Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, Christina Dan Wang
As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community. These challenges impede the AI community's ability to enhance financial tasks effectively. Acknowledging financial analysis's critical role, we aim to devise financial-specialized LLM-based toolchains and democratize access to them through open-source initiatives, promoting wider AI adoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform supporting multiple financially specialized AI agents, each powered by LLM. Specifically, the platform consists of four major layers: 1) the Financial AI Agents layer that formulates Financial Chain-of-Thought (CoT) by breaking sophisticated financial problems down into logical sequences; 2) the Financial LLM Algorithms layer dynamically configures appropriate model application strategies for specific tasks; 3) the LLMOps and DataOps layer produces accurate models by applying training/fine-tuning techniques and using task-relevant data; 4) the Multi-source LLM Foundation Models layer that integrates various LLMs and enables the above layers to access them directly. Finally, FinRobot provides hands-on for both professional-grade analysts and laypersons to utilize powerful AI techniques for advanced financial analysis. We open-source FinRobot at url{https://github.com/AI4Finance-Foundation/FinRobot}.
{"title":"FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models","authors":"Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, Christina Dan Wang","doi":"arxiv-2405.14767","DOIUrl":"https://doi.org/arxiv-2405.14767","url":null,"abstract":"As financial institutions and professionals increasingly incorporate Large\u0000Language Models (LLMs) into their workflows, substantial barriers, including\u0000proprietary data and specialized knowledge, persist between the finance sector\u0000and the AI community. These challenges impede the AI community's ability to\u0000enhance financial tasks effectively. Acknowledging financial analysis's\u0000critical role, we aim to devise financial-specialized LLM-based toolchains and\u0000democratize access to them through open-source initiatives, promoting wider AI\u0000adoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform\u0000supporting multiple financially specialized AI agents, each powered by LLM.\u0000Specifically, the platform consists of four major layers: 1) the Financial AI\u0000Agents layer that formulates Financial Chain-of-Thought (CoT) by breaking\u0000sophisticated financial problems down into logical sequences; 2) the Financial\u0000LLM Algorithms layer dynamically configures appropriate model application\u0000strategies for specific tasks; 3) the LLMOps and DataOps layer produces\u0000accurate models by applying training/fine-tuning techniques and using\u0000task-relevant data; 4) the Multi-source LLM Foundation Models layer that\u0000integrates various LLMs and enables the above layers to access them directly.\u0000Finally, FinRobot provides hands-on for both professional-grade analysts and\u0000laypersons to utilize powerful AI techniques for advanced financial analysis.\u0000We open-source FinRobot at\u0000url{https://github.com/AI4Finance-Foundation/FinRobot}.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider an Ito-financial market at which the risky assets' returns are derived endogenously through a market-clearing condition amongst heterogeneous risk-averse investors with quadratic preferences and random endowments. Investors act strategically by taking into account the impact that their orders have on the assets' drift. A frictionless market and an one with quadratic transaction costs are analysed and compared. In the former, we derive the unique Nash equilibrium at which investors' demand processes reveal different hedging needs than their true ones, resulting in a deviation of the Nash equilibrium from its competitive counterpart. Under price impact and transaction costs, we characterize the Nash equilibrium as the (unique) solution of a system of FBSDEs and derive its closed-form expression. We furthermore show that under common risk aversion and absence of noise traders, transaction costs do not change the equilibrium returns. On the contrary, when noise traders are present, the effect of transaction costs on equilibrium returns is amplified due to price impact.
{"title":"Continuous-time Equilibrium Returns in Markets with Price Impact and Transaction Costs","authors":"Michail Anthropelos, Constantinos Stefanakis","doi":"arxiv-2405.14418","DOIUrl":"https://doi.org/arxiv-2405.14418","url":null,"abstract":"We consider an Ito-financial market at which the risky assets' returns are\u0000derived endogenously through a market-clearing condition amongst heterogeneous\u0000risk-averse investors with quadratic preferences and random endowments.\u0000Investors act strategically by taking into account the impact that their orders\u0000have on the assets' drift. A frictionless market and an one with quadratic\u0000transaction costs are analysed and compared. In the former, we derive the\u0000unique Nash equilibrium at which investors' demand processes reveal different\u0000hedging needs than their true ones, resulting in a deviation of the Nash\u0000equilibrium from its competitive counterpart. Under price impact and\u0000transaction costs, we characterize the Nash equilibrium as the (unique)\u0000solution of a system of FBSDEs and derive its closed-form expression. We\u0000furthermore show that under common risk aversion and absence of noise traders,\u0000transaction costs do not change the equilibrium returns. On the contrary, when\u0000noise traders are present, the effect of transaction costs on equilibrium\u0000returns is amplified due to price impact.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the potential of Bayesian optimization (BO) to optimize the atr multiplier and atr period -the parameters of the Supertrend indicator for maximizing trading profits across diverse stock datasets. By employing BO, the thesis aims to automate the identification of optimal parameter settings, leading to a more data-driven and potentially more profitable trading strategy compared to relying on manually chosen parameters. The effectiveness of the BO-optimized Supertrend strategy will be evaluated through backtesting on a variety of stock datasets.
{"title":"Unlocking Profit Potential: Maximizing Returns with Bayesian Optimization of Supertrend Indicator Parameters","authors":"Abdul Rahman","doi":"arxiv-2405.14262","DOIUrl":"https://doi.org/arxiv-2405.14262","url":null,"abstract":"This paper investigates the potential of Bayesian optimization (BO) to\u0000optimize the atr multiplier and atr period -the parameters of the Supertrend\u0000indicator for maximizing trading profits across diverse stock datasets. By\u0000employing BO, the thesis aims to automate the identification of optimal\u0000parameter settings, leading to a more data-driven and potentially more\u0000profitable trading strategy compared to relying on manually chosen parameters.\u0000The effectiveness of the BO-optimized Supertrend strategy will be evaluated\u0000through backtesting on a variety of stock datasets.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonathan Chávez-Casillas, José E. Figueroa-López, Chuyi Yu, Yi Zhang
A novel high-frequency market-making approach in discrete time is proposed that admits closed-form solutions. By taking advantage of demand functions that are linear in the quoted bid and ask spreads with random coefficients, we model the variability of the partial filling of limit orders posted in a limit order book (LOB). As a result, we uncover new patterns as to how the demand's randomness affects the optimal placement strategy. We also allow the price process to follow general dynamics without any Brownian or martingale assumption as is commonly adopted in the literature. The most important feature of our optimal placement strategy is that it can react or adapt to the behavior of market orders online. Using LOB data, we train our model and reproduce the anticipated final profit and loss of the optimal strategy on a given testing date using the actual flow of orders in the LOB. Our adaptive optimal strategies outperform the non-adaptive strategy and those that quote limit orders at a fixed distance from the midprice.
{"title":"Adaptive Optimal Market Making Strategies with Inventory Liquidation Cos","authors":"Jonathan Chávez-Casillas, José E. Figueroa-López, Chuyi Yu, Yi Zhang","doi":"arxiv-2405.11444","DOIUrl":"https://doi.org/arxiv-2405.11444","url":null,"abstract":"A novel high-frequency market-making approach in discrete time is proposed\u0000that admits closed-form solutions. By taking advantage of demand functions that\u0000are linear in the quoted bid and ask spreads with random coefficients, we model\u0000the variability of the partial filling of limit orders posted in a limit order\u0000book (LOB). As a result, we uncover new patterns as to how the demand's\u0000randomness affects the optimal placement strategy. We also allow the price\u0000process to follow general dynamics without any Brownian or martingale\u0000assumption as is commonly adopted in the literature. The most important feature\u0000of our optimal placement strategy is that it can react or adapt to the behavior\u0000of market orders online. Using LOB data, we train our model and reproduce the\u0000anticipated final profit and loss of the optimal strategy on a given testing\u0000date using the actual flow of orders in the LOB. Our adaptive optimal\u0000strategies outperform the non-adaptive strategy and those that quote limit\u0000orders at a fixed distance from the midprice.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We have designed an innovative portfolio rebalancing mechanism termed the Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal size and number of trades for each asset during the periodic rebalancing process, factoring in the gas fee and slippage. The essence of the model we have created gives indications regarding whether trades should be made on individual assets depending on the uncertainty in the micro - asset level characteristics - and macro - aggregate market factors - environments. In the hyper-volatile crypto market, our approach to daily rebalancing will benefit from volatility. Price movements will cause our algorithm to buy assets that drop in prices and sell as they soar. In fact, the buying and selling happen only when certain boundaries are crossed in order to weed out any market noise and ensure sound trade execution. We have provided several numerical examples to illustrate the steps - including the calculation of several intermediate variables - of our rebalancing mechanism. The Algorithm we have developed can be easily applied outside blockchain to investment funds across all asset classes at any trading frequency and rebalancing duration. Shakespeare As A Crypto Trader: To Trade Or Not To Trade, that is the Question, Whether an Optimizer can Yield the Answer, Against the Spikes and Crashes of Markets Gone Wild, To Quench One's Thirst before Liquidity Runs Dry, Or Wait till the Tide of Momentum turns Mild.
我们设计了一种创新的投资组合再平衡机制，称为级联瀑布循环机制（Cascading Waterfall Round Robin Mechanism）。在定期再平衡过程中，这种算法会为每种资产推荐理想的交易规模和数量，并将气体费和滑点考虑在内。我们所创建模型的本质是，根据微观（资产层面特征）和宏观（总体市场因素）环境的不确定性，就是否应该对单个资产进行交易给出指示。在波动剧烈的加密货币市场，我们的每日再平衡方法将从波动中获益。价格变动将促使我们的算法在资产价格下跌时买入，在资产价格上涨时卖出。事实上，只有在跨越某些界限时才会进行买卖，以剔除市场噪音，确保交易的稳健执行。我们提供了几个数字示例来说明我们的再平衡机制的步骤，包括几个中间变量的计算。我们开发的算法可以在区块链之外轻松地应用于所有资产类别的投资基金，并且可以适用于任何交易频率和再平衡持续时间。作为加密货币交易者的莎士比亚：是交易还是不交易，这是一个问题，一个优化器是否能给出答案，是在市场疯狂的暴涨和暴跌面前，在流动性枯竭之前饮鸩止渴，还是等待势头转缓。
{"title":"To Trade Or Not To Trade: Cascading Waterfall Round Robin Rebalancing Mechanism for Cryptocurrencies","authors":"Ravi Kashyap","doi":"arxiv-2407.12150","DOIUrl":"https://doi.org/arxiv-2407.12150","url":null,"abstract":"We have designed an innovative portfolio rebalancing mechanism termed the\u0000Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends\u0000an ideal size and number of trades for each asset during the periodic\u0000rebalancing process, factoring in the gas fee and slippage. The essence of the\u0000model we have created gives indications regarding whether trades should be made\u0000on individual assets depending on the uncertainty in the micro - asset level\u0000characteristics - and macro - aggregate market factors - environments. In the\u0000hyper-volatile crypto market, our approach to daily rebalancing will benefit\u0000from volatility. Price movements will cause our algorithm to buy assets that\u0000drop in prices and sell as they soar. In fact, the buying and selling happen\u0000only when certain boundaries are crossed in order to weed out any market noise\u0000and ensure sound trade execution. We have provided several numerical examples\u0000to illustrate the steps - including the calculation of several intermediate\u0000variables - of our rebalancing mechanism. The Algorithm we have developed can\u0000be easily applied outside blockchain to investment funds across all asset\u0000classes at any trading frequency and rebalancing duration. Shakespeare As A Crypto Trader: To Trade Or Not To Trade, that is the Question, Whether an Optimizer can Yield the Answer, Against the Spikes and Crashes of Markets Gone Wild, To Quench One's Thirst before Liquidity Runs Dry, Or Wait till the Tide of Momentum turns Mild.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flaws of a continuous limit order book mechanism raise the question of whether a continuous trading session and a periodic auction session would bring better efficiency. This paper wants to go further in designing a periodic auction when both a continuous market and a periodic auction market are available to traders. In a periodic auction, we discover that a strategic trader could take advantage of the accumulated information available along the auction duration by arriving at the latest moment before the auction closes, increasing the price impact on the market. Such price impact moves the clearing price away from the efficient price and may disturb the efficiency of a periodic auction market. We thus propose and quantify the effect of two remedies to mitigate these flaws: randomizing the auction's closing time and optimally designing a transaction fees policy. Our results show that these policies encourage a strategic trader to send their orders earlier to enhance the efficiency of the auction market, illustrated by data extracted from Alphabet and Apple stocks.
{"title":"Clearing time randomization and transaction fees for auction market design","authors":"Thibaut Mastrolia, Tianrui Xu","doi":"arxiv-2405.09764","DOIUrl":"https://doi.org/arxiv-2405.09764","url":null,"abstract":"Flaws of a continuous limit order book mechanism raise the question of\u0000whether a continuous trading session and a periodic auction session would bring\u0000better efficiency. This paper wants to go further in designing a periodic\u0000auction when both a continuous market and a periodic auction market are\u0000available to traders. In a periodic auction, we discover that a strategic\u0000trader could take advantage of the accumulated information available along the\u0000auction duration by arriving at the latest moment before the auction closes,\u0000increasing the price impact on the market. Such price impact moves the clearing\u0000price away from the efficient price and may disturb the efficiency of a\u0000periodic auction market. We thus propose and quantify the effect of two\u0000remedies to mitigate these flaws: randomizing the auction's closing time and\u0000optimally designing a transaction fees policy. Our results show that these\u0000policies encourage a strategic trader to send their orders earlier to enhance\u0000the efficiency of the auction market, illustrated by data extracted from\u0000Alphabet and Apple stocks.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines a trade execution game for two large traders in a generalized price impact model. We incorporate a stochastic and sequentially dependent factor that exogenously affects the market price into financial markets. Our model accounts for how strategic and environmental uncertainties affect the large traders' execution strategies. We formulate an expected utility maximization problem for two large traders as a Markov game model. Applying the backward induction method of dynamic programming, we provide an explicit closed-form execution strategy at a Markov perfect equilibrium. Our theoretical results reveal that the execution strategy generally lies in a dynamic and non-randomized class; it becomes deterministic if the Markovian environment is also deterministic. In addition, our simulation-based numerical experiments suggest that the execution strategy captures various features observed in financial markets.
{"title":"Trade execution games in a Markovian environment","authors":"Masamitsu Ohnishi, Makoto Shimoshimizu","doi":"arxiv-2405.07184","DOIUrl":"https://doi.org/arxiv-2405.07184","url":null,"abstract":"This paper examines a trade execution game for two large traders in a\u0000generalized price impact model. We incorporate a stochastic and sequentially\u0000dependent factor that exogenously affects the market price into financial\u0000markets. Our model accounts for how strategic and environmental uncertainties\u0000affect the large traders' execution strategies. We formulate an expected\u0000utility maximization problem for two large traders as a Markov game model.\u0000Applying the backward induction method of dynamic programming, we provide an\u0000explicit closed-form execution strategy at a Markov perfect equilibrium. Our\u0000theoretical results reveal that the execution strategy generally lies in a\u0000dynamic and non-randomized class; it becomes deterministic if the Markovian\u0000environment is also deterministic. In addition, our simulation-based numerical\u0000experiments suggest that the execution strategy captures various features\u0000observed in financial markets.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Philippe Bergault, Louis Bertucci, David Bouba, Olivier Guéant, Julien Guilbert
In this paper, we introduce a suite of models for price-aware automated market making platforms willing to optimize their quotes. These models incorporate advanced price dynamics, including stochastic volatility, jumps, and microstructural price models based on Hawkes processes. Additionally, we address the variability in demand from liquidity takers through models that employ either Hawkes or Markov-modulated Poisson processes. Each model is analyzed with particular emphasis placed on the complexity of the numerical methods required to compute optimal quotes.
{"title":"Price-Aware Automated Market Makers: Models Beyond Brownian Prices and Static Liquidity","authors":"Philippe Bergault, Louis Bertucci, David Bouba, Olivier Guéant, Julien Guilbert","doi":"arxiv-2405.03496","DOIUrl":"https://doi.org/arxiv-2405.03496","url":null,"abstract":"In this paper, we introduce a suite of models for price-aware automated\u0000market making platforms willing to optimize their quotes. These models\u0000incorporate advanced price dynamics, including stochastic volatility, jumps,\u0000and microstructural price models based on Hawkes processes. Additionally, we\u0000address the variability in demand from liquidity takers through models that\u0000employ either Hawkes or Markov-modulated Poisson processes. Each model is\u0000analyzed with particular emphasis placed on the complexity of the numerical\u0000methods required to compute optimal quotes.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work introduces a framework for evaluating onchain order flow auctions (OFAs), emphasizing the metric of price improvement. Utilizing a set of open-source tools, our methodology systematically attributes price improvements to specific modifiable inputs of the system such as routing efficiency, gas optimization, and priority fee settings. When applied to leading Ethereum-based trading interfaces such as 1Inch and Uniswap, the results reveal that auction-enhanced interfaces can provide statistically significant improvements in trading outcomes, averaging 4-5 basis points in our sample. We further identify the sources of such price improvements to be added liquidity for large swaps. This research lays a foundation for future innovations in blockchain based trading platforms.
{"title":"Quantifying Price Improvement in Order Flow Auctions","authors":"Brad Bachu, Xin Wan, Ciamac C. Moallemi","doi":"arxiv-2405.00537","DOIUrl":"https://doi.org/arxiv-2405.00537","url":null,"abstract":"This work introduces a framework for evaluating onchain order flow auctions\u0000(OFAs), emphasizing the metric of price improvement. Utilizing a set of\u0000open-source tools, our methodology systematically attributes price improvements\u0000to specific modifiable inputs of the system such as routing efficiency, gas\u0000optimization, and priority fee settings. When applied to leading Ethereum-based\u0000trading interfaces such as 1Inch and Uniswap, the results reveal that\u0000auction-enhanced interfaces can provide statistically significant improvements\u0000in trading outcomes, averaging 4-5 basis points in our sample. We further\u0000identify the sources of such price improvements to be added liquidity for large\u0000swaps. This research lays a foundation for future innovations in blockchain\u0000based trading platforms.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yupeng Cao, Zhi Chen, Qingyun Pei, Prashant Kumar, K. P. Subbalakshmi, Papa Momar Ndiaye
In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors. While previous studies have used deep learning-based models to obtain a general view of ECCs, they often fail to capture detailed, complex information. Our study introduces a novel framework: textbf{ECC Analyzer}, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights. The model begins by summarizing the transcript's structure and analyzing the speakers' mode and confidence level by detecting variations in tone and pitch for audio. This analysis helps investors form an overview perception of the ECCs. Moreover, this model uses the Retrieval-Augmented Generation (RAG) based methods to meticulously extract the focuses that have a significant impact on stock performance from an expert's perspective, providing a more targeted analysis. The model goes a step further by enriching these extracted focuses with additional layers of analysis, such as sentiment and audio segment features. By integrating these insights, the ECC Analyzer performs multi-task predictions of stock performance, including volatility, value-at-risk (VaR), and return for different intervals. The results show that our model outperforms traditional analytic benchmarks, confirming the effectiveness of using advanced LLM techniques in financial analytics.
{"title":"ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction","authors":"Yupeng Cao, Zhi Chen, Qingyun Pei, Prashant Kumar, K. P. Subbalakshmi, Papa Momar Ndiaye","doi":"arxiv-2404.18470","DOIUrl":"https://doi.org/arxiv-2404.18470","url":null,"abstract":"In the realm of financial analytics, leveraging unstructured data, such as\u0000earnings conference calls (ECCs), to forecast stock performance is a critical\u0000challenge that has attracted both academics and investors. While previous\u0000studies have used deep learning-based models to obtain a general view of ECCs,\u0000they often fail to capture detailed, complex information. Our study introduces\u0000a novel framework: textbf{ECC Analyzer}, combining Large Language Models\u0000(LLMs) and multi-modal techniques to extract richer, more predictive insights.\u0000The model begins by summarizing the transcript's structure and analyzing the\u0000speakers' mode and confidence level by detecting variations in tone and pitch\u0000for audio. This analysis helps investors form an overview perception of the\u0000ECCs. Moreover, this model uses the Retrieval-Augmented Generation (RAG) based\u0000methods to meticulously extract the focuses that have a significant impact on\u0000stock performance from an expert's perspective, providing a more targeted\u0000analysis. The model goes a step further by enriching these extracted focuses\u0000with additional layers of analysis, such as sentiment and audio segment\u0000features. By integrating these insights, the ECC Analyzer performs multi-task\u0000predictions of stock performance, including volatility, value-at-risk (VaR),\u0000and return for different intervals. The results show that our model outperforms\u0000traditional analytic benchmarks, confirming the effectiveness of using advanced\u0000LLM techniques in financial analytics.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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