Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model (2024)

Abstract: Finding interpretable factors for stock returns is the most vital issue in the empirical asset pricing domain. As data-driven methods, existing factor mining models can be categorized into symbol-based and neural-based models. Symbol-based models are interpretable but inefficient, while neural-based approaches are efficient but lack interpretability. Hence, mining interpretable factors effectively presents a significant challenge. Inspired by the success of Large Language Models (LLMs) in various tasks, we propose a FActor Mining Agent (FAMA) model that enables LLMs to integrate the strengths of both neural and symbolic models for factor mining. In this paper, FAMA consists of two main components: Cross-Sample Selection (CSS) and Chain-of-Experience (CoE). CSS addresses the homogeneity challenges in LLMs during factor mining by assimilating diverse factors as in-context samples, whereas CoE enables LLMs to leverage past successful mining experiences, expediting the mining of effective factors. Experimental evaluations on real-world stock market data demonstrate the effectiveness of our approach by surpassing the SOTA RankIC by 0.006 and RankICIR by 0.105 in predicting S&P 500 returns. Furthermore, the investment simulation shows that our model can achieve superior performance with an annualized return of 38.4% and a Sharpe ratio of 667.2%.

## introduction the task of predicting market trends in finance presents a formidable challenge, given the intricate interplay of various factors @xcite , such as the dynamics of demand and supply @xcite , market sentiment @xcite and government regulations @xcite . in the field of quantitative trading, the conventional approaches often extract factors as indicative signals for market trends from raw historical stock data first, then serve them as input features for machine learning models @xcite @xcite . a pivotal step in this process entails discerning and extracting effective factors that demonstrate robust predictive capabilities for market trends @xcite ). as an illustrative example, the capital asset pricing model (capm) @xcite employed the market's excess return as a predictive factor for the return of a financial asset, thereby contributing a seminal factor to finance. hence discovering factors with high returns has been a trendy topic among investors and researchers. the prevailing methods for mining factors can be in general divided into two groups, namely symbolic factor and neural factor models. as illustrated in figure 1 (a), in symbolic factor models, factors are represented as symbolic expressions, then symbolic regression @xcite serves as a common technique for factor mining @xcite @xcite . for instance, considering two factors, f @xmath0, the factor values are calculated by the opening and closing price, then the two factors are inputted into a symbolic regression model to generate a novel factor, n ewf @xmath1. the interpretability of the symbolic factor model arises from the explicit representation of the calculation process for the factors. however, due to the vast search space of symbolic factors, mining with symbolic factor models often proves inefficient. conversely, neural factor approaches, gaining recent popularity, transform factors into numerical features to optimize factor extraction. as depicted in figure 1 (b), neural factor models predict market trends by extracting numerical factor features from stock data through feature extractors @xcite @xcite . compared with symbolic factor models, neural factor models exhibit proficiency in extracting effective numerical factors. however, the financial interpretability in neural factor models struggles with implicit features. the question we are facing is: can an effective approach be devised for mining financially interpretable factors conducive to predicting market trends? recent advancements in llms have demonstrated success across financial tasks, including sentiment analysis @xcite and financial text generation @xcite . thanks to its powerful in-context learning (icl) ability @xcite , we conceptualize llms as a neurosymbolic model illustrated in figure 1(c ) that bridges two distinct representations, i.e., numerical and symbolic factors, aiming to achieve efficient mining of interpretable ones. it is facile to consider utilizing the factors disclosed @xcite as contextual examples to generate new factors through icl. since the disclosed factors are often limited in number, high correlation, and low complexity, direct mining factors using icl encounter challenges. these issues can be summarized in two aspects: (1) the heightened homogeneity observed among factors, characterized by the uniform structure, culminates in the generation of the singular factor form through icl. (2) the presence of a noteworthy proportion of ineffective factors acts as an impediment, hindering icl from effectively exploring novel patterns. therefore, the efficacy of mining effective factors using llms is contingent upon selecting diversity-guiding factors as contextual samples to mitigate homogeneity. additionally, encouraging icl to explore new patterns is key to increasing the proportion of effective factors. in this paper, we present the factor mining agent (fama), consisting of two main parts: cross-sample selection (css) and chainof-experience (coe) methods. css is designed to ensure the diversification of factor mining by amalgamating low correlation classes of factors as contextual samples, which empowers llms to incorporate diversity-guiding factors and mitigate the homogeneity of mined factors. coe efficiently encourages icl to explore new paradigms by incorporating the paths of mining effective factors as experiential prompts, which contributes to the further optimization of factor mining in llms. our experimental results show better performance of our model in predicting stock market returns compared to previous approaches. moreover, our model also demonstrates a superior annualized return and sharpe ratio in the investment simulations. our contributions can be summarized as follows: ## experiments our experimental investigation revolves around addressing three key questions: ## conclusion in this paper, we consider large language models (llms) as a neural symbolic model for financial factor mining. to facilitate llms to pursue our task, we proposed a model called factor mining agent (fama), which comprises two integral components: cross-sample selection (css) and chain-of-experience (coe). css mitigates the homogeneity in the mined factors by amalgamating diverse guidance factors. coe encourages in-context learning (icl) to explore novel factor paradigms by leveraging the paths leading to the mining of effective factors as experiential prompts. both css and coe components are integrated into our factor mining agent to effectively mine financially inter-pretable factors. experimental results demonstrate the effectiveness of our proposed approach. our future work includes exploring more avenues to enhance the optimization of factor mining and addressing the hallucination phenomenon of llms.