Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to *** random walk hypothesis and efficient market hypothesis essentially state that it is not possible to sy...
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Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to *** random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically,reliably predict future stock prices or forecast changes in the stock market ***,machine learning(ML)techniques that use historical data have been applied to make such *** studies focused on a small number of stocks and claimed success with limited statistical *** this study,we construct feature vectors composed of multiple previous relative returns and apply the random forest(RF),support vector machine(SVM),and long short-term memory(LSTM)ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 *** apply this approach to all S&P 500 companies for the period *** assess performance using accuracy,precision,and recall and compare our results with a random choice *** observe that the LSTM classifier outperforms RF and SVM,and the data-driven ML methods outperform the random choice classifier(p=8.46e^(-17) for accuracy of LSTM).Thus,we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.
Automated Short Answer Grading (ASAG) has emerged as a promising tool for the challenge of assessing open student responses in an efficient and scalable manner as manual grading of such open short answers is labor-int...
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A major problem in system identification is the incorporation of prior knowledge about the physical properties of the given system, such as stability, positivity and passivity. In this paper, we present first steps to...
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In this work, we explore different linear mapping techniques to learn cross-lingual document representations from pre-trained multilingual large language models for low-resource languages. Three different mapping tech...
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Recommendation systems, for documents, have become tools for finding relevant content on the Web. However, these systems have limitations when it comes to recommending documents in languages different from the query l...
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In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price *** review papers in the literature have focused...
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In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price *** review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market ***,no survey study has explored feature selection and extraction techniques for stock market *** survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market *** conduct a systematic search for articles in the Scopus and Web of science databases for the years 2011–*** review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the *** also describe the combination of feature analysis techniques and ML methods and evaluate their ***,we present other survey articles,stock market input and output data,and analyses based on various *** find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.
We summarize the first Workshop on the Design of Responsible Hybrid intelligence (RHI2023), co-located with the 2st International Conference on Hybrid Human-artificialintelligence (HHAI 2022), held on June 27, 2022 i...
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With the growing integration of chatbots, automated writing tools, game AI and similar applications into human society, there is a clear demand for artificially intelligent systems that can successfully collaborate wi...
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Software engineers share their architectural knowledge (AK) in different places on the Web. Recent studies show that architectural blogs contain the most relevant AK, which can help software engineers to make design s...
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Nowadays, electronic waste is no longer considered ordinary waste;instead, it is recognized as valuable and hazardous waste containing significant amounts of precious metals. Therefore, it should not be disposed of il...
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