共查询到20条相似文献，搜索用时 15 毫秒
1.
We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance. 相似文献
2.
Alun Preece 《International Journal of Intelligent Systems in Accounting, Finance & Management》2018,25(2):63-72
Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today. 相似文献
3.
Erdinc Akyildirim Duc Khuong Nguyen Ahmet Sensoy Mario Šikić 《European Financial Management》2023,29(1):22-75
Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency. 相似文献
4.
数据科技是开发网络空间（CYBER SPACE）数据资源所用到的科技,是发展包括智慧金融在内的智慧产业的技术基础。本文介绍了数据科技和数据产业的内涵,探讨了数据在各类智慧系统中的核心作用,以数据科技为技术基础的智慧金融,提出了发展金融数据共享规范、金融数据产品、绿色存储、高可信环境等建议,通过发展数据科技推进智慧金融的进步。 相似文献
5.
Ahmad El Majzoub Fethi A. Rabhi Walayat Hussain 《International Journal of Intelligent Systems in Accounting, Finance & Management》2023,30(3):137-149
This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources. 相似文献
6.
魏伟 《上海金融学院学报》2014,(4):45-51
伴随着信息技术及互联网的快速发展,社会正在走向全面数字化。大数据、云计算等技术的异军突起使得居民的生活行为方式和企业的生产经营方式都发生了显著变化,科技进步带来的数字化变革正在影响并形成全新的生活习惯和商业模式。在这种大背景下,以网络为载体的互联网金融改变了金融运营模式,传统的银行业正在面临严峻考验。商业银行必须全面认识大数据技术变革带来的影响,转变经营策略,积极应对互联网金融带来的深刻变革。 相似文献
7.
Ting Sun Miklos A. Vasarhelyi 《International Journal of Intelligent Systems in Accounting, Finance & Management》2018,25(4):174-189
The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus. 相似文献
8.
从互联网金融的定义和特点出发,总结互联网金融的两大优势,即大规模数据处理能力和资源配置过程“去中介化”,这对传统商业银行产生了较大冲击。鉴于此,互联网金融浪潮下传统商业银行的一个可能发展机遇是,针对中小企业开展大数据征信和网络贷款业务。又以互联网金融模式下的助学贷款业务为例,展示了大数据征信和网络贷款的具体应用方案。 相似文献
9.
传统征信业务必因大数据而发生改变,大数据将为现有征信体系增加海量数据来源并推动普惠金融的发展。但是,由于存在个人隐私权保护、信贷风险控制及管理等限制因素,大数据技术最终如何实现与征信业务的完美结合以及究竟对传统征信业带来何种程度的影响,仍需要时间的检验。 相似文献
10.
Ronald Setty;Yuval Elovici;Dafna Schwartz; 《International Journal of Intelligent Systems in Accounting, Finance & Management》2024,31(1):e1548
In 2022, global startup investments exceeded US$445 billion, sourced from entities like venture capital (VC) funds, angel investors, and equity crowdfunding. Despite their role in driving innovation, startup investments often fall short of S&P 500 returns. Surprisingly, the potential of artificial intelligence (AI) remains untapped by investors, despite AI's growing sway in financial decision-making. Our empirical analysis predicts the success of 10,000 Israeli startups, utilizing diverse machine learning models. Unlike prior research, we employ the MetaCost algorithm to convert models into cost-sensitive variants, minimizing total cost instead of total error. This innovative approach enables varied costs linked to different prediction errors. Our results underscore that these cost-sensitive machine learning models significantly reduce risk for VC funds and startup investors compared to traditional ones. Furthermore, these models provide investors with a distinct capability to tailor their risk profiles, aligning predictions with their risk appetite. However, while cost-sensitive machine learning reduces risk, it may limit potential gains by predicting fewer successful startups. To address this, we propose methods to enhance successful startup identification, including aggregating outcomes from multiple MetaCost models, particularly advantageous for smaller deal flows. Our research advances AI's role in startup investing, presenting a pivotal tool for investors navigating this domain. 相似文献
11.
分布式账本技术的应用使数字货币进入了新的发展阶段。中央银行数字货币是中央银行的电子化负债,其对计价、交易、支付以至货币政策传导等都存在深刻影响。以加拿大为例,重点分析加拿大银行数字货币的发展实践,着重讨论加拿大贾斯珀项目如何测试分布式账本技术在银行间大额支付系统的适用性,同时分析如何将数字货币支付结算拓展至证券和外汇领域,并与外部合作进行跨境、跨币种支付试验。加拿大央行数字货币实践取得的积极进展表明,数字货币发行能力是维系央行功能的基础保障,但仍需权衡中心化管理体系与去中心化技术系统的匹配问题,分布式账本技术及其在央行数字货币的应用仍需深入研究与评估。 相似文献
12.
Internet finance has made significant progress in China. At the same time, it also suffers from legal gaps and inconsistencies. Traditionally, legislation regulates the emerging internet financial market by distinguishing between legal and illegal activities. Users of internet finance engage in regulatory arbitrage and pursue short-term profits, which distort the market. Regulations over internet finance should conform to market logic and utilize informational mechanisms and big data to reduce fraudulent information and market friction, ensuring market transparency, competition, and fair pricing. 相似文献
13.
随着信息和技术革命的推进,数字经济已经成为全球经济发展的新要求和新趋势,跨境电商、数字贸易等数字经济在全球范围内加速发展,经济贸易全球化推动了数据在不同国家之间交互、流动。跨境数据流动治理对发展数字经济、维护国家安全、构建数字红利收入分配体系至关重要。全球各国对跨境数据流动的规制反映了其国际博弈的战略:美欧等发达经济体希望促进数据自由流动;而发展中国家则采取\"本土化\"防御方式,抵御数据领域的长臂管辖。我国选择了适应当前数字经济发展形势的\"本土化\"政策,但也面临对外和对内等多重挑战。建议在总体的监管路径选择上,合理兼顾审慎性和包容性;完善跨境数据流动制度体系,保障立法的全面性和灵活性;构建跨境数据流动治理的统一监管架构,提升治理体系的统筹性和协同性;完善跨境数据流动安全评估体系,平衡金融市场的开放性和安全性。 相似文献
14.
Jan Svanberg Tohid Ardeshiri Isak Samsten Peter Öhman Presha E. Neidermeyer Tarek Rana Natalia Semenova Mats Danielson 《International Journal of Intelligent Systems in Accounting, Finance & Management》2022,29(1):50-68
We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity. 相似文献
15.
Efstathios Kirkos Charalambos Spathis Yannis Manolopoulos 《International Journal of Intelligent Systems in Accounting, Finance & Management》2010,17(1):1-17
Auditor appointment can be regarded as a matter of pursued audit quality and is driven by several factors. The adoption of an effective auditor procurement process increases the likelihood that a company will engage the right auditor at a fair price. In this study, three techniques derived from artificial intelligence (AI) are used to propose models capable of discriminating between cases where companies appoint a Big 4 or a Non‐Big 4 auditor. These three AI methods are then compared with the broadly used method of logistic regression. The results indicate that two of the AI techniques outperform logistic regression. In addition, one method further improves its performance by applying bagging. Finally, significant factors associated with auditor appointment are revealed. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
16.
Daniel E. O'Leary 《International Journal of Intelligent Systems in Accounting, Finance & Management》2023,30(2):101-110
This paper provides some basic definitions associated with digital transformation in organizations and applies those definitions to accounting, electronic commerce, and supply chains. I also drill down on the dimensions associated with digital transformation, including digital everywhere, integration (across applications and with customers and partners), and the need to reengineer processes. I examine several examples of processes ranging from digitization to digital transformation. I also examine the role of people in digitally transformed organizations and some technologies that are important to continued evolution of digitally transformed organizations. Further, we explore a number of scenarios of digital transformation. Finally, these investigations result in the determination of a number of emerging research issues. 相似文献
17.
Monira Essa Aloud 《International Journal of Intelligent Systems in Accounting, Finance & Management》2020,27(2):43-54
In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market. 相似文献
18.
Emna Mnif Anis Jarboui M. Kabir Hassan Khaireddine Mouakhar 《International Journal of Intelligent Systems in Accounting, Finance & Management》2020,27(1):10-21
Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied. 相似文献
19.
We study the rise of digital footprint (DF) users in the U.S. residential mortgage market. The proportion of lenders that use a borrower's DF has witnessed remarkable growth from 6% in 2013 to 34% in 2018 in a short span – according to our analysis. We show that the use of DFs can significantly reduce a lender's overall risk and that the use of DFs can result in considerable societal benefits, by reducing the overall discriminatory forces. In sum, we provide evidence that the informational advantage associated with using a borrower's DF far outweighs that of existing traditional lending methods. 相似文献
20.
田丹 《上海金融学院学报》2011,(1):58-65
围绕金融业和信息产业的发展阶段的论述,通过银行业和信息系统最佳实践的角度,探索利用新思维和新技术构建＂智慧金融＂体系的最基本要素。以SEB银行为便,解析商业银行利用新技术和商业智能功能获取更多信息和洞察力,帮助客户利用最新电子交易渠道和电子服务产品。金融智能化必将是现代金融学理论、金融业界经验、科技发展与运用、社会进步与生活智能化的集大成者。 相似文献