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Svíčky a grafové vzorce v kryptoměnách
dc.contributor.advisorKrištoufek, Ladislav
dc.creatorKuna, Václav
dc.date.accessioned2025-02-25T10:03:03Z
dc.date.available2025-02-25T10:03:03Z
dc.date.issued2025
dc.identifier.urihttp://hdl.handle.net/20.500.11956/197060
dc.description.abstractThis thesis investigates the effectiveness of technical analysis, focusing on can- dlestick patterns, in cryptocurrency markets characterized by high volatility and continuous trading. Using statistical methods, including skewness-adjusted t-test and binomial test, the study evaluates 41 bullish and bearish patterns across five datasets: four datasets covering cryptocurrencies in general (excluding stable- coins) and one specific to stablecoins. Gap-dependent patterns were rare due to the continuous trading nature of cryptocurrency markets. Eight patterns demon- strated predictive potential in the non-stablecoin datasets, though two produced returns contrary to their bearish classification. The most compelling patterns are Hammer Bullish, Rising Window Bullish, On Neek Bearish, and Shooting Star Bearish, which produced returns contrary to its bearish classification, as they ap- pear in three datasets. In contrast, the stablecoin dataset showed Doji Star Bullish and Doji Star Bearish as significant; however, these likely reflect price-stabilization mechanisms rather than intrinsic predictive properties. By leveraging large, di- verse datasets and employing modern trend-definition methodology, the study highlights the limited applicability of traditional candlestick patterns and ques- tions the...cs_CZ
dc.description.abstractThis thesis investigates the effectiveness of technical analysis, focusing on can- dlestick patterns, in cryptocurrency markets characterized by high volatility and continuous trading. Using statistical methods, including skewness-adjusted t-test and binomial test, the study evaluates 41 bullish and bearish patterns across five datasets: four datasets covering cryptocurrencies in general (excluding stable- coins) and one specific to stablecoins. Gap-dependent patterns were rare due to the continuous trading nature of cryptocurrency markets. Eight patterns demon- strated predictive potential in the non-stablecoin datasets, though two produced returns contrary to their bearish classification. The most compelling patterns are Hammer Bullish, Rising Window Bullish, On Neek Bearish, and Shooting Star Bearish, which produced returns contrary to its bearish classification, as they ap- pear in three datasets. In contrast, the stablecoin dataset showed Doji Star Bullish and Doji Star Bearish as significant; however, these likely reflect price-stabilization mechanisms rather than intrinsic predictive properties. By leveraging large, di- verse datasets and employing modern trend-definition methodology, the study highlights the limited applicability of traditional candlestick patterns and ques- tions the...en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Fakulta sociálních vědcs_CZ
dc.subjectTechnická analýzacs_CZ
dc.subjectSvíčkové grafycs_CZ
dc.subjectVzory svíčkových grafůcs_CZ
dc.subjectGrafové vzorycs_CZ
dc.subjectKryptoměnycs_CZ
dc.subjectBitcoincs_CZ
dc.subjectJednoduchý klouzavý průměrcs_CZ
dc.subjectCaginalp-Laurent strategie výstupucs_CZ
dc.subjectTest t s úpravou na šikmostcs_CZ
dc.subjectKladivocs_CZ
dc.subjectNa krkucs_CZ
dc.subjectStoupající oknocs_CZ
dc.subjectPadající hvězdacs_CZ
dc.subjectTechnical analysisen_US
dc.subjectCandlesticksen_US
dc.subjectCandlestick patternsen_US
dc.subjectChart patternsen_US
dc.subjectCryptocurrenciesen_US
dc.subjectBitcoinen_US
dc.subjectSimple moving averageen_US
dc.subjectCaginalp-Laurent exit strategyen_US
dc.subjectSkewness adjusted t-testen_US
dc.subjectHammeren_US
dc.subjectOn Necken_US
dc.subjectRising Windowen_US
dc.subjectShooting Staren_US
dc.titleCandlesticks and graph patterns in cryptocurrenciesen_US
dc.typebakalářská prácecs_CZ
dcterms.created2025
dcterms.dateAccepted2025-02-04
dc.description.departmentInstitute of Economic Studiesen_US
dc.description.departmentInstitut ekonomických studiícs_CZ
dc.description.facultyFaculty of Social Sciencesen_US
dc.description.facultyFakulta sociálních vědcs_CZ
dc.identifier.repId249380
dc.title.translatedSvíčky a grafové vzorce v kryptoměnáchcs_CZ
dc.contributor.refereeTrubelík, Ivan
thesis.degree.nameBc.
thesis.degree.levelbakalářskécs_CZ
thesis.degree.disciplineEconomics and Financeen_US
thesis.degree.disciplineEkonomie a financecs_CZ
thesis.degree.programEconomics and Financeen_US
thesis.degree.programEkonomie a financecs_CZ
uk.thesis.typebakalářská prácecs_CZ
uk.taxonomy.organization-csFakulta sociálních věd::Institut ekonomických studiícs_CZ
uk.taxonomy.organization-enFaculty of Social Sciences::Institute of Economic Studiesen_US
uk.faculty-name.csFakulta sociálních vědcs_CZ
uk.faculty-name.enFaculty of Social Sciencesen_US
uk.faculty-abbr.csFSVcs_CZ
uk.degree-discipline.csEkonomie a financecs_CZ
uk.degree-discipline.enEconomics and Financeen_US
uk.degree-program.csEkonomie a financecs_CZ
uk.degree-program.enEconomics and Financeen_US
thesis.grade.csVýborněcs_CZ
thesis.grade.enExcellenten_US
uk.abstract.csThis thesis investigates the effectiveness of technical analysis, focusing on can- dlestick patterns, in cryptocurrency markets characterized by high volatility and continuous trading. Using statistical methods, including skewness-adjusted t-test and binomial test, the study evaluates 41 bullish and bearish patterns across five datasets: four datasets covering cryptocurrencies in general (excluding stable- coins) and one specific to stablecoins. Gap-dependent patterns were rare due to the continuous trading nature of cryptocurrency markets. Eight patterns demon- strated predictive potential in the non-stablecoin datasets, though two produced returns contrary to their bearish classification. The most compelling patterns are Hammer Bullish, Rising Window Bullish, On Neek Bearish, and Shooting Star Bearish, which produced returns contrary to its bearish classification, as they ap- pear in three datasets. In contrast, the stablecoin dataset showed Doji Star Bullish and Doji Star Bearish as significant; however, these likely reflect price-stabilization mechanisms rather than intrinsic predictive properties. By leveraging large, di- verse datasets and employing modern trend-definition methodology, the study highlights the limited applicability of traditional candlestick patterns and ques- tions the...cs_CZ
uk.abstract.enThis thesis investigates the effectiveness of technical analysis, focusing on can- dlestick patterns, in cryptocurrency markets characterized by high volatility and continuous trading. Using statistical methods, including skewness-adjusted t-test and binomial test, the study evaluates 41 bullish and bearish patterns across five datasets: four datasets covering cryptocurrencies in general (excluding stable- coins) and one specific to stablecoins. Gap-dependent patterns were rare due to the continuous trading nature of cryptocurrency markets. Eight patterns demon- strated predictive potential in the non-stablecoin datasets, though two produced returns contrary to their bearish classification. The most compelling patterns are Hammer Bullish, Rising Window Bullish, On Neek Bearish, and Shooting Star Bearish, which produced returns contrary to its bearish classification, as they ap- pear in three datasets. In contrast, the stablecoin dataset showed Doji Star Bullish and Doji Star Bearish as significant; however, these likely reflect price-stabilization mechanisms rather than intrinsic predictive properties. By leveraging large, di- verse datasets and employing modern trend-definition methodology, the study highlights the limited applicability of traditional candlestick patterns and ques- tions the...en_US
uk.file-availabilityV
uk.grantorUniverzita Karlova, Fakulta sociálních věd, Institut ekonomických studiícs_CZ
thesis.grade.codeB
uk.publication-placePrahacs_CZ
uk.thesis.defenceStatusO


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