A Linguistic Examination of the CapitalCube™ Market Effect Variables

Lusk, Edward J.; Halperin, Michael

doi:10.5430/afr.v5n2p89

Cited by 3 publications

(8 citation statements)

References 13 publications

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“…For this reason, in the interest in the space constraints of the journal, we will only mention these variables and provide an overall summary of the results presented in our previous three studies. Lusk & Halperin (2015) Lusk & Halperin (2016) judged that these four LQs suggest that CA & PS are indications of increases in the stock's future value; and AA & NCE would be indications of decreases in the stock's future value. Testing showed that there was non-random association in the expected direction for the 12 MPVs tested using the expert judgements of the LQs for each of the MPVs.…”

Section: Variables Of the Ccmnpmentioning

confidence: 99%

“…Its functionality, i.e., filtering data to form the linguistic codex, is the intellectual property of the CCMNP. For our purposes, rather than selecting a sub-set of the screens that were used by Lusk & Halperin (2016), we selected all 12 of their linguistic codes. These were tested by experts in Accounting and Finance and were found to be sensitive, specific and overall reasonable action codes.…”

Section: The Linguistic Qualifier [Lq]mentioning

confidence: 99%

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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%

Section: The Linguistic Qualifier [Lq]mentioning

confidence: 99%

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

Lusk¹

2018

AFR

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“…The intention of this research was to determine: (1) if the distribution of LQ on the scale: {Neutral, Unfavorable or Favorable}was differential from a random assignment, (2) if there was general agreement among Experts and Trained pre-professionals regarding these LQ indications, and (3) how often their agreement coincided with a second expect vetting. The results as reported in Lusk & Halperin (2016) is that one can, with confidence, reject the relative three Nulls strongly suggesting that there are inferential differences over the various LQ. This vetting rationalizes the use of the labels: {Neutral, Unfavorable or Favorable} in ferreting out differential indications over the MPV: LQ [DMV] imbedded set.…”

Section: "Financially Screening Investment Candidates Typically Reliementioning

confidence: 72%

“…In the second paper [in this analytic series] Lusk & Halperin (2016) investigated the relationship of 13 selected Market Performance Variables [MPV] relative to their Linguistic Qualifiers [LQ]. Each of the MPV have a unique set of LQs; details on each are found on: http://www.capitalcube.com/blog/index.php/glossary/.…”

Section: Previous Research Summariesmentioning

confidence: 99%

“…Recalling the definitions of the various Linguistic Triage variables as presented Lusk & Halperin (2016) has NO information value and is likely to ONLY serve as a "distraction" or "false information queuing" for an intelligent assessment of the market. However, the contrary is the observation.…”

Section: The Effect Variable As Profilers For the Capitalcube Variablmentioning

confidence: 99%

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

Lusk¹,

Halperin²

2017

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In this third examination of the CapitalCube Market Navigation Platform [CCMNP] In the testing, we are interested in the Sensitivity and the Specificity of these vetted variables over the annual S&P500 Panel from 2005 to 2013. The inference framework employed a Median Split: High or Low for each of the 13 MPV tested and a random selection to avoid the FPE-jeopardy that is part of the Chi2 testing model. We used the Tamhane & Dunlop cut-off to identify Chi2 cells effects of interest and used these to develop the Sensitivity and the Specificity tests. Results: We were able to reject the a priori Nulls proffered for the testing protocols indicating that one may reject the supposition that the labeling of the LQ is formed by random processes in the CCMNP.

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

Lusk¹

2019

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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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A Linguistic Examination of the CapitalCube™ Market Effect Variables

Cited by 3 publications

References 13 publications

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

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

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

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

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