In the domain of quantitative trading,the imperative is to translate historical financial data into predictive signals,commonly referred to as alpha factors,which serves to anticipate future market trends.Notably,form...In the domain of quantitative trading,the imperative is to translate historical financial data into predictive signals,commonly referred to as alpha factors,which serves to anticipate future market trends.Notably,formulaic alphas that are expressible via explicit mathematical formulas are highly sought after by certain investors for better interpretability.The evolving landscape of technology has witnessed the increasing deployment of large language models(LLMs)across various domains,which raises the question of whether LLMs can be effective in the context of formulaic alpha-mining tasks.This paper presents several paradigms aimed at integrating LLMs into the optimization loop of alpha mining,including scenarios where an LLM serves as the sole alpha generator,as well as instances where LLMs enhance existing frameworks.Empirical evaluations on real-world stock data demonstrate significant performance improvements,with our hybrid method achieving an average information coefficient(IC)of 0.0515,a 75%improvement over the baseline—a state-of-the-art reinforcement learning-based framework;backtesting further reveals a cumulative excess return more than double the baseline framework.These results underscore the potential of LLM-enhanced approaches in advancing formulaic alpha discovery and driving innovation in quantitative trading.展开更多
By analyzing the financing difficulties faced by the small and medium-sized firms, the paper built an artificial credit markets with the agent-based computational modeling to simulate the real world credit transaction...By analyzing the financing difficulties faced by the small and medium-sized firms, the paper built an artificial credit markets with the agent-based computational modeling to simulate the real world credit transactions. There are firms, banks, different risk-type projects as well as legal and supervision environments in which debt contracts constitute the financial instruments. The simulation results show that the number of collateral, average success probability of projects, and the prime interest rate have materially impact on bank's average profit, bank's capital, the overall interest rate,the number of borrowing firms, loan size, and the degree of credit rationing. These results in line with those of the classical S-W model in the sense that the relationship between bank profits and interest rates is non-monotonic as well as the relationship between credit rationing and interest rates. And thus there is an adverse selection effect in credit rationing theory.展开更多
This paper aims to contribute to the literature on the explanatory power of behavior models with heterogeneous agents. The authors present a new nonlinear structural stock market model which is a nonlinear determinist...This paper aims to contribute to the literature on the explanatory power of behavior models with heterogeneous agents. The authors present a new nonlinear structural stock market model which is a nonlinear deterministic process buffeted by dynamic noise. An exogenous noise is introduced to the model with the assumption of IID normal innovations of the fundamental value in order to investigate how noisy dynamics interacts with deterministic process. The market is composed of two typical trader types： the rational fundamentalists and the boundedly rational traders governed by greed and fear. The interaction between noise and deterministic element determines the evolution process of the system as key parameters are changed. The authors find the model is able to generate time series that exhibit dynamical and statistical properties closely resembling those of the S＆：P500 index, such as volatility clustering, fat tails （leptokurtosis）, autocorrelation in square and absolute return, larger amplitude, crashes and bubbles. The authors also investigate the nonlinear dependence structure in our data. The results indicate that the GARCH-type model cannot completely account for all nonlinearity in our simulated market, which is thus consistent with the results from real markets. It seems that the nonlinear structural model is more powerful to give a satisfied explanation to market behavior than the traditional stochastic approach.展开更多
基金supported by the National Key R&D Program of China(No.2022YFC3303302)the National Natural Science Foundation of China(Grant Nos.62476263 and U2436209)+1 种基金the Project of Youth Innovation Promotion Association CAS,Beijing Nova Program 20230484430the Innovation Funding of ICT,CAS(No.E461060).
文摘In the domain of quantitative trading,the imperative is to translate historical financial data into predictive signals,commonly referred to as alpha factors,which serves to anticipate future market trends.Notably,formulaic alphas that are expressible via explicit mathematical formulas are highly sought after by certain investors for better interpretability.The evolving landscape of technology has witnessed the increasing deployment of large language models(LLMs)across various domains,which raises the question of whether LLMs can be effective in the context of formulaic alpha-mining tasks.This paper presents several paradigms aimed at integrating LLMs into the optimization loop of alpha mining,including scenarios where an LLM serves as the sole alpha generator,as well as instances where LLMs enhance existing frameworks.Empirical evaluations on real-world stock data demonstrate significant performance improvements,with our hybrid method achieving an average information coefficient(IC)of 0.0515,a 75%improvement over the baseline—a state-of-the-art reinforcement learning-based framework;backtesting further reveals a cumulative excess return more than double the baseline framework.These results underscore the potential of LLM-enhanced approaches in advancing formulaic alpha discovery and driving innovation in quantitative trading.
基金supported by the National Natural Science Foundation of China under Grant Nos.71532009,71320107003 and 71271145Core Projects in Tianjin Education Bureaus Social Science Program under Grant Nos.2012JWZD11 and 2014ZD13Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20110032110031
文摘By analyzing the financing difficulties faced by the small and medium-sized firms, the paper built an artificial credit markets with the agent-based computational modeling to simulate the real world credit transactions. There are firms, banks, different risk-type projects as well as legal and supervision environments in which debt contracts constitute the financial instruments. The simulation results show that the number of collateral, average success probability of projects, and the prime interest rate have materially impact on bank's average profit, bank's capital, the overall interest rate,the number of borrowing firms, loan size, and the degree of credit rationing. These results in line with those of the classical S-W model in the sense that the relationship between bank profits and interest rates is non-monotonic as well as the relationship between credit rationing and interest rates. And thus there is an adverse selection effect in credit rationing theory.
基金This research is supported by MEXT Global COE Program （Kyoto University）, National Natural Science Foundation of China under Grant No.71001036 and No. 71171186, Main Direction Program of Chinese Academy of Sciences KACX1-YW-0906, and the Scientific Research Fund of Hunan Provincial Education Department under Grant No. 10A082.
文摘This paper aims to contribute to the literature on the explanatory power of behavior models with heterogeneous agents. The authors present a new nonlinear structural stock market model which is a nonlinear deterministic process buffeted by dynamic noise. An exogenous noise is introduced to the model with the assumption of IID normal innovations of the fundamental value in order to investigate how noisy dynamics interacts with deterministic process. The market is composed of two typical trader types： the rational fundamentalists and the boundedly rational traders governed by greed and fear. The interaction between noise and deterministic element determines the evolution process of the system as key parameters are changed. The authors find the model is able to generate time series that exhibit dynamical and statistical properties closely resembling those of the S＆：P500 index, such as volatility clustering, fat tails （leptokurtosis）, autocorrelation in square and absolute return, larger amplitude, crashes and bubbles. The authors also investigate the nonlinear dependence structure in our data. The results indicate that the GARCH-type model cannot completely account for all nonlinearity in our simulated market, which is thus consistent with the results from real markets. It seems that the nonlinear structural model is more powerful to give a satisfied explanation to market behavior than the traditional stochastic approach.
