Jeffrey E. Jarrett

Improving forecasting for telemarketing centers by ARIMA modeling with intervention

Abstract In this study we analyze existing and improved methods for forecasting incoming calls to telemarketing centers for the purposes of planning and budgeting. We analyze the use of additive and multiplicative versions of Holt–Winters (HW) exponentially weighted moving average models and compare it to Box–Jenkins (ARIMA) modeling with intervention analysis. We determine the forecasting accuracy of HW and ARIMA models for samples of telemarketing data. Although there is much evidence in recent literature that “simple models” such as Holt–Winters perform as well as or better than more complex models, we find that ARIMA models with intervention analysis perform better for the time series studied.

The quality control chart for monitoring multivariate autocorrelated processes

Previously, quality control and improvement researchers discussed multivariate control charts for independent processes and univariate control charts for autocorrelated processes separately. We combine the two topics and propose vector autoregressive (VAR) control charts for multivariate autocorrelated processes. In addition, we estimate AR(p) models instead of ARMA models for the systematic cause of variation. We discuss the procedures to construct the VAR chart. We examine the effects of parameter shifts and by example present procedures to show the feasibility of VAR control charts. We simulate the average run length to assess the performance of the chart.

Capital market efficiency and the predictability of daily returns

Studies of the weak form of the capital market efficiency theorem infer that there are no predictable properties of the time series of prices of traded securities on organized markets. We examine the weak form of the efficient markets hypothesis with respect to daily closing prices to indicate evidence that daily closing prices have predictable properties. Furthermore, this study of individual securities prices of traded securities on organized markets corroborates previous findings of studies of stock market indexes both in the United States and in other nations’ bourses or stock exchanges. Often, these studies indicated that daily patterns are present in the times series of securities prices. The purpose of this paper is to clarify the existence of time series characteristics of daily stock prices of securities traded on organized exchanges. This study differs from previous studies where the focus was on index numbers of daily stock market prices rather than the actual prices of traded securities in the United States. Furthermore, this study is important because of the weak theory of market efficiency and its application to short-term forecasting of closing prices of traded securities.

Transfer function modeling of processes with dynamic inputs

Time series structures, which are common occurrences with data in many industrial processes, complicate a quality practitioners efforts to accurately position control chart limits. ARIMA modeling and a variety of control charting methods have been recommended for monitoring process data with a time series structure. Estimates of ARIMA model parameters may not be reliable, however, if assignable causes of variation are present in the process data used to fit the time series model. Control limits may also be misplaced if the process inputs are dynamic and exhibiting a time series structure. Our purpose in this paper is to explore the ability of a transfer function model to identify assignable causes of variation and to model dynamic relationships between process inputs and outputs. A transfer function model is developed to monitor biochemical oxygen demand output from a wastewater treatment process, a process with dynamic inputs. This model is used to identify periods of disturbance to the wastewater process and to capture the relationship between the variable nature of the input to the process and the resulting output. Simulation results are included in this study to measure the sensitivity of transfer function models and to highlight conditions where transfer function modeling is critical.

Evidence on the seasonality of stock market prices of firms traded on organized markets

Studies of capital market efficiency are important because they infer that there are predictable properties of the time series of prices of traded securities on organized markets. The weak form of the efficient markets hypothesis is put in dispute by the results of this study. Furthermore, this study of individual securities prices of US traded securities corroborates previous findings of studies of stock market indexes both in the USA and for foreign stock exchanges that seasonality is present in the times of securities prices.

Monitoring Variability and Analyzing Multivariate Autocorrelated Processes

Abstract Traditional multivariate quality control charts are based on independent observations. In this paper, we explain how to extend univariate residual charts to multivariate cases and how to combine the traditional statistical process control (SPC) approaches to monitor changes in process variability in a dynamic environment. We propose using Alts (1984) W chart on vector autoregressive (VAR) residuals to monitor the variability for multivariate processes in the presence of autocorrelation. We study examples jointly using the Hotelling T2 chart on VAR residuals, the W chart, and the Portmanteau test to diagnose the types of shift in process parameters.

Daily Variation, Capital Market Efficiency and Predicting Stock Market Returns

Studies of capital market efficiency are important because they infer that there are predictable properties of the time series of prices of traded securities on organised markets. We examine the weak form of the efficient markets hypothesis to indicate its usefulness in terms of the results of this study. Furthermore, this study of individual securities prices of traded securities on organised markets corroborate previous findings of studies of stock market indexes both in the United States and for foreign stock exchanges that daily patterns are present in the times series of securities prices. You will note also, that the models identified reflect the closing prices on one day less the closing price on the previous day. In this way, we study returns and not average or closing prices.

Efficient markets hypothesis and daily variation in small Pacific-basin stock markets

Purpose - The purpose of this paper is to indicate the existence of certain time series characteristics in daily stock returns of four small Asian (Pacific basin) financial markets. It aims to study efficient capital markets (efficient markets hypothesis (EMH)) as results may infer that there are predictable properties of the time series of prices of traded securities on organized markets in Singapore, Malaysia, Korea and Indonesia. Design/methodology/approach - The paper analyses daily variations in financial market data obtained from the Sandra Ann Morsilli Pacific-basin Capital Markets Research Center (PACAP). Findings - The weak form efficiency test example examines the wide range of trading rules available to common investors. Some theorists try to convince everyone that the weak form of EMH is acceptable due to the weight of academic opinion. The paper finds that for short-term (daily) changes, the markets of four of the smaller Pacific-basin stock markets have predictable properties, which leads to the conclusion that the weak-form EMH does not hold for these markets. Research limitations/implications - The study is limited to those firms and exchanges studied and the time period covered. Originality/value - There have been all too few studies of these small financial markets up to now and there is no other study utilizing these data on the Pacific basin (Asia). The results are unique and original.

Random walk, capital market efficiency and predicting stock returns for Hong Kong Exchanges and Clearing Limited

Purpose – This paper seeks to study capital market efficiency, because results may infer that there are predictable properties of the time series of prices of traded securities on organized markets in Hong Kong, the third largest exchange in the Pacific‐Basin of Asia.Design/methodology/approach – The weak form of the efficient markets hypothesis is examined to indicate its usefulness in terms of the results of this study. Do the data indicate that the times series of closing prices is a random walk or are their predictable properties?Findings – It will be noted from the results that the model identifies predictive short‐term properties that exist in the data of returns of Hong Kong Exchanges for the period studied.Research/limitations/implications – Conclusions are limited to those firms studied and the time period covered.Originality/value – For the securities exchanges in Hong Kong, evidence indicates that the weak form of the efficient markets hypothesis does not characterize the trading market.

ARIMA Modeling With Intervention to Forecast and Analyze Chinese Stock Prices

In this study, we demonstrate the usefulness of ARIMA-Intervention time series analysis as both an analytical and forecast tool. The data base for this study is from the PACAP-CCER China Database developed by the Pacific-Basin Capital Markets (PACAP) Research Center at the University of Rhode Island (USA) and the SINOFIN Information Service Inc, affiliated with the China Center for Economic Research (CCER) of Peking University (China). These data are recent and not fully explored in any published study. The forecasting analysis indicates the usefulness of the developed model in explaining the rapid decline in the values of the price index of Shanghai A shares during the world economic debacle stating in China in 2008. Explanation of the fit of the model is described using the latest development in statistical validation methods. We note that the use of a simpler technique although parsimonious will not explain the variation properly in predicting daily Chinese stock prices. Furthermore, we infer that the daily stock price index contains an autoregressive component; hence, one can predict stock returns.