Control charts in financial applications: An overview

Bisiotis, Konstantinos; Psarakis, Stelios; Yannacopoulos, A. N.

doi:10.1002/qre.2962

Cited by 14 publications

(10 citation statements)

References 92 publications

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“…In industrial production processes, statistical process control (SPC) offers a variety of control charts for tracking abrupt changes in parameters of quality characteristics [1]. This method is commonly employed, notably in the service, chemical industries, and manufacturing sectors to keep quality tabs on production or manufacturing operations [2].…”

Section: Introductionmentioning

confidence: 99%

EWMA Control Chart for Rayleigh Process With Engineering Applications

Al-Duais

Khan

2023

IEEE Access

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Process monitoring is typically performed with quality control charts. The normality assumption is often taken into consideration for the development of control charts. However, this assumption may not hold in many real-life situations. One such circumstance is utilizing the V SQR chart to track the process variability of Rayleigh distributed processes. The structure of the V SQR chart follows the basic design of the Shewhart-type chart, which is not sensitive mainly for smaller to moderate shifts in monitoring parameter. In this paper, an enhanced approach, namely Rayleigh exponential weighted moving average (REWMA) is introduced. The suggested REWMA scheme aims to significantly improve the detection capabilities of the traditional V SQR chart. Designated limits, along with charting parameters, are evaluated for different sample sizes. The effectiveness of the suggested REWMA control chart is assessed in terms of run length distributional parameters. Moreover, the Monte Carlo simulations are made to compare the run length properties of the proposed REWMA chart with the existing competitor design. The comparative assessment demonstrates that the suggested offers a considerable enhancement relative to the competing design. An application of the REWMA chart on simulated data also reveals that the proposed chart is highly sensitive to smaller and persistent shifts in the scaling parameter of Rayleigh distribution. Finally, an example from real-life has been presented to illustrate the importance of the suggested chart.

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Section: Introductionmentioning

confidence: 99%

EWMA Control Chart for Rayleigh Process With Engineering Applications

Al-Duais

Khan

2023

IEEE Access

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“…As a different application, control charts have been widely implemented in different areas of the economic and financial market monitoring (this area of study is sometimes referred to as financial surveillance). Bisiotis, Psarakis [ 9 ] have provided a comprehensive review of this subject in which some general categories could be found. One major category is about monitoring of the portfolio weights with the aid of control charts.…”

Section: Introductionmentioning

confidence: 99%

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Yeganeh

Shongwe

2023

PLoS ONE

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The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method.

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“…These charts are also widely used in several nonindustrial scientific areas including the financial areas. 3 The statistical control charts can generally be classified into two phases: Phase-I and Phase-II. 1,4 First, in Phase-I monitoring, we establish reliable control limits with a clean set of process data and in Phase-II analysis, we use the control limits obtained in Phase-I in the monitoring of a process by comparing the statistic for each successive subgroup as future observations are obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Since their introduction by Shewhart, 2 variable control charts, such as the Shewhart control chart, have been extensively adopted to detect if a process is in a state of control in different fields including industrial manufacturing, service sectors, and healthcare systems, to name just a few. These charts are also widely used in several nonindustrial scientific areas including the financial areas 3 …”

Section: Introductionmentioning

confidence: 99%

Development of robust X‐bar charts with unequal sample sizes

Park

Ouyang

Wang

2023

Quality & Reliability Eng

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The traditional variable control charts, such as the chart, are widely used to monitor variation in a process. They have been shown to perform well for monitoring processes under the general assumptions that the observations are normally distributed without data contamination and that the sample sizes from the process are all equal. However, these two assumptions may not be met and satisfied in many practical applications and thus make them potentially limited for widespread application especially in production processes. In this paper, we alleviate this limitation by providing a novel method for constructing the robust control charts, which can simultaneously deal with both data contamination and unequal sample sizes. The proposed method for the process parameters is optimal in a sense of the best linear unbiased estimation. Numerical results from extensive Monte Carlo simulations and a real data analysis reveal that traditional control charts seriously underperform for monitoring process in the presence of data contamination and are extremely sensitive to even a single contaminated value, while the proposed robust control charts outperform in a manner that is comparable with the traditional ones, whereas they are far superior when the data are contaminated by outliers.

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Control charts in financial applications: An overview

Cited by 14 publications

References 92 publications

EWMA Control Chart for Rayleigh Process With Engineering Applications

EWMA Control Chart for Rayleigh Process With Engineering Applications

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

Development of robust X‐bar charts with unequal sample sizes

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