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Forecasting Stock Market Prices
The Journal of Finance, 1977building techniques to publicly available information could have permitted an investor to earn a portfolio return in excess of the return which was commensurate with the portfolio risk. The question of equity market efficiency over time is an area of constant disagreement, especially between practitioners and theoreticians. The disagreement is really a
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Stock Price prediction using LSTM and SVR
2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), 2020Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have
Gourav Bathla
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Stock Prices and Heteroscedasticity
The Journal of Business, 1976This paper provides evidence that the variance of returns on common stocks is not constant through time but is related to the volume of shares traded. In other words, returns on stocks are heteroscedastic. The work extends the approaches of Osborne, Granger and Morgenstern, and Clark.' Distributions of returns are known to be leptokurtic.
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Powerful CEOs and Stock Price Crash Risk
Journal of Corporate Finance, 2020We find that powerful chief executive officers (CEOs) are associated with higher crash risk. The positive association between CEO power and crash risk holds when controlling for earnings management, tax avoidance, chief executive officer's option ...
MD Al Mamun +2 more
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2023
In this study, the last two years' hourly opening and closing prices of the banks' stocks traded on BIST-30 were used as the dataset. The research is aimed to predict the closing prices of these stocks in the light of machine learning. In this context, the authors propose a new method containing ensemble learning algorithms and fuzzy clustering ...
Ahmet Tezcan Tekin +2 more
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In this study, the last two years' hourly opening and closing prices of the banks' stocks traded on BIST-30 were used as the dataset. The research is aimed to predict the closing prices of these stocks in the light of machine learning. In this context, the authors propose a new method containing ensemble learning algorithms and fuzzy clustering ...
Ahmet Tezcan Tekin +2 more
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Sentiment Analysis for Stock Price Prediction
Conference on Multimedia Information Processing and Retrieval, 2020Stock prices and financial markets are often sentiment-driven, which leads to research efforts to predict stock market trend using public sentiments expressed on social media such as Facebook and Twitter.
Rubi Gupta, Min Chen
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Physical Review E, 2004
We show that the dynamics of stock prices can be accurately described as a continuous time random walk with a time dependent diffusion coefficient. The time evolution of the diffusion coefficient can be derived from tick by tick databases provided the stock price is characterized in terms of a couple of values describing the best ask and the best bid ...
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We show that the dynamics of stock prices can be accurately described as a continuous time random walk with a time dependent diffusion coefficient. The time evolution of the diffusion coefficient can be derived from tick by tick databases provided the stock price is characterized in terms of a couple of values describing the best ask and the best bid ...
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Proceedings of the 2018 2nd International Conference on Cloud and Big Data Computing, 2018
Stock price forecasting has tremendous importance, it is one of the most challenging tasks due to the high volatility of stock market data. Share market investment is often risky, hence the need for an accurate forecasting model to minimize this risk. This article aims to present a new model for forecasting stock prices for a given horizon.
Yassine Touzani +2 more
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Stock price forecasting has tremendous importance, it is one of the most challenging tasks due to the high volatility of stock market data. Share market investment is often risky, hence the need for an accurate forecasting model to minimize this risk. This article aims to present a new model for forecasting stock prices for a given horizon.
Yassine Touzani +2 more
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eLearning and Software for Education, 2020
E-learning, in most contexts, is becoming a more accessible and efficient method of training through which people could acquire new knowledge. Due the difference between epochs, reading and analysing the growing flow of information has become a very challenging task for financial professionals.
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E-learning, in most contexts, is becoming a more accessible and efficient method of training through which people could acquire new knowledge. Due the difference between epochs, reading and analysing the growing flow of information has become a very challenging task for financial professionals.
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Economic Policy Uncertainty and Stock Price Crash Risk
Accounting and Finance, 2019This paper studies the impact of economic policy uncertainty on stock price crash risk using data from China. We develop a new index to measure Chinese economic policy uncertainty and find that economic policy uncertainty has a remarkable positive effect
Xuejun Jin, Ziqing Chen, Xiaolan Yang
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