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Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls 用于财务预测、规划和分析的机器学习：最新发展和陷阱
Digital finance
Pub Date : 2021-07-10 DOI: 10.1007/s42521-021-00046-2
Helmut Wasserbacher, M. Spindler
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引用次数: 12
Profitability of cryptocurrency Pump and Dump schemes 加密货币泵和转储方案的盈利能力
Digital finance
Pub Date : 2021-06-01 DOI: 10.1007/s42521-021-00034-6
Taro Tsuchiya
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引用次数: 2
Correction to: Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009 更正:抵押贷款风险的违约分析与传统和深度机器学习集中在2008-2009年
Digital finance
Pub Date : 2021-06-01 DOI: 10.1007/s42521-021-00039-1
Vikram Ojha, Jeonghoe Lee
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引用次数: 0
CATE meets ML CATE遇见ML
Digital finance
Pub Date : 2021-06-01 DOI: 10.1007/s42521-021-00033-7
D. Jacob
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引用次数: 11
Replicating market makers 复制做市商
Digital finance
Pub Date : 2021-03-26 DOI: 10.1007/s42521-023-00082-0
Guillermo Angeris, A. Evans, Tarun Chitra
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引用次数: 28
Evaluation of multi-asset investment strategies with digital assets 利用数字资产评估多资产投资策略
Digital finance
Pub Date : 2021-03-01 DOI: 10.1007/s42521-021-00031-9
Alla Petukhina, Erin Sprünken
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引用次数: 0
On cointegration and cryptocurrency dynamics 关于协整和加密货币动态
Digital finance
Pub Date : 2021-02-17 DOI: 10.1007/s42521-021-00027-5
Georg Keilbar, Yanfen Zhang
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引用次数: 1
How to gauge investor behavior? A comparison of online investor sentiment measures. 如何衡量投资者行为？在线投资者情绪测量比较。
Digital finance
Pub Date : 2021-01-01 Epub Date: 2021-08-07 DOI: 10.1007/s42521-021-00038-2
Daniele Ballinari, Simon Behrendt

Given the increasing interest in and the growing number of publicly available methods to estimate investor sentiment from social media platforms, researchers and practitioners alike are facing one crucial question - which is best to gauge investor sentiment? We compare the performance of daily investor sentiment measures estimated from Twitter and StockTwits short messages by publicly available dictionary and machine learning based methods for a large sample of stocks. To determine their relevance for financial applications, these investor sentiment measures are compared by their effects on the cross-section of stocks (i) within a Fama and MacBeth (J Polit Econ 81:607-636, 1973) regression framework applied to a measure of retail investors' order imbalances and (ii) by their ability to forecast abnormal returns in a model-free portfolio sorting exercise. Interestingly, we find that investor sentiment measures based on finance-specific dictionaries do not only have a greater impact on retail investors' order imbalances than measures based on machine learning approaches, but also perform very well compared to the latter in our asset pricing application.

鉴于人们对从社交媒体平台估算投资者情绪的兴趣与日俱增，公开可用的估算方法也越来越多，研究人员和从业人员都面临着一个关键问题--哪种方法最能衡量投资者情绪？我们比较了通过 Twitter 和 StockTwits 短消息估算出的每日投资者情绪指标的性能，这些指标是通过公开可用的字典方法和基于机器学习的方法对大量股票样本进行估算得出的。为了确定它们在金融应用中的相关性，我们比较了这些投资者情绪度量对股票横截面的影响：(i) 在 Fama 和 MacBeth（J Polit Econ 81:607-636，1973 年）回归框架中应用于散户投资者订单不平衡度量的影响；(ii) 在无模型投资组合排序中预测异常回报的能力。有趣的是，我们发现，与基于机器学习方法的测量方法相比，基于金融特定词典的投资者情绪测量方法不仅对散户投资者的订单失衡有更大的影响，而且在我们的资产定价应用中与后者相比表现非常出色。
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引用次数: 0
Special Issue on Artificial Intelligence, Machine Learning and Platform Innovation in Quantitative Finance (MathFinance Conference 2020/2021).
Digital finance
Pub Date : 2021-01-01 Epub Date: 2021-11-17 DOI: 10.1007/s42521-021-00043-5
Natalie Packham, Uwe Wystup
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引用次数: 0
Cryptocurrency volatility markets. 加密货币波动市场。
Digital finance
Pub Date : 2021-01-01 Epub Date: 2021-08-02 DOI: 10.1007/s42521-021-00037-3
Fabian Woebbeking

By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market's expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture 'normal' market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.

通过计算加密货币期权价格的波动率指数(CVX)，我们分析了这个市场对未来波动率的预期。我们的方法解决了这一年轻资产类别具有挑战性的流动性环境，并允许我们提取稳定的市场隐含波动率。考虑了两种替代方法，从颗粒状的日内加密货币期权数据中计算波动性，这些数据跨越了COVID-19大流行时期。因此，CVX数据捕捉了“正常”的市场动态以及危机和恢复期。该方法得到两个协整指数序列，其中相应的误差修正模型可以作为市场隐含尾部风险的指标。将我们的CVX与传统资产类别(如VIX(股票)或GVX(黄金))的现有波动性基准进行比较，证实了加密货币的波动性动态通常与传统市场脱节，但却有共同的冲击。
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引用次数: 0
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