arXiv - QuantFin - Statistical Finance最新文献_第4页

Dissecting Multifractal detrended cross-correlation analysis 剖析多分形去趋势交叉相关分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-09 DOI: arxiv-2406.19406

Borko Stosic, Tatijana Stosic

引用次数: 0

Temporal distribution of clusters of investors and their application in prediction with expert advice 投资者集群的时间分布及其在专家建议预测中的应用

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-04 DOI: arxiv-2406.19403

Wojciech Wisniewski, Yuri Kalnishkan, David Lindsay, Siân Lindsay

{"title":"Temporal distribution of clusters of investors and their application in prediction with expert advice","authors":"Wojciech Wisniewski, Yuri Kalnishkan, David Lindsay, Siân Lindsay","doi":"arxiv-2406.19403","DOIUrl":"https://doi.org/arxiv-2406.19403","url":null,"abstract":"Financial organisations such as brokers face a significant challenge in\u0000servicing the investment needs of thousands of their traders worldwide. This\u0000task is further compounded since individual traders will have their own risk\u0000appetite and investment goals. Traders may look to capture short-term trends in\u0000the market which last only seconds to minutes, or they may have longer-term\u0000views which last several days to months. To reduce the complexity of this task,\u0000client trades can be clustered. By examining such clusters, we would likely\u0000observe many traders following common patterns of investment, but how do these\u0000patterns vary through time? Knowledge regarding the temporal distributions of\u0000such clusters may help financial institutions manage the overall portfolio of\u0000risk that accumulates from underlying trader positions. This study contributes\u0000to the field by demonstrating that the distribution of clusters derived from\u0000the real-world trades of 20k Foreign Exchange (FX) traders (from 2015 to 2017)\u0000is described in accordance with Ewens' Sampling Distribution. Further, we show\u0000that the Aggregating Algorithm (AA), an on-line prediction with expert advice\u0000algorithm, can be applied to the aforementioned real-world data in order to\u0000improve the returns of portfolios of trader risk. However we found that the AA\u0000'struggles' when presented with too many trader ``experts'', especially when\u0000there are many trades with similar overall patterns. To help overcome this\u0000challenge, we have applied and compared the use of Statistically Validated\u0000Networks (SVN) with a hierarchical clustering approach on a subset of the data,\u0000demonstrating that both approaches can be used to significantly improve results\u0000of the AA in terms of profitability and smoothness of returns.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516803","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

Machine Learning Methods for Pricing Financial Derivatives 金融衍生品定价的机器学习方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-06-01 DOI: arxiv-2406.00459

Lei Fan, Justin Sirignano

{"title":"Machine Learning Methods for Pricing Financial Derivatives","authors":"Lei Fan, Justin Sirignano","doi":"arxiv-2406.00459","DOIUrl":"https://doi.org/arxiv-2406.00459","url":null,"abstract":"Stochastic differential equation (SDE) models are the foundation for pricing\u0000and hedging financial derivatives. The drift and volatility functions in SDE\u0000models are typically chosen to be algebraic functions with a small number (less\u0000than 5) parameters which can be calibrated to market data. A more flexible\u0000approach is to use neural networks to model the drift and volatility functions,\u0000which provides more degrees-of-freedom to match observed market data. Training\u0000of models requires optimizing over an SDE, which is computationally\u0000challenging. For European options, we develop a fast stochastic gradient\u0000descent (SGD) algorithm for training the neural network-SDE model. Our SGD\u0000algorithm uses two independent SDE paths to obtain an unbiased estimate of the\u0000direction of steepest descent. For American options, we optimize over the\u0000corresponding Kolmogorov partial differential equation (PDE). The neural\u0000network appears as coefficient functions in the PDE. Models are trained on\u0000large datasets (many contracts), requiring either large simulations (many Monte\u0000Carlo samples for the stock price paths) or large numbers of PDEs (a PDE must\u0000be solved for each contract). Numerical results are presented for real market\u0000data including S&P 500 index options, S&P 100 index options, and single-stock\u0000American options. The neural-network-based SDE models are compared against the\u0000Black-Scholes model, the Dupire's local volatility model, and the Heston model.\u0000Models are evaluated in terms of how accurate they are at pricing out-of-sample\u0000financial derivatives, which is a core task in derivative pricing at financial\u0000institutions.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141258807","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

Identifying Extreme Events in the Stock Market: A Topological Data Analysis 识别股市极端事件：拓扑数据分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-25 DOI: arxiv-2405.16052

Anish Rai, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman, Sushovan Majhi

{"title":"Identifying Extreme Events in the Stock Market: A Topological Data Analysis","authors":"Anish Rai, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman, Sushovan Majhi","doi":"arxiv-2405.16052","DOIUrl":"https://doi.org/arxiv-2405.16052","url":null,"abstract":"This paper employs Topological Data Analysis (TDA) to detect extreme events\u0000(EEs) in the stock market at a continental level. Previous approaches, which\u0000analyzed stock indices separately, could not detect EEs for multiple time\u0000series in one go. TDA provides a robust framework for such analysis and\u0000identifies the EEs during the crashes for different indices. The TDA analysis\u0000shows that $L^1$, $L^2$ norms and Wasserstein distance ($W_D$) of the world\u0000leading indices rise abruptly during the crashes, surpassing a threshold of\u0000$mu+4*sigma$ where $mu$ and $sigma$ are the mean and the standard deviation\u0000of norm or $W_D$, respectively. Our study identified the stock index crashes of\u0000the 2008 financial crisis and the COVID-19 pandemic across continents as EEs.\u0000Given that different sectors in an index behave differently, a sector-wise\u0000analysis was conducted during the COVID-19 pandemic for the Indian stock\u0000market. The sector-wise results show that after the occurrence of EE, we have\u0000observed strong crashes surpassing $mu+2*sigma$ for an extended period for\u0000the banking sector. While for the pharmaceutical sector, no significant spikes\u0000were noted. Hence, TDA also proves successful in identifying the duration of\u0000shocks after the occurrence of EEs. This also indicates that the Banking sector\u0000continued to face stress and remained volatile even after the crash. This study\u0000gives us the applicability of TDA as a powerful analytical tool to study EEs in\u0000various fields.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141166369","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

An empirical study of market risk factors for Bitcoin 比特币市场风险因素实证研究

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-24 DOI: arxiv-2406.19401

Shubham Singh

引用次数: 0

Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach 预测金融机构服务中的客户目标：数据驱动的 LSTM 方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-22 DOI: arxiv-2406.19399

Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel Borrajo, Rui Silva

引用次数: 0

A K-means Algorithm for Financial Market Risk Forecasting 用于金融市场风险预测的 K-means 算法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-21 DOI: arxiv-2405.13076

Jinxin Xu, Kaixian Xu, Yue Wang, Qinyan Shen, Ruisi Li

引用次数: 0

Exploiting Distributional Value Functions for Financial Market Valuation, Enhanced Feature Creation and Improvement of Trading Algorithms 利用分布式价值函数进行金融市场估值、增强特征创建和改进交易算法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-19 DOI: arxiv-2405.11686

Colin D. Grab

{"title":"Exploiting Distributional Value Functions for Financial Market Valuation, Enhanced Feature Creation and Improvement of Trading Algorithms","authors":"Colin D. Grab","doi":"arxiv-2405.11686","DOIUrl":"https://doi.org/arxiv-2405.11686","url":null,"abstract":"While research of reinforcement learning applied to financial markets\u0000predominantly concentrates on finding optimal behaviours, it is worth to\u0000realize that the reinforcement learning returns $G_t$ and state value functions\u0000themselves are of interest and play a pivotal role in the evaluation of assets.\u0000Instead of focussing on the more complex task of finding optimal decision\u0000rules, this paper studies and applies the power of distributional state value\u0000functions in the context of financial market valuation and machine learning\u0000based trading algorithms. Accurate and trustworthy estimates of the\u0000distributions of $G_t$ provide a competitive edge leading to better informed\u0000decisions and more optimal behaviour. Herein, ideas from predictive knowledge\u0000and deep reinforcement learning are combined to introduce a novel family of\u0000models called CDG-Model, resulting in a highly flexible framework and intuitive\u0000approach with minimal assumptions regarding underlying distributions. The\u0000models allow seamless integration of typical financial modelling pitfalls like\u0000transaction costs, slippage and other possible costs or benefits into the model\u0000calculation. They can be applied to any kind of trading strategy or asset\u0000class. The frameworks introduced provide concrete business value through their\u0000potential in market valuation of single assets and portfolios, in the\u0000comparison of strategies as well as in the improvement of market timing. They\u0000can positively impact the performance and enhance the learning process of\u0000existing or new trading algorithms. They are of interest from a scientific\u0000point-of-view and open up multiple areas of future research. Initial\u0000implementations and tests were performed on real market data. While the results\u0000are promising, applying a robust statistical framework to evaluate the models\u0000in general remains a challenge and further investigations are needed.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149255","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

"Microstructure Modes" -- Disentangling the Joint Dynamics of Prices & Order Flow "微观结构模式" -- 解读价格与订单流的共同动态变化

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-17 DOI: arxiv-2405.10654

Salma Elomari-Kessab, Guillaume Maitrier, Julius Bonart, Jean-Philippe Bouchaud

{"title":"\"Microstructure Modes\" -- Disentangling the Joint Dynamics of Prices & Order Flow","authors":"Salma Elomari-Kessab, Guillaume Maitrier, Julius Bonart, Jean-Philippe Bouchaud","doi":"arxiv-2405.10654","DOIUrl":"https://doi.org/arxiv-2405.10654","url":null,"abstract":"Understanding the micro-dynamics of asset prices in modern electronic order\u0000books is crucial for investors and regulators. In this paper, we use an order\u0000by order Eurostoxx database spanning over 3 years to analyze the joint dynamics\u0000of prices and order flow. In order to alleviate various problems caused by\u0000high-frequency noise, we propose a double coarse-graining procedure that allows\u0000us to extract meaningful information at the minute time scale. We use Principal\u0000Component Analysis to construct \"microstructure modes\" that describe the most\u0000common flow/return patterns and allow one to separate them into bid-ask\u0000symmetric and bid-ask anti-symmetric. We define and calibrate a Vector\u0000Auto-Regressive (VAR) model that encodes the dynamical evolution of these\u0000modes. The parameters of the VAR model are found to be extremely stable in\u0000time, and lead to relatively high $R^2$ prediction scores, especially for\u0000symmetric liquidity modes. The VAR model becomes marginally unstable as more\u0000lags are included, reflecting the long-memory nature of flows and giving some\u0000further credence to the possibility of \"endogenous liquidity crises\". Although\u0000very satisfactory on several counts, we show that our VAR framework does not\u0000account for the well known square-root law of price impact.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"218 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149307","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

Comparative Study of Bitcoin Price Prediction 比特币价格预测比较研究

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-13 DOI: arxiv-2405.08089

Ali Mohammadjafari

引用次数: 0