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A deep primal-dual BSDE method for optimal stopping problems 优化停止问题的深度原始双 BSDE 方法
Pub Date : 2024-09-11 DOI: arxiv-2409.06937
Jiefei Yang, Guanglian Li
We present a new deep primal-dual backward stochastic differential equationframework based on stopping time iteration to solve optimal stopping problems.A novel loss function is proposed to learn the conditional expectation, whichconsists of subnetwork parameterization of a continuation value and spatialgradients from present up to the stopping time. Notable features of the methodinclude: (i) The martingale part in the loss function reduces the variance ofstochastic gradients, which facilitates the training of the neural networks aswell as alleviates the error propagation of value function approximation; (ii)this martingale approximates the martingale in the Doob-Meyer decomposition,and thus leads to a true upper bound for the optimal value in a non-nestedMonte Carlo way. We test the proposed method in American option pricingproblems, where the spatial gradient network yields the hedging ratio directly.
我们提出了一种新的基于停止时间迭代的深度原始双向后向随机微分方程框架来解决最优停止问题。我们提出了一种新的损失函数来学习条件期望,条件期望由延续值的子网络参数化和从现在到停止时间的空间梯度组成。该方法的显著特点包括(i) 损失函数中的马丁格尔部分减小了随机梯度的方差,这有利于神经网络的训练,并减轻了价值函数近似的误差传播;(ii) 该马丁格尔近似于 Doob-Meyer 分解中的马丁格尔,因此能以非嵌套蒙特卡罗的方式得出最优值的真实上界。我们在美式期权定价问题中检验了所提出的方法,在这些问题中,空间梯度网络可以直接得到对冲比率。
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引用次数: 0
Robust financial calibration: a Bayesian approach for neural SDEs 稳健的财务校准:神经 SDE 的贝叶斯方法
Pub Date : 2024-09-10 DOI: arxiv-2409.06551
Christa Cuchiero, Eva Flonner, Kevin Kurt
The paper presents a Bayesian framework for the calibration of financialmodels using neural stochastic differential equations (neural SDEs). The methodis based on the specification of a prior distribution on the neural networkweights and an adequately chosen likelihood function. The resulting posteriordistribution can be seen as a mixture of different classical neural SDE modelsyielding robust bounds on the implied volatility surface. Both, historicalfinancial time series data and option price data are taken into consideration,which necessitates a methodology to learn the change of measure between therisk-neutral and the historical measure. The key ingredient for a robustnumerical optimization of the neural networks is to apply a Langevin-typealgorithm, commonly used in the Bayesian approaches to draw posterior samples.
本文提出了一个使用神经随机微分方程校准金融模型的贝叶斯框架。该方法基于对神经网络权重的先验分布和适当选择的似然函数的指定。由此产生的后验分布可视为不同经典神经 SDE 模型的混合物,对隐含波动率表面产生稳健的约束。历史金融时间序列数据和期权价格数据都被考虑在内,这就需要一种方法来学习风险中性度量和历史度量之间的度量变化。对神经网络进行稳健数值优化的关键要素是应用朗格文算法,该算法常用于贝叶斯方法中的后验样本提取。
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引用次数: 0
MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction MANA-Net:利用新闻加权减轻聚合情感同质化,增强市场预测能力
Pub Date : 2024-09-09 DOI: arxiv-2409.05698
Mengyu Wang, Tiejun Ma
It is widely acknowledged that extracting market sentiments from news databenefits market predictions. However, existing methods of using financialsentiments remain simplistic, relying on equal-weight and static aggregation tomanage sentiments from multiple news items. This leads to a critical issuetermed ``Aggregated Sentiment Homogenization'', which has been explored throughour analysis of a large financial news dataset from industry practice. Thisphenomenon occurs when aggregating numerous sentiments, causing representationsto converge towards the mean values of sentiment distributions and therebysmoothing out unique and important information. Consequently, the aggregatedsentiment representations lose much predictive value of news data. To addressthis problem, we introduce the Market Attention-weighted News AggregationNetwork (MANA-Net), a novel method that leverages a dynamic market-newsattention mechanism to aggregate news sentiments for market prediction.MANA-Net learns the relevance of news sentiments to price changes and assignsvarying weights to individual news items. By integrating the news aggregationstep into the networks for market prediction, MANA-Net allows for trainablesentiment representations that are optimized directly for prediction. Weevaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along withfinancial news spanning from 2003 to 2018. Experimental results demonstratethat MANA-Net outperforms various recent market prediction methods, enhancingProfit & Loss by 1.1% and the daily Sharpe ratio by 0.252.
人们普遍认为,从新闻数据库中提取市场情绪有利于市场预测。然而,现有的金融情感使用方法仍然过于简单,依赖于等权重和静态聚合来管理来自多个新闻项目的情感。这导致了一个关键问题,即 "聚合情绪同质化",我们通过对行业实践中的大型财经新闻数据集进行分析,对这一问题进行了探讨。这种现象会在汇总众多情感时出现,导致情感分布的表示趋同于平均值,从而抹去了独特而重要的信息。因此,聚合后的情感表征对新闻数据的预测价值大打折扣。为了解决这个问题,我们引入了市场关注加权新闻聚合网络(MANA-Net),这是一种利用动态市场新闻关注机制来聚合新闻情感以进行市场预测的新方法。通过将新闻聚合步骤整合到市场预测网络中,MANA-Net 可以训练可直接优化用于预测的情感表示。我们使用标准普尔 500 指数和纳斯达克 100 指数以及从 2003 年到 2018 年的金融新闻对 MANA-Net 进行了评估。实验结果表明,MANA-Net 的表现优于近期的各种市场预测方法,盈亏率提高了 1.1%,日夏普比率提高了 0.252。
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引用次数: 0
QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE QuantFactor REINFORCE:利用有方差限制的 REINFORCE 挖掘稳定的公式化阿尔法因子
Pub Date : 2024-09-08 DOI: arxiv-2409.05144
Junjie Zhao, Chengxi Zhang, Min Qin, Peng Yang
The goal of alpha factor mining is to discover indicative signals ofinvestment opportunities from the historical financial market data of assets.Deep learning based alpha factor mining methods have shown to be powerful,which, however, lack of the interpretability, making them unacceptable in therisk-sensitive real markets. Alpha factors in formulaic forms are moreinterpretable and therefore favored by market participants, while the searchspace is complex and powerful explorative methods are urged. Recently, apromising framework is proposed for generating formulaic alpha factors usingdeep reinforcement learning, and quickly gained research focuses from bothacademia and industries. This paper first argues that the originally employedpolicy training method, i.e., Proximal Policy Optimization (PPO), faces severalimportant issues in the context of alpha factors mining, making it ineffectiveto explore the search space of the formula. Herein, a novel reinforcementlearning based on the well-known REINFORCE algorithm is proposed. Given thatthe underlying state transition function adheres to the Dirac distribution, theMarkov Decision Process within this framework exhibit minimal environmentalvariability, making REINFORCE algorithm more appropriate than PPO. A newdedicated baseline is designed to theoretically reduce the commonly sufferedhigh variance of REINFORCE. Moreover, the information ratio is introduced as areward shaping mechanism to encourage the generation of steady alpha factorsthat can better adapt to changes in market volatility. Experimental evaluationson various real assets data show that the proposed algorithm can increase thecorrelation with asset returns by 3.83%, and a stronger ability to obtainexcess returns compared to the latest alpha factors mining methods, which meetsthe theoretical results well.
阿尔法因子挖掘的目标是从资产的历史金融市场数据中发现投资机会的指示性信号。基于深度学习的阿尔法因子挖掘方法已被证明是强大的,但缺乏可解释性,使其在风险敏感的真实市场中无法被接受。公式化形式的阿尔法因子更具可解释性,因此受到市场参与者的青睐,而搜索空间非常复杂,因此需要强大的探索方法。最近,一个利用深度强化学习生成公式化阿尔法因子的框架被提出,并迅速得到了学术界和产业界的研究关注。本文首先指出,在阿尔法因子挖掘方面,最初采用的策略训练方法,即近端策略优化(PPO),面临着几个重要问题,使其无法有效探索公式的搜索空间。在此,我们提出了一种基于著名的 REINFORCE 算法的新型强化学习方法。鉴于底层状态转换函数遵循狄拉克分布,该框架内的马尔科夫决策过程表现出最小的环境可变性,使得REINFORCE算法比PPO更合适。为了从理论上降低 REINFORCE 算法普遍存在的高方差,我们设计了一个新的专用基线。此外,还引入了信息比率作为前向塑造机制,以鼓励生成稳定的阿尔法因子,从而更好地适应市场波动的变化。在各种真实资产数据上的实验评估表明,与最新的阿尔法因子挖掘方法相比,所提出的算法与资产收益的相关性提高了 3.83%,获得超额收益的能力更强,很好地满足了理论结果。
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引用次数: 0
Signature of maturity in cryptocurrency volatility 加密货币波动的成熟标志
Pub Date : 2024-09-05 DOI: arxiv-2409.03676
Asim Ghosh, Soumyajyoti Biswas, Bikas K. Chakrabarti
We study the fluctuations, particularly the inequality of fluctuations, incryptocurrency prices over the last ten years. We calculate the inequality inthe price fluctuations through different measures, such as the Gini and Kolkataindices, and also the $Q$ factor (given by the ratio between the highest valueand the average value) of these fluctuations. We compare the results with theequivalent quantities in some of the more prominent national currencies and seethat while the fluctuations (or inequalities in such fluctuations) forcryptocurrencies were initially significantly higher than national currencies,over time the fluctuation levels of cryptocurrencies tend towards the levelscharacteristic of national currencies. We also compare similar quantities for afew prominent stock prices.
我们研究了过去十年中加密货币价格的波动,特别是波动的不平等性。我们通过基尼指数和加尔各答指数等不同指标来计算价格波动的不平等程度,以及这些波动的 Q$ 因子(由最高值和平均值之间的比值表示)。我们将结果与一些更重要的国家货币的同等数量进行了比较,发现虽然加密货币的波动(或这种波动的不平等)最初明显高于国家货币,但随着时间的推移,加密货币的波动水平趋向于国家货币的特征水平。我们还比较了一些著名股票价格的类似数量。
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引用次数: 0
Pricing American Options using Machine Learning Algorithms 使用机器学习算法为美式期权定价
Pub Date : 2024-09-05 DOI: arxiv-2409.03204
Prudence Djagba, Callixte Ndizihiwe
This study investigates the application of machine learning algorithms,particularly in the context of pricing American options using Monte Carlosimulations. Traditional models, such as the Black-Scholes-Merton framework,often fail to adequately address the complexities of American options, whichinclude the ability for early exercise and non-linear payoff structures. Byleveraging Monte Carlo methods in conjunction Least Square Method machinelearning was used. This research aims to improve the accuracy and efficiency ofoption pricing. The study evaluates several machine learning models, includingneural networks and decision trees, highlighting their potential to outperformtraditional approaches. The results from applying machine learning algorithm inLSM indicate that integrating machine learning with Monte Carlo simulations canenhance pricing accuracy and provide more robust predictions, offeringsignificant insights into quantitative finance by merging classical financialtheories with modern computational techniques. The dataset was split intofeatures and the target variable representing bid prices, with an 80-20train-validation split. LSTM and GRU models were constructed using TensorFlow'sKeras API, each with four hidden layers of 200 neurons and an output layer forbid price prediction, optimized with the Adam optimizer and MSE loss function.The GRU model outperformed the LSTM model across all evaluated metrics,demonstrating lower mean absolute error, mean squared error, and root meansquared error, along with greater stability and efficiency in training.
本研究探讨了机器学习算法的应用,尤其是在使用蒙特卡洛模拟法为美式期权定价时的应用。传统模型,如布莱克-斯科尔斯-默顿框架,往往无法充分解决美式期权的复杂性,其中包括提前行使能力和非线性报酬结构。本研究将蒙特卡罗方法与最小二乘法机器学习相结合。这项研究旨在提高期权定价的准确性和效率。研究评估了几种机器学习模型,包括神经网络和决策树,突出了它们优于传统方法的潜力。将机器学习算法应用于LSM 的结果表明,将机器学习与蒙特卡罗模拟相结合可以提高定价的准确性,并提供更稳健的预测,通过将经典金融理论与现代计算技术相结合,为定量金融学提供了重要见解。数据集被分为特征和代表投标价格的目标变量,训练-验证的比例为 80-20。使用 TensorFlow 的 Keras API 构建了 LSTM 和 GRU 模型,每个模型都有四个由 200 个神经元组成的隐藏层和一个禁止价格预测的输出层,并使用 Adam 优化器和 MSE 损失函数进行了优化。GRU 模型在所有评估指标上都优于 LSTM 模型,表现出更低的平均绝对误差、均方误差和均方根误差,以及更高的稳定性和训练效率。
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引用次数: 0
MoA is All You Need: Building LLM Research Team using Mixture of Agents MoA 是你所需要的一切:利用混合代理建立法学硕士研究团队
Pub Date : 2024-09-04 DOI: arxiv-2409.07487
Sandy Chen, Leqi Zeng, Abhinav Raghunathan, Flora Huang, Terrence C. Kim
Large Language Models (LLMs) research in the financial domain is particularlycomplex due to the sheer number of approaches proposed in literature.Retrieval-Augmented Generation (RAG) has emerged as one of the leading methodsin the sector due to its inherent groundedness and data source variability. Inthis work, we introduce a RAG framework called Mixture of Agents (MoA) anddemonstrate its viability as a practical, customizable, and highly effectiveapproach for scaling RAG applications. MoA is essentially a layered network ofindividually customized small language models (Hoffmann et al., 2022)collaborating to answer questions and extract information. While there are manytheoretical propositions for such an architecture and even a few libraries forgenerally applying the structure in practice, there are limited documentedstudies evaluating the potential of this framework considering real businessconstraints such as cost and speed. We find that the MoA framework, consistingof small language models (Hoffmann et al., 2022), produces higher quality andmore grounded responses across various financial domains that are core toVanguard's business while simultaneously maintaining low costs.
金融领域的大型语言模型(LLMs)研究尤为复杂,因为文献中提出的方法数量众多。检索增强生成(RAG)因其固有的基础性和数据源的可变性,已成为该领域的主要方法之一。在这项工作中,我们介绍了一种名为 "代理混合"(MoA)的 RAG 框架,并展示了它作为一种实用、可定制和高效的 RAG 应用扩展方法的可行性。MoA 本质上是一个由单独定制的小语言模型组成的分层网络(Hoffmann 等人,2022 年),它们相互协作回答问题并提取信息。虽然有很多关于这种架构的理论主张,甚至有一些库可以在实践中应用这种结构,但考虑到成本和速度等实际业务限制因素,对这种框架的潜力进行评估的文献研究非常有限。我们发现,由小语言模型组成的 MoA 框架(Hoffmann 等人,2022 年)能在各种金融领域(Vanguard 的核心业务)中产生更高质量和更接地气的响应,同时还能保持低成本。
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引用次数: 0
Attention-Based Reading, Highlighting, and Forecasting of the Limit Order Book 基于注意力的限价订单簿阅读、突出显示和预测
Pub Date : 2024-09-03 DOI: arxiv-2409.02277
Jiwon Jung, Kiseop Lee
Managing high-frequency data in a limit order book (LOB) is a complex taskthat often exceeds the capabilities of conventional time-series forecastingmodels. Accurately predicting the entire multi-level LOB, beyond just themid-price, is essential for understanding high-frequency market dynamics.However, this task is challenging due to the complex interdependencies amongcompound attributes within each dimension, such as order types, features, andlevels. In this study, we explore advanced multidimensionalsequence-to-sequence models to forecast the entire multi-level LOB, includingorder prices and volumes. Our main contribution is the development of acompound multivariate embedding method designed to capture the complexrelationships between spatiotemporal features. Empirical results show that ourmethod outperforms other multivariate forecasting methods, achieving the lowestforecasting error while preserving the ordinal structure of the LOB.
管理限价订单簿(LOB)中的高频数据是一项复杂的任务,往往超出了传统时间序列预测模型的能力。然而,由于每个维度中的复合属性(如订单类型、特征和级别)之间存在复杂的相互依存关系,因此这项任务极具挑战性。在本研究中,我们探索了先进的多维序列到序列模型,以预测整个多级 LOB,包括订单价格和数量。我们的主要贡献在于开发了一种复合多变量嵌入方法,旨在捕捉时空特征之间的复杂关系。实证结果表明,我们的方法优于其他多元预测方法,在保留 LOB 的序数结构的同时,实现了最低的预测误差。
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引用次数: 0
Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS 推进金融预测:神经预测模型 N-HiTS 和 N-BEATS 的比较分析
Pub Date : 2024-08-31 DOI: arxiv-2409.00480
Mohit Apte, Yashodhara Haribhakta
In the rapidly evolving field of financial forecasting, the application ofneural networks presents a compelling advancement over traditional statisticalmodels. This research paper explores the effectiveness of two specific neuralforecasting models, N-HiTS and N-BEATS, in predicting financial market trends.Through a systematic comparison with conventional models, this studydemonstrates the superior predictive capabilities of neural approaches,particularly in handling the non-linear dynamics and complex patterns inherentin financial time series data. The results indicate that N-HiTS and N-BEATS notonly enhance the accuracy of forecasts but also boost the robustness andadaptability of financial predictions, offering substantial advantages inenvironments that require real-time decision-making. The paper concludes withinsights into the practical implications of neural forecasting in financialmarkets and recommendations for future research directions.
在快速发展的金融预测领域,与传统统计模型相比,神经网络的应用是一个引人注目的进步。本研究论文探讨了 N-HiTS 和 N-BEATS 这两种特定神经预测模型在预测金融市场趋势方面的有效性。通过与传统模型的系统比较,本研究证明了神经方法的卓越预测能力,尤其是在处理金融时间序列数据中固有的非线性动态和复杂模式方面。结果表明,N-HiTS 和 N-BEATS 不仅提高了预测的准确性,还增强了金融预测的稳健性和适应性,在需要实时决策的环境中具有很大优势。最后,本文深入分析了神经预测在金融市场中的实际意义,并对未来的研究方向提出了建议。
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引用次数: 0
Trading with Time Series Causal Discovery: An Empirical Study 利用时间序列因果发现进行交易:实证研究
Pub Date : 2024-08-28 DOI: arxiv-2408.15846
Ruijie Tang
This study investigates the application of causal discovery algorithms inequity markets, with a focus on their potential to enhance investmentstrategies. An investment strategy was developed based on the causal structuresidentified by these algorithms, and its performance was evaluated to assess theprofitability and effectiveness in stock market environments. The resultsindicate that causal discovery algorithms can successfully uncover actionablecausal relationships in large markets, leading to profitable investmentoutcomes. However, the research also identifies a critical challenge: thecomputational complexity and scalability of these algorithms when dealing withlarge datasets, which presents practical limitations for their application inreal-world market analysis.
本研究调查了因果发现算法在不公平市场中的应用,重点关注其增强投资策略的潜力。根据这些算法确定的因果结构开发了一种投资策略,并对其性能进行了评估,以评估其在股票市场环境中的盈利能力和有效性。结果表明,因果发现算法可以在大型市场中成功发现可操作的因果关系,从而带来有利可图的投资结果。不过,研究也发现了一个关键挑战:这些算法在处理大型数据集时的计算复杂性和可扩展性,这对它们在真实世界市场分析中的应用造成了实际限制。
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引用次数: 0
期刊
arXiv - QuantFin - Computational Finance
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