Introduction
Artificial Intelligence, as we may all know, is a science or you may call engineering to make intelligent machines/systems for the sole purpose of making complicated tasks easy in other words, those tasks which may be time-consuming for humans to do. It takes into account various algorithms to calculate, analyze, learn from experience and thereby adapt to new situations which makes it powerful enough to solve complicated problems. And that is why, AI is making its way into every domain be it automation of embedded systems, agriculture and farming, security and surveillance, virtual assistant chatbots….you name it.
So now as the topic indicates, AI will also be a useful tool in finance and it's known to be can be a game-changer in stock trading.
How Does It Work?
The concept of artificial intelligence in trading is to help traders improve with the buy and sell process of stocks for faster and efficient trading. Now here’s where a fundamental subset of AI comes in i.e Machine Learning. We need a lumpsum amount of data to identify the relation between the number of features and the target (a.k.a the data we will feed to the machine learning model and what we actually want the machine learning model to output for us), analyze the trend, learn from experience and adapt to it. This can be described briefly in the following three steps:
Collecting the data
Collecting the information through all the available sources. Past events have a significant effect on the future which means collecting historical data will indeed be an excellent source for predicting future price movements in the stock market. Some algorithms may be unable to find a structured pattern in data, a solution to that will be providing neutral information as much as possible.
Data Cleaning
We may have data that might contain missing values, repetitive features, information that has no relation to the target and plays no role in the prediction of the target(outliers). All these data should be removed as they will make the model slower and ineffective.
Build the model
The purpose of an algorithm is to assist us in making price forecasts for an asset that is of interest to the trader. In truth, there are numerous approaches to developing a prediction algorithm. Most of the algorithms, on the other hand, strive to make the problem as simple as possible by using a model with two classes based on the signal and prediction features.
The first part indicates whether inflation or depreciation is expected, while the second reflects how accurate the prediction is. The trader may quickly filter out the most predictable and best-performing subsets of data on the list and trade those with the highest signal strength after the algorithm has gone through the data sets and produced the output.
How are they bringing change?
AI trading systems may learn from a variety of data sources to understand how and why markets move the way they do. When AI is applied to the process of stock trading, computers can figure out when the condition’s requirements are met and produce results with minimal risk. They should also be able to change strategy on the fly with no additional human interaction.
Another advantage is that AI doesn’t work based on emotions. Traders and Investors are human beings and experience emotions such as fear, guilt, greed, loss of money etc. These emotions have a negative impact on the performance of human beings and the chances of making blunders increases. Taking a scenario of the global financial crisis in the year 2008, the financial market was showing signs of downfall, the majority of them ignored the obvious outcomes and had to experience the euphoria stage of the bull market. Algorithms would address the issue by guaranteeing that all trades adhere to preset rules.
The huge potential of risk management by AI makes it worthwhile to implement in growing technologies and AI is already in use by stock markets for that purpose. NASDAQ applied AI on its US stock market to detect malicious trading practices. It aims to strengthen and revolutionize market surveillance through AI and ML.
The U.S. capital markets are the largest, most liquid financial ecosystem in the world and protecting our markets for retail and institutional investors alike is an important responsibility,” said Martina Rejsjo, Vice President and Head of Market Surveillance, North America Equities, Nasdaq. “This means constantly evolving how we adopt and leverage new technologies to better surveil trading activity. By incorporating AI into our monitoring systems, we are sharpening our detection capabilities and broadening our view of market activity to safeguard the integrity of our country’s markets.
You may also utilise AI that analyzes the market sentiment toward a stock by training it through a variety of data such as newspaper articles, social media feeds and posts, tweets etc by NLP(Natural Language Processing). Based on those sentiments people may determine if the market is bullish or bearish for a particular stock.
AI can also give us an idea about where the market is going by finding out the active number of buyers/sellers in the market.
AI can assist you in determining which type of long-term investment is most advantageous in the present market environment. for long-term investors. For a while, a value investor may lose money in the market, and the same is true for growth, dividend, or any other sort of long-term investment strategy. Some investment strategies outperform others which depends on the economy, what the market is concentrating on, and market emotion, but only for a limited period, and AI can assist transition effortlessly to any style to optimise returns.
Pitfalls of AI in Trading
As impressive as AI in stock trading is, it still can’t mimic human intuition. That could leave AI systems open to external manipulation.
For example, if a stock trading algorithm is working based on the sentiments of a person, it would be up to the training data to reach a decision on how to weigh a person’s decisions. Whereas, if compared to a human trader knowing a person’s history in the stock market might reject his opinions and act accordingly.
So there will be a possibility of outside manipulation that would become a hurdle for AI trading systems until they evolve that much to make intuitive decisions on their own.
Another thing is you need a high amount of good quality data. If the data is not close to being good enough to train a machine learning model, it may perform poorly which would be a waste of time and human efforts.
Training an AI system in an environment of high volatility may not perform well in an environment of low volatility and vice-versa. So, One of the primary flaws of these systems is that they require training in conditions that aren’t totally representative of all possible future environments.
Bottom Line
The final thing is AI may have already revolutionized the stock market, its influence is apparent. There is an outstanding chance that AI will continue to evolve new methods to aid investors, brokers and traders. Of course, these AI tools will reduce the time and in-depth knowledge required to develop a good change in society and long-term portfolio. We haven’t reached the point where human traders are obsolete, but today’s AI has significantly reduced the gap between casual investors and experienced traders. And, given how quickly this has occurred, it won’t be long until AI becomes the new cornerstone of future stock markets.