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With the continuous advancement of artificial intelligence, using machine learning to predict market trends is becoming more feasible. Recently, AI has gained significant attention in academia and is widely applied in areas like image recognition and natural language processing. Its impact on quantitative investment, which uses data analysis, model construction, and algorithmic trading to achieve stable returns, is profound. Financial institutions and investors highly favor this investment method. AI-based quantitative investment strategies are emerging as important tools for profit maximization and risk control. Globally, US inflation and Federal Reserve policies influence asset directions, including China’s stock market. This study explores AI’s application in quantitative investment to aid decision-making, simplify investment analysis, and enhance returns for investors.
The traditional quantitative investment strategy leverages classic financial models and computer power to identify market and value patterns in financial data, combining theory with practical experience for stable returns. Utilizing vast financial data and advanced investment models, it achieves precise investment decisions and real-time trading, enhancing accuracy and timeliness. In China, quantitative investment has evolved over the past decade, with AI technology increasingly adopted by investment institutions. AI-driven quantitative investment is now at the forefront of financial innovation. Globally, investors are concerned with US inflation and Federal Reserve interest rate decisions, influencing asset directions, including in China. AI’s application in quantitative investment research holds significant value.
Quantitative investment involves using mathematical models and computer algorithms to analyze large amounts of historical market data and develop investment strategies. This approach aims to reduce investment risk and enhance returns by relying on objective, data-driven decisions rather than subjective judgment. It spans various financial instruments such as stocks, futures, and foreign exchange, constructing and adjusting investment portfolios based on comprehensive data analysis.
Quantitative investing typically involves assigning scores to stocks based on specific features and ranking them accordingly. High-ranked stocks are held in the portfolio, which is periodically rebalanced. The essence of quantitative investment is its reliance on scientific and objective methodologies to make informed investment decisions, minimizing emotional and subjective biases. This method allows investors to better understand market trends and make more accurate predictions.
Quantitative investment has evolved over more than 30 years, gaining recognition for its stable performance and growing market share. Many prominent asset management institutions globally leverage computer technology for investment decisions, with the scale of quantitative funds continuously expanding.
The origins of quantitative investment date back to the 1960s with the advent of scientific stock market systems and convertible bond arbitrage strategies. The 1970s and 1980s saw further advancements with option pricing theory and statistical arbitrage strategies, providing a robust theoretical foundation. The 1990s, known as the “golden decade,” saw the proliferation of standard financial theories and quantitative investment funds, significantly boosting the field.
Despite facing challenges in the 21st century, such as increased market volatility and the subprime mortgage crisis, quantitative investment has continued to evolve. Advances in technology, particularly artificial intelligence and machine learning, have enhanced the precision and intelligence of quantitative strategies. These technologies enable models to process complex data more effectively, uncover hidden investment opportunities, and improve investment efficiency and returns.
Today, quantitative investment is widely applied across various markets, including stocks, futures, and foreign exchange, becoming a crucial tool for asset management.
Artificial intelligence (AI) is a technical field focused on simulating, extending, and expanding human intelligence. It encompasses theories, methods, technologies, and application systems designed to mimic human cognitive functions. AI involves using computers to replicate intelligent behavior, allowing machines to perform tasks traditionally requiring human wisdom. Definitions of AI vary: some experts describe it as the science of acquiring and utilizing knowledge, while others view it as the intelligence demonstrated through human-like work.
The concept of AI dates back to Alan Turing, who explored using machines to simulate human problem-solving. The invention of computers enabled the processing and storage of information, laying the groundwork for AI. Key milestones include the 1956 Dartmouth Conference, marking AI’s birth, and the development of the perceptron algorithm by Frank Rosenblatt in 1957. AI’s evolution can be divided into four waves: the birth stage, the rise of expert systems in the 1980s, the connectionist approach, and the advent of deep learning in the 1990s. The 21st century saw AI’s practical applications expand across various industries, including finance, healthcare, and logistics.
AI has significantly impacted quantitative investment, which relies on statistical analysis, model construction, and algorithmic trading to achieve stable returns. AI-driven quantitative investment strategies leverage powerful analytical models, accurate predictions, and automated trading systems. These technologies minimize human errors and emotional biases, providing enhanced investment experiences. The process of developing AI-based quantitative investment strategies involves data acquisition, processing, analysis, strategy construction, backtesting, and evaluation. Simulated trading tests the strategy’s effectiveness in real-time markets, while real trading applies the strategy in live securities accounts to generate profits.
The quantitative investment strategy construction involves several steps. First, data acquisition gathers market and fundamental data on companies and stocks, using factor data for research and stock index data as benchmarks. Second, data processing prepares the data by excluding delisted or suspended stocks, handling missing data, ensuring completeness, standardizing it, and labeling. Third, data analysis uses AI technologies, including machine learning and deep learning, to uncover relationships between factors and stock yields, aiding in predicting future performance. Fourth, strategy construction involves selecting stock models and trading logic based on data analysis, creating dynamic, responsive investment strategies tailored to specific goals. Fifth, test assessment uses historical market data for backtesting, evaluating performance through indicators like cumulative yield and Sharpe ratio. Lastly, strategy analysis evaluates trading results through various analyses, providing insights for refining and optimizing the investment strategy.
Artificial intelligence methods are complex and include techniques such as random forests, support vector machines, and neural networks. This paper focuses on random forests, an ensemble learning algorithm composed of multiple decision trees. Each decision tree produces a classification result, and the random forest combines these results by majority vote to make a final, robust classification.
AI swiftly analyzes extensive financial data, extracts crucial information, and identifies potential risk factors. Through machine learning, AI continuously refines risk models to better identify and evaluate risks. This enables financial institutions to promptly detect and address risks, thereby mitigating potential losses. AI also monitors financial markets in real time, analyzing transaction data to detect anomalies and issue timely warnings, which helps reduce risks such as market manipulation and insider trading. For customer risk assessment, AI evaluates credit risks and default probabilities, aiding financial institutions in accurately determining creditworthiness and minimizing default risks.
A large investment firm employs an AI-based risk control model to manage its equity portfolio. The model analyzes historical data using deep learning algorithms to identify factors affecting stock prices and predict future trends. It monitors real-time market changes and adjusts the portfolio accordingly to manage risks. The model also detects abnormal trading behaviors and issues early warnings, allowing the firm to take precautionary measures such as adjusting positions or stopping losses, thereby reducing potential losses.
In bond investment, a firm uses AI to develop a credit risk assessment model, evaluating the credit status of bond issuers. The model analyzes financial, business, and industry data to predict default probabilities and credit ratings. This helps the firm avoid high-risk bonds and monitor credit risk changes in real time, providing timely warnings and strategies to mitigate risks. The application of this model has enhanced the firm’s risk control in bond investments, reducing default risks and increasing portfolio security.
Advantages of AI in Quantitative Investment
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- Enhanced Prediction Accuracy: AI technologies like machine learning and deep learning process complex data to build accurate prediction models, forecasting market trends and price movements for better investment decisions.
- Automated Trading Decisions: AI automates trading, reducing human error and emotional bias, and improves efficiency and speed. It also supports complex strategies like statistical arbitrage to boost returns.
- Optimized Risk Management: AI identifies potential risks and predicts market fluctuations through historical data analysis, helping investors develop effective risk control strategies.
- Real-Time Market Monitoring: AI systems provide real-time insights on market dynamics, enabling investors to swiftly adjust strategies in response to market changes.
- Reduced Transaction Costs: Automated AI trading minimizes manual operations and intermediary steps, lowering transaction costs.
- Personalized Investment Advice: AI offers tailored investment recommendations based on individual risk tolerance and objectives, helping investors create suitable strategies.
AI in quantitative investment faces several challenges. First, data quality and sourcing are critical, as the accuracy and completeness of data significantly impact AI model effectiveness. Second, AI models risk overfitting, where they perform well on training data but poorly on new data, and must maintain generalization across different market conditions. Third, the rapid pace of AI technology development necessitates continual updates to investment strategies. Finally, regulatory compliance is essential to avoid risks associated with non-compliance.
The Fed’s pause on interest rate hikes introduces specific challenges. Historical data may become less relevant, requiring continuous model adjustments. Increased market volatility and uncertainty complicate AI model predictions, necessitating cautious evaluation of forecasts. Model risk, including overfitting and incorrect parameter settings, becomes more pronounced. Additionally, evolving regulatory policies demand that AI models adhere to compliance standards to mitigate regulatory risks.
The use of AI in quantitative investment is growing, offering enhanced accuracy and efficiency, thus presenting new opportunities and challenges for investors. As technology advances and data improves, AI’s role in this field will expand, enhancing returns and risk management. Investors should embrace AI, continually learning and adapting to leverage its benefits in a competitive market.
Source:
Shuochen Bi, Wenqing Bao, Jue Xiao, Jiangshan Wang, Tingting Deng, Application and practice of AI technology in quantitative investment. Information Systems and Economics (2024) Vol. 5: 124-132. DOI: http://dx.doi.org/10.23977/infse.2024.050217.