Results 251 to 260 of about 955,547 (308)
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SSRN Electronic Journal, 2010
We study the relation between the ownership structure of financial assets and non-fundamental risk. We define an asset to be fragile if it is susceptible to non-fundamental shifts in demand. An asset can be fragile because of concentrated ownership, or because its owners face correlated or volatile liquidity shocks, i.e., they must buy or sell at the ...
Greenwood, Robin, Thesmar, David
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We study the relation between the ownership structure of financial assets and non-fundamental risk. We define an asset to be fragile if it is susceptible to non-fundamental shifts in demand. An asset can be fragile because of concentrated ownership, or because its owners face correlated or volatile liquidity shocks, i.e., they must buy or sell at the ...
Greenwood, Robin, Thesmar, David
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YMER Digital, 2022
Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks.
V BIKSHAM +4 more
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Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks.
V BIKSHAM +4 more
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Stock price synchronicity and stock price crash risk
China Finance Review International, 2016Purpose– The purpose of this paper is to empirically analyze the effects of stock price synchronicity and herding behavior of qualified foreign institutional investors (QFII) on stock price crash risk, especially the mediating effect of herding behavior of QFII on the relation of stock price synchronicity and stock price crash risk.Design/methodology ...
Yonghong Jin +3 more
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International Review of Economics & Finance, 2021
Abstract We apply concepts form machine learning to forecast stock prices. First, we introduce the general (3 by 3) forecasting model, in which the financial markets are populated by three types of stocks: Overpriced stocks, underpriced stocks and fairly priced stocks.
Arie Harel, Giora Harpaz
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Abstract We apply concepts form machine learning to forecast stock prices. First, we introduce the general (3 by 3) forecasting model, in which the financial markets are populated by three types of stocks: Overpriced stocks, underpriced stocks and fairly priced stocks.
Arie Harel, Giora Harpaz
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Finance and Credit, 2022
Subject. This article deals with stock pricing factors. Objectives. The article aims to identify factors affecting the share prices of Russian companies. Methods. For the study, I used content, logical, and comparative analyses. Results. The article describes the factors that influence the price of a company's shares, but are not always directly ...
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Subject. This article deals with stock pricing factors. Objectives. The article aims to identify factors affecting the share prices of Russian companies. Methods. For the study, I used content, logical, and comparative analyses. Results. The article describes the factors that influence the price of a company's shares, but are not always directly ...
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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 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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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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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
openaire +1 more source