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Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the well-documented return predictability of the strategies based on the ratio of short-term to long-term moving averages can be enhanced by conditioning on information discreteness. Anchoring bias has been the popular explanation for the source of underreaction in the context of moving averages-based strategies. This paper proposes and studies another possible source based on investor inattention that can potentially result in superior performance of these strategies.

The paper uses portfolio sorting as well as Fama-MacBeth cross-sectional regressions. For examining the role of information discreteness in the return predictability of the moving average ratio, the sample stocks are double-sorted based on the moving average ratio and information discreteness measure. The returns to these portfolios are computed using standard approaches in the literature. The regression approach controls for various well-known return predictors.

This study finds that the equally-weighted monthly returns to the long-short moving average ratio quintile portfolios increase monotonically from 0.54% for the discrete information portfolio to 1.37% for the continuous information portfolio over the 3-month holding period. This study observes a similar pattern in risk-adjusted returns, value-weighted portfolios, non-January returns, large and small stocks, for alternative holding periods and the ratio of 50-day to 200-day moving average. The results are robust to control for well-known return predictors in cross-sectional regressions.

To the best of the authors’ knowledge, this is the first paper to document the significant role of investor inattention to continuous information in the return predictability of strategies based on the moving average ratios. There are many underreaction anomalies that have been reported in the literature, and the paper's results can be extended to those anomalies in subsequent research.

The findings of this paper have important practical implications. Strategies based on moving averages are an extremely popular component of a technical analyst's toolkit. Their profitability has been well-documented in the prior literature that attributes the performance to investors' anchoring bias. This paper offers a readily implementable approach to enhancing the performance of these strategies by conditioning on a straightforward measure of information discreteness. In doing so, this study extends the literature on the role of investor inattention to continuous information in anomaly profits.

While there is considerable literature on technical analysis, and especially on the performance of moving averages-based strategies, the novelty of this paper is the analysis of the role of information discreteness in strategy performance. Not only does the paper document robust evidence, but the findings suggest that the investor’s inattention to continuous information is a more dominant source of underreaction compared to anchoring. This is an important result, given that anchoring has so far been considered the source of return predictability in the literature.

The moving median method is suggested as an alternative for short‐term forecasting under some of the standard normal; Student t with degrees of freedom 1, 2 and 3; Cauchy; Chi‐square with degrees of freedom 1 and 2; exponential; and standard normal with outliers. When the Mean Absolute Deviation (MAD) and the Mean Square Error (MSE) are used as the criteria for evaluating forecasting accuracy, the moving average technique is superior to the moving median technique only for time series simulated from the standard normal distribution. The moving median technique is superior to the moving average technique for the fat‐tailed distributions; for example, t‐distributions of degrees 1, 2 and 3, Cauchy distribution, and the contaminated normal cases. An example shows the moving median technique responds to the level changes faster than the moving average technique. An illustrative example is also given for a practical data set.

The purpose of this paper is to analyze the spectral characteristics of moving average operator and to propose a novel time-frequency hybrid sequence operator.

Firstly, the complex data is converted into frequency domain data by Fourier transform. An appropriate frequency domain operator is constructed to eliminate the impact of disturbance. Then, the inverse Fourier transform transforms the frequency domain data in which the disturbance is removed, into time domain data. Finally, an appropriate moving average operator of N items is selected based on spectral characteristics to eliminate the influence of periodic factors and noise.

Through the spectrum analysis of the real-time data sensed and recorded by microwave sensors, the spectral characteristics and the ranges of information, noise and shock disturbance factors in the data can be clarified.

The real-time data analysis results for a drug component monitoring show that the hybrid sequence operator has a good effect on suppressing disturbances, periodic factors and noise implied in the data.

Firstly, the spectral analysis of moving average operator and the novel time-frequency hybrid sequence operator were presented in this paper. For complex data, the ideal effect is difficult to achieve by applying the frequency domain operator or time domain operator alone. The more satisfactory results can be obtained by time-frequency hybrid sequence operator.

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.

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.

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.

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.

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.

The purpose of this paper is to examine three different forms of returns based on the price difference, percentage change, and difference in logarithm price from moving average buy-sell trading rule. Statistical linear correlation, the means of returns from buy/sell days, and the flexibility of long-term moving periods are examined.

Traditional linear correlations, pairwise student t-test, and ϕ coefficient for two binary buy/sell decision variables are studied from the simple block bootstrap (convenience) sampling from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016.

The authors find that different forms of returns from MA(1-50) are strongly linearly correlated via 150 simple block bootstrap (convenience) samples from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016. In other words, the price differences, the percentage returns, and logarithmic returns are exchangeable for returns from S&P, Dow Jones, and NASDAQ. The authors refute the claims from Metghalchi et al.’s (2005, 2011) papers and Brock et al.’s (1992) paper. The authors conclude that the market is efficient and investors cannot gain benefits from moving average technical trading rule. Lastly, the authors find that the decisions from MA(1-50) and MA(1-200) are highly correlated; therefore, the length of periods used in long-period moving average is flexible.

It is one of the first studies about different forms of returns, their conclusions on the market efficiency, and the flexibility of long-term moving period for moving average buy/sell technical rules.

This study examines forecast accuracy associated with the forecast of 55 revenue data series of 18 local governments. The last 18 months (6 quarters; or 2 years) of the data are held-out for accuracy evaluation. Results show that forecast software, damped trend methods, and simple exponential smoothing methods perform best with monthly and quarterly data; and use of monthly or quarterly data is marginally better than annualized data. For monthly data, there is no advantage to converting dollar values to real dollars before forecasting and reconverting using a forecasted index. With annual data, naïve methods can outperform exponential smoothing methods for some types of data; and real dollar conversion generally outperforms nominal dollars. The study suggests benchmark forecast errors and recommends a process for selecting a forecast method.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

The purpose of this paper is to develop a statistical control chart based model for earlier defect identification.

The paper used statistical process control methods and an auto‐regression model to model the identification of the initiation point of a random defect. Conventional statistical process control (SPC) methods have been widely used in process industries for process abnormality detections. However, their practicability and achievable performance are limited due to the assumptions that a continuous process is operated in a particular steady state and that all variables are normally distributed. Because the case considered here does not meet the requirement of conventional SPC methods, we proposed adaptive statistical process control charts based on an autoregressive model to distinguish defects from normal changes in operating conditions. The method proposed has been tested on a set of vibration data of rolling element ball bearings

Several control charts have been used and compared in this paper to identify the initial point of a defect. Overall, the adaptive Shewhart average level chart is a good choice since it overcomes the drawback of adaptive moving charts by working out the limits using all the bearings' data, with no such a need for a subjective threshold level. They are also not very sensitive to the small casual changes in the data.

The model developed can be served as part of a prognosis tool for maintenance decision making since once the earlier warning point has been identified, corrective maintenance actions may be taken. It has practical application areas in vibration based monitoring or any monitoring scheme where a trend in the monitored measurements may exist. The method proposed is easy to use and can be implemented in any condition based maintenance software packages.

The approach proposed in this paper is a new application of existing methods and of original contribution from a point of view of applicability. It adds value to the existing literature and is of value to practitioners.

To investigate the performance of moving average trading rules in an emerging market context, namely that of the Jordanian stock market.

The conditional returns on buy or sell signals from actual data are examined for a range of trading rules. These are compared with conditional returns from simulated series generated by a range of models (random walk with a drift, AR (1), and GARCH‐(M)) and the consistency of the general index series with these processes is examined. Sensitivity analysis of the impact of transaction costs is conducted and standard statistical testing is extended through the use of bootstrap techniques.

The empirical results show that technical trading rules can help to predict market movements, and that there is some evidence that (short) rules may be profitable after allowing for transactions costs, although there are some caveats on this.

New results for the Jordanian market; use of sensitivity analysis to investigate robustness to variations in transactions costs.

Recent developments in the hardware and software mean that the automation of visual fabric inspection tasks is becoming feasible at low cost. This paper investigates the techniques that can be used to solve the problem of repetitive, tedious and physically demanding human inspection for defects in shirt collars. The faults studied in this work are those found in nine types of defects that can be present on shirt collar panels. Two statistical methods: moving group average, and moving divided group average are proposed. In addition, highlighting and variance techniques are applied to an image with moving group average and signature counting. These techniques gave an indication of fast computation time to detect the defects on the image, which is needed in manufacturing, and could be applied to most automated inspection systems.