Artificial intelligence has been shaking up the trading world in recent years, promising to overhaul everything from market analysis to trade execution. While AI still has significant limitations, it has already made considerable waves in some key areas. Here are some of the most critical impacts AI is having on trading.
Crunching market data on an epic scale
Traditional quantitative analysis methods have long crunched numbers to identify potential trading opportunities. But AI goes far beyond just number crunching, using machine learning to identify hidden patterns and signals that even the most brilliant human analysts could never detect. Many hedge funds and proprietary trading firms use AI to scour up-to-the-second market data feeds, news wires, earnings releases, and more to spot potential trading setups as they develop rapidly. The AI constantly weigh each new datum against the ever-shifting context to reevaluate forecasts and opportunities.
Trading strategy optimization and execution
In addition to identifying potential trades, AI is also being leveraged to optimize trading strategies. Firms use AI and machine learning to backtest millions of potential strategies across years of historical data to pinpoint approaches that would have been profitable in the past. However, having AI identify promising strategies amid infinite possibilities provides a significant edge for quantitative firms.
Once an AI system has identified a promising Quantum AI for improving trading decisions in Australia, it uses deep reinforcement learning to continually refine and optimize the plan based on its performance with natural capital at risk in live markets. The AI keeps learning and adapting, fine-tuning trade entries and exits, position sizing, and other parameters to maximize performance. On the execution side, AI is also used to streamline order routing to minimize market impact and achieve better pricing. AI intelligently weigh factors like current market conditions and liquidity to slice and route orders through different exchanges, dark pools, and other venues. The AI system aims to move the desired share amount while minimizing market impact as much as possible.
News and sentiment analysis
Since news and information flow are among the biggest drivers of market movements, AI algorithms are being focused heavily in this area. Systems scan news articles, social media, and other data sources in real-time to detect significant events and rapidly assess their potential market impacts. However, AI goes beyond keyword matching to detect sentiment or subjective slant in news stories, social posts, and other text. Is a given article painting a company’s earnings positively or negatively? Gauging sentiment algorithmically provides a whole new lens for trade decision-making.
AI combines news and events to calculate potential quantitative impacts on a stock’s fundamentals, like earnings estimates, revenue forecasts, legal liabilities, etc. When relevant news hits the wires, the combined quantitative and qualitative analysis generates concrete price targets and trading signals. Many trading firms also use natural language processing (NLP) to convert text-based news and data into structured formats fed into other AI systems for processing. NLP bridges the gap between unstructured data sources and AI model inputs.
Fintech disruption and competitive advantage
In addition to optimizing trade execution and portfolio management internally, major financial institutions use AI in consumer-facing fintech applications to increase efficiency and gain competitive advantage. Banks and brokers leverage AI to provide innovative chatbots and voice assistants to handle customer service and standard requests. The AI behind the banking apps answer questions, resolves issues, and even initiates basic transactions through natural conversations and voice commands.
AI allows banks and brokers to provide automated trading recommendations and intelligent insights based on each client’s portfolio, risk tolerance, trading history and behaviours. The AI algorithms continuously monitor accounts and proactively notify clients of potential trading opportunities or risk management needs. Many firms are also developing AI-enabled personalized marketing capabilities.
