The funnel experiment: The Markov‐based SPC approach

Singer, Gonen; Ben‐Gal, Irad

doi:10.1002/qre.852

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…If the level of transaction costs needed to generate profits from an anomaly (therefore, eliminating it) is far below the level that actually exists in the market, it could explain why a reasonably efficient market allows the anomaly to exist (Wu and Shafer 2007). The VOM tree model that we used in this paper is capable of capturing patterns of reversion to equilibrium (Singer and Ben-Gal, 2007), as long as the reversion is within the window of observation (50 to 100 consecutive trading days in our experiments). This capability to predict beyond random can be explained as follows: when the deviation from equilibrium is small, and the stock market can move either way, the VOM tree can captures only a small predictability level above random (such as seen in leaf 1 in Figure 1).…”

Section: Discussionmentioning

confidence: 99%

Predicting Stock Returns Using a Variable Order Markov Tree Model

Shmilovici

Ben‐Gal

2012

Studies in Nonlinear Dynamics &Amp; Econometrics

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The weak form of the Efficient Market Hypothesis (EMH) states that the current market price fully reflects the information of past prices and rules out predictions based on price data alone. In an efficient market, consistent prediction of the next outcome of a financial time series is problematic because there are no reoccurring patterns that can be used for a reliable prediction.This research offers an alternative test of the weak form of the EMH. It uses a universal prediction algorithm based on the Variable Order Markov tree model to identify re-occurring patterns in the data, constructs explanatory models, and predicts the next time-series outcome. Based on these predictions, it rejects the EMH for certain stock markets while accepting it for other markets.The weak form of the EMH is tested for four international stock exchanges: the German DAX index; the American Dow-Jones30 index; the Austrian ATX index and the Danish KFX index. The universal prediction algorithm is used with sliding windows of 50, 75, and 100 consecutive daily returns for periods of up to 12 trading years. Statistically significant predictions are detected for 17% to 81% of the ATX, KFX and DJ30 stock series for about 3% to 30% of the trading days. A summary prediction analysis indicates that for a confidence level of 99% the more volatile German (DAX) and American (DJ30) markets are indeed efficient. The algorithm detects periods of potential market inefficiency in the ATX and KFX markets that may be exploited for obtaining excess returns.

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Section: Discussionmentioning

confidence: 99%

Predicting Stock Returns Using a Variable Order Markov Tree Model

Shmilovici

Ben‐Gal

2012

Studies in Nonlinear Dynamics &Amp; Econometrics

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“…This implies that an automated process governed by a feedback loop actively controlling the process has to decide when to react and when not to react; do not react when the process is on target, but react when deviance is detected. Methods have been developed to monitor a process in the presence of feedback rules (Singer and Ben Gal 2007), as well as using feedback rules to help detect, explain, and prevent tampering with the processes (Georgantzas and Katsamakas 2008).…”

Section: Literature Tamperingmentioning

confidence: 99%

Operator agency in process intervention: tampering versus application of tacit knowledge

2015

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Statistical process control (SPC) theory takes a negative view of adjustment of process settings, which is termed tampering. In contrast, quality and lean programmes actively encourage operators to acts of intervention and personal agency in the improvement of production outcomes. This creates a conflict that requires operator judgement: How does one differentiate between unnecessary tampering and needful intervention? Also, difficult is that operators apply tacit knowledge to such judgements. There is a need to determine where in a given production process the operators are applying tacit knowledge, and whether this is hindering or aiding quality outcomes. The work involved the conjoint application of systems engineering, statistics, and knowledge management principles, in the context of a case study. Systems engineering was used to create a functional model of a real plant. Actual plant data were analysed with the statistical methods of ANOVA, feature selection, and link analysis. This identified the variables to which the output quality was most sensitive. These key variables were mapped back to the functional model. Fieldwork was then directed to those areas to prospect for operator judgement activities. A natural conversational approach was used to determine where and how operators were applying judgement. This contrasts to the interrogative approach of conventional knowledge management. Data are presented for a case study of a meat rendering plant. The results identify specific areas where operators' tacit knowledge and mental model contribute to quality outcomes and untangles the motivations behind their agency. Also evident is how novice and expert operators apply their knowledge differently. Novices were focussed on meeting throughput objectives, and their incomplete understanding of the plant characteristics led them to inadvertently sacrifice quality in the pursuit of productivity in certain situations. Operators' responses to the plant are affected by their individual mental models of the plant, which differ between operators and have variable validity. Their behaviour is also affected by differing interpretations of how their personal agency should be applied to the achievement of production objectives. The methodology developed here is an integration of systems engineering, statistical analysis, and knowledge management. It shows how to determine where in a given production process the operator intervention is occurring, how it affects quality outcomes, and what tacit knowledge operators are using. It thereby assists the continuous quality improvement processes in a different way to SPC. A second contribution is the provision of a novel methodology for knowledge management, one that circumvents the usual codification barriers to knowledge management. InterventionAlso, although the idealised funnel experiment is highly relevant to the parts manufacturing industries, it is 404 J Ind Eng Int (2015) 11:403-425 J Ind Eng Int (2015) 11:403-425 405

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“…Furthermore, Ben‐Gal et al 48 proposed a context‐based SPC method based on the information theory principles to monitor a state‐dependent process of varying dependent order. The method does not assume prior knowledge of the model parameters but a large storage volume of data is required Singer and Ben‐Gal49 proposed a simplified version of the above methodology based on conventional fixed‐order Markov models.…”

Section: Using Nns To Recognize Shifts In Correlated Process Datamentioning

confidence: 99%

The use of neural networks in statistical process control charts

Psarakis

2011

Quality & Reliability Eng

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Neural networks (NNs) are massively parallel computing mechanism emulating a human brain. It has been proved that they had a satisfactory performance when they were used for a wide variety of applications. In the recent years, the efficiencies that provided the NNs also began to be applied in statistical process control (SPC). SPC charts have become one of the most commonly used tools for monitoring process stability and variability in today's manufacturing environment. These tools are used to determine whether the process is statistically under or out of control but in some cases such as the presence of autocorrelation as well as the presence of a specific pattern in the data do not provide the possibility of correctly and quickly detecting and classifying the existing fault. These problems have led many researchers to propose alternative methods for monitoring processes such as the use of NNs. In this paper, we discuss issues concerning the combination of both tools. Specifically, we study the NNs for the detection and determination of mean and/or variance shifts as well as in pattern recognition in the SPC charts. Furthermore, the use of NNs when the data are correlated is discussed. Finally, the use of NNs in multivariate control charts is addressed. The networks architectures that were used for each case, the way of operation and the performance of the proposed NNs applications are pointed out.

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The funnel experiment: The Markov‐based SPC approach

Cited by 13 publications

References 39 publications

Predicting Stock Returns Using a Variable Order Markov Tree Model

Predicting Stock Returns Using a Variable Order Markov Tree Model

Operator agency in process intervention: tampering versus application of tacit knowledge

The use of neural networks in statistical process control charts

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