{"id":424782,"date":"2026-08-21T00:38:00","date_gmt":"2026-08-20T16:38:00","guid":{"rendered":"https:\/\/library.usj.edu.mo\/?post_type=tnc_col_19631_item&#038;p=424782"},"modified":"2026-08-24T12:17:52","modified_gmt":"2026-08-24T04:17:52","slug":"an-exploratory-study-of-attitudes-and-perceptions-towards-by-using-mobile-payment-in-macau","status":"publish","type":"tnc_col_19631_item","link":"https:\/\/library.usj.edu.mo\/zh\/student-thesis-dissertation\/an-exploratory-study-of-attitudes-and-perceptions-towards-by-using-mobile-payment-in-macau\/","title":{"rendered":"AN EXPLORATORY STUDY OF ATTITUDES AND PERCEPTIONS TOWARDS BY USING MOBILE PAYMENT IN MACAU"},"content":{"rendered":"<p>WITH GLOBALIZATION AND THE INCREASING POPULARITY OF FINANCIAL MARKETS, A GROWING NUMBER OF INDIVIDUALS AND INSTITUTIONS ARE PARTICIPATING IN STOCK INVESTMENT. PREDICTING STOCK PRICES HOLDS VARIOUS LEVEL OF STRATEGIC IMPORTANCE FOR PARTICIPANTS IN THE FINANCIAL MARKETS. THIS STUDY EVALUATES THE EFFICACY OF TWO METHODOLOGIES, THE MACHINE LEARNING (ML) ALGORITHM \u2013 LONG SHORT-TERM MEMORY (LSTM) AND TIME SERIES ANALYSIS (TSA) \u2013 AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA), IN FORECASTING THE STANDARD &amp; POOR\u2019S 500 (S&amp;P 500) INDEX PRICES AND PROVIDES A COMPREHENSIVE COMPARISON OF THEIR ACCURACY. WITH AN EXPLOSION OF FINANCIAL MARKET DATA, ACCURATE STOCK PRICE FORECASTS ARE ESSENTIAL FOR INVESTMENT DECISIONS. NEVERTHELESS, THE SIGNIFICANT VOLATILITY AND NON-LINEAR ATTRIBUTES OF FINANCIAL TIME SERIES MAKE IT CHALLENGING FOR A SINGULAR METHOD TO COMPLETELY ENCAPSULATE THEIR PATTERNS. THIS STUDY EMPLOYS THE LSTM MODEL AS NON-LINEAR MODELLING INSTRUMENT WITH ML, AND ARIMA SERVES AS BENCHMARK METHOD FOR TRADITIONAL STATISTICAL MODEL. BOTH APPROACHES UTILIZED STANDARDIZED PARAMETER OPTIMIZATION AND TRAINING STRATEGIES, APPLYING DATA COLLECTED FROM 2013 TO 2024 TO PREDICT STOCK PRICES. IN THE INITIAL PERIOD, 90% OF THE DATA (FROM 2013 TO 2023) IS DESIGNATED AS TRAINING DATA, WHILE 10% (FROM 2024) IS ALLOCATED FOR TESTING IN THE SUBSEQUENT PERIOD. FOLLOWING THE MODEL PREDICTIONS, THE MEAN SQUARED ERROR (MSE) AND ROOT MEAN SQUARED ERROR (RMSE) ARE EMPLOYED TO ASSESS THE ACCURACY OF THE LSTM AND ARIMA MODELS AND DETERMINE THEIR APPROPRIATENESS FOR THE INDEX. THE FINDINGS INDICATE THAT THE LSTM MODEL POSSESSES CONSIDERABLE ADVANTAGES IN CAPTURING NON-LINEAR PATTERNS AND LONG-TERM DEPENDENCIES, WHEREAS THE ARIMA MODEL EXCEEDS IN FITTING SHORT TERM LINEAR TRENDS, ALBEIT WITH LIMITED CAPACITY TO MANAGE NON-LINEAR STRUCTURE AND ABRUPT FLUCTUATIONS.<\/p>","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","class_list":["post-424782","tnc_col_19631_item","type-tnc_col_19631_item","status-publish","format-standard","hentry","tnc_tax_19912-business-administration","tnc_tax_19818-english","tnc_tax_19716-faculty-of-business-and-law","tnc_tax_19659-master-thesis","tnc_tax_19651-master"],"taxonomy_info":[],"featured_image_src_large":false,"author_info":[],"comment_info":0,"featured_img":false,"coauthors":[],"author_meta":{"author_link":"https:\/\/library.usj.edu.mo\/zh\/author\/emilychan\/","display_name":"emily chan"},"relative_dates":{"created":"Posted 5 \u5929 ago","modified":"Updated 1 \u5929 ago"},"absolute_dates":{"created":"Posted on 21 8 \u6708, 2026","modified":"Updated on 24 8 \u6708, 2026"},"absolute_dates_time":{"created":"Posted on 21 8 \u6708, 2026 12:38 \u4e0a\u5348","modified":"Updated on 24 8 \u6708, 2026 12:17 \u4e0b\u5348"},"featured_img_caption":"","tax_additional":{"tnc_tax_19912":{"linked":["<a href=\"https:\/\/library.usj.edu.mo\/zh\/programme\/business-administration\/\" class=\"advgb-post-tax-term\">BUSINESS ADMINISTRATION<\/a>"],"unlinked":["<span class=\"advgb-post-tax-term\">BUSINESS ADMINISTRATION<\/span>"],"slug":"tnc_tax_19912","name":"PROGRAMME"},"tnc_tax_19818":{"linked":["<a href=\"https:\/\/library.usj.edu.mo\/zh\/language\/english\/\" class=\"advgb-post-tax-term\">ENGLISH<\/a>"],"unlinked":["<span class=\"advgb-post-tax-term\">ENGLISH<\/span>"],"slug":"tnc_tax_19818","name":"LANGUAGE"},"tnc_tax_19765":{"linked":[],"unlinked":[],"slug":"tnc_tax_19765","name":"SUPERVISOR(S)"},"tnc_tax_19716":{"linked":["<a href=\"https:\/\/library.usj.edu.mo\/zh\/faculty-research-unit\/faculty-of-business-and-law\/\" class=\"advgb-post-tax-term\">FACULTY OF BUSINESS AND LAW<\/a>"],"unlinked":["<span class=\"advgb-post-tax-term\">FACULTY OF BUSINESS AND LAW<\/span>"],"slug":"tnc_tax_19716","name":"FACULTY \/ RESEARCH UNIT"},"tnc_tax_19659":{"linked":["<a href=\"https:\/\/library.usj.edu.mo\/zh\/resource-type\/master-thesis\/\" class=\"advgb-post-tax-term\">Thesis &amp; Dissertation<\/a>"],"unlinked":["<span class=\"advgb-post-tax-term\">Thesis &amp; Dissertation<\/span>"],"slug":"tnc_tax_19659","name":"Document Type"},"tnc_tax_19651":{"linked":["<a href=\"https:\/\/library.usj.edu.mo\/zh\/degree-level\/master\/\" class=\"advgb-post-tax-term\">MASTER<\/a>"],"unlinked":["<span class=\"advgb-post-tax-term\">MASTER<\/span>"],"slug":"tnc_tax_19651","name":"DEGREE LEVEL"}},"series_order":"","_links":{"self":[{"href":"https:\/\/library.usj.edu.mo\/zh\/wp-json\/wp\/v2\/tnc_col_19631_item\/424782","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/library.usj.edu.mo\/zh\/wp-json\/wp\/v2\/tnc_col_19631_item"}],"about":[{"href":"https:\/\/library.usj.edu.mo\/zh\/wp-json\/wp\/v2\/types\/tnc_col_19631_item"}],"replies":[{"embeddable":true,"href":"https:\/\/library.usj.edu.mo\/zh\/wp-json\/wp\/v2\/comments?post=424782"}],"version-history":[{"count":1,"href":"https:\/\/library.usj.edu.mo\/zh\/wp-json\/wp\/v2\/tnc_col_19631_item\/424782\/revisions"}],"predecessor-version":[{"id":431920,"href":"https:\/\/library.usj.edu.mo\/zh\/wp-json\/wp\/v2\/tnc_col_19631_item\/424782\/revisions\/431920"}],"wp:attachment":[{"href":"https:\/\/library.usj.edu.mo\/zh\/wp-json\/wp\/v2\/media?parent=424782"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}