PREDICTABILITY OF MOVING AVERAGE RULES AND NONLINEAR PROPERTIES OF STOCK RETURNS: EVIDENCE FROM THE CHINA STOCK MARKET

2011 ◽  
Vol 07 (02) ◽  
pp. 267-279 ◽  
Author(s):  
Zhigang Wang ◽  
Yong Zeng ◽  
Heping Pan ◽  
Ping Li
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Dynamic Process ◽  
Linear Models ◽  
Moving Average ◽  
Ann Model ◽  
Nonlinear Properties ◽  
China Stock Market ◽  
Artificial Neural Network Ann ◽  
Predictability Of Returns

This paper investigates the predictability of moving average rules for the China stock market. We find that buy signals generate higher returns and less volatility, while returns following sell signals are negative and more volatile. Moreover, the bootstrapping results indicate that the asymmetrical patterns of return and volatility between buy and sell signals cannot be explained by four popular linear models of returns, especially the phenomenon of negative sell returns. We then test the nonlinear dynamic process of returns. Although the existing artificial neural network (ANN) model can replicate the negative sell returns, it fails to capture the volatility patterns of buy and sell returns. Furthermore, we introduce the conditional heteroskedasticity structure into the ANN model and find that the revised ANN model cannot only explain the predictability of returns, but can also capture the patterns of buy and sell volatility, which are never achieved by any linear model of returns tested in the related literature. Therefore, we conclude that the moving average trading rules can pick up some of the hidden nonlinear patterns in the dynamic process of stock returns, which may be the reason why they can be used to predict price changes.

Using Hybrid ARIMAX-ANN Model for Simulating Rainfall - Runoff - Sediment Process Case Study

2013 ◽  
Vol 4 (2) ◽  
pp. 44-60 ◽  
Author(s):  
Vahid Nourani ◽  
Samira Roumianfar ◽  
Elnaz Sharghi
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Sediment Load ◽  
Moving Average ◽  
Linear Component ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Non Linear ◽  
Runoff Sediment ◽  
Artificial Neural Network Ann

The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-runoff-sediment time series, they include both linear and nonlinear components; therefore, employing a hybrid model which combines the advantages of both linear and non-linear models improves the accuracy of prediction. In this paper, a hybrid of ARIMAX-ANN model is applied to rainfall-runoff-sediment modeling of a watershed. At the first step of the hybrid modeling, the ARIMAX method is applied to forecast the linear component of the rainfall-runoff process and then in the second step, an ANN model is used to find the non-linear relationship among the residuals of the fitted linear ARIMAX model. Finally, total effective time series of runoff, obtained by the hybrid ARIMAX-ANN model are imposed as input to the proposed ANN model for prediction daily suspended sediment load of the watershed. The proposed model is more appropriate, as it uses the semi-linear relation for prediction of sediment load.

scholarly journals Long Run Returns Predictability and Volatility with Moving Averages

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 105 ◽  
Author(s):  
Chia-Lin Chang ◽  
Jukka Ilomäki ◽  
Hannu Laurila ◽  
Michael McAleer
Keyword(s):  
Financial Performance ◽  
Stock Returns ◽  
Moving Average ◽  
Long Run ◽  
Returns Predictability ◽  
Rolling Window ◽  
Risk Free Rate ◽  
Data Frequency ◽  
Predictability Of Returns ◽  
Free Rate

This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.

Hybrid Neural Models For Rice Yields Times Forecasting

2012 ◽  
Author(s):  
Ruhaidah Samsudin ◽  
Puteh Saad ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Data Sets ◽  
Ann Model ◽  
Yield Data ◽  
Rice Yields ◽  
Artificial Neural Network Ann

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

scholarly journals Predicting Stock Market Prices using Fine-Tuned IndRNN

2020 ◽  
Vol 9 (7) ◽  
pp. 309-315
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Linear Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Activation Function ◽  
Series Data ◽  
Current Time

Prediction and analysis of stock market data have a vital role in current time’s economy. The various methods used for the prediction can be classified into 1) Linear Algorithms like Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA). 2) Non-Linear Models like Artificial Neural Networks and Deep Learning. In this work, we are using the results of previous research papers to demonstrate the potential of some models like ARIMA, Multi-Layer Perception (MLP) ), Convolutional Neural Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM) for forecasting the stock price of an organization based on its available historical data. Then, implementing some of these methods to check and compare their efficiency within the same issue. We used Independently RNN (IndRNN) to explore a better efficiency for stock prediction and we found that it gives better accuracy prevailing methods in the current time. We also proposed an enhancement to IndRNN by replacing its default activation function with a more effective function called Parametric Rectified Linear Unit (PreLU). Our proposed approach can be used as an alternative method for predicting time series data efficiently other than the typical approaches today

scholarly journals The effect of ambiguity on the UK stock market: evidence from a new empirical approach

2017 ◽  
Vol 14 (4) ◽  
pp. 133-147
Author(s):  
Run Qing Tan ◽  
Viktor Manahov ◽  
Jacco Thijssen
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Linear Models ◽  
The Other ◽  
Var Model ◽  
Market Returns ◽  
Stock Market Returns ◽  
Bid Ask Spread ◽  
The Uk

This study developed a new ambiguity measure using the bid-ask spread. The results suggest that the degree of ambiguity has an impact on the daily UK stock market returns, but ambiguity does not cause changes in the returns. This implies that UK stock prices or returns cannot be predicted using variation in the degree of ambiguity through linear models, such as the VAR model, which was used in the study. The two sets of results in the study show that the degree of ambiguity from the previous two days might affect stock market returns. The authors observe that an increase in the degree of ambiguity two days ago is associated with a positive premium required by the investors. On the other hand, the degree of ambiguity tends to be affected by its past five-day values. Thus, the degree of ambiguity seems to persist for five days until investors update their priors. The intuition behind the result is that the degree of ambiguity can affect the returns of the UK stock market and UK stock market returns can in turn have an impact on the degree of ambiguity. The authors also observe that the degree of ambiguity does not seem to predict stock market returns in the UK when one applies linear models. However, this does not mean that there is no non-linear relationship between the degree of ambiguity and stock market returns or stock returns.

Testing asymmetric correlations in stock returns via empirical likelihood method

2014 ◽  
Vol 4 (1) ◽  
pp. 42-57 ◽  
Author(s):  
Zhiyuan Pan ◽  
Xu Zheng ◽  
Qiang Chen
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Empirical Likelihood ◽  
Critical Value ◽  
Likelihood Method ◽  
Content Type ◽  
Empirical Likelihood Method ◽  
Model Free ◽  
International Stock Markets ◽  
China Stock Market

Purpose – This study aims to propose a model-free statistic that tests asymmetric correlations of stock returns, in which stocks move more often with the market when the market goes down than when it goes up, and then empirically analyze the asymmetric correlations of the China stock market and international stock markets, respectively. Design/methodology/approach – Using empirical likelihood method, this study designs and conducts a model-free test, which converges to χ2 distribution under regulated conditions and performs well in the finite sample using bootstrap critical value method. Findings – By analyzing the authors' model-free test, the authors find that compared with Hong et al.'s test that closely relates to the authors, both of the tests are under rejected using asymptotic critical value. However, using the bootstrap critical value method can greatly improve the performance of the two tests. Second, investigating the power of the two tests, the authors find that the proportion of rejections of the authors' test is roughly 10-20 percent larger than Hong et al.'s test in mixed copula model setting. The last finding is the authors find evidence of asymmetric for small-cap size portfolios, but no evidence for middle-cap and large-cap size portfolios in the China stock market. Besides, the authors test asymmetric correlations between the USA and Japan, France and the UK; the asymmetric phenomenon exists in international stock markets, which is similar to Longin and Solnik's findings, but they are not significant according to both the authors' test and Hong et al.'s test. Research limitations/implications – The findings in this study suggest that both the authors' test and Hong et al.'s test are under rejected using asymptotic critical value. When applying these statistics to test asymmetric correlations, the authors should take care with the choice of critical value. Practical implications – The empirical analysis has a significant practical implication for asset allocation, asset pricing and risk management fields. Originality/value – This study constructs a model-free statistic to test asymmetric correlations using empirical likelihood method for the first time and corrects the size performance by bootstrap method, which improves the performance of Hong et al.'s test. To the authors' knowledge, this is the first study to test the asymmetric correlations in the China stock market.

scholarly journals Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index

2021 ◽  
Vol 9 (2) ◽  
pp. 18
Author(s):  
Katleho Makatjane ◽  
Ntebogang Moroke
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Linear Models ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Financial Time ◽  
Johannesburg Stock Exchange ◽  
Non Linear

During the past decades, seasonal autoregressive integrated moving average (SARIMA) had become one of a prevalent linear models in time series and forecasting. Empirical research advocated that forecasting with non-linear models can be an encouraging alternative to traditional linear models. Linear models are often compared to non-linear models with mixed conclusions in terms of superiority in forecasting performance. Therefore, the aim of this study is to build an early warning system (EWS) model for extreme daily losses for financial stock markets. A logistic model tree (LMT) is used in collaboration with a seasonal autoregressive integrated moving average-Markov-Switching exponential generalised autoregressive conditional heteroscedasticity-generalised extreme value distribution (SARIMA-MS-EGARCH-GEVD) estimates. A time series of the study is a five-day financial time series exchange/Johannesburg stock exchange-all share index (FTSE/JSE-ALSI) for the period of 4 January 2010 to 31 July 2020. The study is set into a two-stage framework. Firstly, SARIMA model is fitted to stock returns in order to obtain independently and identically distributed (i.i.d) residuals and fit the MS(k)-EGARCH(p,q)-GEVD to i.i.d residuals; while, in the second stage, we set-up an EWS model. The results of the estimated MS(2)-EGARCH(1,1) -GEVD revealed that the conditional distribution of returns is highly volatile giving the expected duration to approximately 36 months and 4 days in regime one and 58 months and 2 days in regime two. We further found that any degree losses above 25% implies that there will be no further losses. Using the seven statistical loss functions, the estimated SARIMA(2,1,0)×(2,1,0)240−MS(2)−EGARCH(1,1)−GEVD proved to be the most appropriate model for predicting extreme regimes losses as it was ranked at 71%. Finally, the results of EWS model exhibit reasonably an overall performance of 98%, sensitivity of 79.89% and specificity of 98.40% respectively. The model further indicated a success classification rate of 89% and a prediction rate of 95%. This is a promising technique for EWS. The findings also confirmed 63% and 51% of extreme losses for both training sample and validation sample to be correctly classified. The findings of this study are useful for decision makers and financial sector for future use and planning. Furthermore, a base for future researchers for conducting studies on emerging markets, have been contributed. These results are also important to risk managers and and investors.

Prediction of Gold and Silver Prices in an Emerging Economy: Comparative Analysis of Linear, Nonlinear, Hybrid, and Ensemble Models

2019 ◽  
Vol 12 (3) ◽  
pp. 63-78
Author(s):  
Saurabh Kumar
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Emerging Economy ◽  
Series Data ◽  
Ensemble Model ◽  
Ann Model ◽  
Autoregressive Integrated Moving Average ◽  
Ensemble Models ◽  
Artificial Neural Network Ann

This study compares the accuracy of different forecasting techniques for gold and silver returns in a leading emerging economy. The study employs four forecasting models: autoregressive integrated moving average (ARIMA), artificial neural network (ANN), hybrid, and ensemble models. The study takes data of more than 7 years and forecasting is carried out for different forecast horizons varying from 1- to 20-steps ahead. The results reveal that ARIMA model is the best model to predict the gold returns, whereas, the ANN model along with the ensemble model are the best to predict the silver returns. The results also indicate that there exists nonlinear patterns in the time-series data of gold and silver returns. The study has significant implications for investors, academia, and policymakers.

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