Digital finance最新文献_第3页

What drives cryptocurrency returns? A sparse statistical jump model approach 是什么推动了加密货币的回报?一种稀疏统计跳跃模型方法

Digital finance Pub Date : 2023-05-20 DOI: 10.1007/s42521-023-00085-x

Federico P. Cortese, Petter N. Kolm, Erik Lindström

引用次数: 0

Digitalisation promotes adoption of soft information in SME credit evaluation: the case of Indian banks. 数字化促进了软信息在中小企业信用评估中的应用：以印度银行为例。

Digital finance Pub Date : 2023-04-21 DOI: 10.1007/s42521-023-00078-w

Nimbark Hardik

{"title":"Digitalisation promotes adoption of soft information in SME credit evaluation: the case of Indian banks.","authors":"Nimbark Hardik","doi":"10.1007/s42521-023-00078-w","DOIUrl":"10.1007/s42521-023-00078-w","url":null,"abstract":"Small and Medium Enterprises (SMEs) account for half of the employment in developing economies and are a significant part of their economic growth. In spite of this, SMEs are under-financed by banks, which have been disrupted by financial technology (fintech) firms. This qualitative multi-case study examines how Indian banks are utilising digitalisation, soft information, and Big data to improve SME financing. The participants shared their insights on the way banks adopt digital tools, sources of soft information (e.g., customer and supplier relationships, business plans), and factors that influence the implementation of Big data in the SME credit evaluation process. The major themes include: banks are improving SME financing operations through digitalisation, and IT tools can verify SME soft information. Soft information attributes that emerge from addressing SME information opacity include supplier relationships, customer relationships, business plans, and managerial successions. For SME credit managers, developing partnerships to access publicly available soft information created by industry associations and \"online B2B trade platforms\" is a high-priority recommendation. To enhance the efficiency of SME financing, banks should obtain the consent of SMEs before they access their private hard information through trade platforms.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9719860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Financial recommendations on Reddit, stock returns and cumulative prospect theory. Reddit上的财务建议、股票回报率和累积前景理论。

Digital finance Pub Date : 2023-04-18 DOI: 10.1007/s42521-023-00084-y

Felix Reichenbach, Martin Walther

{"title":"Financial recommendations on Reddit, stock returns and cumulative prospect theory.","authors":"Felix Reichenbach, Martin Walther","doi":"10.1007/s42521-023-00084-y","DOIUrl":"10.1007/s42521-023-00084-y","url":null,"abstract":"This study investigates stock recommendations from the three largest finance subreddits on Reddit: wallstreetbets, investing and stocks. A simple strategy that buys recommended stocks weighted by the number of posts per day yields a portfolio with higher average returns at the expense of higher risks than the market for all holding periods, i.e., unfavorable Sharpe ratios. Furthermore, the strategy leads to positive (insignificant) short-term and negative (significant) long-term alphas when considering common risk factors. This is consistent with the idea of \"meme stocks\", meaning that the recommended stocks are artificially inflated in the short term when they are recommended, and that the posts contain no information about long-term success. However, it is likely that Reddit users, especially on the subreddit wallstreetbets, have preferences for bets which are not captured by the mean-variance framework. Therefore, we draw on cumulative prospect theory (CPT). We find that the CPT-valuations of the Reddit portfolio exceed those of the market, which may explain the persistent attractiveness for investors to follow social media stock recommendations despite the unfavorable risk-return ratio.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9717231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors? 深度神经网络在资产定价因素下的股票收益预测中能否胜过Fama-MacBeth回归和其他监督学习方法?

Digital finance Pub Date : 2023-03-01 DOI: 10.1007/s42521-023-00076-y

Huei-Wen Teng, Yu-Hsien Li

引用次数: 0

Deep Learning in Finance 金融领域的深度学习

Digital finance Pub Date : 2023-03-01 DOI: 10.1007/s42521-023-00080-2

Weinan E, Ruimeng Hu, S. Peng

引用次数: 0

A blockchain-based platform for trading weather derivatives 一个基于区块链的天气衍生品交易平台

Digital finance Pub Date : 2023-02-01 DOI: 10.1007/s42521-022-00071-9

Fernando Alves Silveira, Sílvio Parodi Oliveira Camilo

引用次数: 0

Determinants of liquidity in cryptocurrency markets 加密货币市场流动性的决定因素

Digital finance Pub Date : 2023-01-24 DOI: 10.1007/s42521-022-00073-7

J. Westland

引用次数: 0

DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks. DeepVaR:一个利用概率深度神经网络进行投资组合风险评估的框架。

Digital finance Pub Date : 2023-01-01 DOI: 10.1007/s42521-022-00050-0

Georgios Fatouros, Georgios Makridis, Dimitrios Kotios, John Soldatos, Michael Filippakis, Dimosthenis Kyriazis

{"title":"DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks.","authors":"Georgios Fatouros, Georgios Makridis, Dimitrios Kotios, John Soldatos, Michael Filippakis, Dimosthenis Kyriazis","doi":"10.1007/s42521-022-00050-0","DOIUrl":"https://doi.org/10.1007/s42521-022-00050-0","url":null,"abstract":"Determining and minimizing risk exposure pose one of the biggest challenges in the financial industry as an environment with multiple factors that affect (non-)identified risks and the corresponding decisions. Various estimation metrics are utilized towards robust and efficient risk management frameworks, with the most prevalent among them being the Value at Risk (VaR). VaR is a valuable risk-assessment approach, which offers traders, investors, and financial institutions information regarding risk estimations and potential investment insights. VaR has been adopted by the financial industry for decades, but the generated predictions lack efficiency in times of economic turmoil such as the 2008 global financial crisis and the COVID-19 pandemic, which in turn affects the respective decisions. To address this challenge, a variety of well-established variations of VaR models are exploited by the financial community, including data-driven and data analytics models. In this context, this paper introduces a probabilistic deep learning approach, leveraging time-series forecasting techniques with high potential of monitoring the risk of a given portfolio in a quite efficient way. The proposed approach has been evaluated and compared to the most prominent methods of VaR calculation, yielding promising results for VaR 99% for forex-based portfolios.Supplementary information: The online version contains supplementary material available at 10.1007/s42521-022-00050-0.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9380054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Time-varying higher moments in Bitcoin. 比特币中随时间变化的较高时刻。

Digital finance Pub Date : 2022-12-23 DOI: 10.1007/s42521-022-00072-8

Leonardo Ieracitano Vieira, Márcio Poletti Laurini

引用次数: 0

SI women in Fintech and AI 金融科技和人工智能领域的女性

Digital finance Pub Date : 2022-10-20 DOI: 10.1007/s42521-022-00070-w

G. Pisoni, Alessia Paccagnini, C. Tarantola, A. Tanda, Albulena Shala, Kherbouche Meriem

引用次数: 0