Comparative analysis of financial data differentiation techniques using LSTM neural network
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Christos Floros & Shabbar Jaffry & Goncalo Valle Lima, 2007. "Long memory in the Portuguese stock market," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 24(3), pages 220-232, August.
- Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
- Bhardwaj, Geetesh & Swanson, Norman R., 2006.
"An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
- Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics.
- Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
- repec:pri:cepsud:91malkiel is not listed on IDEAS
- Sabzikar, Farzad & Wang, Qiying & Phillips, Peter C.B., 2020. "Asymptotic theory for near integrated processes driven by tempered linear processes," Journal of Econometrics, Elsevier, vol. 216(1), pages 192-202.
- Roel C.A. OOMEN, 2001. "Using high frequency stock market index data to calculate, model and forecast realized return variance," Economics Working Papers ECO2001/06, European University Institute.
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Bouteska, Ahmed & Abedin, Mohammad Zoynul & Hajek, Petr & Yuan, Kunpeng, 2024. "Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods," International Review of Financial Analysis, Elsevier, vol. 92(C).
- Sergio Castellano Gómez & Robert Ślepaczuk, 2021. "Robust optimisation in algorithmic investment strategies," Working Papers 2021-27, Faculty of Economic Sciences, University of Warsaw.
- Maria Rosa Borges, 2010.
"Efficient market hypothesis in European stock markets,"
The European Journal of Finance, Taylor & Francis Journals, vol. 16(7), pages 711-726.
- Maria Rosa Borges, 2008. "Efficient Market Hypothesis in European Stock Markets," Working Papers Department of Economics 2008/20, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023.
"Forecasting mid-price movement of Bitcoin futures using machine learning,"
Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
- Akyildirim, Erdinc & Cepni, Oguzhan & Corbet, Shaen & Uddin, Gazi Salah, 2020. "Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning," Working Papers 20-2020, Copenhagen Business School, Department of Economics.
- Bartosz Bieganowski & Robert 'Slepaczuk, 2024. "Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data," Papers 2411.12753, arXiv.org, revised Nov 2024.
- Chlebus Marcin & Dyczko Michał & Woźniak Michał, 2021.
"Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem,"
Central European Economic Journal, Sciendo, vol. 8(55), pages 44-62, January.
- Marcin Chlebus & Michał Dyczko & Michał Woźniak, 2020. "Nvidia’s stock returns prediction using machine learning techniques for time series forecasting problem," Working Papers 2020-22, Faculty of Economic Sciences, University of Warsaw.
- repec:eme:sef000:10867370710817400 is not listed on IDEAS
- Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
- Lo, Andrew W, 1991.
"Long-Term Memory in Stock Market Prices,"
Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
- Andrew W. Lo, 1989. "Long-term Memory in Stock Market Prices," NBER Working Papers 2984, National Bureau of Economic Research, Inc.
- Lo, Andrew W. (Andrew Wen-Chuan), 1989. "Long-term memory in stock market prices," Working papers 3014-89., Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Tom Doan, "undated". "RSSTATISTIC: RATS procedure to compute R/S Statistic (classical or Lo's modified)," Statistical Software Components RTS00191, Boston College Department of Economics.
- Harvey,Andrew C., 1991.
"Forecasting, Structural Time Series Models and the Kalman Filter,"
Cambridge Books,
Cambridge University Press, number 9780521405737, September.
- Harvey,Andrew C., 1990. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521321969, September.
- Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
- Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
- John Barkoulas & Christopher Baum & Nickolaos Travlos, 2000.
"Long memory in the Greek stock market,"
Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 177-184.
- John T. Barkoulas & Christopher F. Baum & Nickolaos Travlos, 1996. "Long Memory in the Greek Stock Market," Boston College Working Papers in Economics 356., Boston College Department of Economics.
- Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
- Huijian Dong & Helen Bowers & William R. Latham, 2013. "Evidence on the Efficient Market Hypothesis from 44 Global Financial Market Indexes," Working Papers 13-07, University of Delaware, Department of Economics.
- Koustas, Zisimos & Serletis, Apostolos, 2005. "Rational bubbles or persistent deviations from market fundamentals?," Journal of Banking & Finance, Elsevier, vol. 29(10), pages 2523-2539, October.
- Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
- Farzad Sabzikar & Piotr Kokoszka, 2023. "Tempered functional time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 280-293, May.
- C. Lento & N. Gradojevic & C. S. Wright, 2007. "Investment information content in Bollinger Bands?," Applied Financial Economics Letters, Taylor & Francis Journals, vol. 3(4), pages 263-267.
- Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
- Janusz Gajda & Rafał Walasek, 2020. "Fractional differentiation and its use in machine learning," Working Papers 2020-32, Faculty of Economic Sciences, University of Warsaw.
- Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
- Christos Floros & Shabbar Jaffry & Goncalo Valle Lima, 2007. "Long memory in the Portuguese stock market," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 24(3), pages 220-232, August.
- Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
- Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
- Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
- Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Dominik Stempie'n & Robert 'Slepaczuk, 2025. "Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models," Papers 2505.19617, arXiv.org.
- Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023.
"A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting,"
Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
- Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020. "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working Papers 202056, University of Pretoria, Department of Economics.
- Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020. "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working papers 2020-10, University of Connecticut, Department of Economics.
- Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
- Narayan, Seema & Smyth, Russell, 2015.
"The financial econometrics of price discovery and predictability,"
International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
- Seema Narayan & Russell Smyth, 2015. "The Financial Econometrics of Price Discovery and Predictability," Monash Economics Working Papers 06-15, Monash University, Department of Economics.
- Sensoy, Ahmet & Tabak, Benjamin M., 2015. "Time-varying long term memory in the European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 147-158.
- Akber, Ushna & Muhammad, Nabeel, 2013. "Is Pakistan Stock Market moving towards Weak-form efficiency? Evidence from the Karachi Stock Exchange and the Random Walk Nature of free-float of shares of KSE 30 Index," MPRA Paper 49128, University Library of Munich, Germany.
- Firat Melih Yilmaz & Engin Yildiztepe, 2024. "Statistical Evaluation of Deep Learning Models for Stock Return Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 221-244, January.
- Marianna Brunetti & Roberta Luca, 2023.
"Correction to: Pairs trading in the index options market,"
Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 175-176, March.
- Marianna Brunetti & Roberta De Luca, 2023. "Pairs trading in the index options market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 145-173, March.
- Marianna Brunetti & Roberta De Luca, 2021. "Pairs Trading In The Index Options Market," CEIS Research Paper 512, Tor Vergata University, CEIS, revised 02 Sep 2021.
- Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
- Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022. "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(1), pages 1-23, Enero - M.
- Asif, Raheel & Frömmel, Michael, 2022. "Testing Long memory in exchange rates and its implications for the adaptive market hypothesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
- Jitka Veselá & Alžběta Zíková, 2022. "Are the Czech, Polish, German and Dutch markets taking a random walk? [Konají český, polský, německý a nizozemský trh náhodnou procházku?]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2022(2), pages 19-38.
- Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
- Admin Starcevic & Timothy Rodgers, 2011. "Market Efficiency within the German Stock Market: A Comparative Study of the Relative Efficiencies of the DAX, MDAX, SDAX and ASE Indices," International Econometric Review (IER), Econometric Research Association, vol. 3(1), pages 25-37, April.
- Saqib Farid & Rubeena Tashfeen & Tahseen Mohsan & Arsal Burhan, 2023. "Forecasting stock prices using a data mining method: Evidence from emerging market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1911-1917, April.
- Ziliotto, Arianna & Serati, Massimiliano, 2015. "The semi-strong efficiency debate: In search of a new testing framework," Research in International Business and Finance, Elsevier, vol. 34(C), pages 412-438.
- Fayssal Jamhamed & Franck Martin & Fabien Rondeau & Josué Thélissaint & Stéphane Tufféry, 2024. "Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-13, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
- Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2022.
"Forecasting Skills in Experimental Markets: Illusion or Reality?,"
Management Science, INFORMS, vol. 68(7), pages 5216-5232, July.
- Brice Corgnet & Cary Deck & Mark Desantis & David Porter, 2020. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Working Papers halshs-02893291, HAL.
- Brice Corgnet & Cary Deck & Mark Desantis & David Porter, 2022. "Forecasting Skills in Experimental Market : Illusion or Reality?," Post-Print hal-04325544, HAL.
- Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2020. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Working Papers 2020, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
- Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2020. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Working Papers 20-27, Chapman University, Economic Science Institute.
- Chaker Aloui & Duc Khuong Nguyen, 2014.
"On the detection of extreme movements and persistent behaviour in Mediterranean stock markets: a wavelet-based approach,"
Applied Economics, Taylor & Francis Journals, vol. 46(22), pages 2611-2622, August.
- Chaker Aloui & Duc Khuong Nguyen, 2014. "On the detection of extreme movements and persistent behavior in Mediterranean stock markets: a wavelet-based approach," Working Papers 2014-184, Department of Research, Ipag Business School.
- Rompotis, Gerasimos G., 2011. "Testing weak-form efficiency of exchange traded funds market," MPRA Paper 36020, University Library of Munich, Germany.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2025-06-16 (Big Data)
- NEP-CMP-2025-06-16 (Computational Economics)
- NEP-ETS-2025-06-16 (Econometric Time Series)
- NEP-FOR-2025-06-16 (Forecasting)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2505.19243. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.