- Abadi, J.; Brunnermeier, M. Blockchain Economics; Working paper; Princeton University: Princeton, NJ, USA, 2018.
Paper not yet in RePEc: Add citation now
Adcock, R.; Gradojevic, N. Non-Fundamental, Non-Parametric Bitcoin Forecasting. Phys. A Stat. Mech. Its Appl. 2019, 531,
Anatolyev, S.; Gerko, A. A Trading Approach to Testing for Predictability. J. Bus. Econ. Stat. 2005, 23, 455â461. [CrossRef]
Ashley, R.; Granger, C.W.J.; Schmalensee, R. Advertising and Aggregate Consumption: An Analysis of Causality. Econom. J. Econom. Soc. 1980, 48, 1149â1167. [CrossRef]
Athey, S.; Parashkevov, I.; Sarukkai, V.; Xia, J. Bitcoin Pricing, Adoption, and Usage: Theory and Evidence. 2016. Available online: https://ssrn.com/abstract=2826674 (accessed on 30 May 2022).
Atsalakis, G.S.; Atsalaki, I.G.; Pasiouras, F.; Zopounidis, C. Bitcoin Price Forecasting with Neuro-Fuzzy Techniques. Eur. J. Oper. Res. 2019, 276, 770â780. [CrossRef]
Bai, J.; Perron, P. Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica 1998, 66, 47â78. [CrossRef]
Bai, Y. Country Factors in Stock Returns: Reconsidering the Basic Method. Appl. Financ. Econ. 2014, 24, 871â888. [CrossRef]
Baker, M.; Wurgler, J. Investor Sentiment and the Cross-Section of Stock Returns. J. Financ. 2006, 61, 1645â1680. [CrossRef]
Balcilar, M.; Bouri, E.; Gupta, R.; Roubaud, D. Can Volume Predict Bitcoin Returns and Volatility? A Quantiles-Based Approach. Econ. Model. 2017, 64, 74â81. [CrossRef]
Barberis, N.; Shleifer, A.; Vishny, R. A Model of Investor Sentiment. J. Financ. Econ. 1998, 49, 307â343. [CrossRef]
Bikhchandani, S.; Hirshleifer, D.; Welch, I. A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. J. Political Econ. 1992, 100, 992â1026. [CrossRef]
Bolt, W.; van Oordt, M.R.C. On the Value of Virtual Currencies. J. Money Credit. Bank. 2020, 52, 835â862. [CrossRef]
- Bork, L.; Kaltwasser, P.R.; Sercu, P. Commodity Index Construction and the Predictive Power of Exchange Rates. J. Bank. Financ. 2019, 1â49.
Paper not yet in RePEc: Add citation now
Brown, P.P.; Hardy, N. Forecasting Base Metal Prices with the Chilean Exchange Rate. Resour. Policy 2019, 62, 256â281. [CrossRef]
Campbell, J.Y.; Shiller, R.J. The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors. Rev. Financ. Stud. 1988, 1, 195â228. [CrossRef]
Catania, L.; Grassi, S.; Ravazzolo, F. Forecasting Cryptocurrencies under Model and Parameter Instability. Int. J. Forecast. 2019, 35, 485â501. [CrossRef]
Chen, Y.-C.; Rogoff, K.S.; Rossi, B. Can Exchange Rates Forecast Commodity Prices? Q. J. Econ. 2010, 125, 1145â1194. [CrossRef]
Cheung, Y.-W.; Chinn, M.D.; Pascual, A.G. Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive? J. Int. Money Financ. 2005, 24, 1150â1175. [CrossRef]
Ciaian, P.; Rajcaniova, M. Virtual Relationships: Short-and Long-Run Evidence from BitCoin and Altcoin Markets. J. Int. Financ. Mark. Inst. Money 2018, 52, 173â195. [CrossRef]
Clark, T.E.; McCracken, M.W. Tests of Equal Forecast Accuracy and Encompassing for Nested Models. J. Econom. 2001, 105, 85â110. [CrossRef]
Clark, T.E.; West, K.D. Approximately Normal Tests for Equal Predictive Accuracy in Nested Models. J. Econom. 2007, 138, 291â311. [CrossRef]
Clark, T.E.; West, K.D. Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis. J. Econom. 2006, 135, 155â186. [CrossRef]
Cong, L.W.; He, Z.; Li, J. Decentralized Mining in Centralized Pools. Rev. Financ. Stud. 2021, 34, 1191â1235. [CrossRef]
Cong, L.W.; Li, Y.; Wang, N. Tokenomics: Dynamic Adoption and Valuation. Rev. Financ. Stud. 2021, 34, 1105â1155. [CrossRef]
Conlon, T.; Cotter, J.; Eyiah-Donkor, E. The Illusion of Oil Return Predictability: The Choice of Data Matters! J. Bank. Financ. 2022, 134, 106331. [CrossRef]
Corbet, S.; Eraslan, V.; Lucey, B.; Sensoy, A. The Effectiveness of Technical Trading Rules in Cryptocurrency Markets. Financ. Res. Lett. 2019, 31, 32â37. [CrossRef]
Daniel, K.; Hirshleifer, D.; Subrahmanyam, A. Investor Psychology and Security Market Under-and Overreactions. J. Financ. 1998, 53, 1839â1885. [CrossRef]
De Long, J.B.; Shleifer, A.; Summers, L.H.; Waldmann, R.J. Noise Trader Risk in Financial Markets. J. Political Econ. 1990, 98, 703â738. [CrossRef]
Della Corte, P.; Sarno, L.; Tsiakas, I. An Economic Evaluation of Empirical Exchange Rate Models. Rev. Financ. Stud. 2009, 22, 3491â3530. [CrossRef]
Detzel, A.; Liu, H.; Strauss, J.; Zhou, G.; Zhu, Y. Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals. Financ. Manag. 2021, 50, 107â137. [CrossRef]
Diebold, F.X.; Mariano, R.S. Comparing Predictive Accuracy. J. Bus. Econ. Stat. 1995, 20, 134â144. [CrossRef]
Engel, C.; Mark, N.C.; West, K.D.; Rogoff, K.; Rossi, B. Exchange Rate Models Are Not as Bad as You Think [with Comments and Discussion]. NBER Macroecon Annu 2007, 22, 381â473. [CrossRef]
- Felizardo, L.K.; Paiva, F.C.L.; de Vita Graves, C.; Matsumoto, E.Y.; Costa, A.H.R.; Del-Moral-Hernandez, E.; Brandimarte, P. Outperforming Algorithmic Trading Reinforcement Learning Systems: A Supervised Approach to the Cryptocurrency Market. Expert Syst. Appl. 2022, 202, 117259. [CrossRef]
Paper not yet in RePEc: Add citation now
Giudici, P.; Polinesi, G. Crypto Price Discovery through Correlation Networks. Ann. Oper. Res. 2021, 299, 443â457. [CrossRef]
Goyal, A.; Welch, I. Predicting the Equity Premium with Dividend Ratios. Manag. Sci. 2003, 49, 639â654. [CrossRef]
Goyal, A.; Welch, I.; Zafirov, A. A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II. 2021. Available online: https://ssrn.com/abstract=3929119 (accessed on 30 May 2022).
- Hamilton, J.D. Time Series Analysis; Princeton university press: Princeton, NJ, USA, 2020; ISBN 0691218633.
Paper not yet in RePEc: Add citation now
- Hardy, N. âA Bias Recognized Is a Bias Sterilizedâ: The Effects of a Bias in Forecast Evaluation. Mathematics 2022, 10,
Paper not yet in RePEc: Add citation now
- Harvey, D.I.; Leybourne, S.J.; Newbold, P. Forecast Evaluation Tests in the Presence of ARCH. J. Forecast. 1999, 18, 435â445. [CrossRef]
Paper not yet in RePEc: Add citation now
Hong, H.; Lim, T.; Stein, J.C. Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies. J. Financ. 2000, 55, 265â295. [CrossRef]
Hong, H.; Stein, J.C. A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets. J. Financ. 1999, 54, 2143â2184. [CrossRef]
Ince, O.; Molodtsova, T. Rationality and Forecasting Accuracy of Exchange Rate Expectations: Evidence from Survey-Based Forecasts. J. Int. Financ. Mark. Inst. Money 2017, 47, 131â151. [CrossRef]
- Jang, H.; Lee, J. An Empirical Study on Modeling and Prediction of Bitcoin Prices with Bayesian Neural Networks Based on Blockchain Information. IEEE Access 2017, 6, 5427â5437. [CrossRef]
Paper not yet in RePEc: Add citation now
- Jermann, U.J. Bitcoin and Caganâs Model of Hyperinflation. 2018. Available online: https://ssrn.com/abstract=3132050 (accessed on 30 May 2022).
Paper not yet in RePEc: Add citation now
Ji, S.; Kim, J.; Im, H. A Comparative Study of Bitcoin Price Prediction Using Deep Learning. Mathematics 2019, 7, 898. [CrossRef] Mathematics 2022, 10, 2338 26 of 27
Liu, J.; Serletis, A. Volatility in the Cryptocurrency Market. Open Econ. Rev. 2019, 30, 779â811. [CrossRef]
Liu, Y.; Tsyvinski, A. Risks and Returns of Cryptocurrency. Rev. Financ. Stud. 2021, 34, 2689â2727. [CrossRef]
Liu, Y.; Tsyvinski, A.; Wu, X. Common Risk Factors in Cryptocurrency. J. Financ. 2022, 77, 1133â1177. [CrossRef]
- Lyons, R.K. Exchange-Rate Dynamics. Princeton Series in International Economics. J. Econ. Literature 2012, 50, 187â191.
Paper not yet in RePEc: Add citation now
Maciel, L. Cryptocurrencies Value-at-Risk and Expected Shortfall: Do Regime-Switching Volatility Models Improve Forecasting? Int. J. Financ. Econ. 2021, 26, 4840â4855. [CrossRef]
Magner, N.S.; Lavin, J.F.; Valle, M.A.; Hardy, N. The Volatility Forecasting Power of Financial Network Analysis. Complexity 2020, 2020, 7051402. [CrossRef]
Makarov, I.; Schoar, A. Trading and Arbitrage in Cryptocurrency Markets. J. Financ. Econ. 2020, 135, 293â319. [CrossRef]
- McNally, S.; Roche, J.; Caton, S. Predicting the Price of Bitcoin Using Machine Learning. In Proceedings of the 2018 26th Euromicro International Conference on Parallel, Distributed And Network-Based Processing (PDP), Cambridge, UK, 21 Marchâ23 March 2018; pp. 339â343.
Paper not yet in RePEc: Add citation now
Meese, R.; Rogoff, K. The Out-of-Sample Failure of Empirical Exchange Rate Models: Sampling Error or Misspecification. Exch. Rates Int. Macroecon. 1983, 4, 67â112. [CrossRef]
Meese, R.; Rogofp, K. Was It Real? The Exchange Rate-interest Differential Relation over the Modern Floating-rate Period. J. Financ. 1988, 43, 933â948. [CrossRef]
Meese, R.A.; Rogoff, K. Empirical Exchange Rate Models of the Seventies: Do They Fit out of Sample? J. Int. Econ. 1983, 14, 3â24. [CrossRef]
Melvin, M.; Prins, J.; Shand, D. Forecasting Exchange Rates: An Investor Perspective; Elsevier: Amsterdam, The Netherlands, 2013; Volume 2, pp. 721â750, ISBN 1574-0706.
Miller, N.; Yang, Y.; Sun, B.; Zhang, G. Identification of Technical Analysis Patterns with Smoothing Splines for Bitcoin Prices. J. Appl. Stat. 2019, 46, 2289â2297. [CrossRef]
Moosa, I.; Burns, K. The Unbeatable Random Walk in Exchange Rate Forecasting: Reality or Myth? J. Macroecon. 2014, 40, 69â81. [CrossRef]
Moosa, I.A.; Burns, K. The Meese-Rogoff Puzzle. In Demystifying the Meese-Rogoff Puzzle; Springer: Berlin/Heidelberg, Germany, 2015; pp. 1â13.
Muglia, C.; Santabarbara, L.; Grassi, S. Is Bitcoin a Relevant Predictor of Standard & Poorâs 500? J. Risk Financ. Manag. 2019, 12, 93. [CrossRef]
Newey, W.K.; West, K.D. Automatic Lag Selection in Covariance Matrix Estimation. Rev. Econ. Stud. 1994, 61, 631â653. [CrossRef]
Newey, W.K.; West, K.D. Hypothesis Testing with Efficient Method of Moments Estimation. Int. Econ. Rev. 1987, 28, 777â787. [CrossRef]
- Nguyen, D.-T.; Le, H.-V. Predicting the Price of Bitcoin Using Hybrid ARIMA and Machine Learning. In Future Data and Security Engineering; Springer: Berlin/Heidelberg, Germany, 2019; pp. 696â704.
Paper not yet in RePEc: Add citation now
- Pagnotta, E.; Buraschi, A. An Equilibrium Valuation of Bitcoin and Decentralized Network Assets. 2018. Available online: https://ssrn.com/abstract=3142022 (accessed on 30 May 2022).
Paper not yet in RePEc: Add citation now
Pincheira, P.; Hardy, N. Correlation Based Tests of Predictability. 2022. Available online: https://mpra.ub.uni-muenchen.de/1120 14/ (accessed on 30 May 2022).
Pincheira, P.; Hardy, N. Forecasting Aluminum Prices with Commodity Currencies. Resour. Policy 2021, 73, 102066. [CrossRef]
Pincheira, P.; Hardy, N. The Mean Squared Prediction Error Paradox. 2021. Available online: https://mpra.ub.uni-muenchen.de/ 107403/ (accessed on 30 May 2022).
Pincheira, P.; Hardy, N.; Bentancor, A. A Simple Out-of-Sample Test of Predictability against the Random Walk Benchmark. Mathematics 2022, 10, 228. [CrossRef]
Pincheira, P.; Hardy, N.; Muñoz, F. âGo Wild for a While!â: A New Test for Forecast Evaluation in Nested Models. Mathematics 2021, 9, 2254. [CrossRef]
Pincheira, P.M.; Hardy, N. The Predictive Relationship between Exchange Rate Expectations and Base Metal Prices. 2018. Available online: https://ssrn.com/abstract=3263709 (accessed on 30 May 2022).
Pincheira, P.M.; West, K.D. A Comparison of Some Out-of-Sample Tests of Predictability in Iterated Multi-Step-Ahead Forecasts. Res. Econ. 2016, 70, 304â319. [CrossRef]
- Qi, M.; Zhang, J.; Xiao, J.; Wang, P.; Shi, D.; Nnenna, A.B. Interconnectedness and Systemic Risk Measures of Chinese Financial Institutions. Kybernetes 2021, 51, 57â81. [CrossRef]
Paper not yet in RePEc: Add citation now
- Rapach, D.; Zhou, G. Asset Pricing: Time-Series Predictability. 2021. Available online: https://papers.ssrn.com/sol3/papers.cfm? abstract_id=3941499 (accessed on 30 May 2022).
Paper not yet in RePEc: Add citation now
- Rebane, J.; Karlsson, I.; Papapetrou, P.; Denic, S. Seq2Seq RNNs and ARIMA Models for Cryptocurrency Prediction: A Comparative Study. In Proceedings of the SIGKDD Fintechâ18, London, UK, 19â23 August 2018.
Paper not yet in RePEc: Add citation now
Rossi, B. Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability. Macroecon. Dyn. 2005, 10, 20â38. [CrossRef]
Rossi, B. Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them. J. Econ. Lit. 2021, 59, 1135â1190. [CrossRef]
Rossi, B. Optimal Tests for Nested Model Selection with Underlying Parameter Instability. Econ. Theory 2005, 21, 962â990. [CrossRef]
Rossi, B.; Inoue, A. Out-of-Sample Forecast Tests Robust to the Choice of Window Size. J. Bus. Econ. Stat. 2012, 30, 432â453. [CrossRef]
Schilling, L.; Uhlig, H. Some Simple Bitcoin Economics. J. Monet. Econ. 2019, 106, 16â26. [CrossRef]
- Smutny, Z.; Sulc, Z.; Lansky, J. Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies. Mathematics 2021, 9,
Paper not yet in RePEc: Add citation now
Sockin, M.; Xiong, W. Nber Working Paper Series A Model of Cryptocurrencies; National Bureau of Economic Research: Cambridge, MA, USA, 2020.
Stock, J.H.; Watson, M.W. Evidence on Structural Instability in Macroeconomic Time Series Relations. J. Bus. Econ. Stat. 1996, 14, 11â30.
Stock, J.H.; Watson, M.W. Forecasting Output and Inflation: The Role of Asset Prices. J. Econ. Lit. 2003, 41, 788â829. [CrossRef]
Stock, J.H.; Watson, M.W. Why Has US Inflation Become Harder to Forecast? J. Money Credit. Bank. 2007, 39, 3â33. [CrossRef]
Timmermann, A. Elusive Return Predictability. Int. J. Forecast. 2008, 24, 1â18. [CrossRef]
Welch, I.; Goyal, A. A Comprehensive Look at the Empirical Performance of Equity Premium Prediction. Rev. Financ. Stud. 2008, 21, 1455â1508. [CrossRef]
West, K.D. Asymptotic Inference about Predictive Ability. Econom. J. Econom. Soc. 1996, 64, 1067â1084. [CrossRef] Mathematics 2022, 10, 2338 27 of 27
White, H. A Reality Check for Data Snooping. Econometrica 2000, 68, 1097â1126. [CrossRef]
Yae, J.; Tian, G.Z. Out-of-Sample Forecasting of Cryptocurrency Returns: A Comprehensive Comparison of Predictors and Algorithms. Phys. A Stat. Mech. Its Appl. 2022, 598, 127379. [CrossRef]
Yi, Y.; He, M.; Zhang, Y. Out-of-Sample Prediction of Bitcoin Realized Volatility: Do Other Cryptocurrencies Help? N. Am. J. Econ. Financ. 2022, 62, 101731. [CrossRef]