Time series of stock returns

Time series analysis covers a large number of forecasting methods. Researchers have developed numerous modifications to the basic ARIMA model and found 2 Dec 2019 for time series analysis and forecasting. Some studies have been conducted by employing ARIMA models to forecast stock market returns Although conventional time series models employed to explain or forecast stock returns treat a particular asset or index in isolation, a vast literature in finance 27 Jun 2018 Much of the return predictability literature focuses on individual stocks, but the recent TS literature uses a sample of asset classes such as stock Time Series Properties of the Data. 2.1 Standard Deviations of Stock Market Returns. We use daily values of the Standard and Poor's (S&P) composite portfolio time-series approach and the VAR approach for decomposing the variance of stock returns. Section 4 reports empirical results for monthly U.S. data in the period

Is the stock return series ALWAYS stationary? This is because, as Iqbal said, if the time series of stock returns that you are studying are non-stationary then you would get spurious results.

Time Series Analysis on Stock Returns Yiqiao Yin Columbia University March 27, 2017 Abstract This papers applies ARMA model to daily, weekly, and monthly price returns of S&P 500 Index Fund. We present model selection, forecasts, and residual tests. The results for weekly and monthly residuals are consistent with short- and long-run reversal strategy. 1 In the study submitted, selected methods of financial time-series analysis are applied to daily returns of the most liquid stocks at Czech capital market. In most cases, symmetric GARCH(1,1

27 Jun 2018 Much of the return predictability literature focuses on individual stocks, but the recent TS literature uses a sample of asset classes such as stock

16 Jul 2019 For example, suppose you wanted to analyze a time series of daily closing stock prices for a given stock over a period of one year. You would 1 Aug 2019 This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series 4 Mar 2018 This project is about the time series analysis about the log return of the distribution of the stock prices and returns, we try to use ARMA model

Neural Network Time Series Forecasting of Financial Markets John Wiley and Sons Ltd, 1994. Christoffersen, P.F. and F.X. Diebold. Financial asset returns,

1 Aug 2019 This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series 4 Mar 2018 This project is about the time series analysis about the log return of the distribution of the stock prices and returns, we try to use ARMA model Seasonality in stock returns is a closely related to week-form of market efficiency. As suspected, ADF test revealed the time series data (SPY stock data) are 20 Jan 2011 Longin. (1996) found positive autocorrelation for a daily index of stocks. The autocorrelation of weekly stock returns is weakly negative, whilst the

time-series approach and the VAR approach for decomposing the variance of stock returns. Section 4 reports empirical results for monthly U.S. data in the period

Aggregate stock-market returns are asymmetrically distributed. While both the cross-sectional and time-series results are broadly consistent with the theory we 31 Jul 2017 This paper proposes a two-state predictive regression model and shows that stock market 12-month return (TMR), the time-series momentum