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' Forex exchange rate forecasting using deep recurrent neural networks,'Дigital Finance, Springer, vol. Alexander Jakob Dautel & Wolfgang Karl HГ¤rdle & Stefan Lessmann & Hsin-Vonn Seow, 2020.International Journal of Forecasting, Elsevier, vol. ' Deep learning for modeling the collection rate for third-party buyers,' Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J.' Credit spread approximation and improvement using random forest regression,'Д®uropean Journal of Operational Research, Elsevier, vol. Mercadier, Mathieu & Lardy, Jean-Pierre, 2019.' Deep learning with long short-term memory networks for financial market predictions,'ДЎ1/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics. Fischer, Thomas & Krauss, Christopher, 2017.Pacific-Basin Finance Journal, Elsevier, vol. ' Algorithmic and high frequency trading in Asia-Pacific, now and the future,' Physica A: Statistical Mechanics and its Applications, Elsevier, vol. ' Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning,' ' Exploiting social media with higher-order Factorization Machines: Statistical arbitrage on high-frequency data of the S&P 500,'ДЎ3/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics. Knoll, Julian & StГјbinger, Johannes & Grottke, Michael, 2017.' Reinforcement learning in financial markets - a survey,'ДЎ2/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics. ' Deep Portfolio Optimization via Distributional Prediction of Residual Factors,' Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020.' Stock Market Analysis with Text Data: A Review,' Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021.These are the items that most often cite the same works as this one and are cited by the same works as this one. 456(C), pages 10-21.ДЇull references (including those not matched with items on IDEAS)

#STOCKTWITS OMER SERIES#

' Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model,'

Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016.

' Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30,'ДЄpplied Economics Letters, Taylor & Francis Journals, vol.

Terence Tai-Leung Chong & Wing-Kam Ng, 2008.

PLOS ONE, Public Library of Science, vol. ' Statistical and Machine Learning forecasting methods: Concerns and ways forward,'

Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018.

' The profitability of moving average trading rules in South Asian stock markets,'Д®merging Markets Review, Elsevier, vol.

Gunasekarage, Abeyratna & Power, David M., 2001.

Global Journal of Business Research, The Institute for Business and Finance Research, vol. ' A Simplified Perspective Of The Markowitz Portfolio Theory,' ' Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,'

Christopher Krauss & Anh Do & Nicolas Huck, 2017.

' From Efficient Market Theory to Behavioral Finance,'ДЎ385, Cowles Foundation for Research in Economics, Yale University. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. (Department of Electrical Engineering, Imperial College London, London SW7 2AZ, UK) (Media2Day Publishing S.A., 15232 Athens, Greece) (Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

Stocktwits omer