Dijital Varlıklar ve Yapay Zekâ Çağında Yatırım Analizi Yeni Nesil Portföy Yönetimi Stratejileri

Özet

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Referanslar

Arrow, K. J. (1965). Aspects of the Theory of Risk-Bearing. Yrjö Jahnssonin Säätiö.

Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18.

Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261-292.

Bernoulli, D. (1738/1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23-36.

Black, F., Jensen, M. C., & Scholes, M. (1972). The capital asset pricing model: Some empirical tests.

Studies in the Theory of Capital Markets, 79-121.

Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.

Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57- 82.

Cochrane, J. H. (2011). Presidential address: Discount rates. Journal of Finance, 66(4), 1047-1108. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40(3),

793-805.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427-465.

Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.

Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Econo- mics, 116(1), 1-22.

Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Poli- tical Economy, 81(3), 607-636.

Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.

Frazzini, A., & Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics, 111(1), 1-25.

Graham, B., & Dodd, D. (1934). Security Analysis. McGraw-Hill.

Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. American Economic Review, 70(3), 393-408.

Harvey, C. R., Liu, Y., & Zhu, H. (2016). ...and the cross-section of expected returns. Review of Fi- nancial Studies, 29(1), 5-68.

Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econo- metrica, 47(2), 263-291.

Knight, F. H. (1921). Risk, Uncertainty and Profit. Houghton Mifflin.

Lo, A. W. (2004). The adaptive markets hypothesis. Journal of Portfolio Management, 30(5), 15-29. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advan-

ces in Neural Information Processing Systems, 30.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’ optimal? Financial Analysts Journal, 45(1), 31-42.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predicti- ons of any classifier. Proceedings of the 22nd ACM SIGKDD, 1135-1144.

Roll, R. (1977). A critique of the asset pricing theory’s tests. Journal of Financial Economics, 4(2), 129-176.

Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360.

Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long.

Journal of Finance, 40(3), 777-790.

Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk.

Journal of Finance, 19(3), 425-442.

Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in divi- dends? American Economic Review, 71(3), 421-436.

Shleifer, A., & Vishny, R. W. (1997). The limits of arbitrage. Journal of Finance, 52(1), 35-55. Thaler, R. H. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199-214.

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323.

von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.

Adams, H., Zinsmeister, N., & Robinson, D. (2020). Uniswap v2 Core. Uniswap Whitepaper.

Alles, M., Kogan, A., & Vasarhelyi, M. (2002). Feasibility and economics of continuous assurance.

Auditing: A Journal of Practice & Theory, 21(1), 125-138.

Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2), 223-249.

Angeris, G., & Chitra, T. (2020). Improved price oracles: Constant function market makers. Procee- dings of the 2nd ACM Conference on Advances in Financial Technologies, 80-91.

Baumol, W. J. (1986). Unnatural value: Or art investment as floating crap game. American Economic Review, 76(2), 10-14.

Baur, D. G., & Dimpfl, T. (2021). The volatility of Bitcoin and its role as a medium of exchange and a store of value. Empirical Economics, 61, 2663-2683.

Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Jour- nal of International Financial Markets, Institutions and Money, 54, 177-189.

BIS. (2020). Central Bank Digital Currencies: Foundational Principles and Core Features. Bank for International Settlements.

Bordo, M. D., & Levin, A. T. (2017). Central bank digital currency and the future of monetary policy.

NBER Working Paper No. 23711.

Brunnermeier, M. K., & Niepelt, D. (2019). On the equivalence of private and public money. Journal of Monetary Economics, 106, 27-41.

Buterin, V. (2014). Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform. Ethereum Whitepaper.

Caldarelli, G., & Ellul, J. (2021). The blockchain oracle problem in decentralized finance—A multi- vocal approach. Applied Sciences, 11(16), 7572.

Chorzempa, M. (2021). China, the United States, and central bank digital currencies. Peterson Ins- titute for International Economics.

Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2019). GARCH modelling of cryptocurrencies.

Journal of Risk and Financial Management, 10(4), 17.

Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386-405.

Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. Review of Financial Stu- dies, 32(5), 1754-1797.

De Vries, A. (2018). Bitcoin’s growing energy problem. Joule, 2(5), 801-805.

Diamond, D. W., & Dybvig, P. H. (1983). Bank runs, deposit insurance, and liquidity. Journal of Political Economy, 91(3), 401-419.

Dwork, C., & Naor, M. (1993). Pricing via processing or combatting junk mail. Advances in Cryp- tology — CRYPTO’92, 139-147.

ECB. (2023). Progress on the Investigation Phase of a Digital Euro. European Central Bank.

Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). Cryptocur- rency trading: A comprehensive survey. Financial Innovation, 8(1), 1-59.

Fisch, C., & Momtaz, P. P. (2020). Tokenized real estate: Evidence from the secondary market. Wor- king Paper.

FSB. (2023). Global Regulatory Framework for Crypto-Asset Activities. Financial Stability Board. Gudgeon, L., Perez, D., Harz, D., Livshits, B., & Gervais, A. (2020). The decentralized financial crisis.

IEEE Crypto Valley Conference on Blockchain Technology, 1-15.

Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360.

Kajtazi, A., & Moro, A. (2019). The role of bitcoin in well diversified portfolios: A comparative global study. International Review of Financial Analysis, 61, 143-157.

Kareken, J., & Wallace, N. (1978). Deposit insurance and bank regulation: A partial-equilibrium exposition. Journal of Business, 51(3), 413-438.

Kareken, J., & Wallace, N. (1981). On the indeterminacy of equilibrium exchange rates. Quarterly Journal of Economics, 96(2), 207-222.

Kim, T. (2023). Liquid staking derivatives and de-pegging risk. Working Paper.

Kumhof, M., & Noone, C. (2018). Central bank digital currencies — Design principles and balance sheet implications. Bank of England Staff Working Paper No. 725.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-1335.

Lamport, L., Shostak, R., & Pease, M. (1982). The Byzantine generals problem. ACM Transactions on Programming Languages and Systems, 4(3), 382-401.

Liu, W., Makarov, I., & Schoar, A. (2023). Anatomy of a run: The Terra Luna crash. NBER Working Paper No. 31160.

Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. Review of Financial Studies, 34(6), 2689-2727.

Makarov, I., & Schoar, A. (2020). Trading and arbitrage in cryptocurrency markets. Journal of Fi- nancial Economics, 135(2), 293-319.

Merkle, R. C. (1988). A digital signature based on a conventional encryption function. Advances in Cryptology — CRYPTO’87, 369-378.

Nadini, M., Alessandretti, L., Di Giacinto, F., Martino, M., Aiello, L. M., & Baronchelli, A. (2021). Mapping the NFT revolution: Market trends, trade networks, and visual features. Scientific Reports, 11(1), 20902.

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.

Obstfeld, M. (1996). Models of currency crises with self-fulfilling features. European Economic Re- view, 40(3-5), 1037-1047.

Qin, K., Zhou, L., Livshits, B., & Gervais, A. (2021). Attacking the DeFi ecosystem with flash loans for fun and profit. Financial Cryptography and Data Security, 3-32.

Saleh, F. (2021). Blockchain without waste: Proof-of-stake. Review of Financial Studies, 34(3), 1156- 1190.

Szabo, N. (1997). Formalizing and securing relationships on public networks. First Monday, 2(9). Veblen, T. (1899). The Theory of the Leisure Class. Macmillan.

Weber, R. H. (2021). Regulatory approaches to token classification. Journal of International Banking Law and Regulation, 36(2), 45-58.

Williamson, O. E. (1979). Transaction-cost economics: The governance of contractual relations.

Journal of Law and Economics, 22(2), 233-261.

Yermack, D. (2017). Corporate governance and blockchains. Review of Finance, 21(1), 7-31.

Zetzsche, D. A., Buckley, R. P., & Arner, D. W. (2018). The ICO gold rush: It’s a scam, it’s a bubble, it’s a super challenge for regulators. Harvard International Law Journal, 60, 267-315.

Alabi, K. (2017). Digital blockchain networks appear to be following Metcalfe’s Law. Electronic Commerce Research and Applications, 24, 23-29.

Allen, F., & Gale, D. (1992). Stock-price manipulation. Review of Financial Studies, 5(3), 503-529. Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1-33.

Amihud, Y. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2), 223-249.

Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203-228.

Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785-818. Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Let-

ters, 161, 1-4.

Baur, D. G., & Oll, J. (2022). Bitcoin investments and climate change: A financial and carbon inten- sity perspective. Finance Research Letters, 47, 102575.

Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Jour- nal of International Financial Markets, Institutions and Money, 54, 177-189.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econo- metrics, 31(3), 307-327.

Bouri, E., Gupta, R., Lahiani, A., & Shahzad, S. J. H. (2018). Testing for asymmetric nonlinear short- and long-run relationships between Bitcoin, aggregate commodity and gold prices. Resources Policy, 57, 224-235.

Brauneis, A., & Mestel, R. (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Econo- mics Letters, 165, 58-61.

Chainalysis. (2022). The 2022 DeFi Adoption Index. Chainalysis Research.

Chan, S., Chu, J., Nadarajah, S., & Osterrieder, J. (2017). A statistical analysis of cryptocurrencies.

Journal of Risk and Financial Management, 10(2), 12.

Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity mar- kets: An international perspective. Journal of Banking & Finance, 24(10), 1651-1679.

Chen, W., Li, Y., Sun, Y., & Yang, X. (2020). Deep learning for cryptocurrency price prediction: A comparative study. Expert Systems with Applications, 158, 113610.

Chen, W., Xu, Y., Wu, X., & Zheng, Z. (2019). Detecting pump&dump stock market manipulation from Twitter. IEEE International Conference on Big Data, 5891-5900.

Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies.

Journal of Risk and Financial Management, 10(4), 17.

Cong, L. W., Li, X., Tang, K., & Yang, Y. (2023). Crypto wash trading. Management Science, 69(11), 6427-6454.

Cong, L. W., Li, Y., & Wang, N. (2021). Tokenomics: Dynamic adoption and valuation. Review of Financial Studies, 34(3), 1105-1155.

Corbet, S., Larkin, C., & Lucey, B. (2020). The contagion effects of the COVID-19 pandemic: Evi- dence from gold and cryptocurrencies. Finance Research Letters, 35, 101554.

Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relati- onships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34.

Da Gama Silva, P. V. J., Klotzle, M. C., Pinto, A. C. F., & Gomes, L. L. (2019). Herding behavior and contagion in the cryptocurrency market. Journal of Behavioral and Experimental Finance, 22, 41-50.

Damodaran, A. (2018). Investing in cryptocurrencies: Pricing versus valuation. Working Paper, NYU Stern School of Business.

De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738.

Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized au- toregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.

Gkillas, K., & Katsiampa, P. (2018). An application of extreme value theory to cryptocurrencies.

Economics Letters, 164, 109-111.

Glosten, L. R., & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics, 14(1), 71-100.

Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Economet- rics, 2(2), 111-120.

Gudgeon, L., Perez, D., Harz, D., Livshits, B., & Gervais, A. (2020). The decentralized financial crisis.

IEEE Crypto Valley Conference on Blockchain Technology, 1-15.

Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.

Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial inde- pendence of regression residuals. Economics Letters, 6(3), 255-259.

Ji, Q., Bouri, E., Gupta, R., & Roubaud, D. (2020). Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach. Quarterly Review of Economics and Finance, 70, 203-213.

Kalichkin, D. (2018). NVT Signal: Practical uses and limitations. Working Paper.

Kim, T., & Kim, S. (2021). On-chain valuation metrics for digital assets. Working Paper.

Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3, 3415.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-1335. Li, T., Shin, D., & Wang, B. (2021). Cryptocurrency pump-and-dump schemes. Working Paper.

Liu, W. (2022). Cryptocurrency allocation strategies for institutional portfolios. Journal of Portfolio Management, 48(2), 112-128.

Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. Review of Financial Studies, 34(6), 2689-2727.

Lo, A. W. (2004). The adaptive markets hypothesis. Journal of Portfolio Management, 30(5), 15-29. Makarov, I., & Schoar, A. (2020). Trading and arbitrage in cryptocurrency markets. Journal of Fi-

nancial Economics, 135(2), 293-319.

Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36(4), 394- 419.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

Perez, D., Werner, S. M., Xu, J., & Livshits, B. (2021). Liquidations: DeFi on a knife-edge. Financial Cryptography and Data Security, 457-476.

Peterson, T. (2018). Metcalfe’s Law as a model for Bitcoin’s value. Alternative Investment Analyst Review, 7(2), 9-18.

PlanB. (2019). Modeling Bitcoin’s value with scarcity. Medium Working Paper.

Platanakis, E., & Urquhart, A. (2020). Should investors include Bitcoin in their portfolios? A portfo- lio theory approach. British Accounting Review, 52(4), 100837.

Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in divi- dends? American Economic Review, 71(3), 421-436.

Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Université de Paris, 8, 229-231.

Sortino, F. A., & Price, L. N. (1994). Performance measurement in a downside risk framework.

Journal of Investing, 3(3), 59-64.

Timmermann, A., & Granger, C. W. (2004). Efficient market hypothesis and forecasting. Internatio- nal Journal of Forecasting, 20(1), 15-27.

Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking, 1(1), 15-29.

Woo, W. (2017). NVT ratio: A promising indicator for Bitcoin. Working Paper.

Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, 278-288.

Bailey, D. H., & López de Prado, M. (2014). The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting, and non-normality. Journal of Portfolio Management, 40(5), 94-107.

Bailey, D. H., Borwein, J. M., López de Prado, M., & Zhu, Q. J. (2014). Pseudo-mathematics and fi- nancial charlatanism: The effects of backtest overfitting on out-of-sample performance. Notices of the American Mathematical Society, 61(5), 458-471.

Box, G. E., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: An app-

lication to default risk analysis. Bank of England Staff Working Paper No. 816.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199-231.

Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203-216.

CFA Institute. (2019). Investment Professional of the Future. CFA Institute Research Foundation. Chen, L., Kelly, B., & Xiu, D. (2023). Expected returns and large language models. Working Paper. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd

ACM SIGKDD, 785-794.

Cochrane, J. H. (2011). Presidential address: Discount rates. Journal of Finance, 66(4), 1047-1108.

Connor, G., & Korajczyk, R. A. (1988). Risk and return in an equilibrium APT: Application of a new test methodology. Journal of Financial Economics, 21(2), 255-289.

Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Eco- nomic Statistics, 13(3), 253-263.

Elton, E. J., Gruber, M. J., & Blake, C. R. (1996). Survivorship bias and mutual fund performance.

Review of Financial Studies, 9(4), 1097-1120.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

Federal Reserve & OCC. (2011). Supervisory Guidance on Model Risk Management (SR 11-7).

Board of Governors of the Federal Reserve System.

Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273.

Harvey, C. R., Liu, Y., & Zhu, H. (2016). ...and the cross-section of expected returns. Review of Fi- nancial Studies, 29(1), 5-68.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

Jiang, Z., Xu, D., & Liang, J. (2017). A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059.

Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25-37.

Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocompu- ting, 55(1-2), 307-319.

Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702.

Lim, B., Zohren, S., & Roberts, S. (2021). Temporal fusion transformers for interpretable multi-hori- zon time series forecasting. International Journal of Forecasting, 37(4), 1748-1764.

Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, 413-422.

Lo, A. W. (2004). The adaptive markets hypothesis. Journal of Portfolio Management, 30(5), 15-29.

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.

Lussange, J., Lazarevich, I., Bourgeois-Gironde, S., Palminteri, S., & Gutkin, B. (2021). Modelling stock markets by multi-agent reinforcement learning. Computational Economics, 57(1), 113- 147.

López de Prado, M. (2016). Building diversified portfolios that outperform out-of-sample. Journal of Portfolio Management, 42(4), 59-69.

López de Prado, M. (2018). Advances in Financial Machine Learning. John Wiley & Sons. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 Competition: Results, findings,

conclusion and way forward. International Journal of Forecasting, 34(4), 802-808.

McLean, R. D., & Pontiff, J. (2016). Does academic research destroy stock return predictability?

Journal of Finance, 71(1), 5-32.

Merton, R. C. (1971). Optimum consumption and portfolio rules in a continuous-time model. Jour- nal of Economic Theory, 3(4), 373-413.

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529- 533.

Moody, J., & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, 12(4), 875-889.

Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predicti- ons of any classifier. Proceedings of the 22nd ACM SIGKDD, 1135-1144.

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.

Sezer, O. B., & Ozbayoglu, A. M. (2018). Algorithmic financial trading with deep convolutional neu- ral networks: Time series to image conversion approach. Applied Soft Computing, 70, 525-538.

Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review 2005-2019. Applied Soft Computing, 90, 106181.

Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games, 2(28), 307-317.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Resear- ch, 15(1), 1929-1958.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statisti-

cal Society: Series B, 58(1), 267-288.

Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.

Yoon, J., Jarrett, D., & van der Schaar, M. (2019). Time-series generative adversarial networks. Ad- vances in Neural Information Processing Systems, 32.

Zhang, Z., Zohren, S., & Roberts, S. (2019). DeepLOB: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing, 67(11), 3001-3012.

Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism.

Quarterly Journal of Economics, 84(3), 488-500.

Almgren, R., & Chriss, N. (2001). Optimal execution of portfolio transactions. Journal of Risk, 3, 5-40.

Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance, 59(3), 1259-1294.

Aquilina, M., Budish, E., & O’Neill, P. (2022). Quantifying the high-frequency trading “arms race”.

Quarterly Journal of Economics, 137(1), 493-564.

Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Compu- tational Science, 2(1), 1-8.

Budish, E., Cramton, P., & Shim, J. (2015). The high-frequency trading arms race: Frequent batch auctions as a market design response. Quarterly Journal of Economics, 130(4), 1547-1621.

CCPA. (2018). California Consumer Privacy Act. State of California. Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386-405.

De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: Representation, estima- tion, and testing. Econometrica, 55(2), 251-276.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

Farboodi, M., Matray, A., Veldkamp, L., & Venkateswaran, V. (2022). Where has all the data gone?

Review of Financial Studies, 35(7), 3101-3138.

Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs trading: Performance of a relativevalue arbitrage rule. Review of Financial Studies, 19(3), 797-827.

GDPR. (2016). General Data Protection Regulation. European Union.

Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets.

American Economic Review, 70(3), 393-408.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273.

Hansen, S., McMahon, M., & Prat, A. (2018). Transparency and deliberation within the FOMC: A computational linguistics approach. Quarterly Journal of Economics, 133(2), 801-870.

Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity?

Journal of Finance, 66(1), 1-33.

Jensen, M. C. (1978). Some anomalous evidence regarding market efficiency. Journal of Financial Economics, 6(2-3), 95-101.

Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High-frequency trading in an electronic market. Journal of Finance, 72(3), 967-998.

Kolanovic, M., & Krishnamachari, R. T. (2017). Big data and AI strategies: Machine learning and alternative data approach to investing. JPMorgan Global Quantitative & Derivatives Strategy Report.

Krauss, C., & Stübinger, J. (2017). Non-linear dependence modelling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100. Applied Economics, 49(52), 5352-5369.

Kristoufek, L. (2013). BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3, 3415.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-1335. Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety. META

Group Research Note, 6(70), 1.

Lewis, M. (2014). Flash Boys: A Wall Street Revolt. W. W. Norton & Company.

Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35-65.

Mao, H., Counts, S., & Bollen, J. (2015). Quantifying the effects of online bullishness on internatio- nal financial markets. ECB Statistics Paper Series No. 9.

Mayew, W. J., & Venkatachalam, M. (2012). The power of voice: Managerial affective states and fu- ture firm performance. Journal of Finance, 67(1), 1-43.

McLean, R. D., & Pontiff, J. (2016). Does academic research destroy stock return predictability?

Journal of Finance, 71(1), 5-32.

Menkveld, A. J. (2016). The economics of high-frequency trading: Taking stock. Annual Review of Financial Economics, 8, 1-24.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Nevmyvaka, Y., Feng, Y., & Kearns, M. (2006). Reinforcement learning for optimized trade execution. Proceedings of the 23rd International Conference on Machine Learning, 673-680.

Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1532-1543.

Philippon, T. (2019). On fintech and financial inclusion. BIS Working Paper No. 841.

Price, S. M., Doran, J. S., Peterson, D. R., & Bliss, B. A. (2012). Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking & Finance, 36(4), 992-1011.

Renault, T. (2017). Intraday online investor sentiment and return patterns in the U.S. stock market.

Journal of Banking & Finance, 84, 25-40.

Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(3), 341-360.

Scopino, G. (2015). Do automated trading systems dream of manipulating the price of futures cont- racts? Policing markets for improper trading practices by algorithmic robots. Florida Law Re- view, 67(1), 221-293.

SEC-CFTC. (2010). Findings Regarding the Market Events of May 6, 2010. U.S. Securities and Exc- hange Commission and Commodity Futures Trading Commission.

SEC. (2014). Equity Market Structure Literature Review Part II: High Frequency Trading. U.S. Secu- rities and Exchange Commission.

Shen, D., Urquhart, A., & Wang, P. (2019). Does twitter predict Bitcoin? Economics Letters, 174, 118-122.

Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market.

Journal of Finance, 62(3), 1139-1168.

Timmermann, A., & Granger, C. W. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20(1), 15-27.

Yang, Y., UY, M. C. S., & Huang, A. (2020). FinBERT: A pretrained language model for financial communications. arXiv preprint arXiv:2006.08097.

Zhu, H. (2014). Do dark pools harm price discovery? Review of Financial Studies, 27(3), 747-789. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.

Backend Benchmarking. (2023). The Robo Report: Annual Performance Review. Condor Capital Wealth Management.

Baker, T., & Dellaert, B. (2018). Regulating robo advice across the financial services industry. Iowa Law Review, 103, 713-750.

Bardoscia, M., De Vito, A., & Ferrari, L. (2024). Gender bias in algorithmic financial advice. Wor- king Paper.

Capponi, A., Olafsson, S., & Zariphopoulou, T. (2022). Personalized robo-advising: Enhancing in- vestment through client interaction. Management Science, 68(4), 2485-2512.

Chaudhuri, S., Ivkovic, Z., Moulton, J., & Trzcinka, C. (2020). Dynamic rebalancing with reinforce- ment learning: Evidence from robo-advisory portfolios. Working Paper.

Chincarini, L. B., & Kim, D. (2021). Quantitative Equity Portfolio Management (2nd ed.). McG- raw-Hill.

Constantinides, G. M. (1983). Capital market equilibrium with personal tax. Econometrica, 51(3), 611-636.

Cordell, D. M. (2001). RiskPACK: How to evaluate risk tolerance. Journal of Financial Planning, 14(6), 36-40.

D’Acunto, F., & Rossi, A. G. (2021). Robo-advising. In Palgrave Handbook of Technological Finance, 725-749.

D’Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. Review of Financial Studies, 32(5), 1983-2020.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Deloitte. (2022). The Future of Wealth Management: Robo-Advisory and Beyond. Deloitte Insights Report.

Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2022). The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. World Bank.

Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114- 126.

EU AI Act. (2024). Regulation on Artificial Intelligence. European Union.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

Fein, M. L. (2015). Robo-advisors: A closer look. Working Paper.

Fisch, J. E., Laboure, M., & Turner, J. A. (2019). The emergence of the robo-advisor. In The Disrup- tive Impact of FinTech on Retirement Systems, 13-37.

Glaeser, E. L., Iselin, J., Kim, C., & Rauter, T. (2023). Automation bias in digital investment advice.

NBER Working Paper.

Grable, J. E., & Lytton, R. H. (1999). Financial risk tolerance revisited: The development of a risk assessment instrument. Financial Services Review, 8(3), 163-181.

Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360.

Khang, K., Farfan, C., & Rekhi, N. (2019). Advanced tax-loss harvesting. Wealthfront Research White Paper.

Linnainmaa, J. T., Melzer, B. T., & Previtero, A. (2021). The misguided beliefs of financial advisors.

Journal of Finance, 76(2), 587-621.

Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103.

López de Prado, M. (2016). Building diversified portfolios that outperform out-of-sample. Journal of Portfolio Management, 42(4), 59-69.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’ optimal? Financial Analysts Journal, 45(1), 31-42.

Pan, C. H., & Statman, M. (2013). Investor personality in investor questionnaires. Journal of Invest- ment Consulting, 14(1), 48-56.

Reher, M., & Sokolinski, S. (2021). Robo advisors and access to wealth management. Journal of Financial Economics, 145(3), 776-802.

Rossi, A. G., & Utkus, S. P. (2020). Who benefits from robo-advising? Evidence from machine lear- ning. Working Paper, Georgetown University.

Roszkowski, M. J., & Davey, G. (2010). Risk perception and risk tolerance changes attributable to the 2008 economic crisis: A subtle but critical difference. Journal of Financial Service Professionals, 64(4), 42-53.

SEC. (2017). Guidance Update: Robo-Advisers. U.S. Securities and Exchange Commission, Division of Investment Management.

Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long.

Journal of Finance, 40(3), 777-790.

Sun, W., Fan, A., Chen, L. W., Schouwenaars, T., & Albota, M. A. (2006). Optimal rebalancing for institutional portfolios. Journal of Portfolio Management, 32(2), 33-43.

Alexander, C., & Imeraj, A. (2023). Multi-frequency portfolio rebalancing across digital and traditi- onal asset classes. Journal of Financial Data Science, 5(1), 45-67.

Ang, A., & Bekaert, G. (2002). International asset allocation with regime shifts. Review of Financial Studies, 15(4), 1137-1187.

Bailey, D. H., & López de Prado, M. (2014). The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting, and non-normality. Journal of Portfolio Management, 40(5), 94-107.

Bailey, D. H., Borwein, J. M., López de Prado, M., & Zhu, Q. J. (2014). Pseudo-mathematics and fi- nancial charlatanism: The effects of backtest overfitting on out-of-sample performance. Notices of the American Mathematical Society, 61(5), 458-471.

Berg, F., Koelbel, J. F., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings.

Review of Finance, 26(6), 1315-1344.

Bianchi, D., Büchner, M., & Tamoni, A. (2021). Bond risk premiums with machine learning. Review of Financial Studies, 34(2), 1046-1089.

Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.

de Prado, M. L., & Lewis, M. J. (2019). Detection of false investment strategies using unsupervised learning methods. Quantitative Finance, 19(9), 1555-1565.

Dimson, E., Karakaş, O., & Li, X. (2015). Active ownership. Review of Financial Studies, 28(12), 3225-3268.

Elmachtoub, A. N., & Grigas, P. (2022). Smart “predict, then optimize”. Management Science, 68(1), 9-26.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.

Flood, M. D., & Korenko, G. G. (2015). Systematic scenario selection: Stress testing and the nature of uncertainty. Quantitative Finance, 15(1), 43-59.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273.

Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.

Harvey, C. R., & Liu, Y. (2015). Backtesting. Journal of Portfolio Management, 42(1), 13-28.

He, G., & Litterman, R. (1999). The intuition behind Black-Litterman model portfolios. Goldman Sachs Investment Management Research Working Paper.

Herman, D., Googin, C., Liu, X., Sun, Y., Galda, A., Safro, I., ... & Alexeev, Y. (2023). Quantum com- puting for finance. Nature Reviews Physics, 5(8), 450-465.

Idzorek, T. (2005). A step-by-step guide to the Black-Litterman model. Working Paper, Ibbotson Associates.

Jiang, Z., Xu, D., & Liang, J. (2017). A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059.

Kolm, P. N., & Ritter, G. (2019). Modern perspectives on reinforcement learning in finance. Journal of Machine Learning in Finance, 1(1).

Krueger, P., Sautner, Z., & Starks, L. T. (2020). The importance of climate risks for institutional in- vestors. Review of Financial Studies, 33(3), 1067-1111.

Ledoit, O., & Wolf, M. (2004). Honey, I shrunk the sample covariance matrix. Journal of Portfolio Management, 30(4), 110-119.

López de Prado, M. (2016). Building diversified portfolios that outperform out-of-sample. Journal of Portfolio Management, 42(4), 59-69.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

Michaud, R. O. (1989). The Markowitz optimization enigma: Is ‘optimized’ optimal? Financial Analysts Journal, 45(1), 31-42.

Moody, J., & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, 12(4), 875-889.

Orus, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028.

Pastor, L., Stambaugh, R. F., & Taylor, L. A. (2021). Sustainable investing in equilibrium. Journal of Financial Economics, 142(2), 550-571.

Pedersen, L. H., Fitzgibbons, S., & Pomorski, L. (2021). Responsible investing: The ESG-efficient frontier. Journal of Financial Economics, 142(2), 572-597.

Pham, T., Nguyen, K., & Wang, C. (2023). Liquidity-adjusted portfolio optimization for hybrid digi- tal-traditional asset allocation. Working Paper.

Raffinot, T. (2017). Hierarchical clustering-based asset allocation. Journal of Portfolio Management, 44(2), 89-99.

Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2, 21-42.

Uysal, A. S., Li, X., & Mulvey, J. M. (2024). End-to-end deep learning for portfolio optimization.

Journal of Portfolio Management, 50(3), 175-195.

Venturelli, D., & Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimi- zation problems. Quantum Machine Intelligence, 1(1), 17-30.

White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097-1126.

Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep reinforcement learning for trading. Journal of Financial Data Science, 2(2), 25-40.

Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705-1741. Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1-33.

Aramonte, S., Huang, W., & Schrimpf, A. (2021). DeFi risks and the decentralisation illusion. BIS Quarterly Review, December 2021.

Atzei, N., Bartoletti, M., & Cimoli, T. (2017). A survey of attacks on Ethereum smart contracts. Prin- ciples of Security and Trust, 164-186.

Basel Committee on Banking Supervision. (2022). Prudential Treatment of Cryptoasset Exposures.

Bank for International Settlements.

Bernoulli, D. (1738/1954). Exposition of a new theory on the measurement of risk. Econometrica, 22(1), 23-36.

Buterin, V. (2017). The Blockchain Trilemma. Ethereum Foundation Blog.

Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial intelligence, algorithmic pricing, and collusion. American Economic Review, 110(10), 3267-3297.

Chainalysis. (2022). The 2022 Crypto Crime Report. Chainalysis Research. Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386-405.

Danielsson, J., Macrae, R., & Uthemann, A. (2022). Artificial intelligence and systemic risk. Journal of Banking & Finance, 140, 106290.

EU AI Act. (2024). Regulation on Artificial Intelligence. European Union.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417.

FATF. (2021). Updated Guidance for a Risk-Based Approach to Virtual Assets and VASPs. Financial Action Task Force.

Ferrari, V. (2022). The regulation of crypto-assets in the EU: Investment and payment tokens under the radar. Maastricht Journal of European and Comparative Law, 27(3), 320-339.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689-707.

Foley, S., Karlsen, J. R., & Putniņš, T. J. (2019). Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies? Review of Financial Studies, 32(5), 1798-1853.

FSB. (2020). The Use of Supervisory and Regulatory Technology by Authorities and Regulated Ins- titutions. Financial Stability Board.

FSB. (2023). Global Regulatory Framework for Crypto-Asset Activities. Financial Stability Board. Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets.

American Economic Review, 70(3), 393-408.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273.

Gudgeon, L., Perez, D., Harz, D., Livshits, B., & Gervais, A. (2020). The decentralized financial crisis.

IEEE Crypto Valley Conference on Blockchain Technology, 1-15.

Houben, R., & Snyers, A. (2020). Crypto-assets: Key developments, regulatory concerns and respon- ses. European Parliament Policy Department Study.

IMF. (2022). Global Financial Stability Report: Crypto Contagion. International Monetary Fund. Kaufman, G. G., & Scott, K. E. (2003). What is systemic risk, and do bank regulators retard or cont-

ribute to it? The Independent Review, 7(3), 371-391.

Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.

Lakatos, I. (1970). Falsification and the methodology of scientific research programmes. In Criti- cism and the Growth of Knowledge, 91-196.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. MiCA. (2023). Regulation on Markets in Crypto-Assets. European Union.

North, D. C. (1990). Institutions, Institutional Change and Economic Performance. Cambridge Uni- versity Press.

Perez, D., & Livshits, B. (2021). Smart contract vulnerabilities: Vulnerable does not imply exploited.

30th USENIX Security Symposium, 1325-1341.

Rikken, O., Janssen, M., & Kwee, Z. (2021). The ins and outs of decentralized autonomous organiza- tions (DAOs) unraveling the definitions, characteristics, and emerging developments of DAOs. Blockchain: Research and Applications, 2(4), 100034.

Wilson, C., & Van der Velden, M. (2022). Sustainable AI: An integrated model to guide public sector decision-making. Technology in Society, 68, 101926.

Zetzsche, D. A., Annunziata, F., Arner, D. W., & Buckley, R. P. (2020). The Markets in Crypto-Assets Regulation (MiCA) and the EU digital finance strategy. Capital Markets Law Journal, 16(2), 203-225.

Zetzsche, D. A., Buckley, R. P., & Arner, D. W. (2020). Decentralized finance. Journal of Financial Regulation, 6(2), 172-203.

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