such as Gmail have now divided emails into different categories such as socials, promotions, primary, and primary junk. The emails you receive are sorted into different categories automatically without having you do anything as much as lifting a finger.

Why Predict the Stock Market Through an Algorithm?

Predicting the stock market direction helps investors ensure that they will get a profit if they invest in a stock. When this is further aided using an algorithm, the stock’s future value can be predicted using present pointers of commodities and the economy. Investors maximize their profits by ensuring that they do not invest in stocks when they are about to plummet.

Several physical and psychological factors have an immense role in predicting the market trend. A human can’t keep track of all the elements and make a calculated decision, but a machine might. But even though a computer can monitor several factors simultaneously, there are irrational and unexpected behaviors that make it volatile. No matter how refined an algorithm might be, even if way lesser than a human, there is always a chance of an inaccurate prediction.

Are There Any Machine Learning Systems Being Used for the Stock Market Today?

Algorithms for predicting the stock market have been used since the late 1990s when preprogrammed computers were being used for investments in stock markets. However, these computers required human intervention from time to time. They predicted data based upon a simple programming rule that went obsolete because the method attracted many investors, and soon the profits went low.

The central idea of implementing machine learning in the stock market is to make it easier for investors to gain more capital. Machine learning is being implemented almost everywhere in our daily lives. It would be an understatement to say that machine learning is not already being used in the stock market right now.

Many applications revolve around the stock market and trading, even on your smartphones. These applications, too, have machine learning implemented in them to some extent. Examples of such applications are Think Traders, Interactive Brokers, Robinhood, Onada trade, etc. These applications predict the value of the stocks based on the different financial parameters of a company. Then they let investors know if the value of a specific stock will increase or decrease and whether it will be a good investment. Due to the access to enormous data, machine learning efficiently predicts market trends. It leaves the final decision at the disposal of its user. 

Can Machine Learning Predict the Stock Market and How?

Articles that bash the ability of AI to judge the stock market trends only discuss one side of the picture. The claim that AI cannot evaluate stocks’ future as it only focuses on the present and future is valid. But machine learning algorithms go way beyond that. They consider the stocks’ past value and trends and compare them with the present values and variables that may affect the stock. Based on all these factors, machine learning can give you a more accurate prediction than basic AI.

But achieving this is not easy either. We need to implement a large set of algorithms that will work with a sizeable data set of information. Some of the most used algorithms for this purpose are Volume Weighted Average Price (VWAP), Time Weighted Average Price (TWAP), Percent of Value (PoV) as well as Gradient Boosting Algorithm and AdaBoosting Algorithm. These algorithms analyze a company’s financial condition based on its assets and market presence to ensure the best results for the user. 

Since machine learning has high dependability upon data, it would be easy if more data were shared for the analysis. The best algorithm used in the stock market industry is Gradient Boosting Algorithm and AdaBoosting Algorithm. This algorithm is used when highly accurate results are required by analyzing massive amounts of data, especially for predictive results. This algorithm uses several low accuracy models and combines them to create a high accuracy model that can be very helpful for predictive data. 

Conclusion

In my opinion, machine learning has made our lives a lot easier. It is even being implemented in our daily lives, without us even noticing that most systems rely on machine learning in this age. Using machine learning in the stock market should not be a surprise given how long it has been used and how many efficient and better results it can provide to its user. Hence, implementing software with machine learning algorithms and analysis tools is the best choice if one thinks about stepping into the stock market. Such devices must be standard for everyone.