scholarly journals Forecasting Capacity of ARIMA Models; A Study on Croatian Industrial Production and its Sub-sectors

2017 ◽  
Vol 20 (1) ◽  
pp. 81-99 ◽  
Author(s):  
Daniel Tomić ◽  
Saša Stjepanović
Keyword(s):  
Industrial Production ◽  
Moving Average ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Arima Models ◽  
Important Indicator ◽  
Out Of Sample ◽  
Short And Long Term ◽  
Data Evolution

Abstract As one of the most important indicator for monitoring the production in industry as well as for directing investment decisions, industrial production plays important role within growth perspectives. Not only does the composition and/or fluctuation of the goods produced indicate the course of economic activity but it also reflects the changes in cyclical development of the economy thereby providing opportunity to macro-manage with early signs of (short-term) turning-points and (long-term) trend variations. In this paper, we compare univariate autoregressive integrated moving average (ARIMA) models of the Croatian industrial production and its subsectors in order to evaluate their forecasting features within short and long-term data evolution. The aim of this study is not to forecast industrial production but to analyze the out-of-sample predictive performance of ARIMA models on aggregated and disaggregated level inside different forecasting horizons. Our results suggest that ARIMA models do perform very well over the whole rage of the prediction horizons. It is mainly because univariate models often improve the predictive ability of their single component over the short horizons. In that manner ARIMA modelling could be used at least as a benchmark for more complex forecasting methods in predicting the movements of industrial production in Croatia.

scholarly journals Forecasting Security Returns With Simple Moving Averages

2011 ◽  
Vol 7 (11) ◽  
Author(s):  
Camillo Lento
Keyword(s):  
Random Walk ◽  
Moving Average ◽  
Predictive Ability ◽  
Random Walk Model ◽  
Forecasting Model ◽  
Moving Averages ◽  
Data Set ◽  
Out Of Sample ◽  
Security Returns

This study examines the ability of simple moving averages to forecast security returns. Five moving average variants are used to develop a forecasting model using OLS regression for the DJIA, NASDAQ, TSX and CAD-US exchange rate. The forecasting model is compared to the random-walk model without a drift and tested out-of-sample. The results suggest that the moving averages have no predictive ability on the four indices at a 1 day lag. However, the moving averages explain approximately 45% to 48% of the variation in the returns in the following 10 days and clearly outperform the random-walk model. Most of the forecasting ability is derived from the MA (5, 150). Hurst Statistic estimation is used to confirm the long-term dependencies in the lag 10 data set.

scholarly journals A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production

J ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 508-560
Author(s):  
Riccardo Corradini
Keyword(s):  
State Space ◽  
Industrial Production ◽  
Forecast Error ◽  
Moving Average ◽  
Predictive Ability ◽  
State Space Models ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
New Strategy ◽  
Moving Average Model

Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.

Are share prices of Gulf corporate council banks predictable? A time-series analysis

2020 ◽  
Vol 21 (1) ◽  
pp. 9-28
Author(s):  
Hesham I. Almujamed
Keyword(s):  
Moving Average ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Accounting Disclosure ◽  
Content Type ◽  
Share Prices ◽  
Market Participants ◽  
Short And Long Term ◽  
Future Evaluation

Purpose This research aimed to evaluate the predictability of moving-average strategies and examined the validity of the weak form of the efficient market hypothesis (EMH) for securities of banks listed in the Gulf Cooperation Council (GCC) stock markets of Bahrain, Kuwait, Qatar and Saudi Arabia. Design/methodology/approach Several statistical analyses and eight moving-average rules were employed where buy and sell signals were produced by comparing a security price’s short- and long-term moving averages. The study covered the daily closing share prices of 40 GCC-listed banks over the 18-year period ending 31 December 2017. Findings The results suggest that securities of banks in the GCC were not weak-form efficient because share prices were predictable. Investors who traded using moving-average strategies could generate higher profits. Analysis of variance found that securities of Kuwaiti banks were the most efficiently priced. Practical implications The findings supported the idea that profitability depended on the moving-average rules and country chosen. Transaction costs did not affect the returns obtained using different trading rules. Originality/value This work facilitates future evaluation of accounting disclosure environments as well as the market efficiency and the performance of securities in the GCC countries. The performance of moving average rules among representative countries that share similar characteristics was analyzed. Different market participants, including investors, analysts and regulators, can benefit from this study for decision-making. These results suggest that new regulations might be drafted that would improve the timeliness of accounting information and the banks’ level of efficiency.

Rainfall trends in hindsight and in foresight

2020 ◽  
Author(s):  
Theano Iliopoulou ◽  
Demetris Koutsoyiannis
Keyword(s):  
Daily Rainfall ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Annual Rainfall ◽  
Methodological Framework ◽  
Rainfall Trends ◽  
Out Of Sample ◽  
Linear Trends

<p>Trends are customarily identified in rainfall data in the framework of explanatory modelling. Little insight however has been gained by this type of analysis with respect to their performance in foresight. In this work, we examine the out-of-sample predictive performance of linear trends through extensive investigation of 60 of the longest daily rainfall records available worldwide. We devise a systematic methodological framework in which linear trends are compared to simpler mean models, based on their performance in predicting climatic-scale (30-year) annual rainfall indices, i.e. maxima, totals, wet-day average and probability dry, from long-term daily records. Parallel experiments from synthetic timeseries are performed in order to provide theoretical insights to the results and the role of parsimony in predictive modelling is discussed. In line with the empirical findings, it is shown that, prediction-wise, simple is preferable to trendy.</p>

scholarly journals FINANCIAL VARIABLES AND THE OUT-OF-SAMPLE FORECASTABILITY OF THE GROWTH RATE OF INDIAN INDUSTRIAL PRODUCTION

2014 ◽  
Vol 19 (Supplement_1) ◽  
pp. S83-S99 ◽  
Author(s):  
Rangan Gupta ◽  
Yuxiang Ye ◽  
Christopher M. Sako
Keyword(s):  
Growth Rate ◽  
Industrial Production ◽  
Predictive Ability ◽  
Share Price ◽  
Real Effective Exchange Rate ◽  
Dividend Yield ◽  
Term Spread ◽  
Financial Variables ◽  
Lending Rate ◽  
Out Of Sample

In this paper, we consider the forecasting power, both in- and out-of-sample, of 11 financial variables with respect to the growth rate of Indian industrial production over the monthly out-ofsample period of 2005:4–2011:4, using an in-sample of 1994:1–2005:3. The financial variables used are: M0, M1, M2, M3, lending rate, 3-month Treasury bill rate, term spread, real effective exchange rate, real stock prices, dividend yield and non-food credit growth. We observe that that, at times, in-sample and out-of-sample predictive ability of the financial variables tend to coincide. We find relatively strong evidence of out-of-sample predictability for at least one of the horizons for M0, M1, M2, M3, the lending rate and real share price growth rate. The term-spread and dividend yield are added to the list when weaker versions of the out-of-sample test statistics are considered as well. Given that we consider a large number of financial variables, when we checked the significant results by accounting for data mining across the 11 financial variables, majority of these results ceases to be significant, with only M0, M1 and M2 retaining some of its predictive ability.

scholarly journals Technical trading revisited: evidence from the Asian stock markets

2014 ◽  
Vol 11 (2) ◽  
pp. 511-532 ◽  
Author(s):  
Thorben Lubnau ◽  
Neda Todorova
Keyword(s):  
Transaction Costs ◽  
Moving Average ◽  
Predictive Ability ◽  
Excess Returns ◽  
Short Term ◽  
Technical Trading ◽  
Developed Markets ◽  
Stock Indices ◽  
Bootstrap Tests

We examine the forecasting power and profitability of moving average (MA) and trading range break (TRB) rules for the daily prices of ten Asian stock indices from January 1990 to September 2012 using bootstrap tests. The results confirm the predictive ability of MA rules whereas the picture uncovered by the TRB rules is more mixed. The MA rules consistently generate positive excess returns after transaction costs, with highest magnitudes often achieved for less developed markets. However, more developed markets surprisingly seem to be far from informationally efficient as well. Furthermore, short-term variants of the trading rules outperform systematically long-term variants.

scholarly journals Market valuation and acquiring firm performance in the short and long term: Out-of-sample evidence from Spain

2020 ◽  
Vol 23 (1) ◽  
pp. 234094442090104
Author(s):  
José E. Farinós ◽  
Begoña Herrero ◽  
Miguel A. Latorre
Keyword(s):  
Jel Classification ◽  
Target Firm ◽  
Listed Firms ◽  
Market Sentiment ◽  
Out Of Sample ◽  
Short And Long Term ◽  
The Moment ◽  
Long Term Performance ◽  
Term Performance

We investigate bidder’s short- and long-term performance in periods of high and low valuation market in response to announcements of acquisitions carried out by Spanish listed firms over the period 1991–2016. We find that acquirers of unlisted targets fully react at the announcement date in high valuation periods, meanwhile the underreaction of listed target bidders at the moment of the announcement in low valuation markets is the result of return continuations. In addition, we find that the market reaction do not depend on recent merger history. Therefore, we provide evidence that bidder reaction to acquisitions is not consistent with the predictions of market sentiment (optimism) after controlling for the listing status of the target firm, not supporting, for a thinner market as the Spanish one, the evidence observed in US and UK markets. JEL CLASSIFICATION G14; G34; L33; D81

scholarly journals Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 211-229
Author(s):  
Ulrich Gunter ◽  
Irem Önder ◽  
Egon Smeral
Keyword(s):  
Moving Average ◽  
Tourism Demand ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Forecast Combinations ◽  
The Stability ◽  
Forecasting Errors ◽  
Macroeconomic Environment

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

scholarly journals Behavioral modeling and linearization of a millimeter-wave power amplifier

2009 ◽  
Vol 1 (2) ◽  
pp. 127-136
Author(s):  
Ilan Melczarsky ◽  
Pere L. Gilabert ◽  
Valeria Di Giacomo ◽  
Eduard Bertran ◽  
Fabio Filicori
Keyword(s):  
Millimeter Wave ◽  
Power Amplifier ◽  
Moving Average ◽  
Behavioral Model ◽  
Long Term Memory ◽  
Wave Power ◽  
Auto Regressive ◽  
Transistor Model ◽  
Short And Long Term

The use of digital predistortion for linearizing a millimeter-wave power amplifier (PA) is investigated. A PA operating at 38 GHz is designed using an accurate non-quasi-static transistor model, taking into account both short- and long-term memory effects. A realistic test signal is then used for the identification of a nonlinear auto-regressive moving average (NARMA) behavioral model of the PA. The NARMA-based digital predistorter is then derived and formulated in terms of basic predistortion cells, especially suitable for efficient implementation in an FPGA. The performance of the predistortion solution is preliminarily assessed by means of computer simulations.

scholarly journals INTRODUCTION TO TIME-VARYING MODELING WITH MACROECONOMIC AND FINANCIAL DATA

2012 ◽  
Vol 16 (S2) ◽  
pp. 167-175 ◽  
Author(s):  
Fredj Jawadi
Keyword(s):  
Time Series ◽  
Time Variation ◽  
Moving Average ◽  
Statistical Properties ◽  
Financial Data ◽  
Time Varying ◽  
Arch Model ◽  
Financial Variables ◽  
Short And Long Term

The dynamics of macroeconomic and financial series has evolved swiftly and asymmetrically since the end of the 1970s, and their statistical properties have also changed over time, suggesting complex relationships between economic and financial variables. The transformations can be explained by considerable changes in householder's behavior, market structures, and economic systems and by the alternation of exogenous shocks and financial crises that have affected the economic cycle, with significant evidence of time variation in the major economic variables. Hence, there is a need for new econometric protocols to take such changes into consideration. The introduction of ARMA (autoregressive moving average models) by Box and Jenkins (1970) led to the development of time-series econometrics, which had a major impact on the conceptual analysis of economic and financial data. This type of modeling offered a transition from a static setup to a new modeling process that reproduces the time-varying features of macroeconomic and financial series. However, the ARMA modeling system retains the constancy of the first and second moments, limits the phases of a cycle to symmetrical instances, and only reproduces the dynamics of stationary variables. It thus fails to adequately reproduce the nonstationary relationships between major economic and financial variables. Abrupt changes in economies and financial systems have given evidence of nonstationary series whose statistical properties are also time-varying, making it necessary to develop new econometric tools to capture the time variation of economic and financial series in the mean and in the variance, and to apprehend their dynamics in the short and long term. Among the most important and influential studies in the 1980s' econometrics literature were therefore those that dealt with the introduction of the ARCH (autoregressive conditional heteroskedasticity) model by Engle (1982) and the cointegration theory by Engle and Granger (1987). The ARCH model, which focuses on the time-varying features of volatility structure, was a major breakthrough, as it highlighted the importance of the second moment of time series, while the cointegration framework enabled the short- and long-term dynamics of nonstationary variables to be modeled.

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