Can I use AI for trading?

Ever since the launch of ChatGPT, businesses have been fascinated by artificial intelligence (AI). It’s no surprise that many traders and investors are exploring new ways to use AI in investing and trading.

In fact, AI has transformed the financial industry, with traders and investors looking for ways to use AI to enhance their trading strategies and improve their returns.

In this article, we’ll look at AI, what it involves, how it can be used in investing and trading, and a lot more.

What is AI?

Artificial intelligence (AI) takes form in different ways. The most widely used application of AI is machine learning, where computers are trained with data to draw inferences that would normally require human thinking. This enables image recognition, like recognising faces or plant species.

Other popular uses of AI include computer vision, which is vital for applications like autonomous vehicles, and natural language processing, which underpins technology like ChatGPT and other generative AI tools.

Additionally, robotics has a wide range of uses. Examples include industrial robots for welding or painting and domestic robots like robot vacuum cleaners.

Another example involves neural networks, which mimic the connectivity of the human brain and support technologies like speech recognition and natural language processing.

AI trading relies on key technologies

AI trading relies on several key technologies: machine learning, natural language processing, and big data analytics. Machine learning algorithms analyse vast datasets to identify patterns and guide trading decisions.

Natural language processing analyses news articles and different information sources to identify market trends and opportunities.

Big data analytics analyses huge amounts of data to find market patterns and trends. In addition, AI trading platforms use advanced algorithms to automate trade execution. These algorithms capitalise on new market opportunities, enabling traders to make better decisions and increase their profits.

Overall, AI trading is a quickly changing field that offers several opportunities for traders. By using advanced algorithms and technologies, traders can efficiently analyse extensive data, identify market trends and opportunities, and capitalise on market opportunities around the clock.

Uses of AI in trading

1. Algorithmic (AI) trading

Algorithmic trading is probably the most direct way in which AI is used in trading.

Traders use computer algorithms to execute trades. Vast amounts of data are analysed based on market trends and patterns.

AI has become popular in trading as computer algorithms have the ability to analyse data much faster than humans can, giving them an advantage in high-frequency trading. Also, human biases don’t affect AI algorithms.

Algorithmic trading mainly focuses on taking advantage of price discrepancies like the bid-ask spread, with gains often being small and relying on high trading volumes.

While algorithmic trading is effective, no trading strategy guarantees a 100% success rate due to rapid adjustments to market conditions and trader responses to new information.

2. Sentiment analysis

Another application of AI in trading is sentiment analysis. Market movements are influenced by factors such as macroeconomic data, earnings reports, geopolitical issues, interest rates, and market sentiment.

Although sentiment is hard to measure, investor emotions often influence the stock market more than any other data.

AI programs can help traders assess market sentiment by analysing news articles, social media posts, and other online activity to gauge the overall sentiment of the market and predict market movements.

Understanding market sentiment helps traders make well-informed decisions about the right time to buy and sell.

3. Portfolio optimisation

Portfolio optimisation is crucial in investing, where money managers aim to find a balance between diversification, risk, and factors like income and growth. AI helps fund managers optimize portfolios to achieve these objectives and prioritise specific goals.

In the future, generative AI technologies like ChatGPT may be used in portfolio management. Research suggests that ChatGPT can be a helpful assistant in creating a portfolio, particularly for retail investors who lack experience in investment management.

Furthermore, AI investing bots can offer insights to money managers on gaps in their portfolios, informing them how to better balance them.

4. Risk management

AI also plays a role in risk management by analysing historical market data, Volatilité, and correlations that impact returns, helping money managers and companies.

Machine learning techniques also help improve efficiency and reduce costs.

In some instances, AI can substitute human labour as it rapidly analyses large datasets with minimal human intervention.

These models are better than traditional regression models, demonstrating better forecasting accuracy and capturing nonlinear relationships between risk factors and variables.

5. Personalised investment advice

With innovations like ChatGPT and generative AI, AI programs are now offering personalised investment advice.

One example is Magnifi, a recently launched app that uses ChatGPT and other AI tools to provide real-time investment advice.

Magnifi functions like a trading platform that can answer questions with a chatbot interface like ChatGPT.

The challenges of AI

Market volatility and AI interpretability are two of the main challenges of AI in trading.

Volatility of the market

A major challenge of AI in trading is its inability to predict black swan events and extreme market conditions, which are difficult to forecast. Even though AI models can be trained on historical data, it’s possible that they won’t be able to predict sudden market movements or unforeseen events that can significantly influence the market. This could result in serious financial losses and inaccurate predictions.

Another challenge is increased market volatility due to AI trading algorithms. These algorithms can make decisions based on real-time data and react rapidly to market changes. However, they may also increase market volatility as they may all react at once to the same market signals.

AI models are challenging

Traders may find it difficult to understand the algorithm’s decisions because AI models can be hard to interpret. This can lead to a lack of trust in the AI model, making it hard for traders to make informed decisions. Furthermore, AI models can also be affected by discrepancies and errors in the data, resulting in inaccurate predictions. It’s important to ensure that the data used for training AI models is accurate and up-to-date.

Future trends and innovations

As artificial intelligence continues to become more popular in trading, algorithms are also evolving. Traditional technical analysis-based algorithms are being replaced by machine learning algorithms. This allows faster and more accurate predictions. Additionally, deep learning algorithms learn from past data to make predictions, allowing them to adapt to changing market conditions for more accurate predictions.

Deloitte reports that top global investment banks can improve their front-office productivity by 27%–35% with generative AI. This represents an additional $3.5 million in revenue per employee by 2026.

AI-driven algorithmic trading is set to expand, allowing fast trade execution to capitalise on market inefficiencies, making markets more efficient and lowering trading costs.

However, the increasing role of AI raises concerns about market stability, potentially leading to unexpected movements in the market and increased volatility. While AI has great potential for trading, it’s essential to recognise and address potential risks.

Is AI the future of trading?

While there’s no definite answer to whether artificial intelligence is the future of trading, it’s clear that investors are likely to adopt any new technology that can improve performance and reduce the labour of investing.

Artificial intelligence fits both criteria. Although there will always be a human element in investing, such as stock selection and portfolio management, artificial intelligence is likely to play an increasingly significant role in investing as AI technology develops.

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Ces informations ne doivent pas être considérées comme un conseil ou une recommandation d'investissement, mais uniquement comme une communication marketing