Simple Moving Average Research Articles

Stabilization of Wind Generator by Using STATCOM/SMES

In this work, the STATCOM/SMES system with a voltage-source IGBT converter is modeled as a controllable energy source. The objective of the proposed STATCOM/SMES topology is to provide both active and reactive power, which can significantly decrease the voltage and power fluctuations of grid connected fixed speed wind generators. One major problem in wind generator output power smoothing is setting of the reference output power. Constant output power reference is not a good choice because there can be some cases where wind speed is very low and then sufficient power cannot be obtained. In that case, energy storage device can solve the problem but large energy capacity may be needed. To generate output power reference, a Simple Moving Average (SMA) technique is used which corresponds to the energy storage capacity. Thus the capacity of SMES can be small (50% of wind farm capacity). Real wind speed data is used in the simulation analyses, which validates the effectiveness of the proposed control strategy. Keywords: STATCOM/SMES; Energy storage system (ESS); Simple moving average (SMA); Fixed speed wind generator; Voltage source converter (VSC).© 2009 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.DOI: 10.3329/jsr.v1i3.2605 J. Sci. Res. 1 (3), 528-538 (2009)

Performance of a CDMA system employing AMC and multicodes in the presence of channel estimation errors

The impact of channel state estimation errors in a CDMA system employing adaptive modulation and coding in conjunction with multicodes is studied. The channel is modelled as a finite-state Markov chain and the performances using (1) a simple moving average (SMA) filter (2) a hidden Markov model (HMM) filter to estimate the channel state are compared. The results show that the HMM filter is more robust and provides a significant throughput improvement over the SMA filter, especially when the channel estimate is quite noisy or the normalized Doppler rate is small.

The Profitability of Simple Technical Trading Strategies: The Case of Hong Kong

In this paper, we investigate two simple but very popular trading rules, simple moving average (SMA) and trading range break (TRB), by looking at a series of data of Hang Seng Index over thirty-five years, from 1972 to 2006. To avoid the data-snooping biases, we apply the out-of-sample tests in additional to the traditional tests to check for the validity of the profitability of the trading rules. In general, our results show that there is one trading rule, the (1, 50) rule, outperforms the market (HSI) over the 35 years testing period and in the pre-1986 sub-periods in our study in the SMA trading rule. These average returns from SMA rule are higher than the returns found in the US market (Brock et al., 1992) and Hong Kong market (Bessembinder and Chan, 1995). Besides, the VMA (1, 50) rule performs better than the FMA (1, 50) rule because it includes the information of the first nine days into investors' decision. With the flexibility of buy or sell within the first nine days, the VMA (1, 50) rule can generate 2.5% to 7.5% (annual) more profit than the FMA rule before transaction costs. However, the returns of the TRB rules are all small and insignificant. We find that the high trading returns are unlikely to be explained by the difference risk in difference trading strategies and transaction costs. However, they can be partly explained by the positive first order autocorrelation in the Hong Kong stock market and the time-varying premium. Furthermore, the inconsistent subperiod abnormal average returns suggest that the stock market integration leads to a more efficient dissemination of information over time in Hong Kong.

Profiting from Technical Analysis in Indian Equity Markets: Using Moving Averages

This study employs the Simple Moving Average (SMA) and the Displaced Moving Average (DMA) trading rules to test the weak form efficiency of the Indian equity markets. The indicators were applied on the S&P CNX Nifty, BSE Sensex as well as multiple individual stocks for a time period spanning 15 years (1991-2005). Our results provide sufficient evidence that the DMA indicator is a highly successful trading rule that generated profitable signals even after adjusting for transaction and other costs.

Is smarter better? A comparison of adaptive, and simple moving average trading strategies

This study examines the comparative performance of an Adaptive Moving Average (AMA) on the Australian All Ordinaries, Dow Jones Industrial Average, and Standard and Poor's 500 stock market indices. The theoretical advantage of the Adaptive Moving Average over fixed length Simple Moving Average (SMA) trading systems is its ability to automatically respond to changing market conditions dependant upon the level of volatility in the market. While the strategy is confirmed to have some market timing ability, the overall results show returns to the Adaptive Moving Average cannot compensate for the cost of trade therefore lending support for the use of a long run passive strategy.

Time Series Forecasting Using Wavelet Denoising an Application to Saudi Stock Index

In recent years, wavelet transform has become very popular in many application areas such as physics, engineering, biomedical, signal processing, mathematics, and statistics. In this paper, we present the role of wavelet transform in time series analysis. Saudi Stock Index (SSI) time series was used as a vehicle to highlight the benefits of wavelet transform usage in time series analysis in general and in time series denoising, in particular. SSI was modeled as a deterministic function plus random noise (white or colored). Different denoising techniques were considered with Haar, Daubechies’, and Biorthogonal wavelets. Several Forecasting models of SSI were developed for original and denoised series. Namely, Linear Regression (LR), Simple Moving Average (SMA), Exponential Smoothing (ES), Autoregressive (AR), and Autoregressive Moving Average (ARMA). Computational results have shown that more information could be exploited from SSI when it is decomposed into several series with different resolutions using wavelet transform. Moreover, forecasting errors of SSI can be substantially reduced when the index was first denoised using soft thresholding with white noise assumption.

A robust forecasting system, based on the combination of two simple moving averages

For series with negligible growth and seasonality, simple moving averages are frequently used to estimate the current level of a process, and the resultant value projected as a forecast for future observations. This paper shows that a linear combination of two simple moving averages (SMA) can provide an improved estimate of the underlying level of the process. The proposition is demonstrated by simulation, and good combinations are listed. The theory underlying the improvement is developed. The general rules are then illustrated through an application in an inventory situation.

Some properties of a simple moving average when applied to forecasting a time series

Simple (equally weighted) moving averages are frequently used to estimate the current level of a time series, with this value being projected as a forecast for future observations. A key measure of the effectiveness of the method is the sampling error of the estimator, which this paper defines in terms of characteristics of the data. This enables the optimal length of the average for any steady state model to be established and the lead time forecast error derived. A comparison of the performance of a simple moving average (SMA) with an exponentially weighted moving average (EWMA) is made. It is shown that, for a steady state model, the variance of the forecast error is typically less than 3% higher than the appropriate EWMA. This relatively small difference may explain the inconclusive results from the empirical studies about the relative predictive performance of the two methods.