arXiv - QuantFin - Computational Finance最新文献_第6页

Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns 利用时空模式预测金融资产依赖性

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-13 DOI: arxiv-2406.11886

Haoren Zhu, Pengfei Zhao, Wilfred Siu Hung NG, Dik Lun Lee

{"title":"Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns","authors":"Haoren Zhu, Pengfei Zhao, Wilfred Siu Hung NG, Dik Lun Lee","doi":"arxiv-2406.11886","DOIUrl":"https://doi.org/arxiv-2406.11886","url":null,"abstract":"Financial assets exhibit complex dependency structures, which are crucial for\u0000investors to create diversified portfolios to mitigate risk in volatile\u0000financial markets. To explore the financial asset dependencies dynamics, we\u0000propose a novel approach that models the dependencies of assets as an Asset\u0000Dependency Matrix (ADM) and treats the ADM sequences as image sequences. This\u0000allows us to leverage deep learning-based video prediction methods to capture\u0000the spatiotemporal dependencies among assets. However, unlike images where\u0000neighboring pixels exhibit explicit spatiotemporal dependencies due to the\u0000natural continuity of object movements, assets in ADM do not have a natural\u0000order. This poses challenges to organizing the relational assets to reveal\u0000better the spatiotemporal dependencies among neighboring assets for ADM\u0000forecasting. To tackle the challenges, we propose the Asset Dependency Neural\u0000Network (ADNN), which employs the Convolutional Long Short-Term Memory\u0000(ConvLSTM) network, a highly successful method for video prediction. ADNN can\u0000employ static and dynamic transformation functions to optimize the\u0000representations of the ADM. Through extensive experiments, we demonstrate that\u0000our proposed framework consistently outperforms the baselines in the ADM\u0000prediction and downstream application tasks. This research contributes to\u0000understanding and predicting asset dependencies, offering valuable insights for\u0000financial market participants.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520791","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}

引用次数: 0

Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism 通过门控交叉注意力机制进行多模态稳定融合的股票走势预测

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-06 DOI: arxiv-2406.06594

Chang Zong, Jian Shao, Weiming Lu, Yueting Zhuang

{"title":"Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism","authors":"Chang Zong, Jian Shao, Weiming Lu, Yueting Zhuang","doi":"arxiv-2406.06594","DOIUrl":"https://doi.org/arxiv-2406.06594","url":null,"abstract":"The accurate prediction of stock movements is crucial for investment\u0000strategies. Stock prices are subject to the influence of various forms of\u0000information, including financial indicators, sentiment analysis, news\u0000documents, and relational structures. Predominant analytical approaches,\u0000however, tend to address only unimodal or bimodal sources, neglecting the\u0000complexity of multimodal data. Further complicating the landscape are the\u0000issues of data sparsity and semantic conflicts between these modalities, which\u0000are frequently overlooked by current models, leading to unstable performance\u0000and limiting practical applicability. To address these shortcomings, this study\u0000introduces a novel architecture, named Multimodal Stable Fusion with Gated\u0000Cross-Attention (MSGCA), designed to robustly integrate multimodal input for\u0000stock movement prediction. The MSGCA framework consists of three integral\u0000components: (1) a trimodal encoding module, responsible for processing\u0000indicator sequences, dynamic documents, and a relational graph, and\u0000standardizing their feature representations; (2) a cross-feature fusion module,\u0000where primary and consistent features guide the multimodal fusion of the three\u0000modalities via a pair of gated cross-attention networks; and (3) a prediction\u0000module, which refines the fused features through temporal and dimensional\u0000reduction to execute precise movement forecasting. Empirical evaluations\u0000demonstrate that the MSGCA framework exceeds current leading methods, achieving\u0000performance gains of 8.1%, 6.1%, 21.7% and 31.6% on four multimodal datasets,\u0000respectively, attributed to its enhanced multimodal fusion stability.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501642","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}

引用次数: 0

Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy 采用 TPE 贝叶斯优化的门控递归神经网络提高股指预测准确性

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-02 DOI: arxiv-2406.02604

Bivas Dinda

{"title":"Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy","authors":"Bivas Dinda","doi":"arxiv-2406.02604","DOIUrl":"https://doi.org/arxiv-2406.02604","url":null,"abstract":"The recent advancement of deep learning architectures, neural networks, and\u0000the combination of abundant financial data and powerful computers are\u0000transforming finance, leading us to develop an advanced method for predicting\u0000future stock prices. However, the accessibility of investment and trading at\u0000everyone's fingertips made the stock markets increasingly intricate and prone\u0000to volatility. The increased complexity and volatility of the stock market have\u0000driven demand for more models, which would effectively capture high volatility\u0000and non-linear behavior of the different stock prices. This study explored\u0000gated recurrent neural network (GRNN) algorithms such as LSTM (long short-term\u0000memory), GRU (gated recurrent unit), and hybrid models like GRU-LSTM, LSTM-GRU,\u0000with Tree-structured Parzen Estimator (TPE) Bayesian optimization for\u0000hyperparameter optimization (TPE-GRNN). The aim is to improve the prediction\u0000accuracy of the next day's closing price of the NIFTY 50 index, a prominent\u0000Indian stock market index, using TPE-GRNN. A combination of eight influential\u0000factors is carefully chosen from fundamental stock data, technical indicators,\u0000crude oil price, and macroeconomic data to train the models for capturing the\u0000changes in the price of the index with the factors of the broader economy.\u0000Single-layer and multi-layer TPE-GRNN models have been developed. The models'\u0000performance is evaluated using standard matrices like R2, MAPE, and RMSE. The\u0000analysis of models' performance reveals the impact of feature selection and\u0000hyperparameter optimization (HPO) in enhancing stock index price prediction\u0000accuracy. The results show that the MAPE of our proposed TPE-LSTM method is the\u0000lowest (best) with respect to all the previous models for stock index price\u0000prediction.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550542","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}

引用次数: 0

Gaussian Recombining Split Tree 高斯重组分裂树

arXiv - QuantFin - Computational Finance Pub Date : 2024-05-25 DOI: arxiv-2405.16333

Yury Lebedev, Arunava Banerjee

{"title":"Gaussian Recombining Split Tree","authors":"Yury Lebedev, Arunava Banerjee","doi":"arxiv-2405.16333","DOIUrl":"https://doi.org/arxiv-2405.16333","url":null,"abstract":"Binomial trees are widely used in the financial sector for valuing securities\u0000with early exercise characteristics, such as American stock options. However,\u0000while effective in many scenarios, pricing options with CRR binomial trees are\u0000limited. Major limitations are volatility estimation, constant volatility\u0000assumption, subjectivity in parameter choices, and impracticality of\u0000instantaneous delta hedging. This paper presents a novel tree: Gaussian\u0000Recombining Split Tree (GRST), which is recombining and does not need\u0000log-normality or normality market assumption. GRST generates a discrete\u0000probability mass function of market data distribution, which approximates a\u0000Gaussian distribution with known parameters at any chosen time interval. GRST\u0000Mixture builds upon the GRST concept while being flexible to fit a large class\u0000of market distributions and when given a 1-D time series data and moments of\u0000distributions at each time interval, fits a Gaussian mixture with the same\u0000mixture component probabilities applied at each time interval. Gaussian\u0000Recombining Split Tre Mixture comprises several GRST tied using Gaussian\u0000mixture component probabilities at the first node. Our extensive empirical\u0000analysis shows that the option prices from the GRST align closely with the\u0000market.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166277","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}

引用次数: 0

Inference of Utilities and Time Preference in Sequential Decision-Making 顺序决策中的效用和时间偏好推断

arXiv - QuantFin - Computational Finance Pub Date : 2024-05-24 DOI: arxiv-2405.15975

Haoyang Cao, Zhengqi Wu, Renyuan Xu

{"title":"Inference of Utilities and Time Preference in Sequential Decision-Making","authors":"Haoyang Cao, Zhengqi Wu, Renyuan Xu","doi":"arxiv-2405.15975","DOIUrl":"https://doi.org/arxiv-2405.15975","url":null,"abstract":"This paper introduces a novel stochastic control framework to enhance the\u0000capabilities of automated investment managers, or robo-advisors, by accurately\u0000inferring clients' investment preferences from past activities. Our approach\u0000leverages a continuous-time model that incorporates utility functions and a\u0000generic discounting scheme of a time-varying rate, tailored to each client's\u0000risk tolerance, valuation of daily consumption, and significant life goals. We\u0000address the resulting time inconsistency issue through state augmentation and\u0000the establishment of the dynamic programming principle and the verification\u0000theorem. Additionally, we provide sufficient conditions for the identifiability\u0000of client investment preferences. To complement our theoretical developments,\u0000we propose a learning algorithm based on maximum likelihood estimation within a\u0000discrete-time Markov Decision Process framework, augmented with entropy\u0000regularization. We prove that the log-likelihood function is locally concave,\u0000facilitating the fast convergence of our proposed algorithm. Practical\u0000effectiveness and efficiency are showcased through two numerical examples,\u0000including Merton's problem and an investment problem with unhedgeable risks. Our proposed framework not only advances financial technology by improving\u0000personalized investment advice but also contributes broadly to other fields\u0000such as healthcare, economics, and artificial intelligence, where understanding\u0000individual preferences is crucial.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166351","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}

引用次数: 0

Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning 利用强化学习处理非累积目标的决策过程

arXiv - QuantFin - Computational Finance Pub Date : 2024-05-22 DOI: arxiv-2405.13609

Maximilian Nägele, Jan Olle, Thomas Fösel, Remmy Zen, Florian Marquardt

{"title":"Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning","authors":"Maximilian Nägele, Jan Olle, Thomas Fösel, Remmy Zen, Florian Marquardt","doi":"arxiv-2405.13609","DOIUrl":"https://doi.org/arxiv-2405.13609","url":null,"abstract":"Markov decision processes (MDPs) are used to model a wide variety of\u0000applications ranging from game playing over robotics to finance. Their optimal\u0000policy typically maximizes the expected sum of rewards given at each step of\u0000the decision process. However, a large class of problems does not fit\u0000straightforwardly into this framework: Non-cumulative Markov decision processes\u0000(NCMDPs), where instead of the expected sum of rewards, the expected value of\u0000an arbitrary function of the rewards is maximized. Example functions include\u0000the maximum of the rewards or their mean divided by their standard deviation.\u0000In this work, we introduce a general mapping of NCMDPs to standard MDPs. This\u0000allows all techniques developed to find optimal policies for MDPs, such as\u0000reinforcement learning or dynamic programming, to be directly applied to the\u0000larger class of NCMDPs. Focusing on reinforcement learning, we show\u0000applications in a diverse set of tasks, including classical control, portfolio\u0000optimization in finance, and discrete optimization problems. Given our\u0000approach, we can improve both final performance and training time compared to\u0000relying on standard MDPs.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151250","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}

引用次数: 0

Trading Volume Maximization with Online Learning 通过在线学习实现交易量最大化

arXiv - QuantFin - Computational Finance Pub Date : 2024-05-21 DOI: arxiv-2405.13102

Tommaso Cesari, Roberto Colomboni

{"title":"Trading Volume Maximization with Online Learning","authors":"Tommaso Cesari, Roberto Colomboni","doi":"arxiv-2405.13102","DOIUrl":"https://doi.org/arxiv-2405.13102","url":null,"abstract":"We explore brokerage between traders in an online learning framework. At any\u0000round $t$, two traders meet to exchange an asset, provided the exchange is\u0000mutually beneficial. The broker proposes a trading price, and each trader tries\u0000to sell their asset or buy the asset from the other party, depending on whether\u0000the price is higher or lower than their private valuations. A trade happens if\u0000one trader is willing to sell and the other is willing to buy at the proposed\u0000price. Previous work provided guidance to a broker aiming at enhancing traders'\u0000total earnings by maximizing the gain from trade, defined as the sum of the\u0000traders' net utilities after each interaction. In contrast, we investigate how\u0000the broker should behave to maximize the trading volume, i.e., the total number\u0000of trades. We model the traders' valuations as an i.i.d. process with an\u0000unknown distribution. If the traders' valuations are revealed after each\u0000interaction (full-feedback), and the traders' valuations cumulative\u0000distribution function (cdf) is continuous, we provide an algorithm achieving\u0000logarithmic regret and show its optimality up to constant factors. If only\u0000their willingness to sell or buy at the proposed price is revealed after each\u0000interaction ($2$-bit feedback), we provide an algorithm achieving\u0000poly-logarithmic regret when the traders' valuations cdf is Lipschitz and show\u0000that this rate is near-optimal. We complement our results by analyzing the\u0000implications of dropping the regularity assumptions on the unknown traders'\u0000valuations cdf. If we drop the continuous cdf assumption, the regret rate\u0000degrades to $Theta(sqrt{T})$ in the full-feedback case, where $T$ is the time\u0000horizon. If we drop the Lipschitz cdf assumption, learning becomes impossible\u0000in the $2$-bit feedback case.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151260","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}

引用次数: 0

NIFTY Financial News Headlines Dataset NIFTY 金融新闻标题数据集

arXiv - QuantFin - Computational Finance Pub Date : 2024-05-16 DOI: arxiv-2405.09747

Raeid Saqur, Ken Kato, Nicholas Vinden, Frank Rudzicz

{"title":"NIFTY Financial News Headlines Dataset","authors":"Raeid Saqur, Ken Kato, Nicholas Vinden, Frank Rudzicz","doi":"arxiv-2405.09747","DOIUrl":"https://doi.org/arxiv-2405.09747","url":null,"abstract":"We introduce and make publicly available the NIFTY Financial News Headlines\u0000dataset, designed to facilitate and advance research in financial market\u0000forecasting using large language models (LLMs). This dataset comprises two\u0000distinct versions tailored for different modeling approaches: (i) NIFTY-LM,\u0000which targets supervised fine-tuning (SFT) of LLMs with an auto-regressive,\u0000causal language-modeling objective, and (ii) NIFTY-RL, formatted specifically\u0000for alignment methods (like reinforcement learning from human feedback (RLHF))\u0000to align LLMs via rejection sampling and reward modeling. Each dataset version\u0000provides curated, high-quality data incorporating comprehensive metadata,\u0000market indices, and deduplicated financial news headlines systematically\u0000filtered and ranked to suit modern LLM frameworks. We also include experiments\u0000demonstrating some applications of the dataset in tasks like stock price\u0000movement and the role of LLM embeddings in information acquisition/richness.\u0000The NIFTY dataset along with utilities (like truncating prompt's context length\u0000systematically) are available on Hugging Face at\u0000https://huggingface.co/datasets/raeidsaqur/NIFTY.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059900","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}

引用次数: 0

Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems 利用模块化动态故障树分析和蒙特卡罗模拟对无人系统进行基于状态的维护的成本效益分析

arXiv - QuantFin - Computational Finance Pub Date : 2024-05-15 DOI: arxiv-2405.09519

Joseph M. Southgate, Katrina Groth, Peter Sandborn, Shapour Azarm

{"title":"Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems","authors":"Joseph M. Southgate, Katrina Groth, Peter Sandborn, Shapour Azarm","doi":"arxiv-2405.09519","DOIUrl":"https://doi.org/arxiv-2405.09519","url":null,"abstract":"Recent developments in condition-based maintenance (CBM) have helped make it\u0000a promising approach to maintenance cost avoidance in engineering systems. By\u0000performing maintenance based on conditions of the component with regards to\u0000failure or time, there is potential to avoid the large costs of system shutdown\u0000and maintenance delays. However, CBM requires a large investment cost compared\u0000to other available maintenance strategies. The investment cost is required for\u0000research, development, and implementation. Despite the potential to avoid\u0000significant maintenance costs, the large investment cost of CBM makes decision\u0000makers hesitant to implement. This study is the first in the literature that\u0000attempts to address the problem of conducting a cost-benefit analysis (CBA) for\u0000implementing CBM concepts for unmanned systems. This paper proposes a method\u0000for conducting a CBA to determine the return on investment (ROI) of potential\u0000CBM strategies. The CBA seeks to compare different CBM strategies based on the\u0000differences in the various maintenance requirements associated with maintaining\u0000a multi-component, unmanned system. The proposed method uses modular dynamic\u0000fault tree analysis (MDFTA) with Monte Carlo simulations (MCS) to assess the\u0000various maintenance requirements. The proposed method is demonstrated on an\u0000unmanned surface vessel (USV) example taken from the literature that consists\u0000of 5 subsystems and 71 components. Following this USV example, it is found that\u0000selecting different combinations of components for a CBM strategy can have a\u0000significant impact on maintenance requirements and ROI by impacting cost\u0000avoidances and investment costs.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059899","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}

引用次数: 0

Can machine learning unlock new insights into high-frequency trading? 机器学习能否开启高频交易的新视角？

arXiv - QuantFin - Computational Finance Pub Date : 2024-05-13 DOI: arxiv-2405.08101

G. Ibikunle, B. Moews, K. Rzayev

引用次数: 0