QuantZ uses machine learning to create plug-and-play Composite Signals constructed from the ESBs, as presented below. They 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, consider focusing on the curated composites they have created for each factor family and the subsequent ESB combos.
To see the 2019 Factor Recap – QMIT by QuantZ visit:
- Beta-Neutrality for a bullish tape – As one might expect for many Composite Signals being $-hedged can result in being “over-hedged” (in net beta terms). This is evident in the significantly net negative realized 20year betas. Clearly, in a market melt-up with SPX ~+31% for FY19 being “over-hedged” can be hugely consequential. Hence, we also display 2019 performance based on Beta-Neutrality to show the impact of de-levering of the higher beta side (to equalize betas) of the Long-Short factor portfolios.
- Beta-Neutrality led to a substantial benefit for 8/10 Composite Signals while only Value tilted signals, such as Value Composite and Quality-Value Composite did not benefit in a bullish tape due to their Longs having higher beta & given the disastrous year for Value.
- On the other hand, Sizzling Seven increased by ~15%, Fabulous Fourteen by ~12%, Famous Five by ~19% & Enterprise 18 by 12.8%.
- On a Beta-Neutral basis, 8/ 10 Composite Signals beat the HFRX EMN benchmark at -1.87% YTD by anywhere from +3.8% to 26.64%.
- 3 of our Composite Signals – Sizzling Seven, Famous Five and Qual+Mo – delivered staggering 20+% hedged returns.
- In Long Only terms our Sizzling Seven, Famous Five and Momentum Composite Signals beat the S&P500 (with Sizzling Seven at +650 bps).
- Our Sizzling Seven (Long Only). In Beta-Neutral terms its only 1 had down year of 20 in 2018 averaging ~29% with a 6.7 Sortino.
- Our Momentum Composite which combines Enhanced Momentum with Analyst Ratings & Targets, Analyst Revisions & Growth delivered +18.5% (Beta-Neutral) and 32.3% on the Long-only side in 2019.
- Similarly, our Famous Five Composite delivered +22.75% (Beta-Neutral) and +31.1% on the Long-only side in 2019.
Our ESBs used in the Composite Signals are based on Best Flavor of the Month (BFOMs) 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:
- Equal Weighted
- Max Sharpe Ratio optimization (on an expanding window to prevent look ahead bias)
- Risk Parity optimization (on an expanding window to prevent look ahead bias)
- Top 3 factors based on cumulative return but Equal Weighted (on an expanding window to prevent look ahead bias)
- 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.
Beta neutral – Daily heatmap YTD:
$ Neutral – Daily heatmap YTD:
Beta Neutral – 19y Monthly &1y Daily heatmap LTD:
$ Neutral – 19y Montly &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.
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 ~2500 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 factor top & bottom portfolios are equally weighted 5%-tiles. While the combined ESB spreads also represent top vs bottom 5%-tiles they are based on the best (cumulative return LTD) of five methodologies listed above.
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 without transaction costs or fees as is customary in the factor literature.
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.
Both Max Sharpe & Risk Parity optimization routines are based on a Hybrid methodology where we 1] find the optimal factor mix within the Smart Beta cohort based on signal blending/ “mixing” but 2] subsequently run the combined ESB spreads outsample on a fully “integrated” basis not just as the linear combination of factor returns.
LTD data commences January 2000.
Disclosure: QMIT – QuantZ Machine Intelligence Technologies
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.
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