Literatura académica sobre el tema "Stock price prediction"
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Artículos de revistas sobre el tema "Stock price prediction"
BIKSHAM, V., B. VISHAL KUMAR, C. RAHUL, G. VENU y M. BHARGAV SAI. "STOCK PRICE PREDICTION". YMER Digital 21, n.º 05 (2 de mayo de 2022): 1–6. http://dx.doi.org/10.37896/ymer21.05/01.
Texto completoCheruvu, Sai Manoj. "Stock Price Prediction Using Time Series". International Journal for Research in Applied Science and Engineering Technology 9, n.º 12 (31 de diciembre de 2021): 375–81. http://dx.doi.org/10.22214/ijraset.2021.39296.
Texto completoAl-Hasnawi, Salim Sallal y Laith Haleem Al-Hchemi*. "CLOSING PRICE PREDICTION OF STOCK LISTED ON THE IRAQ STOCK EXCHANGE USING ANN-LSTM". JURISMA : Jurnal Riset Bisnis & Manajemen 12, n.º 2 (30 de octubre de 2022): 173–85. http://dx.doi.org/10.34010/jurisma.v12i2.8103.
Texto completoPeng, Luna. "Stock Price Prediction of “Google” based on Machine Learning". BCP Business & Management 34 (14 de diciembre de 2022): 912–18. http://dx.doi.org/10.54691/bcpbm.v34i.3111.
Texto completoRammurthy, Shruthi Komarla y Sagar B. Patil. "An LSTM-Based Approach to Predict Stock Price Movement for IT Sector Companies". International Journal of Cognitive Informatics and Natural Intelligence 15, n.º 4 (octubre de 2021): 1–12. http://dx.doi.org/10.4018/ijcini.20211001.oa3.
Texto completoBhavanagarwala, Mustafa Shabbir, Nagarjun K N, Tanzim Abbas Charolia, Vishal M y Ashwini M. "STOCK AND CRYPTOCURRENCY PREDICTION". International Journal of Innovative Research in Advanced Engineering 9, n.º 8 (12 de agosto de 2022): 182–86. http://dx.doi.org/10.26562/ijirae.2022.v0908.06.
Texto completoTOKMAK, Mahmut. "Stock Price Prediction Using Long-Short-Term Memory Network". Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 6, n.º 2 (29 de septiembre de 2022): 309–22. http://dx.doi.org/10.31200/makuubd.1164099.
Texto completoZhong, Shan y David Hitchcock. "S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data". Statistics, Optimization & Information Computing 9, n.º 4 (18 de noviembre de 2021): 769–88. http://dx.doi.org/10.19139/soic-2310-5070-1362.
Texto completoZhao, Cheng, Xiaohui Liu, Jie Zhou, Yuefeng Cen y Xiaomin Yao. "GCN-based stock relations analysis for stock market prediction". PeerJ Computer Science 8 (11 de agosto de 2022): e1057. http://dx.doi.org/10.7717/peerj-cs.1057.
Texto completoL, Dushyanth. "A SURVEY ON STOCK PRICE PREDICTION USING DEEP LEARNING". International Research Journal of Computer Science 9, n.º 2 (28 de febrero de 2022): 5–8. http://dx.doi.org/10.26562/irjcs.2022.v0902.002.
Texto completoTesis sobre el tema "Stock price prediction"
Ghodsi, Boushehri Ali. "Applying fuzzy logic to stock price prediction". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ54332.pdf.
Texto completoEadie, Edward Norman. "Small resource stock share price behaviour and prediction". Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.
Texto completoWang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction". Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.
Texto completoDen nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction". Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.
Texto completoBurgard, Andrew. "Can Business News Provide Insight into a Stock’s Future Price Performance?" Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1673.
Texto completoTroeman, Reamflar Elvio Estebano y Lisa Fischer. "Politics, Artificial Intelligence, Twitter and Stock Return : An Interdisciplinary Test for Stock Price Prediction Based on Political Tweets". Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48436.
Texto completoVan, Niekerk J. P. de T. "The application of neural networks to the prediction of share price indices on the JSE". Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/53086.
Texto completoENGLISH ABSTRACT: The dream of finding the ultimate tool for forecasting market instruments like share prices has long eluded investors throughout the world. Various forecasting techniques have been examined with a view to helping the investor or analyst to gain a better understanding of price behaviour in the open market. These techniques have been based mainly on traditional statistical analysis of data to forecast price behaviour. Though used by almost all serious investors, these techniques have yielded limited success as investment instruments. The reason for this is that most of these methods explored linear relationships between variables in the forecasting model, while in fact, most relationships found between variables in the share market are non-linear. Neural networks present a unique opportunity for the investor to overcome this problem. Neural networks are mathematical models of the human brain and have the ability to map complex nonlinear relationships between data sets. This study focuses on developing a neural network model to predict the price changes of the ALSI index on the JSE one and five days into the future. The results of the neural network model were then compared to forecasting results obtained by using a traditional statistical forecasting technique namely ARIMA modelling. The study found that the neural network models did not significantly perform better than the ARIMA models. A further test was done to determine the performance of the five-day forecasting model when analysing different time windows within the given data set. The test indicated that the model did perform better when using the inputs of certain time frames. This indicates that the neural network model needs to be updated regularly to ensure optimum model performance. The results of the neural network models were also used in a trading simulation to determine whether these results could be applied successfully to trading the ALSI index on the JSE. Unfortunately, the results of the trading simulation showed that using the neural network results as trading strategy yielded poorer results than using a buy/hold investment strategy. It can therefore be concluded that, although the neural network models performed relatively well relative to traditional forecasting techniques in forecasting the ALSI index, the forecasts were still not accurate enough to be useful as inputs in a trading strategy.
AFRIKAANSE OPSOMMING: Die droom om die perfekte vooruitskattingsinstrument te vind om die prysgedrag van verskillende markinstrumente vooruit te skat, ontwyk al generasies lank die meeste beleggers. Verskillende tegnieke is al ondersoek om die belegger te help om ’n beter gevoel van prysveranderinge in die vrye mark te verkry. Die meeste van hierdie tegnieke het gefokus op tradisionele statistiese vooruitskattingstegnieke. Alhoewel hierdie tegnieke wêreldwyd deur investeerders gebruik word, was hierdie metodes se sukses as investeringsinstrument beperk. Die rede vir hierdie beperkte sukses lê in die feit dat hierdie tegnieke slegs die lineêre verwantskappe tussen veranderlikes gebruik het om voorspellings te maak, terwyl die meeste verwantskappe wat tussen veranderlikes in die vrye mark bestaan, nie-lineêr is. Neurale netwerke bied ’n unieke geleentheid vir beleggers om bogenoemde probleme te oorkom. Neurale netwerke is wiskundige modelle wat op die werking van die menslike brein gebaseer is en besit die vermoë om komplekse nie-lineêre verwantskappe tussen datastelle te herken. Hierdie studie fokus op die ontwikkeling van ’n neurale netwerk(e) om die prysverandering van die ALSI indeks op die JEB een en vyf dae in die toekoms vooruit te skat. Die resultate van die neurale netwerk model is verder vergelyk met die resultate van tradisionele statistiese vooruitskattingstegnieke soos byvoorbeeld ARIMA tegnieke. Die studie het gevind dat die neurale netwerk modelle nie beduidend beter gevaar het as die ARIMA modelle in die vooruitskatting van die ALSI indeks in beide die een- en vyfdag vooruitskattings nie. ’n Verdere toets is gedoen om die toepaslikheid van die gekose vyfdagmodel op verskillende tydvensters van die tydreeks te bepaal. Die toets het aangetoon dat die model baie meer akkuraat is vir sekere tydvensters as vir ander tydvensters. Dit dui dus daarop dat die neurale netwerk model gereeld heropgelei behoort te word om seker te maak dat die model optimaal presteer gegewe die spesifieke insetdata. Die resultate van die neurale netwerk model is ook gebruik in ’n simulasie om te bepaal of die resultate die belegger kan help om beter investeringsbesluite rakende die ALSI indeks op die JEB te maak. Ongelukkig het die simulasie resultate gewys dat ’n beleggingstrategie gebaseer op die neurale netwerk resultate swakker opbrengste gerealiseer het as ’n koop/hou beleggingstrategie. Ten slotte het die studie getoon dat alhoewel die neurale netwerk modelle relatief goed in vergelyking met tradisionele statistiese modelle gevaar het in die vooruitskatting van die ALSI indeks, hierdie vooruitskattings nie akkuraat genoeg is om as inset tot ’n investeringstrategie gebruik te word nie.
Karlemstrand, Roderick y Ebba Leckström. "Using Twitter Attribute Information to Predict Stock Prices". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299835.
Texto completoAtt kunna förutspå aktiekurser kan sägas vara aktiespararnas outtalade önskan. Även om aktievärden är komplicerade att förutspå finns det många teorier om vad som påverkar dess rörelser, bland annat räntor, nyheter och sociala medier. Med hjälp av maskininlärning kan mönster i data identifieras bortom människans intellekt. I detta examensarbete skapas och testas en modell inom maskininlärning i syfte att beräkna framtida aktiepriser. Modellen baseras på ett neuralt nätverk med flera lager av LSTM och fullt kopplade lager. Den tränas med historiska aktievärden, tekniska indikatorer och Twitter-attributinformation. De är hämtad, extraherad och beräknad från inlägg på den sociala plattformen Twitter. Dessa attribut är sentiment-värde, antal favorit-markeringar, följare, retweets och om kontot är verifierat. För att samla in data från Twitter används Twitters API och sentimentanalys genomförs genom VADER. Resultatet visar att genom att lägga till fler Twitter attribut förbättrade MSE mellan de förutspådda värdena och de faktiska värdena med 3%. Genom att ta teknisk analys i beaktande minskar MSE från 0,1617 till 0,1437, vilket är en förbättring på 11%. Begränsningar i denna studie innefattar bland annat att den utvalda aktien ska vara publikt listad på börsen och populär på Twitter och bland småspararna. Dessutom skiljer sig aktiemarknadens öppettider från Twitter då den är ständigt tillgänglig. Detta kan då introducera brus i modellen.
Kennedy, Pauline. "Three essays on the prediction and identification of currency crises /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3102540.
Texto completoYin, Pei. "Volatility estimation and price prediction using a hidden Markov model with empirical study". Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4795.
Texto completoThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on December 18, 2007) Vita. Includes bibliographical references.
Libros sobre el tema "Stock price prediction"
J, Millard Brian. Channel analysis: The key to share price prediction. 2a ed. Chichester, England: J. Wiley, 1997.
Buscar texto completoChannel analysis: The key to share price prediction. Bramhall: Qudos, 1990.
Buscar texto completoGoval, Amit. A comprehensive look at the empirical performance of equity premium prediction. Cambridge, MA: National Bureau of Economic Research, 2004.
Buscar texto completoLin, Jie-Shin. The top management changes of construction firms: Stock price reaction and possibility of prediction. Manchester: UMIST, 1996.
Buscar texto completoStevens, Leigh. Essential technical analysis: Tools and techniques to spot market trends. New York, NY: Wiley, 2002.
Buscar texto completoEssential technical analysis: Tools and techniques to spot market trends. New York: Wiley, 2002.
Buscar texto completoThe spiral calendar: And its effects on financial markets and human events. Gainesville, Ga: New Classics Library, 1992.
Buscar texto completoGoyal, Amit. Predicting the equity premium with dividend ratios. Cambridge, MA: National Bureau of Economic Research, 2002.
Buscar texto completoI, Ellison George, ed. Stock returns cyclicity, prediction and economic consequences. Hauppauge, NY: Nova Science Publishers, 2009.
Buscar texto completoBaker, Malcolm. Pseudo market timing and predictive regressions. Cambridge, Mass: National Bureau of Economic Research, 2004.
Buscar texto completoCapítulos de libros sobre el tema "Stock price prediction"
Joyce, Philip. "Stock Price Prediction". En Practical Numerical C Programming, 73–92. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6128-6_4.
Texto completoD’Mello, Lynette, Aditya Jeswani y Janice Johnson. "Stock Price Prediction Using Grammatical Evolution". En Algorithms for Intelligent Systems, 379–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_36.
Texto completoRani, Poonam, Jyoti Shokeen, Anshul Singh, Anmol Singh, Sharlin Kumar y Naman Raghuvanshi. "Stock Price Prediction Using Reinforcement Learning". En Advances in Intelligent Systems and Computing, 69–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2597-8_6.
Texto completoLiu, Ping. "Stock Price Prediction Using Deep Learning". En Applied Economics and Policy Studies, 196–200. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0564-3_20.
Texto completoDevi, Sanjana y Virrat Devaser. "Stock Market Price Prediction Using SAP Predictive Service". En Communications in Computer and Information Science, 135–48. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3140-4_13.
Texto completoR., Abirami y Vijaya M.S. "Stock Price Prediction Using Support Vector Regression". En Communications in Computer and Information Science, 588–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29219-4_67.
Texto completoChanduka, Bhabesh, Swati S. Bhat, Neha Rajput y Biju R. Mohan. "A TFD Approach to Stock Price Prediction". En Intelligent Computing and Communication, 635–44. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_61.
Texto completoPawar, Kriti, Raj Srujan Jalem y Vivek Tiwari. "Stock Market Price Prediction Using LSTM RNN". En Advances in Intelligent Systems and Computing, 493–503. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2285-3_58.
Texto completoBarik, Rhada, Amine Baina y Mostafa Bellafkih. "The Prediction Stock Market Price Using LSTM". En Lecture Notes on Data Engineering and Communications Technologies, 444–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15191-0_42.
Texto completoDas, Suman Kumar, Soumyabrata Saha y Suparna DasGupta. "Prediction of Stock Price Using Machine Learning". En Studies in Autonomic, Data-driven and Industrial Computing, 141–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7305-4_15.
Texto completoActas de conferencias sobre el tema "Stock price prediction"
Agarwal, Shiva y Naresh Babu Muppalaneni. "Stock Market Price and Cryptocurrency Price Prediction". En 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2022. http://dx.doi.org/10.1109/icdcece53908.2022.9793088.
Texto completoVora, Chintan, Dhairya Sheth, Bhavya Shah y Nasim Banu Shah. "Stock Price Analysis and Prediction". En 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. http://dx.doi.org/10.1109/iccict50803.2021.9510159.
Texto completoBai, Muqing y Yu Sun. "An Intelligent and Social-Oriented Sentiment Analytical Model for Stock Market Prediction using Machine Learning and Big Data Analysis". En 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121819.
Texto completoLi, Wei, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu y Qi Su. "Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/626.
Texto completoGupta, Rubi y Min Chen. "Sentiment Analysis for Stock Price Prediction". En 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2020. http://dx.doi.org/10.1109/mipr49039.2020.00051.
Texto completoRoy, Ranjan Kumar, Koyel Ghosh y Apurbalal Senapati. "Stock Price Prediction: LSTM Based Model". En Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.19.
Texto completoRuan, Jinlong, Wei Wu y Jiebo Luo. "Stock Price Prediction Under Anomalous Circumstances". En 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378030.
Texto completoTiwari, Shashank, Akshay Bharadwaj y Sudha Gupta. "Stock price prediction using data analytics". En 2017 International Conference on Advances in Computing, Communication and Control (ICAC3). IEEE, 2017. http://dx.doi.org/10.1109/icac3.2017.8318783.
Texto completoYoshida, Kenichi. "Interpreting Attention of Stock Price Prediction". En 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2022. http://dx.doi.org/10.1109/compsac54236.2022.00200.
Texto completoSidogi, Thendo, Rendani Mbuvha y Tshilidzi Marwala. "Stock Price Prediction Using Sentiment Analysis". En 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2021. http://dx.doi.org/10.1109/smc52423.2021.9659283.
Texto completoInformes sobre el tema "Stock price prediction"
Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani y Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, marzo de 2019. http://dx.doi.org/10.34074/ocds.12019.
Texto completoDerbentsev, V., A. Ganchuk y Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.
Texto completoWideman, Jr., Robert F., Nicholas B. Anthony, Avigdor Cahaner, Alan Shlosberg, Michel Bellaiche y William B. Roush. Integrated Approach to Evaluating Inherited Predictors of Resistance to Pulmonary Hypertension Syndrome (Ascites) in Fast Growing Broiler Chickens. United States Department of Agriculture, diciembre de 2000. http://dx.doi.org/10.32747/2000.7575287.bard.
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