Exploit momentum in Cryptocurrency Market
DOI:
https://doi.org/10.54691/bcpbm.v42i.4547Keywords:
Momentum Strategy, Cryptocurrency market.Abstract
Researchers put efforts into explanations of the momentum phenomenon and improvements of the momentum strategy since the emergence of momentum in 1993. Interested in anomalies appearing as exhibited in traditional asset markets, adequate studies are launched on the nascent phenomenon emergers in the last decade, the cryptocurrency market. Recent studies have shown that there is hardly any cross-sectional momentum in the cryptocurrency market. To explore the momentum anomaly additionally in the cryptocurrency market, this paper implemented a time-series momentum on cross-sectional winners for improvement. Previous studies have introduced detecting the turning point between long-term slow time-series factor and short-term fast time-series factor contributes to predicting the trend well. Furthermore, a threshold decided by a certain machine learning model suggests better performance. In this paper. A multilayer perceptron (MLP) is utilized to learn the weights of time-series factors. The combination of cross-sectional momentum and time-series momentum shows advantages and the MLP learned weighted strategy is preferable.
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Jegadeesh, N., Titman, S.: Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance 48(1), 65–91 (1993), http://www.jstor.org/stable/2328882 DOI: https://doi.org/10.1111/j.1540-6261.1993.tb04702.x
Singh, S., Walia, N.: Momentum investing: a systematic literature review and bibliometric analysis. Management Review Quarterly 72(1), 87–113 (Feb 2022). https://doi.org/10.1007/s11301-020-00205-6, https://doi.org/10.1007/s11301-02000205-6 DOI: https://doi.org/10.1007/s11301-020-00205-6
Shen, D., Urquhart, A., Wang, P.: A three-factor pricing model for cryptocurrencies. Fi-nance Research Letters 34, 101248 (2020) DOI: https://doi.org/10.1016/j.frl.2019.07.021
Nguyen, H., Liu, B., Parikh, N.Y.: Exploring the short-term momentum effect in the crypto-currency market. Evolutionary and Institutional Economics Review 17(2), 425–443 (2020) DOI: https://doi.org/10.1007/s40844-020-00176-z
Garg, A., Goulding, C., Harvey, C.R., Mazzoleni, M.: Momentum turning points. Capital Markets: Asset Pricing & Valuation eJournal (2019) DOI: https://doi.org/10.2139/ssrn.3489539
George, T.J., Hwang, C.Y.: The 52-week high and momentum investing. The Journal of Fi-nance 59(5), 2145–2176 (2004), http://www.jstor.org/stable/3694820 DOI: https://doi.org/10.1111/j.1540-6261.2004.00695.x
David, B., Joop, H., P.E., M.M.: Residual momentum. Behavioral & Experimental Finance eJournal (2009). https://doi.org/10.1016/j.jempfin.2011.01.003 DOI: https://doi.org/10.1016/j.jempfin.2011.01.003
Moskowitz, T.J., Ooi, Y.H., Pedersen, L.H.: Time series momentum. Journal of Financial Economics 104(2), 228–250 (May 2012), https://www.sciencedirect.com/science/article/pii/S0304405X11002613 DOI: https://doi.org/10.1016/j.jfineco.2011.11.003
Cheng, E., Kostyuchyk, N., Lee, W., Liu, P., Ma, C.: Trending fast and slow. The Journal of Portfolio Management 48(3), 103–116 (2022) DOI: https://doi.org/10.3905/jpm.2021.1.312
Lim, B., Zohren, S., Roberts, S.: Enhancing time-series momentum strategies using deep neural networks. The Journal of Financial Data Science 1(4), 19–38 (2019) DOI: https://doi.org/10.3905/jfds.2019.1.015
Wood, K., Roberts, S., Zohren, S.: Slow momentum with fast reversion: A trading strategy using deep learning and changepoint detection. The Journal of Financial Data Science 4(1), 111–129 (2022). https://doi.org/10.3905/jfds.2021.1.081, https://jfds.pm-research.com/content/4/1/111 DOI: https://doi.org/10.3905/jfds.2021.1.081
Grobys, K., Sapkota, N.: Cryptocurrencies and momentum. Economics Letters 180, 6–10 (2019). https://doi.org/https://doi.org/10.1016/j.econlet.2019.03.028, https://www.sciencedirect.com/science/article/pii/S0165176519301077 DOI: https://doi.org/10.1016/j.econlet.2019.03.028
Liu, Y., Tsyvinski, A., Wu, X.: Common Risk Factors in Cryptocurrency. NBER Working Papers 25882, National Bureau of Economic Research, Inc (May 2019), https://ideas.repec.org/p/nbr/nberwo/25882.html DOI: https://doi.org/10.3386/w25882
Dobrynskaya, V.: Cryptocurrency momentum and reversal. Available at SSRN 3913263 (2021) DOI: https://doi.org/10.2139/ssrn.3913263
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