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QMIT by QuantZ presents the Composite Signal Monitor – July 2020 YTD flash

By:

CEO, QMIT – QuantZ Machine Intelligence Technologies

QMIT by QuantZ presents the monthly Composite Signal Monitor. To learn more about QuantZ/ QMIT and to get their factor research + heatmaps daily or even real-time, please get in touch!

QuantZ provides Enhanced Smart Betas (ESBs) enabling you to express almost any view on Equities and plug-and-play Composite Signals constructed from the ESBs, as presented below. We do the machine learning for you, and you get the game-changing factor investing insights. Our Smart Beta Book presents factor heatmaps + Sectors ranked by bottom up aggregation of our ESBs + cross-sectional factor rank correlations + return correlations & hints at a better risk model based directly on these ESBs. In this missive, we instead highlight curated factor portfolios i.e., Composite Signals based on ESB combos. Given that the set of N choose k combinations in this case is quite large, it’s particularly instructive to focus on the curated composites we have created for each factor family and the subsequent ESB combos.


Our ESBs used in the Composite Signals are based on Best Flavor of the Month (BFOMs) investable strategies which systematically switch between the 5 ESB models. While our ESB heatmaps only display the best of 5 models YTD/ LTD for each ESB – for the sake of brevity – we do provide the full history of all 5 models as well as in live mode so you can pick any flavor:

  1. Equal Weighted
  2. Max Sharpe Ratio optimization (on an expanding window to prevent look ahead bias)
  3. Risk Parity optimization (on an expanding window to prevent look ahead bias)
  4. Top 3 factors based on cumulative return but Equal Weighted (on an expanding window to prevent look ahead bias)
  5. Top 3 factors based on Sharpe ratio but Equal Weighted (based on cumulative return on an expanding window to prevent look ahead bias

These 5 alternate models can be viewed as an “ensemble” of learners on an expanding window (to prevent look-ahead bias). Further, these models must re-optimize monthly (using only data through T-1 month-end) in order to solve for the optimal factor weights which are used in calculating performance from T to T+1. To prevent look-ahead bias in representing a single 20y time series for a given ESB such as DV (Deep Value) we must ensure that such a time series represents the performance of a BFOM trading strategy which picks the “Best Flavor of the Month” based on the highest cum LTD performance (or some other criterion like Sortino) through month end T-1 as we do not know a-priori if that BFOM will in fact be the best performer in the coming month. Subsequently, we take Equal Weighted combos (of the BFOMs) to obtain our Composite Signal Monitor.

NB combos below are as of 2020-07-31

Sector-Beta neutral – Daily heatmap YTD:

Quant QMIT

Beta neutral – Daily heatmap YTD:

Quant QMIT

$ Neutral – Daily heatmap YTD:

Quant QMIT

Beta Neutral – 19y Monthly &1y Daily heatmap LTD:

$ Neutral – 19y Monthly &1y Daily heatmap LTD:

Composite Signal Definitions

Type I:  (EW combos of ESBs based on the best flavor)
Value Composite: DV + RV
Growth+Momentum Composite: ARS + ART + EnMOM + GroH
Quality Composite: CSU + Eff + EQ + Lev + Stab + Prof
Fabulous Fourteen:  ARS + ART + CSU + DV + Eff + EnMOM + EQ + GroH + Lev + Prof + Rev + Risk + RV + Size
Enterprise Eighteen:  All 18 ESBs

Type II:  (EW combos of EW combos)
Value Momentum Composite:  Value Composite + Growth+Momentum Composite
Quality Value Composite:  Quality Composite + Value Composite
Quality Momentum Composite:  Quality Composite + Growth+Momentum Composite

Type III:  (EW combos of EW combos & ESBs based on the best flavor)
Famous Five:  Quality Composite + Value Composite + Growth+Momentum Composite + Risk + Size
Sizzling Seven:  Quality Composite + Value Composite + Growth+Momentum Composite + Risk + Size + Rev + SIRF

Enhanced Smart Beta Definitions

ARS:  This smart beta composite shows our Analyst Revisions cohort based on measures of estimate revisions, dispersion, Standardized Unexpected Earnings surprise (SUE score) & consensus change in both earnings as well as revenues which can outperform traditional metrics like a 1mo consensus change.
ART:  This smart beta composite shows our Analyst Ratings & Targets cohort based on measures of analyst recommendations, target price, changes & diffusion which can outperform traditional metrics like a 1mo consensus change.
CSU:  This smart beta composite shows our Capital Structure/Usage cohort based on measures including Buybacks, Total yield, Capex, capital usage ratios etc which can outperform traditional metrics like Cash/MC.
Dividends:  This smart beta composite shows our Dividends related cohort based on measures including Yield, payout, growth, forward yield etc which can outperform traditional metrics like Dividend Yield.
DV:  This smart beta composite shows our Deep Value (or intrinsic value) cohort based on measures including tangible book & sales which can outperform traditional Book yield.
Efficiency:  This smart beta composite shows our Efficiency cohort based on measures including Asset Turnover, Current Liabilities, Receivables etc which can outperform traditional metrics like Asset Turnover.
EnMOM:  This smart beta composite shows our Enhanced Momentum cohort which can outperform traditional 12 month price momentum in both return & risk adjusted terms particularly at market inflection points.
EQ:  This smart beta composite shows our Earnings Quality cohort based on a variety of Accrual measures which can outperform traditional metrics like Total Accruals.
Growth:  This smart beta composite shows our Historical Growth cohort based on a variety of Earnings, Sales, Margins & CF related growth measures which can outperform traditional metrics like 3yr Sales growth.
Leverage:  This smart beta composite shows our Leverage related cohort based on measures of Balance Sheet leverage which can outperform traditional metrics like Debt To Equity.
PMOM:  This smart beta composite shows our PMOM related cohort which can outperform traditional 12 month price momentum using a variety of traditional momentum factors.
Profit:  This smart beta composite shows our Profitability cohort based on measures like ROA, ROE, ROCE, ROTC, Margins etc which can outperform traditional metrics like ROE.
RV:  This smart beta composite shows our Relative Value cohort based on measures of EPS, CFO, EBITDA etc which can outperform traditional Earnings yield.
Reversals:  This smart beta composite shows our Reversals cohort which is comprised of metrics like short term reversals, RSI, DMA & other technical factors which can outperform traditional metrics like a 1 month total return.
Risk:  This smart beta composite shows our Risk/ Low Vol cohort which is comprised of metrics like Beta, Low volatility etc.
SIRF:  This smart beta composite shows our Short Interest cohort which is comprised of metrics related to Short Interest and its normalization by Float, trading volume etc.
Size:  This smart beta composite shows our Size cohort which is comprised of metrics related to firm size including market capitalization.
Stability:  This smart beta composite shows our Stability cohort which is comprised of metrics like Dispersion of EPS/ SPS estimates as well as the stability of Margins, EPS & CFs etc.
EXPLANATORY FOOTNOTES:

Factor portfolios are not sector neutral.
Generated weekly as of last night’s close this report shows the DTD, MTD, YTD and LTD returns for our smart beta composite spreads.
Factors within the cohort spreads are long-short based on top vs bottom 5%-tile (~125×125) of the largest liquid US traded stocks (usually ~2,500 depending upon market capitalization & minimum $ price criterion for stocks listed on NYSE & Nasdaq).
Certain industries like Biotechs and REITS are excluded due to event risk or because a generic quant model is not appropriate for those industries.
Individual Composites are top & bottom 5%-tile portfolios based on equally weighted ESB BFOM scores mentioned above.
Daily vs Monthly rebalaning Our Monthly Heatmaps are based on a T-1 month end optimization to solve for the optimal factor weights. While we use optimal factor weights based on month end optimization, in Daily Heatmaps, we refresh individual Factor Ranks on a daily basis therefore resulting in an intra-month varying Long/Short Portfolio. The optimal factor weights & selection of the “Best Flavor of the Month” is still static intra-month in order to prevent excessive turnover & unnecessarily noisy ESBs.
Dollar neutral vs Beta Neutral Beta-neutrality implies daily de-levering of the higher beta side of the Long/Short factor portfolio. Indeed, the beta neutrality has to be enforced via daily rebalance since the factor ranks (& top/ bottom portfolios) are being refreshed daily.
MTD returns/ spreads are geometrically chain-linked DTD returns/ spreads where both are based on factor portfolios formed at the prior month end close.
YTD & LTD returns are based on geometric chain-linking of monthlies
Multi-period spread returns are not the difference of cumulative top vs bottom returns. Instead, they represent the daily geometrically compounded & rebalancing of the market neutral “active return” differential of the top vs bottom portfolios.
Since liquid equity commissions are now de minimus for both the institutional & even the retail world and market impact is really a function of the investor’s AUM we simply leave out the impact of TCosts as is customary in factor research.
LTD data commences January 2000.

QMIT by QuantZ presents the monthly Composite Signal Monitor. To learn more about QuantZ/ QMIT and to get their factor research + heatmaps daily or even real-time, please get in touch!

Disclosure: QMIT

DISCLAIMERS: QMIT is a data provider and not an investment advisor. This information has been prepared by QMIT for informational purposes only. This information should not be construed as investment, legal and/or tax advice. Additionally, this content is not intended as an offer to sell or a solicitation of any investment product or service. Opinions expressed are based on statistical forecasting from historical data. Past performance does not guarantee future performance. Further, the assumptions and the historical data based used could be erroneous. All results and analyses expressed are merely hypothetical and are NOT guaranteed. Trading securities involves substantial risk. Please consult a qualified investment advisor before risking any capital. The performance results for live portfolios following the screens presented herein may differ from the performance hypotheticals contained in this report for a variety of reasons, including differences related to transaction costs, market impact, fees, as well as differences in the time and price of execution. The performance results for individuals following the strategy could also differ based on differences in treatment of dividends received, including the amount received and whether and when such dividends were reinvested. We do not request personal information in any unsolicited email correspondence from our customers. Any correspondence offering trading advice or unsolicited message asking for personal details should be treated as fraudulent and reported to QMIT. Neither QMIT nor its third-party content providers shall be liable for any errors, inaccuracies or delays in content, or for any actions taken in reliance thereon. QMIT EXPRESSLY DISCLAIMS ALL WARRANTIES, EXPRESSED OR IMPLIED, AS TO THE ACCURACY OF ANY THE CONTENT PROVIDED, OR AS TO THE FITNESS OF THE INFORMATION FOR ANY PURPOSE. Although QMIT makes reasonable efforts to obtain reliable content from third parties, QMIT does not guarantee the accuracy of or endorse the views or opinions given by any third-party content provider. All content herein is owned by QuantZ Machine Intelligence Technologies and/ or its affiliates and protected by United States and international copyright laws. QMIT content may not be reproduced, transmitted or distributed without the prior written consent of QMIT.

Disclosure: Interactive Brokers

Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from QMIT and is being posted with permission from QMIT. The views expressed in this material are solely those of the author and/or QMIT and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

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