{"id":"https://openalex.org/W4406081070","doi":"https://doi.org/10.1007/s10462-024-11037-1","title":"Improving Real Estate Investment Trusts (REITs) time-series prediction accuracy using machine learning and technical analysis indicators","display_name":"Improving Real Estate Investment Trusts (REITs) time-series prediction accuracy using machine learning and technical analysis indicators","publication_year":2025,"publication_date":"2025-01-06","ids":{"openalex":"https://openalex.org/W4406081070","doi":"https://doi.org/10.1007/s10462-024-11037-1"},"language":"en","primary_location":{"id":"doi:10.1007/s10462-024-11037-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-024-11037-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-024-11037-1.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10462-024-11037-1.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018430729","display_name":"Fatim Z. Habbab","orcid":"https://orcid.org/0000-0002-6731-8553"},"institutions":[{"id":"https://openalex.org/I110002522","display_name":"University of Essex","ror":"https://ror.org/02nkf1q06","country_code":"GB","type":"education","lineage":["https://openalex.org/I110002522"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Fatim Z. Habbab","raw_affiliation_strings":["Centre for Computational Finance and Economics Agents, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Computational Finance and Economics Agents, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, UK","institution_ids":["https://openalex.org/I110002522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087235995","display_name":"Michael Kampouridis","orcid":"https://orcid.org/0000-0003-0047-7565"},"institutions":[{"id":"https://openalex.org/I110002522","display_name":"University of Essex","ror":"https://ror.org/02nkf1q06","country_code":"GB","type":"education","lineage":["https://openalex.org/I110002522"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Michael Kampouridis","raw_affiliation_strings":["Centre for Computational Finance and Economics Agents, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Computational Finance and Economics Agents, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, UK","institution_ids":["https://openalex.org/I110002522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010875322","display_name":"Tasos Papastylianou","orcid":"https://orcid.org/0000-0001-5010-6979"},"institutions":[{"id":"https://openalex.org/I110002522","display_name":"University of Essex","ror":"https://ror.org/02nkf1q06","country_code":"GB","type":"education","lineage":["https://openalex.org/I110002522"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tasos Papastylianou","raw_affiliation_strings":["Institute of Public Health and Wellbeing, University of Essex, Wivenhoe Park, Colchester, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Public Health and Wellbeing, University of Essex, Wivenhoe Park, Colchester, UK","institution_ids":["https://openalex.org/I110002522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018430729"],"corresponding_institution_ids":["https://openalex.org/I110002522"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":7.0795,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.95654329,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"58","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10632","display_name":"Housing Market and Economics","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10632","display_name":"Housing Market and Economics","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6332780718803406},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.604595422744751},{"id":"https://openalex.org/keywords/real-estate-investment-trust","display_name":"Real estate investment trust","score":0.597438395023346},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5312266945838928},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5272276401519775},{"id":"https://openalex.org/keywords/real-estate","display_name":"Real estate","score":0.5029265284538269},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45460718870162964},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.4478706121444702},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4204673767089844},{"id":"https://openalex.org/keywords/portfolio","display_name":"Portfolio","score":0.41328659653663635},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3993085026741028},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3915863335132599},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37782222032546997},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.23796826601028442},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.23119577765464783},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.21367040276527405}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6332780718803406},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.604595422744751},{"id":"https://openalex.org/C38815200","wikidata":"https://www.wikidata.org/wiki/Q697852","display_name":"Real estate investment trust","level":3,"score":0.597438395023346},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5312266945838928},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5272276401519775},{"id":"https://openalex.org/C82279013","wikidata":"https://www.wikidata.org/wiki/Q684740","display_name":"Real estate","level":2,"score":0.5029265284538269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45460718870162964},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.4478706121444702},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4204673767089844},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.41328659653663635},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3993085026741028},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3915863335132599},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37782222032546997},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.23796826601028442},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.23119577765464783},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.21367040276527405}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10462-024-11037-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-024-11037-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-024-11037-1.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10462-024-11037-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10462-024-11037-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10462-024-11037-1.pdf","source":{"id":"https://openalex.org/S122814990","display_name":"Artificial Intelligence Review","issn_l":"0269-2821","issn":["0269-2821","1573-7462"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Review","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406081070.pdf","grobid_xml":"https://content.openalex.org/works/W4406081070.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W85561527","https://openalex.org/W1519302679","https://openalex.org/W1575985025","https://openalex.org/W1910154508","https://openalex.org/W1982553641","https://openalex.org/W1982981909","https://openalex.org/W1986782333","https://openalex.org/W1993496161","https://openalex.org/W1993657680","https://openalex.org/W1997364324","https://openalex.org/W2016597455","https://openalex.org/W2019448438","https://openalex.org/W2047094503","https://openalex.org/W2085317472","https://openalex.org/W2109316012","https://openalex.org/W2115992801","https://openalex.org/W2117502083","https://openalex.org/W2501286582","https://openalex.org/W2508977414","https://openalex.org/W2576905002","https://openalex.org/W2588718483","https://openalex.org/W2610314771","https://openalex.org/W2612690371","https://openalex.org/W2811507150","https://openalex.org/W2901852093","https://openalex.org/W2935877504","https://openalex.org/W2967131053","https://openalex.org/W2977860057","https://openalex.org/W2978722213","https://openalex.org/W3010613921","https://openalex.org/W3036201112","https://openalex.org/W3036910872","https://openalex.org/W3044123268","https://openalex.org/W3083080466","https://openalex.org/W3110845139","https://openalex.org/W3124554734","https://openalex.org/W3125435015","https://openalex.org/W3144813758","https://openalex.org/W3163308004","https://openalex.org/W3183637089","https://openalex.org/W3191081466","https://openalex.org/W3206048862","https://openalex.org/W4237481653","https://openalex.org/W4240503114","https://openalex.org/W4280516539","https://openalex.org/W4283031855","https://openalex.org/W4294811498","https://openalex.org/W4299833825","https://openalex.org/W4300337452","https://openalex.org/W4308381112","https://openalex.org/W4312640728","https://openalex.org/W4384024076","https://openalex.org/W4385488618","https://openalex.org/W4385880715","https://openalex.org/W4387007542"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W3162092757","https://openalex.org/W2610455874","https://openalex.org/W3135881084","https://openalex.org/W2380590035","https://openalex.org/W31566076","https://openalex.org/W2266644394","https://openalex.org/W407002902","https://openalex.org/W2351712633","https://openalex.org/W93644405"],"abstract_inverted_index":{"Abstract":[0],"The":[1,132],"primary":[2],"goal":[3],"of":[4,26,62,126,173,286,298],"investors":[5],"who":[6],"include":[7,75],"Real":[8],"Estate":[9],"Investment":[10],"Trusts":[11],"(REITs)":[12],"in":[13,108,117,123,195,229,234,238,244,290,306],"their":[14,27,121],"portfolios":[15],"is":[16,48],"to":[17,50,69,106,137,170,242,282],"achieve":[18,41],"better":[19],"returns":[20,43],"while":[21,44],"reducing":[22,45],"the":[23,59,124,145,196,230,284,287,291,299],"overall":[24],"risk":[25],"investments.":[28],"REITs":[29,127,143],"are":[30,99,114],"entities":[31],"responsible":[32],"for":[33,141],"owning":[34],"and":[35,90,150,156,191,218,247,280],"managing":[36],"real":[37],"estate":[38],"properties.":[39],"To":[40],"greater":[42],"risk,":[46],"it":[47],"essential":[49],"accurately":[51],"predict":[52,70,138],"future":[53,139],"REIT":[54,71,97],"prices.":[55,72,270],"This":[56],"study":[57,133,273],"explores":[58],"predictive":[60],"capability":[61],"five":[63],"different":[64],"machine":[65,185],"learning":[66,186],"algorithms":[67,74,136,187,232],"used":[68,116,202,289],"These":[73,293],"Ordinary":[76],"Least":[77],"Squares":[78],"Linear":[79,208],"Regression,":[80,83,86],"Support":[81],"Vector":[82],"k-Nearest":[84],"Neighbours":[85],"Extreme":[87],"Gradient":[88],"Boosting,":[89],"Long/Short-Term":[91],"Memory":[92],"Neural":[93],"Networks.":[94],"Additionally,":[95],"historical":[96],"prices":[98,140],"supplemented":[100],"with":[101,153],"Technical":[102,226,257,303],"Analysis":[103,227,258,304],"indicators":[104,113,228,259,305],"(TAIs)":[105],"aid":[107],"price":[109,162],"predictions.":[110],"While":[111],"TA":[112],"commonly":[115,201],"stock":[118],"market":[119],"forecasting,":[120],"application":[122],"context":[125],"has":[128],"remained":[129],"relatively":[130],"unexplored.":[131],"applied":[134],"these":[135],"30":[142,154,157],"from":[144,268],"United":[146,148],"States,":[147],"Kingdom,":[149],"Australia,":[151],"along":[152],"stocks":[155],"bonds.":[158],"After":[159],"obtaining":[160],"our":[161],"predictions,":[163],"we":[164],"employ":[165],"a":[166,174,235,249,261],"Genetic":[167],"Algorithm":[168],"(GA)":[169],"optimise":[171],"weights":[172],"diversified":[175],"portfolio.":[176],"Our":[177],"results":[178],"reveal":[179],"several":[180],"key":[181],"findings:":[182],"(i)":[183],"all":[184],"demonstrated":[188],"low":[189],"average":[190],"standard":[192],"deviation":[193],"values":[194],"error":[197],"rate":[198],"distributions,":[199],"outperforming":[200],"statistical":[203],"benchmarks":[204],"such":[205],"as":[206],"Holt\u2019s":[207],"Trend":[209],"Method":[210],"(HLTM),":[211],"Trigonometric":[212],"Box-Cox":[213],"Autoregressive":[214,219],"Time":[215],"Series":[216],"(TBATS),":[217],"Integrated":[220],"Moving":[221],"Average":[222],"(ARIMA);":[223],"(ii)":[224],"incorporating":[225],"ML":[231],"resulted":[233],"significant":[236],"reduction":[237],"prediction":[239],"errors,":[240],"up":[241],"60%":[243],"some":[245],"cases;":[246],"(iii)":[248],"multi-asset":[250],"portfolio":[251,262],"constructed":[252],"using":[253],"predictions":[254,266],"that":[255],"incorporated":[256],"outperformed":[260],"based":[263],"solely":[264],"on":[265],"derived":[267],"past":[269],"Furthermore,":[271],"this":[272,307],"employed":[274],"Shapley":[275],"Value-based":[276],"techniques,":[277],"specifically":[278],"SHAP":[279],"SAGE,":[281],"analyse":[283],"importance":[285],"features":[288],"analysis.":[292],"techniques":[294],"provided":[295],"additional":[296],"evidence":[297],"value":[300],"added":[301],"by":[302],"context.":[308]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-01-06T00:00:00"}
