An Associational Examination of the CaptialCube Effect Context for the MPV over the Linguistic Partitions: Testing Sensitivity &amp; Specificity

Lusk, Edward J.; Halperin, Michael

doi:10.5430/afr.v6n2p1

Cited by 2 publications

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“…4.1.3 Integrated testing of the MPV[LQ]s over the MNISV. Lusk & Halperin (2017) tested the ensemble of the variables of the CCMNP and determined that there was non-random association of the profiles of the MPV[LQ] sets relative to the MNISV.…”

Section: Variables Of the Ccmnpmentioning

confidence: 99%

Evaluation of the Predictive Validity of the CapitalCube™ Market Navigation Platform

Lusk¹

2018

AFR

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Introduction This is the fourth research report where various aspects of the CapitalCube™ Market Navigation Platform [CCMNP] of AnalytixInsight™ have been examined. Previous Results In our previous three studies, we have tested many of the CCMNP-variables as expressed through the S&P500; we have rejected the Nulls of their inter-and intra-group association in favor of the likelihood that the variables that constitute the CCMNP are not produced by random generating processes. This suggests that the CCMNP is capable of creating market relevant information that may inform the investment decision. Current Study The previous results beg the question that is the focus of this report: Given the Non-Random character of the various CCMNP panel variables, does this panel of information enable the identification of a particular stock that will, in the near future, experience a turning-point? Results: We find no evidence that the CCMNP aids in detecting turning-points for the S&P500 Panel of data tested. Various caveats to this study are detailed in the summary section of this research report. Finally, we offer that the methodology used in investigating the CCMNP is a simple, transparent, and useful model for evaluating the acuity of a MNP in detecting turning-points.

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Section: Variables Of the Ccmnpmentioning

confidence: 99%

Evaluation of the Predictive Validity of the CapitalCube™ Market Navigation Platform

Lusk¹

2018

AFR

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“…The focus of this research report is to follow-up on the research reports of Lusk & Halperin (2015& 2017 which addressed the nature of the associational analysis of selected variables of the CapitalCube™ Market Navigation Platform [CCMNP: [<http://www.capitalcube.com/>]] a commercial product of AnalytixInsight™ [<http://www.analytixinsight.com/>]. For the CCMNP variable-set selected, they report that the Nulls of their interand intra-group associations may be rejected in favor of the likelihood that these CCMNP variables are not produced by random generating processes.…”

Section: Context Of This Research Reportmentioning

confidence: 99%

A Benchmarked Evaluation of a Selected CapitalCube Interval-Scaled Market Performance Variable

Lusk¹

2019

AFR

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ContextIn this fifth analysis of the CapitalCube™ Market Navigation Platform [CCMNP], the focus is on the CaptialCube Closing Price Latest [CCPL] which, is an Interval Scaled Market Performance [ISMP] variable that seems, a priori, the key CCMNP information for tracking the price of stocks traded on the S&P500. This study follows on the analysis of the CCMNP's Linguistic Category MPVs [LCMPV] where it was reported that the LCMPV were not effective in signaling impending Turning Points [TP] in stock prices. Study Focus As the TP of an individual stock is the critical point in the Panel and was used previously in the evaluation of the CCMNP, this study adopts the TP as the focal point in the evaluation montage used to determine the market navigation utility of the CCPL. This study will use the S&P500 Panel in an OLS Time Series [TS] two-parameter linear regression context: Y[S&P500] = X[TimeIndex] as the Benchmark for the performance evaluation of the CCPL in the comparable OLS Regression: Y[S&P500] = X [CCPL]. In this regard, the inferential context for this comparison will be the Relative Absolute Error [RAE] using the Ergodic Mean Projection [termed the Random Walk[RW]] of the matched-stock price forecasts three periods after the TP. Results Using the difference in the central tendency of the RAEs as the effect-measure, the TS: S&P Panel did not test to be different from the CCPL-arm of the study; further neither outperformed the RW; all three had Mean and Median RAEs that were greater than 1.0-the standard cut-point for rationalizing the use of a particular forecasting model. Additionally, an exploratory analysis used these REA datasets blocked on: (i) horizons and (ii) TPs of DownTurns & UpTurns; this analysis identified interesting possibilities for further analyses. The summary of the Lusk (2018) study is that: The CCMNP does NOT provide information from its LMP[LQ] variable-set that would flag or signal an impending TP.This, of course, leads to the next study, which is the point of departure of this study for which a question of interest is: Do the Set of Interval Scaled Market Performance [ISMP] Variables provide forecast acuity for time periods after a detected TP?This, then, is a corollary to the Lusk(2018) paper. Lusk (2018) found that the CCMNP set of linguistic variables was not likely to identify a TP from the currently available information of the CCMNP. This study then asks:What-If a Decision Maker could have ferreted out from all the available information that a particular month would be a TP, is there an ISMP-variable in the CCMNP that would allow the DM to forecast the stock price a few periods after the TP that would outperformed using just a Time Series projection?This question will form the nexus of this research. The rationale underlying this study is to determine if the variables offered in the CCMNP are sensitive to future trajectory changes in the market for selected firms. If this is not the case then it would be difficult to justify the allocation of time and resources in using the C...

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An Associational Examination of the CaptialCube Effect Context for the MPV over the Linguistic Partitions: Testing Sensitivity & Specificity

Cited by 2 publications

References 7 publications

Evaluation of the Predictive Validity of the CapitalCube™ Market Navigation Platform

Evaluation of the Predictive Validity of the CapitalCube™ Market Navigation Platform

A Benchmarked Evaluation of a Selected CapitalCube Interval-Scaled Market Performance Variable

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