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Stock price prediction is regarded as one of most difficult task to accomplish in financial forecasting due to complex nature of stock market [1, 2, 3]. The desire of many . work are historical daily stock prices obtained from two countries stock exchanged. The data composed of four elements, namely: open price, low price, high price and
1. BASIC INTRODUCTION OF STOCK MARKET A stock market is a public market for trading of company stocks. Stock market prediction is the task to find the future price of a company stock. The price of a share depends on the number of people who want to buy or sell it. If there are more buyers, then prices will rise. If the seller has a number of .
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price could yield significant profit. The stock market is not an efficient market.
the close price of a stock is used when training and predicting stock prices. All data was retrieved from Yahoo. Historical stock prices from 1 January 1990 until 1 June 2019 results in approximately 7000 data points (trading days) per stock. Data preparation In order to create enough data for the two models to
stock prices then an increase in the -rm s own stock price informativeness reduces the sensitivity of its investment to its peer stock price (prediction 1). Indeed, as the signal conveyed by its own . stock price (prediction 2), but not otherwise. The same prediction holds for an increase in the correlation of the fundamentals of a -rm .
stock market turning points. The proposed turning points prediction model is tested using stock market datasets which are the historical data of stocks listed as components of S&P500 index of New York Stock Exchange. These data are stock prices that are either moving upward, downward, or sideways. From the findings, the
Forecasting prices in stock markets is a matter of great interest both in the academic field and in business. The forecasting of stock prices and stock returns is possible using various techniques and methods. Many researchers study price trends in stock markets with the help of artificial neural networks [1-2] or fuzzy-trends [3, 4]. The
7.1 Results for the 30-minute ANN with NHY stock data . . . 76 7.2 Results for the 30-minute ANN with DNBNOR stock data . 76 7.3 Results for the 2-hour ANN with DNBNOR stock data . . . 77 7.4 Results for the 2-hour ANN with STB stock data . . . . . . 77 7.5 Results for the 2-day ANN with DNBNOR stock data . . . 79
In the event studies, I regress stock returns on market returns and other factors over a time span well before the events of a tax change, creating a model of how the stock returns behave. Then I use the deviation of stock prices from the model's prediction around the events of the tax change to establish the stock's abnormal returns.
stock return predictability. The predictability of stock returns has been under debate for a long time (Campbell & Yogo (2006); Ang & Bekaert (2007); Cochrane (2011); Fama & French (1988)). Now many financial economists agree that long-term stock returns are predictable. In particular, the predictable part of stock returns is risk premium.
Issuing its stock, stock options, or other equity instruments Incurring liabilities to pay cash, the amounts of which are based, at least in part, on the price of the company's stock or other equity instruments Incurring liabilities that may be settled through issuance of the company's stock or other equity instruments.
negative stock return and a subsequent decline in household spending and employment. We use a local labor market analysis to address this empirical challenge and provide quantitative evidence on the stock market consumption wealth e ect. Our empirical strategy combines regional heterogeneity in stock market wealth with aggregate movements in stock