AI Stock Trading: Future Trends

The important AI stock trading future trends are as follows:

1. Managing the stock market risks with artificial intelligence (AI)

Financial institutions of all kinds can use AI to identify, analyze, and mitigate risks. Some banks have already started this, furthermore, hedge funds can also take advantage of it.

Professional traders, retail investors, and institutional investors need to identify risks from various data points. They need to gain insights from this data. These insights help them to identify risks.

However, much of this data is unstructured. Investors find it hard to gather insights from unstructured data using traditional analytics software products.

AI and ML can extract insights from unstructured data. This helps investors to identify and analyze risks.

Example: Bank d’ Italia used AI for conducting sentiment analysis of tweets. It used this for predicting risks for well-known Italian banks.

2. Improving the stock trading decisions with the help of AI stock trading solutions

Both institutional and individual investors would want to improve their stock trading decisions. They would like to buy or sell stocks with more confidence.

Experienced investors make informed decisions since they have the expertise. However, retail investors that are new to the world of stock trading might often have to depend on intuitions.

Retail investors might not have unlimited access to high-quality data, which is a constraint. That’s not the case for institutional investors, who have access to comprehensive market data. Here, extracting insights from the data is the constraint. That’s especially true for unstructured data.

AI performs the task of extracting insights from vast data sets surprisingly well, and it works with unstructured data. Think of self-driving cars. AI systems in these cars make the best driving-related decision based on data. Institutional investors can make better trading decisions with the help of AI and ML.

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Example: JP Morgan Chase uses machine learning and big data analysis to extract insights from the massive data sets that they have. It uses these to make investment decisions, and it utilizes these to predict the course of markets.

3. Assessing compliance risk in financial markets using machine learning (ML)

Many traders need help to assess compliance risks in financial markets. That requires plenty of analysis, which isn’t easy.

Trading rules in stock markets and money markets can be complex. Many users lack the requisite expertise to analyze the historical data about markets.

All of these factors make it hard to assess compliance risks. This especially impacts day traders and individual investors.

AI platforms that use machine learning algorithms can analyze vast data sets containing historical data. They can simultaneously focus on multiple markets, and they can detect underlying patterns in trading data. Investors can get a realistic assessment of compliance risks from such platforms.

Example: Trading Technologies has made a notable entry into this space with its 2017 acquisition of Neurensic. The AI platform of Neurensic uses ML and big data to identify complex patterns from many markets simultaneously.

4. Providing insights to traders from financial data and notes

Traders in financial markets can’t make the decisions to buy or sell stocks or other investment instruments based on intuitions alone. Such decisions might lack objectivity. They need insights.

Many of their activities tend to be complex, e.g.:

• Traders might need to fine-tune their day-trading strategies.
• They might need to utilize technical analysis and fundamental analysis for swing trading.
• Individual investors may need help with breakout detection.
• Retail investors might need to gain a deeper understanding of what they see in chart windows.

A vast amount of data about financial markets is available in unstructured data. This could include briefing calls with analysts, media reports, market reports, press notes, etc. Traders find it hard to gather actionable insights from this data.

AI platforms can use capabilities like speech recognition and natural language processing (NLP) to scan this data. They can use ML algorithms to extract insights from this, which helps traders.

Example: GreenKey Technologies offers an AI platform with speech recognition and NLP. This platform extracts meaningful insights from conversational data.

5. Recommending top stocks using an AI platform

Traders look for high-performing stocks as part of portfolio management. They want stocks that perform well consistently when the market closes. Researching about top stocks takes time, and extracting insights from varied materials can be hard.

AI systems with their ML algorithms can quickly analyze vast data sets. They can apply statistical models and ranking algorithms to rank top-performing stocks. Traders can gain confidence in their recommendations due to the data-driven approach.

Example: Kavout, an American company offers an AI platform to recommend top-ranking stocks. Its “Kai Intelligence Platform” provides a rating called the “K Score”. It’s a predictive equity rating with a score between 0 and 9. Kavout uses machine learning algorithms and apples over 200 factors to produce a “K Score”.

6. Providing a platform to create trading strategies

Institutional investors and Hedge Funds want to be ahead of the market. Brokerage firms like Interactive Broker would also like to identify market opportunities in advance.

These organizations employ acclaimed experts to get the right advice in this regard. These experts undoubtedly do a good job, however, financial markets are complex.

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Plenty of data points around financial markets is unstructured, which compounds the challenge. Technology solutions that enable investors to gain access to high-quality market intelligence are useful. AI/ML platforms combined with data can help investors to identify opportunities and create strategies.

Example: Auquan provides a data science platform for portfolio management. It provides a suite of AI/ML and data science tools, which helps investors to identify risks and opportunities. The platform offered by Auquan has additional features like a portfolio activity monitor.

7. Refining trading strategies and the future performance of trading

High-net-worth individual investors and institutional investors would want to have the right trading strategies. They want to improve the performance of trading by learning lessons from past trading activities.

Formulating the right trading strategy and refining it manually can take a lot of time. These investors can’t afford to spend that time. Such a select group of investors can invest in the appropriate tools for this.

AI-trading platforms can “learn” for vast data sets to refine trading strategies. These platforms can improve trading performance based on historical data.

Example: Epoque Plus is a Swiss company that offers AI trading services for high-net-worth individual investors and institutional investors. Its platform manages orders, refines trading strategies, and “learns” from past trades.

8. Making the investment process systematic using an AI-powered trading platform

Institutional investors prefer a systematic investment process. They want investment strategies based on sound logic. Furthermore, they want to factor in the risks. Institutional investors want to make informed decisions.

Due to human limitations, they can’t process vast data sets rapidly. The insights that they need lie hidden in those data sets.

AI trading solutions can find promising investment opportunities by processing this data quickly, which includes unstructured data. They can make the trading process more systematic.

Example: EquBot uses proprietary machine learning algorithms and IBM Watson. It finds the right trading opportunities using AI/ML. It caters to asset managers, asset owners, banks, wealth managers, and individual investors.

9. Finding the most promising trading algorithms by testing them against many trading scenarios

Traders want to test their trading strategies. They want enough trading scenarios for this testing. However, they don’t want to lose money in that process.

Simulating a large number of trading scenarios can be hard, however, AI-powered systems can help. Such platforms can recommend trading strategies with a high potential for success.

Example: Trade Ideas, an American company provides an advanced simulated trading platform. Traders can learn to navigate the stock market, and they don’t need to spend real money. The company offers “Holly”, an AI-powered robo-trading platform. It tests trading algorithms against a large number of scenarios. Trade Ideas offers an automated trading platform. Among additional features, it offers an integration with Interactive Brokers.

10. Optimizing the trading process for growth stocks, equities, etc.

Investors want to optimize their trading performance. They need to minimize market impact and adverse selection. Traders also want an appropriate spread of stocks to hedge risks.

They find it hard to achieve all of these by manual research and analysis. AI trading platforms can help since they can scan vast data sets at scale. They can use ML algorithms for optimal trading.

Example: Imperative Execution Inc., an American company has built IntelligentCross, an AI trading platform. It optimizes trading performance for US equities. IntelligentCross utilizes AI to process orders by factoring in market impact and adverse selection.

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11. Using AI-powered trading platforms for crypto trading

The global cryptocurrency market is highly volatile. Crypto traders often find it hard to make sense of this volatility.

There are often new blockchain-cryptocurrency projects. Investors need to know whether they should invest in them.

There are also malicious actors operating in the crypto market. Crypto traders face risks like scams and cyber-attacks. They need guidance to secure their investments. Companies using AI to guide crypto traders in this volatile market can be helpful. They are even more valuable if they help traders in areas like security.

Example: Infinite Alpha is a UK-based company that provides an AI platform for crypto trading. It helps crypto traders with security, encryption, and authentication too.

12. Using AI technology for market analysis, technical analysis, risk analysis, etc.

Institutional investors want real-time market analysis. They also want technical analysis, risk analysis, etc. High-net-worth individual investors want them too. Performing these analyses manually can take plenty of time.

AI-powered systems can perform these analyses at scale. Thanks to machine learning algorithms, they can identify hidden patterns in complex and large data sets. They can extract insights from unstructured data too.

Example: WOA Technology is a UK-based company. It utilizes AI and ML to perform real-time market analysis. The company offers this service to high-net-worth individual investors, hedge funds, institutional investors, and wealth funds.

13. Automated trading of stocks without human intervention

Many investors want automated trading solutions. They prefer a system without human intervention, alternatively, they would want a trading system with minimal human intervention.

However, they know that stock market analysis requires human intelligence. Therefore, they want an “intelligent” automated trading system that can “think” like human beings. AI trading platforms with ML algorithms can provide this.

Example: Techtrader is an American company, which was founded in 2012. It offers an autonomous stock trading platform. Human beings don’t need to make any adjustments, nor do they need to intervene in any other way. Techtrader examines stocks the way human experts analyze them. It also offers an autonomous hedge fund.

14. Developing quantitative trading and investment strategies using AI

It takes plenty of expertise and effort to develop quantitative trading and investment strategies. This task involves plenty of subjectivity, therefore, you can’t fully automate it. However, combining technology solutions and human expertise can help. AI and ML can make a difference here.

Example: Sentient Investment Management is a subsidiary of Sentient Technologies, an American company. It uses AI capabilities like deep learning to create proprietary quantitative trading and investment strategies.

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