[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118229-en":3,"doc-seo-118229-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118229,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Forecasting Bitcoin returns - Econometric time series analysis vs. machine learning","Study of Bitcoin return series centers on statistical properties and a structured forecasting exercise. Machine learning methods are calibrated alongside econometric time series models to benchmark predictive performance. Empirical results show that machine learning delivers higher forecasting precision relative to econometric benchmarks for both in-sample and out-of-sample forecasts. Deep learning architectures and complex LSTM layers do not improve daily forecast precision. A simple recurrent neural network is identified as a sensible approach for forecasting daily returns.","Received: 5 June 2023 Revised: 28 March 2024 Accepted: 19 May 2024  \nDOI: 10.1002/for.3165  \nRESE ARCH ARTICL E  \nForecasting Bitcoin returns: Econometric time series analysis vs. machine learning  \nTheo Berger 1,2  | Jana Koubov3  \n1Department of Business and Computer Science, University of Applied Sciences Hannover, Hanover, Germany 2Department of Business and Administration, University of Bremen, Bremen, Germany  \n3Sparkassen Rating und Risikosysteme, Berlin, Germany  \nCorrespondence  \nTheo Berger, Department of Business and Computer Science, University of Applied Sciences Hannover, Ricklinger Stadtweg 120, D-30459 Hannover, Germany.  \nEmail: [theo.berger@hs-hannover.de](theo.berger@hs-hannover.de)  \nAbstract  \nWe study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate state-of-the-art machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the application of machine learning techniques outperforms econometric benchmarks in terms of forecasting precision for both in- and out-of-sample forecasts. We find that both deep learning architectures as well as complex layers, such as LSTM, do not increase the precision of daily forecasts. Specifically, a simple recurrent neural network describes a sensible choice for forecasting daily return series.  \nKEYWOR DS  \nforecasting, machine learning, risk measurement, time series analysis  \nJE L C L A SSIF ICAT ION  \nC33, C58, G17, G23  \n1 | INTRODUCTION  \nFinding the adequate methodological approach to forecast individual financial return series describes a staggering task. In order to achieve precise out-of-sample forecasts, it is necessary to understand the properties of the underlying time series. In this vein, the statistical properties of financial time series have been widely discussed and the application of Autoregressive Moving Average (ARMA) Models is widely accepted (Berger & Gencay, 2018; Halbleib & Pohlmeier, 2012) .  \nDue to steadily growing computational power, as well as increasing data availability, forecasting economic time series via machine learning techniques describes a novel string of research. As discussed by Kraus et al. (2020), machine learning is less restrictive regarding the  \nassumptions on the underlying data and current machine learning approaches can adjust to properties of economic time series individually. Therefore machine learning describes a fruitful alternative to econometric modelling. Although machine learning techniques are characterized as black boxes, recent studies provide empirical evidence that machine learning achieves higher forecasting precision than widely accepted econometric approaches. Guet al. (2020) provide a thorough empirical assessment and discuss competing machine-learning techniques applied to economic data sets. As a result, adequately calibrated machine learning approaches outperform interpretable econometric models in terms of forecasting accuracy. Feng et al. (2020) confirm these results for stock returns, Longo et al. (2022) for GDP forecasts, and Makridakiset al. (2018) for various economic data sets.  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n© 2024 The Author(s). Journal of Forecasting published by John Wiley & Sons Ltd.  \nThis study adds to this string of literature, and we focus on forecasts for innovative economic return series, namely Bitcoin returns. As described in Alessandretti et al. (2018) and Tandon et al. (2019), Bitcoins belong to the asset class of cryptocurrencies and are characterized by higher volatility than classical currencies. Furthermore, as cryptocurrencies are not controlled by national central banks, Bitcoins a","cbCailU5T6ck24rx","https://ap.wps.com/l/cbCailU5T6ck24rx","pdf",691305,1,13,"English","en",105,"# Introduction\n## Econometric benchmarks for financial time series\n## Machine learning approaches for economic forecasting\n## Motivation for forecasting Bitcoin returns\n## Study design and contribution\n## Forecasting models compared","[{\"question\":\"What does the document investigate about Bitcoin?\",\"answer\":\"It studies the statistical properties of Bitcoin return series and conducts a forecasting exercise for daily Bitcoin returns.\"},{\"question\":\"How are machine learning methods compared to econometric time series models?\",\"answer\":\"State-of-the-art machine learning techniques are calibrated and directly benchmarked against econometric time series models using in-sample and out-of-sample forecasting precision.\"},{\"question\":\"Which model is most effective for daily Bitcoin return forecasting according to the results?\",\"answer\":\"A simple recurrent neural network provides a sensible choice, while deep learning architectures and complex LSTM layers do not increase daily forecast precision.\"}]","Forecasting Bitcoin returns - 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