This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Boyd, S., Vandenberghe, L., 2004. ‘Convex Optimization’ Cambridge University Press New York, NY, USA.BoydS.VandenbergheL.2004New York, NY, USA10.1017/CBO9780511804441Search in Google Scholar
Cawley, G., Talbot, N., 2010. ‘On over-fitting in model selection and subsequent selection bias in performance evaluation’ J. Mach. Learn. Res. 11, 2079–2107.CawleyG.TalbotN.2010‘On over-fitting in model selection and subsequent selection bias in performance evaluation’112079–2107Search in Google Scholar
Chen M., Narwal N., Schultz. M., 2018, Stanford University. Predicting Price Changes in Ethereum. URL: https://pdfs.semanticscholar.org/ceff/65e02b2b9b6b181cfc956350351b8e284a01.pdf?_ga=2.139748214.472574922.1533418619-1566045322.1533418619ChenM.NarwalN.SchultzM.2018https://pdfs.semanticscholar.org/ceff/65e02b2b9b6b181cfc956350351b8e284a01.pdf?_ga=2.139748214.472574922.1533418619-1566045322.1533418619Search in Google Scholar
Chordia, T., Swaminathan, B., 2002. ‘Trading volume and cross-autocorrelations in stock returns’ J. Financ. 55 (2), 913–935.ChordiaT.SwaminathanB.2002‘Trading volume and cross-autocorrelations in stock returns’552913–93510.1111/0022-1082.00231Search in Google Scholar
Cristianini, N. and Shawe-Taylor, J. 2000, ‘An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods’ Cambridge University Press Cambridge.CristianiniN.Shawe-TaylorJ.2000Cambridge10.1017/CBO9780511801389Search in Google Scholar
David Meyer. Support Vector Machines. The Interface to libsvm in package e1071. URL: https://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdfhttps://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdfSearch in Google Scholar
Hastie, T., Tibshirani, R., Friedman, J., ‘The Elements of Statistical Learning’ Data Mining, Inference, and Prediction.HastieT.TibshiraniR.FriedmanJ.Search in Google Scholar
Huang, W., Nakamori, Y., Wang, S.-Y., 2005. ‘Forecasting stock market movement direction with support vector machine’ Comput. Oper. Res 32 (10), 2513–2522.HuangW.NakamoriY.WangS.-Y.2005‘Forecasting stock market movement direction with support vector machine’32102513–252210.1016/j.cor.2004.03.016Search in Google Scholar
Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S., 2004. ‘Credit rating analysis with support vector machines and neural networks: A market comparative study’ Decis. Support Syst. 37 (4), 543–558.HuangZ.ChenH.HsuC.J.ChenW.H.WuS.2004‘Credit rating analysis with support vector machines and neural networks: A market comparative study’374543–55810.1016/S0167-9236(03)00086-1Search in Google Scholar
Huerta, R., Corbacho, F., Elkan, C., 2013. ‘Nonlinear support vector machines can systematically identify stocks with high and low future returns’ IOS Press. Algorithmic Finance 2, (45–58).HuertaR.CorbachoF.ElkanC.2013‘Nonlinear support vector machines can systematically identify stocks with high and low future returns’245–5810.3233/AF-13016Search in Google Scholar
Huffman, S., Moll, C., 2011. ‘The impact of asymmetry on expected stock returns: An investigation of alternative risk measures’ Algorithmic Financ 1 (2), 79–93.HuffmanS.MollC.2011‘The impact of asymmetry on expected stock returns: An investigation of alternative risk measures’1279–9310.3233/AF-2011-008Search in Google Scholar
James, G., Witten, D., Hastie, T., Tibshirani, R., ‘An Introduction to Statistical Learning with Applications in R’.JamesG.WittenD.HastieT.TibshiraniR.Search in Google Scholar
Jegadeesh, N., Titman, S., 2012. ‘Returns to buying winners and selling losers: Implications for stock market efficiency’ J Financ. 48 (1), 65–91.JegadeeshN.TitmanS.2012‘Returns to buying winners and selling losers: Implications for stock market efficiency’48165–9110.1111/j.1540-6261.1993.tb04702.xSearch in Google Scholar
Joachims, T. 1998. ‘Text categorization with Support Vector Machines: Learning with many relevant features. European Conference on Machine Learning ECML’ 1998: Machine Learning: ECML-98 pp 137–142.JoachimsT.1998‘Text categorization with Support Vector Machines: Learning with many relevant features. European Conference on Machine Learning ECML’ 1998137–14210.1007/BFb0026683Search in Google Scholar
Kim, K., 2003. ‘Financial time series forecasting using support vector machines. Neurocomputing’ 55 (1), 307–319.KimK.2003‘Financial time series forecasting using support vector machines551307–31910.1016/S0925-2312(03)00372-2Search in Google Scholar
Kość K., Sakowski P., Ślepaczuk R., 2018,Momentum and Contrarian Effects on the Cryptocurrency Market Physica A 523, 691–701, https://www.sciencedirect.com/science/article/pii/S037843711930216X?dgcid=authorKośćK.SakowskiP.ŚlepaczukR.2018Physica A 523691–701https://www.sciencedirect.com/science/article/pii/S037843711930216X?dgcid=author10.1016/j.physa.2019.02.057Search in Google Scholar
Meyer D., Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. Package ‘e1071’. URL: https://cran.r-project.org/web/packages/e1071/e1071.pdfMeyerD.TU WienPackage ‘e1071’. URLhttps://cran.r-project.org/web/packages/e1071/e1071.pdfSearch in Google Scholar
Muller, K., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B., 2001. ‘An introduction to kernel-based learning algorithms’ IEEE Neural Network 12 (2), 181–201.MullerK.MikaS.RatschG.TsudaK.ScholkopfB.2001‘An introduction to kernel-based learning algorithms’122181–20110.1201/9781315220413-4Search in Google Scholar
Package “TTR”. URL: https://cran.r-project.org/web/packages/TTR/TTR.pdfPackage “TTR”. URLSearch in Google Scholar
Performance Analytics in R. URL: https://cran.r-project.org/web/packages/PerformanceAnalytics/vignettes/portfolio_returns.pdfPerformance Analytics in R. URLSearch in Google Scholar
Pistole, T.C. ‘Comparison of three technical trading methods vs. buy-and-hold for the S&P 500 market. Graduate Student of Finance’, University of Houston – Victoria. URL: http://swdsi.org/swdsi2010/SW2010_Preceedings/papers/PA153.pdfPistoleT.C.University of Houston – Victoriahttp://swdsi.org/swdsi2010/SW2010_Preceedings/papers/PA153.pdfSearch in Google Scholar
Rouwenhorst, K., 2002. ‘International momentum strategies’ J. Financ. 53 (1), 267–284.RouwenhorstK.2002‘International momentum strategies’531267–28410.1111/0022-1082.95722Search in Google Scholar
Sewell, M., 2010. ‘The Application of Intelligent Systems to Financial Time Series Analysis, PhD thesis, PhD dissertation, Department of Computer Science’, University College London University of London.SewellM.2010University of LondonSearch in Google Scholar
Ślepaczuk R., Sakowski P., Zakrzewski G., 2018, Investment strategies beating the market. What can we squeeze from the market? eFinanse Vol. 14, no. 4, s. 36-55, https://e-finanse.com/current-issue/?number=59&id=421ŚlepaczukR.SakowskiP.ZakrzewskiG.2018Investment strategies beating the market. What can we squeeze from the market?Vol. 14436–55https://e-finanse.com/current-issue/?number=59&id=42110.2139/ssrn.2508647Search in Google Scholar
Tay, F., Cao, L., 2001. ‘Application of support vector machines in financial time series forecasting’ Omega 29 (4), 309–317.TayF.CaoL.2001‘Application of support vector machines in financial time series forecasting’294309–31710.1142/9789812791375_0007Search in Google Scholar
Tay, F., Cao, L., 2002. ‘Modified support vector machines in financial time series forecasting’ Neurocomputing 48 (1), 847–861.TayF.CaoL.2002‘Modified support vector machines in financial time series forecasting’481847–86110.1016/S0925-2312(01)00676-2Search in Google Scholar
Van Gestel, T., Suykens, J., Baestaens, D., Lambrechts, A., Lanckriet, G., Vandaele, B., et. hal., 2001. ‘Financial time series prediction using least squares support vector machines within the evidence framework’ Neural Netw., IEEE Trans 12 (4), 809–821.Van GestelT.SuykensJ.BaestaensD.LambrechtsA.LanckrietG.VandaeleB.ethal.2001‘Financial time series prediction using least squares support vector machines within the evidence framework’124809–82110.1109/72.935093Search in Google Scholar
Vapnik, V., 1999. ‘The Nature of Statistical Learning Theory’ Springer, Heidelberg, Germany.VapnikV.1999Germany10.1007/978-1-4757-3264-1Search in Google Scholar