New Start Graduate School of Business’s Journey into Artificial Intelligence
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Phase 1: Quantitative Trading
In the early days of NSG School of Business, Professor Robert Clayton tried to create a ‘lazy investment system’. He realized very early on that quantitative trading would be applicable to all investment markets and types in the future, such as securities markets, various futures markets, cryptocurrency markets, foreign exchange markets, etc.
Compared with subjective trading, quantitative trading can help investors/traders deal with many problems:
1. Emotional trading: Quantitative trading can help investors eliminate the influence of emotional factors on trading decisions, to trade more objectively and rationally.
2. Trading execution: Quantitative trading can automatically execute trading strategies and respond quickly to market changes, reducing human errors and delays.
3. Big data analysis: Quantitative trading can use large-scale data and analytical tools to mine and analyze market patterns and trends to discover potential trading opportunities.
4. Risk control: Quantitative trading can apply strict risk management and stop-loss strategies to protect investment portfolios from significant losses.
5. Statistical advantages: Through quantitative trading, investors can use statistical principles and mathematical models to improve the return rate and risk management capabilities of their investment portfolios.
6. Market arbitrage: By quickly responding to market price differences and potential conflicts of interest, quantitative trading can achieve market arbitrage and thus make profits.
7. Transaction cost optimization: Quantitative trading can reduce transaction costs through algorithms and execution strategies, such as low-latency trading and high-frequency trading.
8. Diversified investment: Through quantitative trading, it is easy to implement diversified investment strategies, including trading in stocks, futures, foreign exchange and other asset classes.
Overall, quantitative trading can help investors improve trading efficiency and profitability in terms of decision-making, execution and risk management.
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Phase 2: The leap from quantitative trading to artificial intelligence.
Although both quantitative trading and AI trading are methods of using technology to make trading decisions, they also have some shortcomings. The following are some of the weaknesses of quantitative trading compared to AI trading:
1. Dependence on historical data: Quantitative trading is usually based on the analysis and model building of historical data. Therefore, for emerging markets or markets with drastic changes in economic conditions, quantitative trading may not be as flexible as artificial intelligence trading.
2. Lack of subjective judgment: Quantitative trading mainly relies on rules and algorithms to make trading decisions and lacks the intuition and subjective judgment of human traders. This sometimes leads to the inability to capture certain irregular market sentiments or events, resulting in the instability of trading strategies.
3. Sensitivity to data quality: The results of quantitative trading are heavily dependent on the accuracy and reliability of the historical data used. If the data is wrong or missing, or if it cannot accurately reflect the current market conditions due to market changes, it will have a negative impact on the success of the trading strategy.
4. High initial cost: Quantitative trading requires the establishment and maintenance of a large amount of technical infrastructure, including high-performance computers, data storage and processing systems, etc. These facilities require a lot of capital investment and expertise to maintain and the initial cost is high.
5. Sensitivity to model risk: Quantitative trading models are usually built based on historical data and there are defects in accuracy and stability during the investment process for investment targets with less historical market data. For example, there are a lot of opportunities in the rise of emerging market cryptocurrency markets, but quantitative trading loses the opportunity because of this defect.
With the development of science and technology, the application of artificial intelligence technology has had a profound impact on quantitative trading. Quantitative trading is a trading strategy that uses mathematical models and a large amount of historical data to make investment decisions and the introduction of artificial intelligence makes quantitative trading more accurate, efficient and intelligent.
First, artificial intelligence technology can analyze and process huge financial data through methods such as data mining and machine learning, and discover the laws and patterns in the financial market. Compared with traditional quantitative trading methods, artificial intelligence can more accurately capture the dynamics and changes of the market and improve the accuracy of investment decisions.
Secondly, artificial intelligence technology can also realize automated trading, that is, to perform trading operations through algorithms and programs, reducing the intervention and operational risks of traders. This makes trading execution faster and more accurate, and can monitor market changes in real time and adjust investment portfolios in a timely manner.
In addition, artificial intelligence technology can also help optimize and improve quantitative trading strategies. Through the training and optimization of machine learning algorithms, the parameters of quantitative trading models can be effectively adjusted and optimized to improve the profitability and risk control capabilities of trading strategies.
Given that AI trading can obtain data in real time and make decisions based on real-time market conditions, it can better adapt to market changes. AI can process more complex data and patterns to obtain more accurate market judgments. AI trading can monitor market changes in real time and automatically make trading decisions, and can respond quickly when market opportunities arise. AI trading can continuously optimize its own trading strategies through machine learning and deep learning algorithms to adapt to market changes... etc. AI has stronger adaptability and decision-making capabilities. Since 2018, NSG has begun to leap from quantitative trading to the field of AI trading.
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Phase 3: NSG’s journey towards artificial intelligence.
【Academic Programs】
NSG offers a range of AI-related academic courses, such as machine learning, deep learning, natural language processing, etc. These courses are designed to help students gain a deep understanding of the core concepts and technologies of AI and provide them with opportunities to apply these technologies in practice.
【Research Projects】
NSG actively promotes cooperation with industry to carry out artificial intelligence research projects. By working with enterprises, the school hopes to deepen students' understanding of the field of artificial intelligence and provide them with solutions to practical problems. These research projects can also help the school stay close to industry and keep abreast of the latest technological developments and trends.
【Innovation Center】
NSG has set up a dedicated innovation center dedicated to promoting innovation and entrepreneurship in the field of artificial intelligence. The school encourages engineers, practical experts, employees and students to actively participate in the activities of the innovation center, providing creative and entrepreneurial support such as incubators, mentor guidance and innovation funds. The innovation center will also organize various innovation competitions to encourage students to provide innovative solutions in the field of artificial intelligence.
【Talent Cultivation】
1. Provide professional courses: Offer AI-related courses covering basic knowledge, algorithms, programming skills, and practical projects. Courses should be taught by experienced teachers and industry experts to ensure that students acquire the latest knowledge and skills.
2. Carry out practical projects: Cooperate with companies in the field of AI to provide practical projects to students. Students can apply what they have learned in practical projects, solve practical problems, and interact with industry professionals. This will help improve students' practical skills and problem-solving abilities.
3. Provide industry mentors: Invite professionals in the AI industry as mentors to guide students' learning and development. Mentors can provide students with practical experience, industry insights, and professional advice to help them better understand and adapt to industry development.
4. Build laboratories and research centers: Establish AI laboratories and research centers on campus to provide students with an innovative environment. Such laboratories and centers can provide professional equipment and resources to encourage students to conduct research, develop new technologies, and solve practical problems.
5. Hold academic forums and seminars: Regularly organize academic forums and seminars, and invite scholars and industry experts to share the latest research results and industry trends.
Phase 4: Prototype and future vision of the ‘AI Wealth Navigation 4.0’ investment system.
With the participation of many experts, scholars, and scientific and technological talents, NSG developed ‘AI Wealth Navigation 1.0’, which improves many deficiencies in quantitative trading models and is more efficient, fast, and intelligent.
‘AI Wealth Navigation 1.0’ is mainly based on rule and pattern matching, including knowledge-based reasoning, expert systems, etc. However, AI 1.0 has some limitations when dealing with complex and ambiguous problems. In order to overcome these limitations, the NSG expert team began to seek new methods to develop more advanced AI systems.
‘AI Wealth Navigation 2.0’ refers to the introduction of machine learning technology on the basis of version 1.0. Machine learning allows AI systems to learn and improve their performance through large amounts of data. The representative of this method is deep learning technology. By building a multi-layer neural network, the AI system can extract more advanced features from the data and has made many important breakthroughs.
Based on version 2.0, ‘AI Wealth Navigation 3.0’ introduces more perception and adaptive capabilities. AI systems can collect data in the environment through data sensors and adjust their behavior and decisions based on this data. This ability makes AI systems more adaptable to different environments and tasks, becoming intelligent assistants in the real world.
‘AI Wealth Navigation 4.0’ is the latest development stage, which focuses on the application of artificial intelligence in the entire market of the financial industry. Version 4.0 emphasizes the combination of artificial intelligence with technologies such as the Internet of Things, cloud computing, and big data to build intelligent solutions.
At present, ‘AI Wealth Navigation 4.0’ includes four major trading and investment systems: ‘Trading Signal Decision System’, ‘AI Programmatic Trading System’, ‘Investment Strategy Decision System’, and ‘Expert and Investment Advisory System’.
In the future, we hope to make these four frameworks (systems) achieve the following investment effects and purposes:
Trading Signal Decision System helps us make subjective judgments, and prompts buying and selling points in real time, with an accuracy rate of more than 80%.
Ai Programmatic Trading System is an AI computer trading system. After manually adjusting the parameters, it will automatically help us complete the transaction and achieve the goal of stable profit.
Investment Strategy Decision System is an analysis system that makes big data analysis of mainstream investment projects in major markets and gives rating decisions, especially for new investment projects, provide accurate investment strategies.
Expert and Investment Advisory System is a set of accurate and powerful investment advisory systems formed by many famous investment experts to help high-quality users and future funds make investment decisions and plans.
