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Muhteşem Beşli Teknoloji Şirketlerinin Hisse Değerleri ile Bitcoin ve Altın Fiyatlarının Karşılaştırmalı Değerlendirmesi

Yıl 2025, Cilt: 9 Sayı: 1, 583 - 600, 25.02.2025
https://doi.org/10.25295/fsecon.1490060

Öz

Özellikle yeni nesil yatırımcılar, ileri teknolojilerin kullanıldığı popüler şirketlerin hisse senetleri ile kripto paraları yatırım aracı olarak kullanmayı tercih edebilmektedirler. Klasik yatırım araçlarından olan altında hâlâ dünya genelinde yatırımcıların portföylerinde yer alan emtia varlıklar arasında yerini korumaktadır. Bu çalışmada bu varlık grupları değerlendirilmiştir. İlk grup yatırım aracı olarak; yeni nesil muhteşem beşli olarak adlandırılan decacorn ve hectocorn teknoloji şirketleri; Apple, Microsoft, Amazon, Alphabet, Nvidia Corporation ve Tesla şirket hisse değeri getirileri analiz edilmiştir. Ayrıca ikinci finansal varlık olarak çalışmada, günümüzde teknolojinin evrimi ile birlikte gündelik hayatta kullanılmanın yanı sıra yatırım aracı olarak da değerlendirilen kripto paralar ve bu kripto paralar arasında popülerliğini koruyan Bitcoin (BTC) çalışmaya konu edilmiştir. Çalışmada son olarak dünyanın en eski kıymetli yatırım araçlarından olan altın madeni diğer finansal varlıklarla karşılaştırmalı değerlendirilmiştir. Çalışma, 2020:01 ile 2023:12 dönemleri arasında muhteşem beşli hisse senetleri, BTC ve altın ons fiyatı karşılıklı olarak eşbütünleşme, vektör hata düzeltmeli (VEC) ve Granger nedensellik analizlerinden yararlanılarak incelenmiştir. Çalışmanın bulguları; değişkenlerin BTC’de meydana getirdiği kısa dönemli şoklar yaklaşık bir ay sonra dengeye gelmektedir. Bu süreçte NVDA hisseleri artış gösterdikçe BTC değeri düşmekte, altın değeri arttıkça BTC değeri de artmaktadır.

Proje Numarası

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Kaynakça

  • Aggarwal, D., Chandrasekaran, S. & Annamalai, B. (2020). A complete empirical ensemble mode decomposition and support vector machine-based approach to predict bitcoin prices. Journal of Behavioral and Experimental Finance, 27, 1-12. https://doi.org/10.1016/j.jbef.2020.100335
  • Ahn, Y. & Kim, D. (2020). Sentiment disagreement and bitcoin price fluctuations: A psycholinguistic. Applied Economics Letters, 27(5), 412-416. https://doi.org/10.1080/13504851.2019.1619013
  • Ali, M. & Shatabda, S. (2020). A data selection methodology to train linear regression model to predict bitcoin price. 2020 2nd International Conference on Advanced Information and Communication Technology (s. 330-335).
  • Bağcı, H. & Köylü, K. M. (2019). Cryptocurrency: Determining the correlation between bitcoin cryptocurrency and gold prices. F. Ayhan & B. Darıcı (Ed.), Cryptocurrency in all aspects (p. 193-206). Peter Lang, Berlin. https://doi.org/10.3726/b15365
  • Benli, Y. K. & Yıldız, A. (2015). Forecasting the gold price with time series methods and artificial neural networks. Dumlupınar University Journal of Social Sciences, (42). 213-224.
  • Bouktif, S., Fiaz, A. & Awad, M. (2020). Augmented textual features-based stock market prediction. IEEE Access, 8, 40269-40282.
  • Bouoiyour, J., Selmi, R. & Tiwari, A. K. (2015). Is bitcoin business income or speculative foolery? New ideas through an improved frequency domain analysis. Annals of Financial Economics, 10(01), 1-23.
  • Brooks, C. & Prokopczuk, M. (2013). The dynamics of commodity prices. Quantitative Finance, 13(4), 527-542. https://doi.org/10.1080/14697688.2013.769689
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  • Carpenter, A. (2016). Portfolio diversification with bitcoin. Journal of Undergraduate in France, 1-27
  • Çelik, İ., Özdemir, A., Gürsoy, S. & Ünlü, H. U. (2018). Return and volatility spillover between emerging stock markets and precious metals. Ege Academic Review, 18(2), 217-230. 10.21121/eab.2018237351
  • Chen, Y. (2021). Empirical analysis of bitcoin price. Journal of Economics and Finance, 692-715. https://doi.org/10.1007/s12197-021-09549-5
  • Chen, Z. (2022). An empirical test of CAPM: Application in Apple and Tesla Stocks. 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022).
  • Chkili, W. (2021). Modelling bitcoin price volatility: Long memory vs markov switching. Eurasian Economic Review: A Journal in Applied Macroeconomics and Finance, 11(3), 433-448. https://doi.org/10.1007/s40822-021-00180-7
  • Conlon, T., Corbet, S. & McGee, R. J. (2024). The bitcoin volume-volatility relationship: A high-frequency analysis of futures and spot exchanges. International Review of Financial Analysis, 91, 103013. https://doi.org/10.1016/j.irfa.2023.103013
  • De Almeida, L. A. G. (2020). Technical indicators for rational investing in the technology companies: The evidence of FAANG stocks. Jurnal Pengurusan, 59, 75-87. https://doi.org/10.17576/pengurusan-2020-59-08
  • Dickey, D. A. & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://www.jstor.org/stable/2286348
  • Dirk, G. B. & McDermott, T. K. (2010). Is gold safe haven? International evidence. Journal of Banking and Finance 34, 1886–1898. https://doi.org/10.1016/j.jbankfin. 2009.12.008
  • Dumitrescu, B. A., Obreja, C., Leonida, I., Mihai, D. G. & Trifu, L. C. (2023). The link between bitcoin price changes and the exchange rates in European countries with non-euro currencies. Journal of Risk and Financial Management, 16(4), 232.
  • Dyhrberg, A. H. (2015). Hedging capabilities of bitcoin. is it the virtual gold?. Finance Research Letters, 139-144. https://doi.org/10.1016/j.frl.2015.10.025
  • Edgari, E., Thiojaya, J. & Qomariyah, N. N. (2022). The impact of twitter sentiment analysis on bitcoin price during COVID-19 with XGBoost. 2022 5th International Conference on Computing and Informatics (ICCI) (p. 337-342).
  • Ekapure, S., Jiruwala, N., Patnaik, S. & SenGupta, I. (2021). A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks. Arxiv Preprint arXiv:2107.14695. CFP20P17-ART
  • Eswara, M. (2017). Cryptocurrency gyration and bitcoin volatility. International Journal of Business and Administration Research Review, 3(18), 187-195
  • Gayathri, V. & Dhanabhakyam, D. (2014). Cointegration and causal relationship between gold price and nifty –An empirical study. Abhinav International Monthly Refereed Journal of Research in Management & Technology, 3(7), 14-21. Online ISSN-2320-0073
  • Gilmore, C.G., Mcmanus, G.M., Sharma, R. & Tezel, A. (2009). The dynamics of gold prices, gold mining stock prices and stock market prices comovements. Research in Applied Economics, 1(1), 1-19 ISSN 1948-5433
  • Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. & Siering, M. (2014). Bitcoin- asset or currency? Revealing users’ hidden intentions. Twenty Second European Conference on Information Systems (p. 1-14). Tel Aviv. https://ssrn.com/abstract=2425247
  • Guégan, D. & Renault, T. (2021). Does investor sentiment on social media provide robust information for Bitcoin return predictability?. Finance Research Letters, 38, 1-7. https://doi.org/10.1016/j.frl.2020.101494
  • Guizani, S. & Nafti, I. K. (2019). The determinants of bitcoin price volatility: An investigation with ARDL model. Procedia Computer Science, 164, 233-238. https://doi.org/10.1016/j.procs.2019.12.177
  • Guo, S. (2024). Analysis of investors’ choices in technology companies. Highlights in Business, Economics and Management, 24, 1682-1687. 10.54097/05jeve55
  • Ibrahim, A. (2021). Forecasting the early market movement in bitcoin using twitter's sentiment analysis: An ensemble-based prediction model. 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).
  • Jarque, C. & Bera, A. (1980) Efficient tests for normality homoscedasticity and serial independence of regression residuals, Econometric Letters, 6, 255–259
  • Jin Lim, S. & Masih, M. (2017). Exploring portfolio diversification opportunities in Islamic capital markets through bitcoin: Evidence from MGARCHDCC and Wavelet approaches. MPRA Paper No. 79752
  • Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.
  • Kahraman, İ., Küçükşahin, H. & Çağlak, E. (2019). The volatility structure of cryptocurrencies: The comparison of GARCH models. Fiscaoeconomia, 3(2) 21-45. 10.25295/fsecon.2019.02.002
  • Kajtazi, A. & Moro, A. (2017). Bitcoin, portfolio diversification and Chinese financial markets. SSRN 3062064. The UK.
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Comparative Evaluation of Share Values of Five Magnificent Technology Companies with Bitcoin and Gold Prices

Yıl 2025, Cilt: 9 Sayı: 1, 583 - 600, 25.02.2025
https://doi.org/10.25295/fsecon.1490060

Öz

Especially new generation investors may prefer to use stocks of popular companies that use advanced technologies and cryptocurrencies as investment instruments. Gold, one of the classical investment instruments, still maintains its place among the commodity assets in the portfolios of investors around the world. These asset groups were evaluated in this study. As the first group investment tool, decacorn and hectocorn technology companies called the new generation the magnificent five; Company stock returns of Apple, Microsoft, Amazon, Alphabet, Nvidia Corporation and Tesla were analyzed. In addition, as the second financial asset, cryptocurrencies, which are used as investment instruments as well as being used in daily life with the evolution of technology, and Bitcoin (BTC), which remains popular among these cryptocurrencies, were the subject of the study. Finally, the study evaluated gold mines, one of the world's oldest valuable investment instruments, compared with other financial assets. The study examined the magnificent five stocks, BTC and gold ounce prices between the periods of 2020:01 and 2023:12, using mutual cointegration, vector error correction (VEC) and Granger causality analyses. Findings of the study; Short-term shocks caused by variables in BTC stabilise after about a month. In this process, as NVDA shares increase, BTC value decreases, and as gold value increases, BTC value increases.

Etik Beyan

Bu çalışmanın tüm hazırlanma süreçlerinde etik kurallara uyulduğunu yazar olarak beyan ediyorum. Aksi bir durumun tespiti halinde Fiscaoeconomia Dergisinin hiçbir sorumluluğu olmayıp, tüm sorumluluk çalışmanın yazarına aittir.

Destekleyen Kurum

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Proje Numarası

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Teşekkür

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Kaynakça

  • Aggarwal, D., Chandrasekaran, S. & Annamalai, B. (2020). A complete empirical ensemble mode decomposition and support vector machine-based approach to predict bitcoin prices. Journal of Behavioral and Experimental Finance, 27, 1-12. https://doi.org/10.1016/j.jbef.2020.100335
  • Ahn, Y. & Kim, D. (2020). Sentiment disagreement and bitcoin price fluctuations: A psycholinguistic. Applied Economics Letters, 27(5), 412-416. https://doi.org/10.1080/13504851.2019.1619013
  • Ali, M. & Shatabda, S. (2020). A data selection methodology to train linear regression model to predict bitcoin price. 2020 2nd International Conference on Advanced Information and Communication Technology (s. 330-335).
  • Bağcı, H. & Köylü, K. M. (2019). Cryptocurrency: Determining the correlation between bitcoin cryptocurrency and gold prices. F. Ayhan & B. Darıcı (Ed.), Cryptocurrency in all aspects (p. 193-206). Peter Lang, Berlin. https://doi.org/10.3726/b15365
  • Benli, Y. K. & Yıldız, A. (2015). Forecasting the gold price with time series methods and artificial neural networks. Dumlupınar University Journal of Social Sciences, (42). 213-224.
  • Bouktif, S., Fiaz, A. & Awad, M. (2020). Augmented textual features-based stock market prediction. IEEE Access, 8, 40269-40282.
  • Bouoiyour, J., Selmi, R. & Tiwari, A. K. (2015). Is bitcoin business income or speculative foolery? New ideas through an improved frequency domain analysis. Annals of Financial Economics, 10(01), 1-23.
  • Brooks, C. & Prokopczuk, M. (2013). The dynamics of commodity prices. Quantitative Finance, 13(4), 527-542. https://doi.org/10.1080/14697688.2013.769689
  • BTC (2024). Market summary. https://bitcoin.org Market summary
  • Cabarcos M., Angeles L., Pico, A. M. P., Chousa, J. P. & Sevi´c, A. (2021). Bitcoin volatility, stock market and investor sentiment. Are they connected?. Finance Research Letters, 38, 1-8.
  • Carpenter, A. (2016). Portfolio diversification with bitcoin. Journal of Undergraduate in France, 1-27
  • Çelik, İ., Özdemir, A., Gürsoy, S. & Ünlü, H. U. (2018). Return and volatility spillover between emerging stock markets and precious metals. Ege Academic Review, 18(2), 217-230. 10.21121/eab.2018237351
  • Chen, Y. (2021). Empirical analysis of bitcoin price. Journal of Economics and Finance, 692-715. https://doi.org/10.1007/s12197-021-09549-5
  • Chen, Z. (2022). An empirical test of CAPM: Application in Apple and Tesla Stocks. 2022 2nd International Conference on Financial Management and Economic Transition (FMET 2022).
  • Chkili, W. (2021). Modelling bitcoin price volatility: Long memory vs markov switching. Eurasian Economic Review: A Journal in Applied Macroeconomics and Finance, 11(3), 433-448. https://doi.org/10.1007/s40822-021-00180-7
  • Conlon, T., Corbet, S. & McGee, R. J. (2024). The bitcoin volume-volatility relationship: A high-frequency analysis of futures and spot exchanges. International Review of Financial Analysis, 91, 103013. https://doi.org/10.1016/j.irfa.2023.103013
  • De Almeida, L. A. G. (2020). Technical indicators for rational investing in the technology companies: The evidence of FAANG stocks. Jurnal Pengurusan, 59, 75-87. https://doi.org/10.17576/pengurusan-2020-59-08
  • Dickey, D. A. & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://www.jstor.org/stable/2286348
  • Dirk, G. B. & McDermott, T. K. (2010). Is gold safe haven? International evidence. Journal of Banking and Finance 34, 1886–1898. https://doi.org/10.1016/j.jbankfin. 2009.12.008
  • Dumitrescu, B. A., Obreja, C., Leonida, I., Mihai, D. G. & Trifu, L. C. (2023). The link between bitcoin price changes and the exchange rates in European countries with non-euro currencies. Journal of Risk and Financial Management, 16(4), 232.
  • Dyhrberg, A. H. (2015). Hedging capabilities of bitcoin. is it the virtual gold?. Finance Research Letters, 139-144. https://doi.org/10.1016/j.frl.2015.10.025
  • Edgari, E., Thiojaya, J. & Qomariyah, N. N. (2022). The impact of twitter sentiment analysis on bitcoin price during COVID-19 with XGBoost. 2022 5th International Conference on Computing and Informatics (ICCI) (p. 337-342).
  • Ekapure, S., Jiruwala, N., Patnaik, S. & SenGupta, I. (2021). A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks. Arxiv Preprint arXiv:2107.14695. CFP20P17-ART
  • Eswara, M. (2017). Cryptocurrency gyration and bitcoin volatility. International Journal of Business and Administration Research Review, 3(18), 187-195
  • Gayathri, V. & Dhanabhakyam, D. (2014). Cointegration and causal relationship between gold price and nifty –An empirical study. Abhinav International Monthly Refereed Journal of Research in Management & Technology, 3(7), 14-21. Online ISSN-2320-0073
  • Gilmore, C.G., Mcmanus, G.M., Sharma, R. & Tezel, A. (2009). The dynamics of gold prices, gold mining stock prices and stock market prices comovements. Research in Applied Economics, 1(1), 1-19 ISSN 1948-5433
  • Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. & Siering, M. (2014). Bitcoin- asset or currency? Revealing users’ hidden intentions. Twenty Second European Conference on Information Systems (p. 1-14). Tel Aviv. https://ssrn.com/abstract=2425247
  • Guégan, D. & Renault, T. (2021). Does investor sentiment on social media provide robust information for Bitcoin return predictability?. Finance Research Letters, 38, 1-7. https://doi.org/10.1016/j.frl.2020.101494
  • Guizani, S. & Nafti, I. K. (2019). The determinants of bitcoin price volatility: An investigation with ARDL model. Procedia Computer Science, 164, 233-238. https://doi.org/10.1016/j.procs.2019.12.177
  • Guo, S. (2024). Analysis of investors’ choices in technology companies. Highlights in Business, Economics and Management, 24, 1682-1687. 10.54097/05jeve55
  • Ibrahim, A. (2021). Forecasting the early market movement in bitcoin using twitter's sentiment analysis: An ensemble-based prediction model. 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).
  • Jarque, C. & Bera, A. (1980) Efficient tests for normality homoscedasticity and serial independence of regression residuals, Econometric Letters, 6, 255–259
  • Jin Lim, S. & Masih, M. (2017). Exploring portfolio diversification opportunities in Islamic capital markets through bitcoin: Evidence from MGARCHDCC and Wavelet approaches. MPRA Paper No. 79752
  • Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.
  • Kahraman, İ., Küçükşahin, H. & Çağlak, E. (2019). The volatility structure of cryptocurrencies: The comparison of GARCH models. Fiscaoeconomia, 3(2) 21-45. 10.25295/fsecon.2019.02.002
  • Kajtazi, A. & Moro, A. (2017). Bitcoin, portfolio diversification and Chinese financial markets. SSRN 3062064. The UK.
  • Kalyvas, A., Papakyriakou, P., Sakkas, A. & Urquhart, A. (2020). What drives Bitcoin’s price crash risk?. Economics Letters, 191, 1-4.
  • Keskin Köylü, M. & Köylü, T.Ç. (2017). The application potential of the block chain technology in financial markets. International Journal of Social Science, 63, 359-372, 10.9761/JASSS7446
  • Keskin Köylü, M. & Yücel, A. (2022), Determination of the relationship between BIST 100 index and gold prices by artificial neural networks (1988-2020). MANAS Journal of Social Studies. 11(2), 600-611. 1694-7215
  • Keskin Köylü, M. (2018). Crypto currency and their position in the financial markets. International Journal of Academic Value Studies, 4(21), 814-821.
  • Keskin Köylü, M. (2024). Examples of financial asset dependency in markets from past to present. Silk Road 3rd International Scientific Research Congress, March 6-8, 2024, Samarkand, Uzbekistan..
  • Koçoğlu, Ş., Çevik, Y. E. & Tanrıöven, C. (2016). Efficiency, liquidity and volatility of bitcoin markets. Journal of Business Research-Turk, 8(2), 77-97. 10.20491/isarder.2016.170
  • Kothari, A. & Gulati, D. (2015). Investment in gold and stock market: An analytical comparison. Pacific Business Review International, 7(9), 65-68. http://www.pbr.co.in/2015/ 2015_month/March/8.pdf
  • Kristoufek, L. (2015). What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. PloS One, 10(4), 1-15. https://doi.org/10.1371/journal.pone.0123923
  • Kumar, S., Kumar, A. & Singh, G. (2023). Causal relationship among international crude oil, gold, exchange rate, and stock market: fresh evidence from NARDL testing approach. International Journal of Finance & Economics, 28(1), 47-57. DOI: 10.1002/ijfe.2404
  • Li, Z., Yu, H., Xu, J., Liu, J. & Mo, Y. (2023). Stock market analysis and prediction using LSTM: A case study on technology stocks. Innovations in Applied Engineering and Technology, 1-6. https://doi.org/10.62836/iaet.v2i1.162
  • Liu, Y., Zeng, Q., Ordieres Meré, J. & Yang, H. (2019). Anticipating stock market of the renowned companies: A knowledge graph approach. Hindawi Complexity 2019. 1-15 https://doi.org/10.1155/2019/9202457
  • Lotz, A. (2018). Big Tech isn't one big monopoly, it's 5 companies all different businesses. http://theconversation.com/big-tech-isnt-one-big-monopoly-its-5-companies-all-in-different-businesses-927 91 (5 May2024).
  • Maleki, N., Nikoubin, A., Rabbani, M. & Zeinali, Y. (2023). Bitcoin price prediction based on other cryptocurrencies using machine learning and time series analysis. Scientia Iranica, 30(1), 285-301. 10.24200/sci.2020.55034.4040
  • Mao, Y., Wei, W., Wang, B. & Liu, B. (2012). Correlating S&P 500 stocks with Twitter data. Proceedings of The First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research (p. 69-72).
  • Meador, C. & Gluck, J. (2010). Analyzing the relationship between tweets, box-office performance and stocks. methods. Swarthmore College. https://www.sccs. swarthmore.edu/users/12/jgluck/resources/TwitterSentiment.pdf
  • Millera, N., Yanga, Y., Sunb, B. & Zhang, G. (2019). Identification of technical analysis patterns with smoothing splines for bitcoin prices. Journal of Applied Statistics, 46(12), 2289-2297. https://doi.org/10.1080/02664763.2019.1580251
  • Mittal, A., Dhiman, V., Singh, A. & Prakash, C. (2019). Short-term bitcoin price fluctuation prediction using social media and web search data. Twelfth International Conference on Contemporary Computing (p. 1-16).
  • Nakamoto, S. (2008, Ocak 3). Bitcoin: A peer-to-peer electronic cash system. Bitcoin.org Web Side: https://bitcoin.org/en/bitcoin-paper
  • Nasdaq (2024). Nasdaq, Inc. market activity. https://www.nasdaq.com/
  • Nguyen, K. Q. (2022). The correlation between the stock market and bitcoin during COVID-19 and other uncertainty periods. Finance Research Letters, 46(A), 1-5. https://doi.org/10.1016/j.frl.2021.102284
  • Omag, A. (2012). An observation of the relationship between gold prices and selected financial variables in Turkey. Muhasebe ve Finansman Dergisi, 195-206 https://www.proquest.com/scholarly-journals/observation-relationship-between-gold-prices/docview/1809054333/se-2?accountid=142289
  • Pandey, V. & Vipul, V. (2017). Volatility spillover from crude oil and gold to BRICS equity markets. Journal of Economics Studies. 45(2), 426-440. 10.1108/JES-01-2017-0025
  • Patel, S. A. (2013). Causal relationship between stock market indices and gold price: Evidence from India. The IUP Journal of Applied Finance, 19(1), 99-109.
  • Philippasa, D., Rjiba, H., Guesmi, K. & Goutte, S. (2019). Media attention and bitcoin prices. Finance Research Letters, 30, 37-43. https://doi.org/10.1016/j.frl.2019.03.031
  • Puell, D. (2019). A new barometer of bitcoin’s market cycles. https://medium.com/unconfiscatable/the-puell-multiple-bed755cfe358
  • Raju, N. G., Padullaparti, S. S. S. & Allam, S. P. R. (2020). Inclination of tech stocks using time series analysis and prophecy of returns using recurrent neural network. 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (p. 792-795).
  • Şahin, E. E. (2018). Crypto money bitcoin: Price estimation with ARIMA and artificial neural networks. Fiscaoeconomia, 2(2), 74-92. 10.25295/fsecon.2018.02.005
  • Sinlapates, P. & Chancharat, S. (2024). Impact of oil and gold prices on southeast asian stock markets: Empirical evidence from quantile regression analysis. ABAC Journal, 44(2), 123-137. https://doi.org/10.59865/abacj.2024.17
  • Smailović, J., Grčar, M., Žnidaršič, M. & Lavrač, N. (2012). Sentiment analysis on tweets in a financial domain. 4th Jožef Stefan International Postgraduate School Students Conference (Vol. 1, p. 169-175).
  • Su, X. & Li, Y. (2020). Dynamic sentiment spillover among crude oil, gold, and bitcoin markets: Evidence from time and frequency domain analyses. PLoS ONE 15(12), e0242515. https://doi.org/10.1371/journal.pone.0242515
  • Tolu, F. (2020). Relationship between London Ftse100 exchange index and gold prices. Journal of Knowledge Economy & Knowledge Management, 15(1). 59-70.
  • US Apple Store (2024). https://www.apple.com/
  • Vo, A., Chapman, T. A. & Lee, Y.-S. (2021). Examining bitcoin and economic determinants: An evolutionary perspective. Journal of Computer Information Systems, 1-15. https://doi.org/10.1080/08874417.2020.1865851
  • Vu, T. T., Chang, S., Ha, Q. T. & Collier, N. (2012). An experiment in integrating sentiment features for tech stock prediction in Twitter. Proceedings of the Workshop on Information Extraction and Entity Analytics on Social Media Data (p. 23-38). https://aclanthology.org/W12-5503.pdf
  • Wang, Z., Bouri, E., Ferreira, P., Shahzad, S. J. H. & Ferrer, R. (2022). A grey-based correlation with multi-scale analysis: S&P 500 VIX and individual VIXs of large US company stocks. Finance Research Letters, 48, 102872.
  • XAUUSD (2024). XAU/USD | Gold Spot US Dollar Price. https://www.investing.com/ currencies/xau-usd
  • Yermack, D. (2013). Is bitcoin a real currency?. NBER Working Paper No. 19747, National Bureau of Economic Research, 1-12. http://www.nber.org/papers/w19747
Toplam 73 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sermaye Piyasaları
Bölüm Makaleler
Yazarlar

Meltem Keskin 0000-0002-8536-4940

Proje Numarası -
Yayımlanma Tarihi 25 Şubat 2025
Gönderilme Tarihi 26 Mayıs 2024
Kabul Tarihi 18 Kasım 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 9 Sayı: 1

Kaynak Göster

APA Keskin, M. (2025). Comparative Evaluation of Share Values of Five Magnificent Technology Companies with Bitcoin and Gold Prices. Fiscaoeconomia, 9(1), 583-600. https://doi.org/10.25295/fsecon.1490060

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