Long Run Returns Predictability and Volatility with Moving Averages

Chang, Chia‐Lin; Ilomäki, Jukka; Laurila, Hannu; McAleer, Michael

doi:10.3390/risks6040105

Cited by 9 publications

(7 citation statements)

References 51 publications

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“…For example, Neely et al [43], Ni et al [44], and Marshall et al [45] found that investors benefit from the use of MA rules, whereas Hudson et al [46] claim that MA rules are useless in intra-day trading. The main inspiration for this paper comes from the findings in Chang et al [47] that the DJIA index returns are predictable in the long run according to MA rules, which provides empirical support for the analytical result in Reference [42].…”

Section: Literature Reviewsupporting

confidence: 52%

“…The theoretical model closely follows Ilomäki et al [4], and Chang et al [47]. We consider all the energy stocks as financial assets, and assume risk averse investors live for two periods in an overlapping generations economy with a continuum of young and old investors [0, 1].…”

Section: Model Specificationmentioning

confidence: 99%

“…The Sharpe ratio is calculated as [r i − 0.004]/σ i = SR i , where r i is the average annualized returns with dividends, and σ i is the annualized daily volatility of returns for asset i. have actually produced a negative return to risk value over the last decade in Europe, while fossil energy stocks have performed well. Chang et al [47] and Ilomäki et al [51] find that market timing with MA rules adds value for a risk averse investor, as compared with random timing for Dow Jones Industrial Average stocks, and with the DJIA index itself. We followed the same procedure by choosing a 200 trading days rolling window so that the sample size of each asset sums to 2564 daily observations.…”

Section: Empirical Analysismentioning

confidence: 99%

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Moving Average Market Timing in European Energy Markets: Production Versus Emissions

et al. 2018

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This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing.

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Section: Literature Reviewsupporting

confidence: 52%

Section: Model Specificationmentioning

confidence: 99%

Section: Empirical Analysismentioning

confidence: 99%

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Moving Average Market Timing in European Energy Markets: Production Versus Emissions

et al. 2018

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“…They found a good prediction of the stock price even only for no more than 200 days, so we believe this justifies the level of daily data for our study. In addition, Chang, Ilomäki, Laurila & McAleer (2018) and Williams (2006) used the same period in their research to predict the volatility of the return.…”

Section: Methodology 31 Datamentioning

confidence: 99%

The Asymmetric Volatility of the Islamic Capital Market During the Covid-19 Pandemic

Nurdany

Ibrahim

Romadoni

2021

JIMF

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This study attempts to identify the existence of asymmetric volatility in the Islamic capital market in Indonesia during the Covid-19 pandemic. The paper employs the symmetric analysis of the GARCH (1,1) model and the asymmetric analysis of the TGARCH (1,1) model in order to identify Islamic capital market behaviour duringthe first 200 days after the first Covid-19 cases were confirmed. We used the daily closing prices of the Indonesia Sharia Stock Index (ISSI). The symmetric analysis of the GARCH (1,1) model revealed that the current value of return on the ISSI does not have a significant impact on its future value. On the other hand, the TGARCH (1,1) model showed that the asymmetric parameter coefficient was positive and statistically significant. Good news and bad news does not have the same level of impact on the volatility of returns on the ISSI. Furthermore, coefficients αi and γi in the variance equation indicate that good news has a higher volatility impact than bad news. The results indicate that investors should not to worry about the bad news effect of theCovid-19 pandemic, while the government should continue the mitigation of the spread of the coronavirus along with its economic recovery policy.

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“…High debt-by-price ratio and low GDP growth rate relate to high equity risk premiums. While the out-of-sample R-squared of many well-known predictive variables can hardly beat the historical average [30], as evidenced by the results in this paper, the current research adds to the stock return predictability literature [15,[31][32][33][34][35][36]. This new approach describing the impact of US economic growth and government debt on stock returns also adds new insights into recent methodological advances (see [35]).…”

Section: Introductionmentioning

confidence: 73%

Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns

Mousavi

2021

Mathematics

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With the prominent role of government debt in economic growth in recent decades, one would expect that government debt alongside economic growth to be a risk factor priced in the time series of stock returns. In this paper, this idea is investigated by applying a nonparametric model, namely, a local-linear kernel smoother with the aim of forecasting long-term stock returns where the model and smoothing parameters are chosen by cross-validation. While a wide range of predictive variables are examined, we find that our newly introduced debt-by-price ratio and the third to fourth quarter economic growth are robust predictors of stock returns, beating the well-known predictive variables in the literature by a significant difference. The combination of these two covariates can explain almost 30% variation of stock returns at a one-year horizon. This is very crucial considering the difficulty in capturing even a small proportion of movements in stock returns.

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Long Run Returns Predictability and Volatility with Moving Averages

Cited by 9 publications

References 51 publications

Moving Average Market Timing in European Energy Markets: Production Versus Emissions

Moving Average Market Timing in European Energy Markets: Production Versus Emissions

The Asymmetric Volatility of the Islamic Capital Market During the Covid-19 Pandemic

Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns

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