Political uncertainty and behavior of Tunisian stock market cycles: Structural unobserved components time series models

2017 ◽  
Vol 39 ◽  
pp. 206-214 ◽  
Author(s):  
Afef Trabelsi Mnif
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Models ◽  
Political Uncertainty ◽  
Unobserved Components ◽  
And Behavior

scholarly journals A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION

2012 ◽  
Vol 13 (2) ◽  
pp. 275-293 ◽  
Author(s):  
A. Nazif Çatık ◽  
Mehmet Karaçuka
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Unobserved Components ◽  
Inflation Forecasting ◽  
Long Run ◽  
Univariate Time Series ◽  
Forecasting Performance ◽  
Unobserved Components Model ◽  
One Step ◽  
Conventional Models

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run.

scholarly journals Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks

2020 ◽  
Vol 6 (2) ◽  
pp. 137-148
Author(s):  
J. Oliver Muncharaz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Stock Market ◽  
Recurrent Neural Network ◽  
Stock Prices ◽  
Arima Model ◽  
Time Series Models ◽  
Prediction Errors ◽  
The Creation

In the financial literature, there is great interest in the prediction of stock prices. Stock prediction is necessary for the creation of different investment strategies, both speculative and hedging ones. The application of neural networks has involved a change in the creation of predictive models. In this paper, we analyze the capacity of recurrent neural networks, in particular the long short-term recurrent neural network (LSTM) as opposed to classic time series models such as the Exponential Smooth Time Series (ETS) and the Arima model (ARIMA). These models have been estimated for 284 stocks from the S&P 500 stock market index, comparing the MAE obtained from their predictions. The results obtained confirm a significant reduction in prediction errors when LSTM is applied. These results are consistent with other similar studies applied to stocks included in other stock market indices, as well as other financial assets such as exchange rates.

scholarly journals Can the Baidu Index predict realized volatility in the Chinese stock market?

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wei Zhang ◽  
Kai Yan ◽  
Dehua Shen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Power ◽  
Realized Volatility ◽  
Trading Strategies ◽  
Time Series Models ◽  
Chinese Stock Market ◽  
Continuous Components ◽  
Long Term Impact

AbstractThis paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index. Furthermore, the predictability of the Baidu Index is found to rise as the forecasting horizon increases. We also find that continuous components enhance predictive power across all horizons, but that increases are only sustained in the short and medium terms, as the long-term impact on volatility is less persistent. Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

Stock prices prediction using time series models in Saudian stock market

2017 ◽  
Vol 2 (4) ◽  
Author(s):  
مهدي صالح عبدالقادر قاسم أغا ◽  
روهات زادة
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Prices ◽  
Time Series Models
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