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

Abstract

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...

OLS-Regression Forecasting Confidence Intervals Capture Rates: Precision Profiling in the Forecasting Model Selection Process

“…The panels selected were identified as the ten monthly closing prices preceding a stock price TP-dramatic change in the trajectory of the stock price. The details of this TP analysis are best found in Lusk and Halperin (2015) and Lusk (2019b). For each of these 32 Panels there were This profiling would be typical for inspecting the contribution components of the various forecasting protocols.…”

“…TheLusk (2019b) report was not accurate in characterizing the CCPL as not a useful conditioning Market Navigation Platform-variable. Rather, as an elaboration, it seems that in the TP-context using the CCPL as a conditioning variable did in fact emerge as a possibility not inferentially different from the TS-versions as noted above.…”

“…Stage A: The Decomposition of the OLSR-[Model & Version]-component set regarding relative precision, and Stage B for the inferentially significant OLSR-components from Stage A, a multilevel quality-sensitive algorithm is suggested and parameterized to evaluate the Quality of the respective 95%CI Capture Rates, 5. The [D&S-Triage] protocol is illustrated for an accrual of firms from the S&P500; the forecasting context selected is a very challenging forecasting context introduced by Lusk & Halperin (2015) and Lusk (2019b) where forecasts are produced and evaluated after Panel trajectory turning points, and 6. Suggest further research possibilities for the forecasting domain.…”

“…Panel TP, and (ii) the Relative Absolute Error analysis reported byLusk (2019b) found that there was no forecasting model that outperformed the Random Walk [RW]-the Occam's version as the forecast/model. Even for this context, using the [D&S Triage & Ex[A]], we learned the following related inferential impacts that will certainly inform the forecasting model selection process:1.…”

Time Series Forecasting in Stock Trading Markets

“…This result was essentially that reported in a recent study of the CapitalCube™ market navigation platform. See [Lusk (2019a)].…”