Simple Moving Average Research Articles

Trends in Satellite-Based Ocean Parameters through Integrated Time Series Decomposition and Spectral Analysis. Part I: Chlorophyll, Sea Surface Temperature, and Sea Level Anomaly

Abstract This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and wavelet transforms. Globally, SST and SLA trend upward, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time series components (seasonality, trend, and noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background trends, e.g., the 2010–11 SLA drop due to La Niña–induced rainfall over land. The continuous analysis clarifies the warming hiatus debate, affirming sustained warming. Decadal trend rates per grid cell are also mapped. These are ubiquitously significant for SST and SLA, whereas Chl-a trend rates are globally low but extreme across coasts and boundary currents. Significance Statement This work introduces a versatile scheme combining decomposition-based trend methods and spectral analysis while considering multiple seasonal periods. Trends in ocean parameters reveal planetary changes, and space-based observations are vital for in situ sparse regions. However, selecting reliable data and accounting for multiple periods are crucial to discerning real trends amid inherent variabilities. Resulting trends highlight hotspots (areas of significant changes) and, alongside other information, may uncover change drivers. The purpose is to (i) summarize decomposition methods, spanning foundational to the latest, (ii) derive trend lines and trend-rate maps for biophysical parameters, and (iii) characterize geospatial variations. These maps at native grid resolutions may inform background conditions for other prospective ocean applications. Extending this scheme to additional variables is forthcoming.

Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM

BackgroundMissing observations within the univariate time series are common in real-life and cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in every incomplete univariate time series. Most of the existing studies focus on comparing the distributions of imputed data. There is a gap of knowledge on how different imputation methods for univariate time series affect the forecasting performance of time series models. We evaluated the prediction performance of autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network models on imputed time series data using ten different imputation techniques.MethodsMissing values were generated under missing completely at random (MCAR) mechanism at 10%, 15%, 25%, and 35% rates of missingness using complete data of 24-h ambulatory diastolic blood pressure readings. The performance of the mean, Kalman filtering, linear, spline, and Stineman interpolations, exponentially weighted moving average (EWMA), simple moving average (SMA), k-nearest neighborhood (KNN), and last-observation-carried-forward (LOCF) imputation techniques on the time series structure and the prediction performance of the LSTM and ARIMA models were compared on imputed and original data.ResultsAll imputation techniques either increased or decreased the data autocorrelation and with this affected the forecasting performance of the ARIMA and LSTM algorithms. The best imputation technique did not guarantee better predictions obtained on the imputed data. The mean imputation, LOCF, KNN, Stineman, and cubic spline interpolations methods performed better for a small rate of missingness. Interpolation with EWMA and Kalman filtering yielded consistent performances across all scenarios of missingness. Disregarding the imputation methods, the LSTM resulted with a slightly better predictive accuracy among the best performing ARIMA and LSTM models; otherwise, the results varied. In our small sample, ARIMA tended to perform better on data with higher autocorrelation.ConclusionsWe recommend to the researchers that they consider Kalman smoothing techniques, interpolation techniques (linear, spline, and Stineman), moving average techniques (SMA and EWMA) for imputing univariate time series data as they perform well on both data distribution and forecasting with ARIMA and LSTM models. The LSTM slightly outperforms ARIMA models, however, for small samples, ARIMA is simpler and faster to execute.

Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50

This study evaluates machine learning models for stock market prediction in the European stock market EU50, with emphasis on the integration of key technical indicators. Advanced techniques, such as ANNs, CNNs and LSTMs, are applied to analyze a large EU50 dataset. Key indicators, such as the simple moving average (SMA), exponential moving average (EMA), moving average convergence/divergence (MACD), stochastic oscillator, relative strength index (RSI) and accumulation/distribution (A/D), were employed to improve the model’s responsiveness to market trends and momentum shifts. The results show that CNN models can effectively capture localized price patterns, while LSTM models excel in identifying long-term dependencies, which is beneficial for understanding market volatility. ANN models provide reliable benchmark predictions. Among the models, CNN with RSI obtained the best results, with an RMSE of 0.0263, an MAE of 0.0186 and an R2 of 0.9825, demonstrating high accuracy in price prediction. The integration of indicators such as SMA and EMA improves trend detection, while MACD and RSI increase the sensitivity to momentum, which is essential for identifying buy and sell signals. This research demonstrates the potential of machine learning models for refined stock prediction and informs data-driven investment strategies, with CNN and LSTM models being particularly well suited for dynamic price prediction.

Comparing Moving Averages And Polynomial Regression In Financial Trend Analysis

Trend analysis forms the backbone of studies related to financial markets as they give investors and market participants insights into expected market behavior, aiding in investment decisions. This paper compares two methods for determining financial trends: Moving Averages (MAs) and Polynomial Regression (PR). Widely used technical indicators like Simple Moving Averages (SMA) and Exponential Moving Averages (EMA) are popular due to their ease of understanding and effectiveness in smoothing price data by removing noise. Although simpler models like linear or logistic regression can be used if the data is suitable, polynomial regression is considered more flexible as it fits a non-linear curve to the data. This study aims to examine the effectiveness of these two approaches in detecting and predicting financial trends accurately. The research involves calculating SMA and EMA on historical stock price data and applying polynomial regressions of various degrees. A comprehensive comparison will focus on the fit, precision, and predictability of each method in financial trend analysis

Artificial Intelligence Workload Allocation Method for Vehicular Edge Computing

Real-time applications such as smart transportation systems require minimum response time to increase performance. Incorporating edge computing, processing units near end devices, achieving fast response time. The collaboration between edge servers and cloud servers is beneficial in achieving the lowest response time by using edge servers and high computational resources by using cloud servers. The workload allocation between edge–cloud servers is challenging, especially in a highly dynamic system with multiple factors varying over time. In this paper, the workload allocation decisions among the edge servers and cloud are considered for autonomous vehicle systems. The autonomous vehicle system generates multiple tasks belonging to different AI applications running on the vehicles. The proposed method considers allocating the tasks to edge or cloud servers. The cloud servers can be reached through a cellular network or a wireless network. The proposed method is based on designing a neural network model and using a high number of features that contribute to the decision-making process. A huge dataset has also been generated for the implementation. The EdgeCloudSim is used as a simulator for implementation. The competitor's methods considered for the comparison are random, simple moving average (SMA) based, multi-armed bandit (MAB) theory-based, game theory-based, and machine learning-based workload allocation methods. The result shows an improvement in the average Quality of Experience (QoE), ranging from 8.33% to 28.57%, while the average failure rate achieved enhancement up to 50%.

Suggestions and Comparisons of Two Algorithms for the Simplification of Bluetooth Sensor Data in Traffic Cordons.

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin-destination (i-j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements.

Evaluation of Different Filtering Methods Devoted to Magnetometer Data Denoising

In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs).

Evaluation of Mean Square Errors in Simple Moving Average versus Exponential Smoothing Method and Assessment of Time as Predictor in Forecasting Myocardial Infarction Cases in the Philippines

This study aimed to identify the best forecasting model and evaluate if time (in months and years) is a predictor for Myocardial Infarction cases in the Philippines. The research design was quantitative research, specifically descriptive study design. Secondary data on monthly reported cases of myocardial infarction for the past five years (November 2018-November 2023) from Google Trends was utilized. Microsoft Excel and SPSS were the software used to obtain results. Simple Moving Average (SMA) and Exponential Smoothing Method (ESM) were the forecasting techniques used in this study. At the same time, Mean, Standard Deviation, Analysis of Variance, and Independent sample t-test were the statistical tools utilized for both descriptive and inferential analyses. Results revealed that the mean level of cases reported for the past five years was 54. Also, according to SMA and ESM, the forecasted cases for the next succeeding month (December 2023) were 66 and 61, respectively. Results also showed that the Mean Squared Error (MSE) value of SMA is lower than ESM, making SMA a better forecasting method. Moreover, there was a significant difference between the reported cases when grouped according to year, with Year 5 having the highest number of cases and Year 1 having the lowest. Further, there was no significant difference between the SMA and ESM forecasted cases. Furthermore, time (year and month) significantly predicted the number of myocardial infarction cases reported in the Philippines.

Leveraging transfer learning with deep learning for crime prediction.

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam

The aims of this study are to predict the stock price trend in the stock market in an emerging economy. Using the Long Short Term Memory (LSTM) algorithm, and the corresponding technical analysis indicators for each stock code include: simple moving average (SMA), convergence divergence moving average (MACD), and relative strength index (RSI); and the secondary data from VN-Index and VN-30 stocks, the research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model and the test set data is used to evaluate the model’s performance. The research results showed that the forecasting model has a high accuracy of 93% for most of the stock data used, demonstrating the appropriateness of the LSTM model in analyzing and forecasting stock price movements on the machine learning platform.

Hourly PM2.5 Concentration Prediction Based on Empirical Mode Decomposition and Geographically Weighted Neural Network

Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time series data observed from stations, and it is difficult to avoid the redundancy between features during feature selection. To further improve the accuracy, this study proposes a hybrid model based on empirical mode decomposition (EMD), minimal-redundancy-maximal-relevance (mRMR), and geographically weighted neural network (GWNN) for hourly PM2.5 concentration prediction, named EMD-mRMR-GWNN. Firstly, the original PM2.5 concentration sequence with distinct nonlinearity and non-stationarity is decomposed into multiple intrinsic mode functions (IMFs) and a residual component using EMD. IMFs are further classified and reconstructed into high-frequency and low-frequency components using the one-sample t-test. Secondly, the optimal feature subset is selected from high-frequency and low-frequency components with mRMR for the prediction model, thus holding the correlation between features and the target variable and reducing the redundancy among features. Thirdly, the residual component is predicted with the simple moving average (SMA) due to its strong trend and autocorrelation, and GWNN is used to predict the high-frequency and low-frequency components. The final prediction of the PM2.5 concentration value is calculated by an artificial neural network (ANN) composed of the predictive values of each component. PM2.5 concentration prediction experiments in three representational cities, such as Beijing, Wuhan, and Kunming were carried out. The proposed model achieved high accuracy with a coefficient of determination greater than 0.92 in forecasting PM2.5 concentration for the next 1 h. We compared this model with four baseline models in forecasting PM2.5 concentration for the next few hours and found it performed the best in PM2.5 concentration prediction. The experimental results indicated the proposed model can improve prediction accuracy.

Trend-Enhanced Improved Bollinger Bands Trend-Following High-Frequency Trading Strategy for Futures Market

The application of digital scientific technologies in the global financial markets has become widespread, which has led to the prevalence of the concept of quantitative trading. Among various quantitative trading strategies, high-frequency trading has gained significant popularity. In this study, we propose an improved Bollinger Bands trend-following high-frequency trading strategy based on trend enhancement. Instead of using the simple moving average (SMA), we replace it with the exponential moving average (EMA). Furthermore, we introduce a 3-times average true range (ATR) limit price rebound exit system to achieve timely profit-taking and stop-loss. By incorporating trend measurement indicators such as the average directional index (ADX), moving average convergence divergence (MACD), and commodity channel index (CCI), we determine the futures trend. Subsequently, this study formulates suitable parameters for the high-frequency trading strategy and conduct backtesting on the 2022 CSI 300 stock index futures, resulting in a remarkably high annualized return. These results present a novel approach to high-frequency trading strategy and provide valuable insights for the market.

Analisis Teknikal Dan Analisis Fundamental Terhadap Kelayakan Pembelian Saham PT Bank Negara Indonesiaia Tbk (BBNI)

The decision on the feasibility of purchasing a share is an indicator or criterion that is considered before finally deciding to invest or invest in the issuer. Researchers conducted technical analysis and fundamental analysis of PT Bank Negara Indonesia, Tbk. (BBNI) shares to find out whether the issuer's shares are suitable as issuers for investing or just for trading. Technical analysis and fundamental analysis are performed to see opportunity whether the shares are worthy of stock investment or not. Technical analysis is carried out using the simple moving average (SMA) and RSI methods. In addition, fundamental analysis is also carried out with several indicators such as price book value (PBV), price earning ratio (PER), debt equity ratio (DER), net probability margin (NPM), and earnings per share (EPS). In this study, the approach taken is a qualitative descriptive approach where data is presented in the form of narratives or detailed elaboration from experts. PT Bank Negara Indonesia (BBNI) shares are the oldest bank stocks that have a large market capitalization. This stock can also be said to have a relatively stable level of price fluctuation with a high level of profit and price. In terms of technical analysis and fundamental analysis, PT Bank Negara Indonesia (BBNI) shares are stocks that deserve to be an investment choice.

An internet of things enabled machine learning model for Energy Theft Prevention System (ETPS) in Smart Cities

Energy theft is a significant problem that needs to be addressed for effective energy management in smart cities. Smart meters are highly utilized in smart cities that help in monitoring the energy utilization level and provide information to the users. However, it is not able to detect energy theft or over-usage. Therefore, we have proposed a multi-objective diagnosing structure named an Energy Theft Prevention System (ETPS) to detect energy theft. The proposed system utilizes a combination of machine learning techniques Gated Recurrent Unit (GRU), Grey Wolf Optimization (GWO), Deep Recurrent Convolutional Neural Network (DDRCNN), and Long Short-Term Memory (LSTM). The statistical validation has been performed using the simple moving average (SMA) method. The results obtained from the simulation have been compared with the existing technique in terms of delivery ratio, throughput, delay, overhead, energy conversation, and network lifetime. The result shows that the proposed system is more effective than existing systems.

COMPARISON OF THE EFFECTIVENESS OF TIME SERIES ANALYSIS METHODS: SMA, WMA, EMA, EWMA, AND KALMAN FILTER FOR DATA ANALYSIS

In time series analysis, signal processing, and financial analysis, simple moving average (SMA), weighted moving average (WMA), exponential moving average (EMA), exponential weighted moving average (EWMA), and Kalman filter are widely used methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific application and data characteristics. It is important for researchers and practitioners to understand the properties and limitations of these methods in order to make informed decisions when analyzing time series data. This study investigates the effectiveness of time series analysis methods using data modeled with a known exponential function with overlaid random noise. This approach allows for control of the underlying trend in the data while introducing the variability characteristic of real-world data. The relationships were written using scripts for the construction of dependencies, and graphical interpretation of the results is provided.

Advanced Deep Learning-Based Predictive Modelling for Analyzing Trends and Performance Metrics in Stock Market

Objective: This study's main goal is to investigate how deep learning approaches may be used to analyze stock market performance. The complex patterns and nonlinear interactions present in stock market data may be difficult to completely capture using traditional approaches, which are mostly based on statistical models.
 Methodology: Our work uses a large dataset of historical stock prices, macroeconomic indices, and other crucial financial factors to address this. Simple Moving Averages (SMA) are one of the feature engineering approaches that are used to combine fundamental and technical indicators. To capture the temporal dynamics of the stock market, the study goes further into a variety of deep learning architectures, including as long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
 Findings: The results show that thorough feature engineering combined with deep learning approaches may effectively capture the complexity of the stock market and provide forecasts that are more accurate.
 Implications: This highlights how deep learning may revolutionize financial market research and points to a paradigm change toward more trustworthy instruments for investors and decision-makers.

Prediction of US 30‐years‐treasury‐bonds movement and trading entry point using the robust 1DCNN‐BiLSTM‐XGBoost algorithm

AbstractThis article presents a novel algorithm that accurately predicts market trends and identifies trading entry points for US 30‐year Treasury bonds. The proposed method employs a hybrid approach, integrating a 1‐dimensional convolutional neural network (1DCNN), long‐short term memory (LSTM), and XGBoost algorithms. The 1DCNN is used to learn local and short‐term patterns, while LSTM is employed to capture both short and long‐term dependencies. Furthermore, we have implemented an algorithm that utilizes hull moving average (HMA) and simple moving average (SMA) crossover data to detect trading entry points and major trends in the market. The combination of the SMA–HMA crossover algorithm and predictions provided by the 1DCNN‐BiLSTM‐XGBoost algorithm yields exceptional results in terms of prediction accuracy and profitability. Additionally, these integrated techniques effectively filter out noise and mitigate false breakouts, which are often observed with US 30‐year Treasury bonds. In the field of financial time series prediction, the effectiveness of 1DCNN and LSTM in identifying trading entry points and market perturbations has not been comprehensively studied. Therefore, our work fills this gap by demonstrating through experiments that the proposed 1DCNN‐BiLSTM‐XGBoost algorithm, in combination with moving average crossovers, effectively reduces noise and market perturbations. This leads to the precise identification of trading entry points and accurate recognition of trend signals for US 30‐year Treasury bonds. We demonstrate through experiments that our proposed approach achieves an average root mean squared error of 0.0001 and an R‐square value of 0.9999, highlighting its promise as a method for predicting market trends and trading entry points for US 30‐year Treasury bonds.

UAV instantaneous power consumption prediction using LR‐TCN with simple moving average

SummaryThe usage of multi‐rotor unmanned aerial vehicles (UAVs) has rapidly increased in a variety of industries recently, which has caused a quick rise in the quantity of research works on the topic. Remaining battery life prediction, anomaly detection and instantaneous power consumption prediction are among the topics that attract the most attention of researchers. This article presents the development and utilization of a modified temporal convolutional network (TCN) model, a commonly employed approach for anomaly detection and instantaneous power consumption prediction in unmanned aerial vehicles (UAVs). The first modification to the TCN model was to use the Leaky ReLu activation function in place of the rectified linear unit (ReLu) activation function from the original TCN model. In the next step, instantaneous power consumption prediction for UAVs was performed by using the data obtained from both sensors and simple moving average (SMA) algorithm. As a result of the tests performed with the created simulation setup, it has been clearly shown that the proposed method gives better results compared to other deep learning models used for comparison with the lowest RMSE of 0.0496.

Analysis and Exploration of the Impact of Average Sea Level Change on Navigational Safety in Ports

The primary clientele of a harbor is vessels, and vessels are primarily influenced by external forces such as wind (on the water surface), currents (underwater), and waves (affecting vessel stability). Therefore, it is necessary to comprehensively consider safety factors such as marine environmental forces and port characteristics. As ship sailing falls under applied science, acquiring marine meteorological information regarding ship routes can enhance port navigational safety. However, in the face of changes in the environmental conditions of harbor waters, it is essential to fully consider the impact of the external environment on ship maneuvering. One can effectively navigate complex operating environments by devising reasonable ship-handling plans. In the context of sea level rise caused by extreme climatic events, long-term variations, trends, and random factors are at play. Previous assessments of sea level rise have often relied on linear regression and the least squares method to determine coefficients. However, these methods fail to accurately capture the actual trend of sea level rise. Additionally, traditional harmonic analysis methods are unable to analyze sea level rise as well. Therefore, in this study, the techniques of simple moving average (SMA), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) were applied to analyze sea level rise. The obtained results of sea level rise under different analysis conditions were integrated with a hydrodynamic model that incorporates both wave and tidal characteristics to calculate the overall coastal dynamics parameters, which are crucial for ship navigation. The research findings contribute to the study of ship navigational safety issues by examining the distribution characteristics of port meteorology under climate change conditions. They offer valuable insights for mariners to assess navigational safety and devise maneuvering strategies based on the actual water flow conditions. Furthermore, the findings help identify and address potential risks and issues, ultimately ensuring the safety of navigation.

Practical Applications of Leading Economic Indicator and Global Stock Market Returns

In <ext-link><bold><italic>Leading Economic Indicator and Global Stock Market Returns</italic></bold></ext-link>, from the Summer 2022 issue of <bold><italic>The Journal of Wealth Management</italic></bold>, <bold>Todd Feldman</bold> and <bold>Alan Jung</bold>, both of <bold>San Francisco State University</bold>, present a market-timing strategy based on combining the Conference Board’s Leading Economic Index (LEI) with a 200-day simple moving average (SMA). This LEI/SMA strategy signals when to exit the stock market and move to cash and when to return to the stock market. In a 2015 study (<xref>Feldman, Jung, and Klein 2015</xref>), the authors demonstrated that for the US stock market, the LEI/SMA strategy outperformed other simple market-timing strategies, as well as a buy-and-hold strategy. In this article, Feldman and Jung expand their analysis of the LEI/SMA strategy and include the stock markets of the United States and seven other countries. They find that the LEI/SMA strategy performs better than a buy-and-hold strategy in all but two of those countries. Additionally, the authors show how global economic indicators can be useful in timing US market decisions.