Sosyal medya paylaşımları ile NFT koleksiyonlarının fiyatı ve satış miktarı arasındaki ilişkinin ekonometrik analizi
Yıl 2025,
Cilt: 15 Sayı: 1, 314 - 339, 23.03.2025
Uğur Bakan
,
Özge Korkmaz
,
Ufuk Bakan
Öz
Sosyal medya platformlarının ortaya çıkışı, bilgi dağıtımında devrim yaratarak piyasa duygularının hızla aktarılabileceği dinamik bir ortam yaratmaktadır. Birçok sosyal medya platformu olmasına rağmen, insanların duygu ve düşüncelerini rahatça paylaştıkları önemli bir mecra olarak X (Twitter) görülmektedir. Öyle ki, bu mecradaki etkileşime göre insanların düşüncelerinin ve taleplerinin değiştiği, tasarruf ve tüketim davranışlarında anlamlı etkilere yol açtığı görülmektedir. Benzer şekilde bu mecradaki etkileşim neticesinde, sürü davranışları gözlenmektedir. Değişen ve gelişen dünyada teknolojik unsurların dikkate alınması gerçeği neticesinde, bu çalışma X (Twitter) ve NFT etkileşimini incelemesi yönüyle özgün bir nitelik taşımaktadır. Bu bağlamda çalışmada, X (Twitter)’deki sosyal medya etkileşimlerinden elde edilen duygu değeri ile seçilmiş NFT koleksiyonlarının satış fiyatı ve satış miktarı arasındaki ilişki incelenmektedir. Çalışmada Hacker Hatemi (2010) nedensellik testinden yararlanılmış ve 25/01/2023-26/01/2024 dönemi günlük verileri ile çalışılmıştır. Çalışma sonucunda duygu değerinin NFT koleksiyonunun satış miktarına ve satış fiyatına neden olduğu saptanmıştır.
Kaynakça
- Agarwal, B., Mittal, N., Bansal, P., & Garg, S. (2015). Sentiment Analysis Using Common-Sense and Context Information. Computational Intelligence and Neuroscience, 2015, 1–9. https://doi.org/10.1155/2015/715730
- Ángeles Oviedo-García, M., Muñoz-Expósito, M., Castellanos-Verdugo, M., & Sancho-Mejías, M. (2014). Metric proposal for customer engagement in Facebook. Journal of Research in Interactive Marketing, 8(4), 327-344. https://doi.org/10.1108/JRIM-05-2014-0028
- Antweiler, W., & Frank, M. Z. (2001). Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards. SSRN Electronic Journal, 59(3): 1259-1294.https://doi.org/10.2139/ssrn.282320.
- Baker, M., & Wurgler, J. (2007). Investor Sentiment in the Stock Market. Journal of Economic Perspectives, 21(2), 129-151. https://doi.org/10.1257/jep.21.2.129
- Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
- Broadstock, D. C., & Zhang, D. (2019). Social-media and intraday stock returns: The pricing power of sentiment. Finance Research Letters, 30, 116–123. https://doi.org/10.1016/j.frl.2019.03.030
- Brown, C., Hendrickson, E., & Littau, J. (2014). New Opportunities For Diversity Twitter Journalists and Traditionally Underserved Communities. Journal of Social Media Studies, 1(1), 1–16. https://doi.org/10.15340/2147336611760
- Casale-Brunet, S., Ribeca, P., Doyle, P., & Mattavelli, M. (2021). Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem. 2021 IEEE International Conference on Blockchain (Blockchain), 188-195. https://doi.org/10.1109/Blockchain53845.2021.00033
- Cho, J. B., Serneels, S., & Matteson, D. S. (2023). Non-Fungible Token Transactions: Data and Challenges. Data Science in Science, 2(1), 2151950. https://doi.org/10.1080/26941899.2022.2151950
- Dorsey J., Biz, S., Williams, E. (2006). The Birth and Early Development of Twitter. Twitter Inc.
- Dowling, M. (2022). Fertile LAND: Pricing non-fungible tokens. Finance Research Letters, 44, 102096. https://doi.org/10.1016/j.frl.2021.102096
- Du, C., Sun, H., Wang, J., Qi, Q., & Liao, J. (2020). Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4019-4028. https://doi.org/10.18653/v1/2020.acl-main.370
- Dukedom, C. (2021). The Nft Revolution - Real Estate Edition: 2 1 practical guide for beginners to create, buy and sell Non-fungible tokens disruptive projects of virtual land, properties and worlds. Independently published.
- Enders, W. (2004). Applied Econometric Time Series (Second ed.). Hoboken: John Wiley & Sons.
- Ge, Y., Qiu, J., Liu, Z., Gu, W., & Xu, L. (2020). Beyond negative and positive: Exploring the effects of emotions in social media during the stock market crash. Information Processing & Management, 57(4), 102218. https://doi.org/10.1016/j.ipm.2020.102218
Gomez‐Carrasco, P., & Michelon, G. (2017). The Power of Stakeholders’ Voice: The Effects of Social Media Activism on Stock Markets. Business Strategy and the Environment, 26(6), 855-872. https://doi.org/10.1002/bse.1973
- Gričar, S., Šugar, V., Baldigara, T., & Folgieri, R. (2023). Potential Integration of Metaverse, Non-Fungible Tokens, and Sentiment Analysis in Quantitative Tourism Economic Analysis. Journal of Risk and Financial Management, 17(1), 15. https://doi.org/10.3390/jrfm17010015
- Gu, C., & Kurov, A. (2020). Informational role of social media: Evidence from Twitter sentiment. Journal of Banking & Finance, 121, 105969. https://doi.org/10.1016/j.jbankfin.2020.105969
- Hacker, S. & Hatemi-J, A. (2010). The Properties of Procedures Dealing with Uncertainty about Intercept and Deterministic Trend in Unit Root Testing. CESIS Electronic Working Paper Series, Paper No. 214. Centre of Excellence for Science and Innovation Studies, The Royal Institute of Technology, Stockholm, Sweden.
- Hailiang Chen, De, P., Yu Hu, & Byoung-Hyoun Hwang. (2011). Sentiment is revealed in social media and affects the stock market. 2011 IEEE Statistical Signal Processing Workshop (SSP), 25–28. https://doi.org/10.1109/SSP.2011.5967675
- Holotescu, C., Grosseck, G., & Danciu, E. (2014). Educational Digital Stories in 140 Characters: Towards a Typology of Micro-blog Storytelling in Academic Courses. Procedia - Social and Behavioral Sciences, 116, 4301-4305. https://doi.org/10.1016/j.sbspro.2014.01.936
- Fortnow, M., Quharrison, T., Nguyen, K. (2022). The NFT Handbook: How to Create, Sell, and Buy Non-Fungible Tokens. Hoboken New Jersey: John Wiley & Sons.
- Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
- Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. https://doi.org/10.1016/j.bushor.2009.09.003
- Kaur, M., Kalra, T., Malik, S., & Kapoor, A. (2018). Significance of Social Networking Media for Influencing Investor Behaviour in the Stock Market. İçinde A. K. Kar, S. Sinha, & M. P. Gupta (Ed.), Digital India (ss. 83-98). Springer International Publishing. https://doi.org/10.1007/978-3-319-78378-9_5
- Kim, W., Jeong, O.-R., & Lee, S.-W. (2010). On social websites. Information Systems, 35(2), 215–236. https://doi.org/10.1016/j.is.2009.08.003
- Larsson, A. O., & Moe, H. (2012). Studying political microblogging: Twitter users in the 2010 Swedish election campaign. New Media & Society, 14(5), 729-747. https://doi.org/10.1177/1461444811422894
- Lazzini, A., Lazzini, S., Balluchi, F., & Mazza, M. (2022). Emotions, moods and hyperreality: Social media and the stock market during the first phase of COVID-19 pandemic. Accounting, Auditing & Accountability Journal, 35(1), 199-215. https://doi.org/10.1108/AAAJ-08-2020-4786
- Li, D., Wang, Y., Madden, A., Ding, Y., Tang, J., Sun, G. G., Zhang, N., & Zhou, E. (2019). Analysing stock market trends using social media user moods and social influence. Journal of the Association for Information Science and Technology, 70(9), 1000-1013. https://doi.org/10.1002/asi.24173
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011
- 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. https://doi.org/10.1038/s41598-021-00053-8
- Nyakurukwa, K., & Seetharam, Y. (2023). The evolution of studies on social media sentiment in the stock market: Insights from bibliometric analysis. Scientific African, 20, e01596. https://doi.org/10.1016/j.sciaf.2023.e01596
- Oduncu, S. (2022). NFT, Kripto Sanatı ve Türkiye’deki Yansımaları. Art-e Sanat Dergisi 15 (29), 195-224. https://doi.org/10.21602/sduarte.1080813.
- Öztürk, S. A. (2022). Yeni Bir Dijital Varlık Olarak NFT: Pazarlama Dünyasındaki Yeri Üzerine Değerlendirmeler. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(4), 1151-1164. https://doi.org/10.18037/ausbd.1225897
- Perron, P. (1989). The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis. Econometrica, 57 (6), 1361-1401
- Piñeiro‐Chousa, J., Vizcaíno‐González, M., & Pérez‐Pico, A. M. (2017). Influence of Social Media over the Stock Market. Psychology & Marketing, 34(1), 101-108. https://doi.org/10.1002/mar.20976
- Saygın, E. P., & Fındıklı, S. (2021). Tuvalden tuşa: Sanat pazarındaki dijital dönüşümde NFT’lerin rolü. Business & Management Studies: An International Journal, 9(4), 1452-1466. https://doi.org/10.15295/bmij.v9i4.1930
- Shiller, R. J. (2000). Measuring Bubble Expectations and Investor Confidence. Journal of Psychology and Financial Markets, 1 (1), 49-60. https://doi.org/10.1207/S15327760JPFM0101_05.
- Smith, A. N., Fischer, E., & Yongjian, C. (2012). How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113. https://doi.org/10.1016/j.intmar.2012.01.002
- Stokel-Walker, C. (2023). Why is Twitter becoming X? New Scientist, 259(3449), 9. https://doi.org/10.1016/S0262-4079(23)01398-2
- Sul, H. K., Dennis, A. R., & Yuan, L. (Ivy). (2017). Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns. Decision Sciences, 48(3), 454-488. https://doi.org/10.1111/deci.12229
- Tetlock, P. C. (2005). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.685145
- Valeonti, F., Bikakis, A., Terras, M., Speed, C., Hudson-Smith, A., & Chalkias, K. (2021). Crypto Collectibles, Museum Funding and OpenGLAM: Challenges, Opportunities and the Potential of Non-Fungible Tokens (NFTs). Applied Sciences, 11(21), 9931. https://doi.org/10.3390/app11219931
- Werner, S. M., Pritz, P. J., & Perez, D. (2020). Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price. Içinde P. Pardalos, I. Kotsireas, Y. Guo, & W. Knottenbelt (Ed.), Mathematical Research for Blockchain Economy (pp. 161-177). Springer International Publishing. https://doi.org/10.1007/978-3-030-53356-4_10
Wilson, K. B., Karg, A., & Ghaderi, H. (2022). Prospecting non-fungible tokens in the digital economy: Stakeholders and ecosystem, risk and opportunity. Business Horizons, 65(5), 657-670. https://doi.org/10.1016/j.bushor.2021.10.007
- Yang, S. Y., Mo, S. Y. K., & Liu, A. (2015). Twitter financial community sentiment and its predictive relationship to stock market movement. Quantitative Finance, 15(10), 1637-1656. https://doi.org/10.1080/14697688.2015.1071078
- Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”. Procedia - Social and Behavioral Sciences, 26, 55-62. https://doi.org/10.1016/j.sbspro.2011.10.562
- Zivot, E., & Andrews, D. W. K. (1992). Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis. Journal of Business & Economic Statistics, 10(3), 251. https://doi.org/10.2307/1391541
Econometric analysis of the relationship between social media shares and price and sales amount of NFT collections
Yıl 2025,
Cilt: 15 Sayı: 1, 314 - 339, 23.03.2025
Uğur Bakan
,
Özge Korkmaz
,
Ufuk Bakan
Öz
The emergence of social media platforms is revolutionizing the distribution of information, creating a dynamic environment where market sentiment can be conveyed quickly. Although many social media platforms exist, X (Twitter) is an important medium where people easily share their feelings and thoughts. So much so that it is observed that people's thoughts and demands change according to the interaction in this medium, causing significant effects on savings and consumption behaviors. Similarly, herd behavior is observed due to the interaction in this medium. As a result of the fact that technological elements are taken into account in the changing and developing world, this study is unique in that it examines the interaction of X (Twitter) and NFT. In this context, the study examines the relationship between the sentiment value obtained from social media interactions on X (Twitter), specifically for X (Twitter) and the sales price and sales amount of selected NFT collections. In the study, Hacker Hatemi (2010) causality test was used and daily data for 25/01/2023-26/01/2024 was studied. As a result of the study, it was determined that the sentiment value caused the NFT collection sales amount and sales price.
Kaynakça
- Agarwal, B., Mittal, N., Bansal, P., & Garg, S. (2015). Sentiment Analysis Using Common-Sense and Context Information. Computational Intelligence and Neuroscience, 2015, 1–9. https://doi.org/10.1155/2015/715730
- Ángeles Oviedo-García, M., Muñoz-Expósito, M., Castellanos-Verdugo, M., & Sancho-Mejías, M. (2014). Metric proposal for customer engagement in Facebook. Journal of Research in Interactive Marketing, 8(4), 327-344. https://doi.org/10.1108/JRIM-05-2014-0028
- Antweiler, W., & Frank, M. Z. (2001). Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards. SSRN Electronic Journal, 59(3): 1259-1294.https://doi.org/10.2139/ssrn.282320.
- Baker, M., & Wurgler, J. (2007). Investor Sentiment in the Stock Market. Journal of Economic Perspectives, 21(2), 129-151. https://doi.org/10.1257/jep.21.2.129
- Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
- Broadstock, D. C., & Zhang, D. (2019). Social-media and intraday stock returns: The pricing power of sentiment. Finance Research Letters, 30, 116–123. https://doi.org/10.1016/j.frl.2019.03.030
- Brown, C., Hendrickson, E., & Littau, J. (2014). New Opportunities For Diversity Twitter Journalists and Traditionally Underserved Communities. Journal of Social Media Studies, 1(1), 1–16. https://doi.org/10.15340/2147336611760
- Casale-Brunet, S., Ribeca, P., Doyle, P., & Mattavelli, M. (2021). Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem. 2021 IEEE International Conference on Blockchain (Blockchain), 188-195. https://doi.org/10.1109/Blockchain53845.2021.00033
- Cho, J. B., Serneels, S., & Matteson, D. S. (2023). Non-Fungible Token Transactions: Data and Challenges. Data Science in Science, 2(1), 2151950. https://doi.org/10.1080/26941899.2022.2151950
- Dorsey J., Biz, S., Williams, E. (2006). The Birth and Early Development of Twitter. Twitter Inc.
- Dowling, M. (2022). Fertile LAND: Pricing non-fungible tokens. Finance Research Letters, 44, 102096. https://doi.org/10.1016/j.frl.2021.102096
- Du, C., Sun, H., Wang, J., Qi, Q., & Liao, J. (2020). Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4019-4028. https://doi.org/10.18653/v1/2020.acl-main.370
- Dukedom, C. (2021). The Nft Revolution - Real Estate Edition: 2 1 practical guide for beginners to create, buy and sell Non-fungible tokens disruptive projects of virtual land, properties and worlds. Independently published.
- Enders, W. (2004). Applied Econometric Time Series (Second ed.). Hoboken: John Wiley & Sons.
- Ge, Y., Qiu, J., Liu, Z., Gu, W., & Xu, L. (2020). Beyond negative and positive: Exploring the effects of emotions in social media during the stock market crash. Information Processing & Management, 57(4), 102218. https://doi.org/10.1016/j.ipm.2020.102218
Gomez‐Carrasco, P., & Michelon, G. (2017). The Power of Stakeholders’ Voice: The Effects of Social Media Activism on Stock Markets. Business Strategy and the Environment, 26(6), 855-872. https://doi.org/10.1002/bse.1973
- Gričar, S., Šugar, V., Baldigara, T., & Folgieri, R. (2023). Potential Integration of Metaverse, Non-Fungible Tokens, and Sentiment Analysis in Quantitative Tourism Economic Analysis. Journal of Risk and Financial Management, 17(1), 15. https://doi.org/10.3390/jrfm17010015
- Gu, C., & Kurov, A. (2020). Informational role of social media: Evidence from Twitter sentiment. Journal of Banking & Finance, 121, 105969. https://doi.org/10.1016/j.jbankfin.2020.105969
- Hacker, S. & Hatemi-J, A. (2010). The Properties of Procedures Dealing with Uncertainty about Intercept and Deterministic Trend in Unit Root Testing. CESIS Electronic Working Paper Series, Paper No. 214. Centre of Excellence for Science and Innovation Studies, The Royal Institute of Technology, Stockholm, Sweden.
- Hailiang Chen, De, P., Yu Hu, & Byoung-Hyoun Hwang. (2011). Sentiment is revealed in social media and affects the stock market. 2011 IEEE Statistical Signal Processing Workshop (SSP), 25–28. https://doi.org/10.1109/SSP.2011.5967675
- Holotescu, C., Grosseck, G., & Danciu, E. (2014). Educational Digital Stories in 140 Characters: Towards a Typology of Micro-blog Storytelling in Academic Courses. Procedia - Social and Behavioral Sciences, 116, 4301-4305. https://doi.org/10.1016/j.sbspro.2014.01.936
- Fortnow, M., Quharrison, T., Nguyen, K. (2022). The NFT Handbook: How to Create, Sell, and Buy Non-Fungible Tokens. Hoboken New Jersey: John Wiley & Sons.
- Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
- Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. https://doi.org/10.1016/j.bushor.2009.09.003
- Kaur, M., Kalra, T., Malik, S., & Kapoor, A. (2018). Significance of Social Networking Media for Influencing Investor Behaviour in the Stock Market. İçinde A. K. Kar, S. Sinha, & M. P. Gupta (Ed.), Digital India (ss. 83-98). Springer International Publishing. https://doi.org/10.1007/978-3-319-78378-9_5
- Kim, W., Jeong, O.-R., & Lee, S.-W. (2010). On social websites. Information Systems, 35(2), 215–236. https://doi.org/10.1016/j.is.2009.08.003
- Larsson, A. O., & Moe, H. (2012). Studying political microblogging: Twitter users in the 2010 Swedish election campaign. New Media & Society, 14(5), 729-747. https://doi.org/10.1177/1461444811422894
- Lazzini, A., Lazzini, S., Balluchi, F., & Mazza, M. (2022). Emotions, moods and hyperreality: Social media and the stock market during the first phase of COVID-19 pandemic. Accounting, Auditing & Accountability Journal, 35(1), 199-215. https://doi.org/10.1108/AAAJ-08-2020-4786
- Li, D., Wang, Y., Madden, A., Ding, Y., Tang, J., Sun, G. G., Zhang, N., & Zhou, E. (2019). Analysing stock market trends using social media user moods and social influence. Journal of the Association for Information Science and Technology, 70(9), 1000-1013. https://doi.org/10.1002/asi.24173
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011
- 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. https://doi.org/10.1038/s41598-021-00053-8
- Nyakurukwa, K., & Seetharam, Y. (2023). The evolution of studies on social media sentiment in the stock market: Insights from bibliometric analysis. Scientific African, 20, e01596. https://doi.org/10.1016/j.sciaf.2023.e01596
- Oduncu, S. (2022). NFT, Kripto Sanatı ve Türkiye’deki Yansımaları. Art-e Sanat Dergisi 15 (29), 195-224. https://doi.org/10.21602/sduarte.1080813.
- Öztürk, S. A. (2022). Yeni Bir Dijital Varlık Olarak NFT: Pazarlama Dünyasındaki Yeri Üzerine Değerlendirmeler. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(4), 1151-1164. https://doi.org/10.18037/ausbd.1225897
- Perron, P. (1989). The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis. Econometrica, 57 (6), 1361-1401
- Piñeiro‐Chousa, J., Vizcaíno‐González, M., & Pérez‐Pico, A. M. (2017). Influence of Social Media over the Stock Market. Psychology & Marketing, 34(1), 101-108. https://doi.org/10.1002/mar.20976
- Saygın, E. P., & Fındıklı, S. (2021). Tuvalden tuşa: Sanat pazarındaki dijital dönüşümde NFT’lerin rolü. Business & Management Studies: An International Journal, 9(4), 1452-1466. https://doi.org/10.15295/bmij.v9i4.1930
- Shiller, R. J. (2000). Measuring Bubble Expectations and Investor Confidence. Journal of Psychology and Financial Markets, 1 (1), 49-60. https://doi.org/10.1207/S15327760JPFM0101_05.
- Smith, A. N., Fischer, E., & Yongjian, C. (2012). How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113. https://doi.org/10.1016/j.intmar.2012.01.002
- Stokel-Walker, C. (2023). Why is Twitter becoming X? New Scientist, 259(3449), 9. https://doi.org/10.1016/S0262-4079(23)01398-2
- Sul, H. K., Dennis, A. R., & Yuan, L. (Ivy). (2017). Trading on Twitter: Using Social Media Sentiment to Predict Stock Returns. Decision Sciences, 48(3), 454-488. https://doi.org/10.1111/deci.12229
- Tetlock, P. C. (2005). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.685145
- Valeonti, F., Bikakis, A., Terras, M., Speed, C., Hudson-Smith, A., & Chalkias, K. (2021). Crypto Collectibles, Museum Funding and OpenGLAM: Challenges, Opportunities and the Potential of Non-Fungible Tokens (NFTs). Applied Sciences, 11(21), 9931. https://doi.org/10.3390/app11219931
- Werner, S. M., Pritz, P. J., & Perez, D. (2020). Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price. Içinde P. Pardalos, I. Kotsireas, Y. Guo, & W. Knottenbelt (Ed.), Mathematical Research for Blockchain Economy (pp. 161-177). Springer International Publishing. https://doi.org/10.1007/978-3-030-53356-4_10
Wilson, K. B., Karg, A., & Ghaderi, H. (2022). Prospecting non-fungible tokens in the digital economy: Stakeholders and ecosystem, risk and opportunity. Business Horizons, 65(5), 657-670. https://doi.org/10.1016/j.bushor.2021.10.007
- Yang, S. Y., Mo, S. Y. K., & Liu, A. (2015). Twitter financial community sentiment and its predictive relationship to stock market movement. Quantitative Finance, 15(10), 1637-1656. https://doi.org/10.1080/14697688.2015.1071078
- Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”. Procedia - Social and Behavioral Sciences, 26, 55-62. https://doi.org/10.1016/j.sbspro.2011.10.562
- Zivot, E., & Andrews, D. W. K. (1992). Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis. Journal of Business & Economic Statistics, 10(3), 251. https://doi.org/10.2307/1391541