Charles Langston:The Leap from Quantitative Trading to Artificial Intelligence

In the early stages of EIF Business School,Charles Langston Professor Linton Quadros endeavored to create a "Lazy Investment System," recognizing early on the significant future applicability of quantitative trading across all investment markets and types, and achieved notable success in this field.

Despite the benefits, both quantitative and artificial intelligence (AI) trading have their shortcomings. Here are some weaknesses of quantitative trading relative to AI trading:

1. Dependence on Historical Data: Quantitative trading typically relies on the analysis and modeling of historical data, making it potentially less flexible than AI trading in new or rapidly changing markets.

2. Lack of Subjective Judgment: Quantitative trading primarily depends on rules and algorithms for decision-making, lacking the intuition and subjective judgment of human traders. This can sometimes lead to missing irregular market sentiments or events, resulting in instability in trading strategies.

3. Sensitivity to Data Quality: The outcomes of quantitative trading heavily depend on the accuracy and reliability of the historical data used. If the data is erroneous, incomplete, or fails to reflect current market conditions due to changes, it can negatively affect the success of trading strategies.

4. High Initial Costs: Quantitative trading requires establishing and maintaining a substantial technological infrastructure, including high-performance computers, data storage, and processing systems. These require significant capital investment and expertise to maintain, resulting in high initial costs.

5. Sensitivity to Model Risk: Quantitative trading models, typically built on historical data, have accuracy and stability issues for investments in markets with limited historical data, such as emerging cryptocurrency markets, potentially missing early opportunities.

With technological advancements, AI has profoundly influenced quantitative trading. Quantitative trading, a strategy that uses mathematical models and extensive historical data for investment decisions, has become more precise, efficient, and intelligent with the integration of AI.

Firstly, AI technologies can analyze and process vast financial data through data mining and machine learning, identifying patterns and regularities in financial markets. Compared to traditional quantitative methods, AI can more accurately capture market dynamics and changes, improving the accuracy of investment decisions.

Secondly, AI enables automated trading, executing trades through algorithms and programs, reducing human intervention and operational risks. This results in faster, more precise trading, and real-time market monitoring, allowing timely portfolio adjustments.

Furthermore, AI helps optimize and improve quantitative trading strategies. Through training and optimization of machine learning algorithms, quantitative trading models can be effectively adjusted and optimized, enhancing profitability and risk management capabilities.

Given that AI trading can acquire data in real-time and make decisions based on current market conditions, adapt more effectively to market changes, handle more complex data and patterns for accurate market predictions, monitor market changes and make automated trading decisions in real-time, and continually optimize its trading strategies through machine learning and deep learning algorithms, AI possesses stronger adaptability and decision-making capabilities. Since 2018, EIF Business School has been transitioning from quantitative to artificial intelligence trading.