ML for risk management systems
Risk management, an essential discipline in finance, has undergone a paradigm shift in the age of quantitative trading. Traditionally, risk management’s role was to mitigate potential losses through diversification, hedging, and other strategies. However, with the inception of quantitative trading, where decisions are driven by algorithms and mathematical models, risk management has taken on a more dynamic and proactive role.
Advanced quantitative trading operations require instant decision-making and real-time portfolio adjustments. Traditional risk management strategies, while effective in various contexts, may not always keep pace with the complexity and speed of today’s financial markets.
This is where ML comes into play. ML, a subset of AI, involves algorithms that learn and make decisions from data. Instead of being explicitly programmed, these algorithms adapt based on the data they process, making them well-suited for the dynamic...
