Pub Date : 2024-08-05DOI: 10.1186/s40854-024-00643-1
Oluwadamilare Omole, David Enke
This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural network–long short-term memory (CNN–LSTM), long- and short-term time-series network, temporal convolutional network, and ARIMA (benchmark) models for predicting Bitcoin prices using on-chain data. Feature-selection methods—i.e., Boruta, genetic algorithm, and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set. Results indicate that combining Boruta feature selection with the CNN–LSTM model consistently outperforms other combinations, achieving an accuracy of 82.44%. Three trading strategies and three investment positions are examined through backtesting. The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions. This study provides evidence of the potential profitability of predictive models in Bitcoin trading.
{"title":"Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared","authors":"Oluwadamilare Omole, David Enke","doi":"10.1186/s40854-024-00643-1","DOIUrl":"https://doi.org/10.1186/s40854-024-00643-1","url":null,"abstract":"This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural network–long short-term memory (CNN–LSTM), long- and short-term time-series network, temporal convolutional network, and ARIMA (benchmark) models for predicting Bitcoin prices using on-chain data. Feature-selection methods—i.e., Boruta, genetic algorithm, and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set. Results indicate that combining Boruta feature selection with the CNN–LSTM model consistently outperforms other combinations, achieving an accuracy of 82.44%. Three trading strategies and three investment positions are examined through backtesting. The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions. This study provides evidence of the potential profitability of predictive models in Bitcoin trading.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02DOI: 10.1186/s40854-024-00642-2
Arturo Leccadito, Alessandro Staino, Pietro Toscano
This study introduces the dynamic Gerber model (DGC) and evaluates its performance in the prediction of Value at Risk (VaR) and Expected Shortfall (ES) compared to alternative parametric, non-parametric and semi-parametric methods for estimating the covariance matrix of returns. Based on ES backtests, the DGC method produces, overall, accurate ES forecasts. Furthermore, we use the Model Confidence Set procedure to identify the superior set of models (SSM). For all the portfolios and VaR/ES confidence levels we consider, the DGC is found to belong to the SSM.
本研究介绍了动态格伯模型（DGC），并评估了该模型在预测风险价值（VaR）和预期亏空（ES）方面与其他参数、非参数和半参数收益协方差矩阵估计方法相比的性能。根据 ES 回溯测试，DGC 方法总体上能准确预测 ES。此外，我们还使用模型置信集程序来确定优越的模型集（SSM）。对于我们考虑的所有投资组合和 VaR/ES 置信度水平，我们发现 DGC 属于 SSM。
{"title":"A novel robust method for estimating the covariance matrix of financial returns with applications to risk management","authors":"Arturo Leccadito, Alessandro Staino, Pietro Toscano","doi":"10.1186/s40854-024-00642-2","DOIUrl":"https://doi.org/10.1186/s40854-024-00642-2","url":null,"abstract":"This study introduces the dynamic Gerber model (DGC) and evaluates its performance in the prediction of Value at Risk (VaR) and Expected Shortfall (ES) compared to alternative parametric, non-parametric and semi-parametric methods for estimating the covariance matrix of returns. Based on ES backtests, the DGC method produces, overall, accurate ES forecasts. Furthermore, we use the Model Confidence Set procedure to identify the superior set of models (SSM). For all the portfolios and VaR/ES confidence levels we consider, the DGC is found to belong to the SSM.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141887096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1186/s40854-024-00640-4
Xin-Jiang He, Sha Lin
The effects of stochastic volatility, jump clustering, and regime switching are considered when pricing variance swaps. This study established a two-stage procedure that simplifies the derivation by first isolating the regime switching from other stochastic sources. Based on this, a novel probabilistic approach was employed, leading to pricing formulas with time-dependent and regime-switching parameters. The formulated solutions were easy to implement and differed from most existing results of variance swap pricing, where Fourier inversion or fast Fourier transform must be performed to obtain the final results, since they are completely analytical without involving integrations. The numerical results indicate that jump clustering and regime switching have a significant influence on variance swap prices.
{"title":"A probabilistic approach for the valuation of variance swaps under stochastic volatility with jump clustering and regime switching","authors":"Xin-Jiang He, Sha Lin","doi":"10.1186/s40854-024-00640-4","DOIUrl":"https://doi.org/10.1186/s40854-024-00640-4","url":null,"abstract":"The effects of stochastic volatility, jump clustering, and regime switching are considered when pricing variance swaps. This study established a two-stage procedure that simplifies the derivation by first isolating the regime switching from other stochastic sources. Based on this, a novel probabilistic approach was employed, leading to pricing formulas with time-dependent and regime-switching parameters. The formulated solutions were easy to implement and differed from most existing results of variance swap pricing, where Fourier inversion or fast Fourier transform must be performed to obtain the final results, since they are completely analytical without involving integrations. The numerical results indicate that jump clustering and regime switching have a significant influence on variance swap prices.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-29DOI: 10.1186/s40854-023-00606-y
María José Ayala, Nicolás Gonzálvez-Gallego, Rocío Arteaga-Sánchez
This study systematically reviewed the literature on using the Google Search Volume Index (GSVI) as a proxy variable for investor attention and stock market movements. We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases. The articles were classified and synthesized based on the selection criteria for building the GSVI: keywords of the search term, market region, and frequency of the data sample. Next, we analyze the effect of returns, volatility, and trading volume on the financial variables. The main results can be summarized as follows. (1) The GSVI is positively related to volatility and trading volume regardless of the keyword, market region, or frequency used for the sample. Hence, increasing investor attention toward a specific financial term will increase volatility and trading volume. (2) The GSVI can improve forecasting models for stock market movements. To conclude, this study consolidates, for the first time, the research literature on GSVI, which is highly valuable for academic practitioners in the area.
本研究系统回顾了将谷歌搜索量指数（GSVI）作为投资者关注度和股市走势替代变量的相关文献。我们使用 Web of Sciences 和 ScienceDirect 数据库分析了 2010 年至 2021 年间发表的 56 篇学术研究。根据建立 GSVI 的选择标准：搜索关键词、市场区域和数据样本的频率，对文章进行了分类和综合。接下来，我们分析了收益率、波动率和交易量对金融变量的影响。主要结果总结如下(1) 无论使用何种关键词、市场区域或样本频率，GSVI 与波动率和交易量都呈正相关。因此，增加投资者对特定金融术语的关注会增加波动性和交易量。(2）GSVI 可以改善股市波动的预测模型。总之，本研究首次整合了有关 GSVI 的研究文献，对该领域的学术从业人员极具参考价值。
{"title":"Google search volume index and investor attention in stock market: a systematic review","authors":"María José Ayala, Nicolás Gonzálvez-Gallego, Rocío Arteaga-Sánchez","doi":"10.1186/s40854-023-00606-y","DOIUrl":"https://doi.org/10.1186/s40854-023-00606-y","url":null,"abstract":"This study systematically reviewed the literature on using the Google Search Volume Index (GSVI) as a proxy variable for investor attention and stock market movements. We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases. The articles were classified and synthesized based on the selection criteria for building the GSVI: keywords of the search term, market region, and frequency of the data sample. Next, we analyze the effect of returns, volatility, and trading volume on the financial variables. The main results can be summarized as follows. (1) The GSVI is positively related to volatility and trading volume regardless of the keyword, market region, or frequency used for the sample. Hence, increasing investor attention toward a specific financial term will increase volatility and trading volume. (2) The GSVI can improve forecasting models for stock market movements. To conclude, this study consolidates, for the first time, the research literature on GSVI, which is highly valuable for academic practitioners in the area.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The informativeness of environmental, social, and governance (ESG) scores and their actual impact on firms remains understudied. To address this gap in the literature, we make theoretical predictions and conduct empirical research revealing that a high ESG score is associated with a lower probability of ESG scandals and lower stock returns during a scandal event. Our results suggest that ESG scores are heterogeneous but informative, and that a strong ESG reputation may have both positive and negative consequences for firms. Drawing on our findings, we develop a model and showcase that firms face an optimization problem when determining optimal ESG investment levels. Two equilibria may exist based on the trade-off between ESG scandal losses and ESG adjustment costs. Our model explains why certain firms make heterogeneous ESG decisions
{"title":"ESG scores, scandal probability, and event returns","authors":"Wenya Sun, Yichen Luo, Siu-Ming Yiu, Luping Yu, Wenzhi Ding","doi":"10.1186/s40854-024-00635-1","DOIUrl":"https://doi.org/10.1186/s40854-024-00635-1","url":null,"abstract":"The informativeness of environmental, social, and governance (ESG) scores and their actual impact on firms remains understudied. To address this gap in the literature, we make theoretical predictions and conduct empirical research revealing that a high ESG score is associated with a lower probability of ESG scandals and lower stock returns during a scandal event. Our results suggest that ESG scores are heterogeneous but informative, and that a strong ESG reputation may have both positive and negative consequences for firms. Drawing on our findings, we develop a model and showcase that firms face an optimization problem when determining optimal ESG investment levels. Two equilibria may exist based on the trade-off between ESG scandal losses and ESG adjustment costs. Our model explains why certain firms make heterogeneous ESG decisions","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-16DOI: 10.1186/s40854-024-00641-3
Md. Bokhtiar Hasan, Gazi Salah Uddin, Md. Sumon Ali, Md. Mamunur Rashid, Donghyun Park, Sang Hoon Kang
{"title":"Examining time–frequency quantile dependence between green bond and green equity markets","authors":"Md. Bokhtiar Hasan, Gazi Salah Uddin, Md. Sumon Ali, Md. Mamunur Rashid, Donghyun Park, Sang Hoon Kang","doi":"10.1186/s40854-024-00641-3","DOIUrl":"https://doi.org/10.1186/s40854-024-00641-3","url":null,"abstract":"","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141643084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-16DOI: 10.1186/s40854-024-00637-z
P. Foroutan, Salim Lahmiri
{"title":"Deep learning systems for forecasting the prices of crude oil and precious metals","authors":"P. Foroutan, Salim Lahmiri","doi":"10.1186/s40854-024-00637-z","DOIUrl":"https://doi.org/10.1186/s40854-024-00637-z","url":null,"abstract":"","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The consideration of environmental, social, and governance (ESG) aspects has become an integral part of investment decisions for individual and institutional investors. Most recently, corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts. While stock market prediction is a complex and challenging task, several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices. To address this challenge, we propose an integrated computational framework to implement deep learning model architectures, specifically long short-term memory (LSTM), gated recurrent unit, and convolutional neural network, to predict the volatility of the ESG index in an identical environment. A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data, technical indicators, and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction. The performance of the constructed models was evaluated using standard assessment metrics. Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model. Furthermore, a series of statistical analyses was conducted to validate the robustness and reliability of the model. Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.
{"title":"Implementation of deep learning models in predicting ESG index volatility","authors":"Hum Nath Bhandari, Nawa Raj Pokhrel, Ramchandra Rimal, Keshab R. Dahal, Binod Rimal","doi":"10.1186/s40854-023-00604-0","DOIUrl":"https://doi.org/10.1186/s40854-023-00604-0","url":null,"abstract":"The consideration of environmental, social, and governance (ESG) aspects has become an integral part of investment decisions for individual and institutional investors. Most recently, corporate leaders recognized the core value of the ESG framework in fulfilling their environmental and social responsibility efforts. While stock market prediction is a complex and challenging task, several factors associated with developing an ESG framework further increase the complexity and volatility of ESG portfolios compared with broad market indices. To address this challenge, we propose an integrated computational framework to implement deep learning model architectures, specifically long short-term memory (LSTM), gated recurrent unit, and convolutional neural network, to predict the volatility of the ESG index in an identical environment. A comprehensive analysis was performed to identify a balanced combination of input features from fundamental data, technical indicators, and macroeconomic factors to delineate the cone of uncertainty in market volatility prediction. The performance of the constructed models was evaluated using standard assessment metrics. Rigorous hyperparameter tuning and model-selection strategies were implemented to identify the best model. Furthermore, a series of statistical analyses was conducted to validate the robustness and reliability of the model. Experimental results showed that a single-layer LSTM model with a relatively small number of neurons provides a superior fit with high prediction accuracy relative to more complex models.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-26DOI: 10.1186/s40854-024-00646-y
Alessio Brini, Jimmie Lenz
The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.
{"title":"A comparison of cryptocurrency volatility-benchmarking new and mature asset classes","authors":"Alessio Brini, Jimmie Lenz","doi":"10.1186/s40854-024-00646-y","DOIUrl":"https://doi.org/10.1186/s40854-024-00646-y","url":null,"abstract":"The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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