Introduction To Machine Learning In Stock Markets

Machine learning is the usage of Artificial Intelligence for predicting future results or trends by feeding it historical human data. The ai gradually learns to adapt to the past trends and improves over time in extrapolating those trends to future scenarios. Machine learning has been in the limelight for the past decade, and it is gradually getting better by the day as it gets fed more and more data.

“Artificial Intelligence is to trading what fire was to the caveman.” 

It means that we shouldn’t be scared of AI replacing us. Instead, we should embrace it and use it as a tool to maximize our efficiency. AI isn’t a threat; it is a solution.

Uses Of Machine Learning In The Stock Market

1. Machine Learning can be used to predict future market trends based on past historical data and can suggest to the users the general trend the market is about to follow.

2. It can help traders keep track of their transactions in the market without any extra effort. It can also provide meaningful insights about those transactions and give a risk report of the portfolio.

3. AI can also analyze stocks using technical and fundamental indicators and recommend favorable stocks to the user.

4. AI can remove the need for active participation in the market. It can execute the instructions provided by you on your behalf.

5. Since anyone with basic coding knowledge and some work can develop a machine learning algorithm, it reduces the gap between the casual investor and the investing companies.

6. Besides predicting stock market trends, AI can also predict general sectoral trends and can give a list of emerging sectors that could potentially become big in the future.

7. AI can also be programmed to invest into new sectors where the investor does not have any knowledge or experience.

8. Some machine learning softwares are dedicated to speed trading i.e., to grab the opportunities at the earliest and to book a profit.

Disadvantages Of Machine Learning In The Stock Market

1. AI-related tools and methods are wholly dependent on the historical data provided to them during development. They can’t verify the quality of the data fed to them. If the data provided is inadequate or faulty, it can hamper the predictions.

2. AI doesn’t have the emotional intelligence which humans possess, hence it’s difficult for it to predict the human influence in the stock market.

3. According to Seth Weingram, director of the client advisory at $US97 billion Acadian Asset Management, “people chasing the market with AI might end up gaining nothing, and the risk is that there is not enough data to train the algorithms.” (Galouchko 2019)

4. Ai is also completely powered by electricity and is completely virtual, meaning it is prone to cyber attacks and electrical outages.

5. Ai-driven programs can sometimes over analyse the data, which might cause uncertainty, and hence investors need to be cautious.

6. Using machine learning-based software can prove dangerous if the user does not have adequate knowledge about the working of the software. They might take a few wrong steps and could result in losses.

Real-Life Examples

1. Sigmoidal is a consulting firm that offers end-to-end machine learning, data science, AI, and software development for business — including the trading sector. In one case, its team of experts helped formulate an investment strategy by developing an intelligent asset allocation system that used deep learning to predict every asset in a particular portfolio.

2.Startup AITrading’s “trading ecosystem” combines AI and the trading community to increase earnings by scanning markets to locate optimal trading opportunities. Deals are done via blockchain-based smart contracts.  

(source – https://builtin.com/artificial-intelligence/ai-trading-stock-market-tech)

Bottom Line –

The future for machine learning in the field of stock markets looks positive. It will act as a tool for investors to safely place their bets and also to increase their efficiency and overall market returns. It will also help reduce market inefficiencies and streamline operations. Ai with sound data can do wonders for the investor but it should be used cautiously otherwise it might result in incorrect predictions and huge losses.

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References –

https://builtin.com/artificial-intelligence/ai-trading-stock-market-tech

https://www.fastcompany.com/90502428/artificial-intelligence-beat-the-stock-market

https://www.ibm.com/in-en/cloud/learn/machine-learning#:~:text=Machine%20learning%20is%20a%20branch,learn%2C%20gradually%20improving%20its%20accuracy.