Forecasting NFT coin prices using machine learning: Insights into feature significance and portfolio strategies

Achelis, 2013 Adcock, 2019, Non-fundamental, non-parametric bitcoin forecasting, Physica A: Statistical Mechanics and its Applications, 531, 10.1016/j.physa.2019.121727 Aharon, 2022, NFTs and asset class spillovers: Lessons from the period around the COVID-19 pandemic, Finance Research Letters, 47, 10.1016/j.frl.2021.102515 Akyildirim, 2021, Prediction of cryptocurrency returns using machine learning, Annals of Operations Research, 297, 3, 10.1007/s10479-020-03575-y Atsalakis, 2019, Bitcoin price forecasting with neuro-fuzzy techniques, European Journal of Operational Research, 276, 770, 10.1016/j.ejor.2019.01.040 Baker, 2016, Measuring economic policy uncertainty, The Quarterly Journal of Economics, 131, 1593, 10.1093/qje/qjw024 Ballings, 2015, Evaluating multiple classifiers for stock price direction prediction, Expert Systems with Applications, 42, 7046, 10.1016/j.eswa.2015.05.013 Bao, 2022, Non-fungible token: A systematic review and research agenda, Journal of Risk and Financial Management, 15(5), Article 5 Barberis, 2003, Chapter 18 a survey of behavioral finance, Vol. 1, 1053, 10.1016/S1574-0102(03)01027-6 Basak, 2019, Predicting the direction of stock market prices using tree-based classifiers, The North American Journal of Economics and Finance, 47, 552, 10.1016/j.najef.2018.06.013 Basher, 2022, Forecasting bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?, Machine Learning with Applications, 9, 10.1016/j.mlwa.2022.100355 Bergmeir, 2018, A note on the validity of cross-validation for evaluating autoregressive time series prediction, Computational Statistics & Data Analysis, 120, 70, 10.1016/j.csda.2017.11.003 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 Bustos, 2020, Stock market movement forecast: A systematic review, Expert Systems with Applications, 156, 10.1016/j.eswa.2020.113464 Chen, 1986, Economic forces and the stock market, The Journal of Business, 59, 383, 10.1086/296344 Chen, 2016, XGBoost: A scalable tree boosting system, 785 Chen, 2020, Bitcoin price prediction using machine learning: An approach to sample dimension engineering, Journal of Computational and Applied Mathematics, 365, 10.1016/j.cam.2019.112395 Corbet, 2022, Cryptocurrency liquidity and volatility interrelationships during the COVID-19 pandemic, Finance Research Letters, 45, 10.1016/j.frl.2021.102137 Corbet, 2018, Exploring the dynamic relationships between cryptocurrencies and other financial assets, Economics Letters, 165, 28, 10.1016/j.econlet.2018.01.004 Divakaruni, 2021, Uncovering Retail Trading in Bitcoin: The Impact of COVID-19 Stimulus Checks, 10.26509/frbc-wp-202113 Dowling, 2022, Fertile LAND: Pricing non-fungible tokens, Finance Research Letters, 44 Dowling, 2022, Is non-fungible token pricing driven by cryptocurrencies?, Finance Research Letters, 44 Gargano, 2014, Forecasting commodity price indexes using macroeconomic and financial predictors, International Journal of Forecasting, 30, 825, 10.1016/j.ijforecast.2013.09.003 Geurts, 2006, Extremely randomized trees, Machine Learning, 63, 3, 10.1007/s10994-006-6226-1 Ghosh, 2023, Return and volatility properties: Stylized facts from the universe of cryptocurrencies and NFTs, Research in International Business and Finance, 65, 10.1016/j.ribaf.2023.101945 Ghosh, 2023, Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI, International Review of Financial Analysis, 87, 10.1016/j.irfa.2023.102558 Gray, 2016 Greenwell Guzmán, 2021, Trading cryptocurrencies as a pandemic pastime: COVID-19 lockdowns and bitcoin volume, Mathematics, 9, 15, 10.3390/math9151771 Hamayel, 2021, A novel cryptocurrency Price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms, AI, 2, 4, 10.3390/ai2040030 Hastie, 2009 James, 2022 Jang, 2018, An empirical study on modeling and prediction of bitcoin prices with Bayesian neural networks based on Blockchain information, IEEE Access, 6, 5427, 10.1109/ACCESS.2017.2779181 Jaquart, 2021, Short-term bitcoin market prediction via machine learning, The Journal of Finance and Data Science, 7, 45, 10.1016/j.jfds.2021.03.001 Karim, 2022, Examining the interrelatedness of NFTs, DeFi tokens and cryptocurrencies, Finance Research Letters, 47, 10.1016/j.frl.2022.102696 Ko, 2022, The economic value of NFT: Evidence from a portfolio analysis using mean–variance framework, Finance Research Letters, 47, 10.1016/j.frl.2022.102784 Kuhn Lahmiri, 2019, Cryptocurrency forecasting with deep learning chaotic neural networks, Chaos, Solitons & Fractals, 118, 35, 10.1016/j.chaos.2018.11.014 Lee, 1992, Causal relations among stock returns, interest rates, real activity, and inflation, The Journal of Finance, 47, 1591, 10.1111/j.1540-6261.1992.tb04673.x Leung, 2000, Forecasting stock indices: A comparison of classification and level estimation models, International Journal of Forecasting, 16, 173, 10.1016/S0169-2070(99)00048-5 Lohrmann, 2019, Classification of intraday S&P500 returns with a random Forest, International Journal of Forecasting, 35, 390, 10.1016/j.ijforecast.2018.08.004 Lundberg, 2016 Lundberg, 2017, A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems, 30 Malkiel, 2003, The efficient market hypothesis and its critics, Journal of Economic Perspectives, 17, 59, 10.1257/089533003321164958 Mallqui, 2019, Predicting the direction, maximum, minimum and closing prices of daily bitcoin exchange rate using machine learning techniques, Applied Soft Computing, 75, 596, 10.1016/j.asoc.2018.11.038 Maouchi, 2022, Understanding digital bubbles amidst the COVID-19 pandemic: Evidence from DeFi and NFTs, Finance Research Letters, 47, 10.1016/j.frl.2021.102584 Meyer Mudassir, 2020, Time-series forecasting of bitcoin prices using high-dimensional features: A machine learning approach, Neural Computing and Applications, 10.1007/s00521-020-05129-6 Nakano, 2018, Bitcoin technical trading with artificial neural network, Physica A: Statistical Mechanics and its Applications, 510, 587, 10.1016/j.physa.2018.07.017 Neely, 2014, Forecasting the equity risk premium: The role of technical indicators, Management Science, 60, 1772, 10.1287/mnsc.2013.1838 Nyberg, 2011, Forecasting the direction of the US stock market with dynamic binary probit models, International Journal of Forecasting, 27, 561, 10.1016/j.ijforecast.2010.02.008 Nyberg, 2016, International sign predictability of stock returns: The role of the United States, Economic Modelling, 58, 323, 10.1016/j.econmod.2016.06.013 Pabuçcu, 2020, Forecasting the movements of bitcoin prices: An application of machine learning algorithms, Quantitative Finance and Economics, 4, 679, 10.3934/QFE.2020031 Pesaran, 2002, Market timing and return prediction under model instability, Journal of Empirical Finance, 9, 495, 10.1016/S0927-5398(02)00007-5 Peterson Pönkä, 2016, Real oil prices and the international sign predictability of stock returns, Finance Research Letters, 17, 79, 10.1016/j.frl.2016.01.011 Qiao, 2023, Time-frequency extreme risk spillover network of cryptocurrency coins, DeFi tokens and NFTs, Finance Research Letters, 51, 10.1016/j.frl.2022.103489 R Core Team, 2022 Rubbaniy, 2021, COVID-19, lockdowns and herding towards a cryptocurrency market-specific implied volatility index, Economics Letters, 207, 10.1016/j.econlet.2021.110017 Thorbecke, 1997, On stock market returns and monetary policy, The Journal of Finance, 52, 635, 10.1111/j.1540-6261.1997.tb04816.x Ulrich Umar, 2022, Covid-19 impact on NFTs and major asset classes interrelations: Insights from the wavelet coherence analysis, Finance Research Letters, 47, 10.1016/j.frl.2022.102725 Urom, 2022, Dynamic dependence and predictability between volume and return of non-fungible tokens (NFTs): The roles of market factors and geopolitical risks, Finance Research Letters, 50, 10.1016/j.frl.2022.103188 Vidal-Tomás, 2022, The new crypto niche: NFTs, play-to-earn, and metaverse tokens, Finance Research Letters, 47, 10.1016/j.frl.2022.102742 Wang, 2022, Volatility spillovers across NFTs news attention and financial markets, International Review of Financial Analysis, 83, 10.1016/j.irfa.2022.102313 Wang, 2020, Forecasting commodity prices out-of-sample: Can technical indicators help?, International Journal of Forecasting, 36, 666, 10.1016/j.ijforecast.2019.08.004 Wright Yin, 2016, Predicting the oil prices: Do technical indicators help?, Energy Economics, 56, 338, 10.1016/j.eneco.2016.03.017 Yin, 2017, Predictability of structural co-movement in commodity prices: The role of technical indicators, Quantitative Finance, 17, 795, 10.1080/14697688.2016.1225977 Yousaf, 2022, Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication, Global Finance Journal, 53, 10.1016/j.gfj.2022.100719 Yousaf, 2022, The relationship between trading volume, volatility and returns of non-fungible tokens: Evidence from a quantile approach, Finance Research Letters, 50, 10.1016/j.frl.2022.103175