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

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

High-Frequency Stock Market Order Transitions during the US-China Trade War 2018: A Discrete-Time Markov Chain Analysis 2018年中美贸易战期间的高频股市订单转换：离散时间马尔可夫链分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-09 DOI: arxiv-2405.05634

Salam Rabindrajit Luwang, Anish Rai, Md. Nurujjaman, Om Prakash, Chittaranjan Hens

{"title":"High-Frequency Stock Market Order Transitions during the US-China Trade War 2018: A Discrete-Time Markov Chain Analysis","authors":"Salam Rabindrajit Luwang, Anish Rai, Md. Nurujjaman, Om Prakash, Chittaranjan Hens","doi":"arxiv-2405.05634","DOIUrl":"https://doi.org/arxiv-2405.05634","url":null,"abstract":"Statistical analysis of high-frequency stock market order transaction data is\u0000conducted to understand order transition dynamics. We employ a first-order\u0000time-homogeneous discrete-time Markov chain model to the sequence of orders of\u0000stocks belonging to six different sectors during the USA-China trade war of\u00002018. The Markov property of the order sequence is validated by the Chi-square\u0000test. We estimate the transition probability matrix of the sequence using\u0000maximum likelihood estimation. From the heat-map of these matrices, we found\u0000the presence of active participation by different types of traders during high\u0000volatility days. On such days, these traders place limit orders primarily with\u0000the intention of deleting the majority of them to influence the market. These\u0000findings are supported by high stationary distribution and low mean recurrence\u0000values of add and delete orders. Further, we found similar spectral gap and\u0000entropy rate values, which indicates that similar trading strategies are\u0000employed on both high and low volatility days during the trade war. Among all\u0000the sectors considered in this study, we observe that there is a recurring\u0000pattern of full execution orders in Finance & Banking sector. This shows that\u0000the banking stocks are resilient during the trade war. Hence, this study may be\u0000useful in understanding stock market order dynamics and devise trading\u0000strategies accordingly on high and low volatility days during extreme\u0000macroeconomic events.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929882","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

Complex network analysis of cryptocurrency market during crashes 崩盘期间加密货币市场的复杂网络分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-09 DOI: arxiv-2405.05642

Kundan Mukhia, Anish Rai, SR Luwang, Md Nurujjaman, Sushovan Majhi, Chittaranjan Hens

{"title":"Complex network analysis of cryptocurrency market during crashes","authors":"Kundan Mukhia, Anish Rai, SR Luwang, Md Nurujjaman, Sushovan Majhi, Chittaranjan Hens","doi":"arxiv-2405.05642","DOIUrl":"https://doi.org/arxiv-2405.05642","url":null,"abstract":"This paper identifies the cryptocurrency market crashes and analyses its\u0000dynamics using the complex network. We identify three distinct crashes during\u00002017-20, and the analysis is carried out by dividing the time series into\u0000pre-crash, crash, and post-crash periods. Partial correlation based complex\u0000network analysis is carried out to study the crashes. Degree density\u0000($rho_D$), average path length ($bar{l}$), and average clustering coefficient\u0000($overline{cc}$) are estimated from these networks. We find that both $rho_D$\u0000and $overline{cc}$ are smallest during the pre-crash period, and spike during\u0000the crash suggesting the network is dense during a crash. Although $rho_D$ and\u0000$overline{cc}$ decrease in the post-crash period, they remain higher than\u0000pre-crash levels for the 2017-18 and 2018-19 crashes suggesting a market\u0000attempt to return to normalcy. We get $bar{l}$ is minimal during the crash\u0000period, suggesting a rapid flow of information. A dense network and rapid\u0000information flow suggest that during a crash uninformed synchronized panic\u0000sell-off happens. However, during the 2019-20 crash, the values of $rho_D$,\u0000$overline{cc}$, and $bar{l}$ did not vary significantly, indicating minimal\u0000change in dynamics compared to other crashes. The findings of this study may\u0000guide investors in making decisions during market crashes.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"128 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929827","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

Generalization of the Alpha-Stable Distribution with the Degree of Freedom 用自由度概括阿尔法稳定分布

arXiv - QuantFin - Statistical Finance Pub Date : 2024-05-07 DOI: arxiv-2405.04693

Stephen H. Lihn

{"title":"Generalization of the Alpha-Stable Distribution with the Degree of Freedom","authors":"Stephen H. Lihn","doi":"arxiv-2405.04693","DOIUrl":"https://doi.org/arxiv-2405.04693","url":null,"abstract":"A Wright function based framework is proposed to combine and extend several\u0000distribution families. The $alpha$-stable distribution is generalized by\u0000adding the degree of freedom parameter. The PDF of this two-sided super\u0000distribution family subsumes those of the original $alpha$-stable, Student's t\u0000distributions, as well as the exponential power distribution and the modified\u0000Bessel function of the second kind. Its CDF leads to a fractional extension of\u0000the Gauss hypergeometric function. The degree of freedom makes possible for\u0000valid variance, skewness, and kurtosis, just like Student's t. The original\u0000$alpha$-stable distribution is viewed as having one degree of freedom, that\u0000explains why it lacks most of the moments. A skew-Gaussian kernel is derived\u0000from the characteristic function of the $alpha$-stable law, which maximally\u0000preserves the law in the new framework. To facilitate such framework, the\u0000stable count distribution is generalized as the fractional extension of the\u0000generalized gamma distribution. It provides rich subordination capabilities,\u0000one of which is the fractional $chi$ distribution that supplies the needed\u0000'degree of freedom' parameter. Hence, the \"new\" $alpha$-stable distribution is\u0000a \"ratio distribution\" of the skew-Gaussian kernel and the fractional $chi$\u0000distribution. Mathematically, it is a new form of higher transcendental\u0000function under the Wright function family. Last, the new univariate symmetric\u0000distribution is extended to the multivariate elliptical distribution\u0000successfully.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929881","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

A novel portfolio construction strategy based on the core-periphery profile of stocks 基于股票核心-外围特征的新型投资组合构建策略

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-27 DOI: arxiv-2405.12993

Imran Ansari, Charu Sharma, Akshay Agrawal, Niteesh Sahni

引用次数: 0

Systematic Comparable Company Analysis and Computation of Cost of Equity using Clustering 利用聚类对可比公司进行系统分析并计算股本成本

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-25 DOI: arxiv-2405.12991

Mohammed Perves

引用次数: 0

Analysis of market efficiency in main stock markets: using Karman-Filter as an approach 主要股票市场的市场效率分析：使用卡曼滤波器作为一种方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-25 DOI: arxiv-2404.16449

Beier Liu, Haiyun Zhu

引用次数: 0

BERT vs GPT for financial engineering 金融工程的 BERT 与 GPT

arXiv - QuantFin - Statistical Finance Pub Date : 2024-04-24 DOI: arxiv-2405.12990

Edward Sharkey, Philip Treleaven

{"title":"BERT vs GPT for financial engineering","authors":"Edward Sharkey, Philip Treleaven","doi":"arxiv-2405.12990","DOIUrl":"https://doi.org/arxiv-2405.12990","url":null,"abstract":"The paper benchmarks several Transformer models [4], to show how these models\u0000can judge sentiment from a news event. This signal can then be used for\u0000downstream modelling and signal identification for commodity trading. We find\u0000that fine-tuned BERT models outperform fine-tuned or vanilla GPT models on this\u0000task. Transformer models have revolutionized the field of natural language\u0000processing (NLP) in recent years, achieving state-of-the-art results on various\u0000tasks such as machine translation, text summarization, question answering, and\u0000natural language generation. Among the most prominent transformer models are\u0000Bidirectional Encoder Representations from Transformers (BERT) and Generative\u0000Pre-trained Transformer (GPT), which differ in their architectures and\u0000objectives. A CopBERT model training data and process overview is provided. The CopBERT\u0000model outperforms similar domain specific BERT trained models such as FinBERT.\u0000The below confusion matrices show the performance on CopBERT & CopGPT\u0000respectively. We see a ~10 percent increase in f1_score when compare CopBERT vs\u0000GPT4 and 16 percent increase vs CopGPT. Whilst GPT4 is dominant It highlights\u0000the importance of considering alternatives to GPT models for financial\u0000engineering tasks, given risks of hallucinations, and challenges with\u0000interpretability. We unsurprisingly see the larger LLMs outperform the BERT\u0000models, with predictive power. In summary BERT is partially the new XGboost,\u0000what it lacks in predictive power it provides with higher levels of\u0000interpretability. Concluding that BERT models might not be the next XGboost\u0000[2], but represent an interesting alternative for financial engineering tasks,\u0000that require a blend of interpretability and accuracy.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149266","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