{"id":25899,"date":"2019-11-20T09:45:22","date_gmt":"2019-11-20T14:45:22","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=25899"},"modified":"2023-02-27T16:35:19","modified_gmt":"2023-02-27T21:35:19","slug":"qmit-by-quantz-weekly-smart-beta-book-11-18-2019","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/qmit-by-quantz-weekly-smart-beta-book-11-18-2019\/","title":{"rendered":"QMIT by QuantZ Presents the Weekly Smart Beta Book"},"content":{"rendered":"\n

ESBs (Enhanced Smart Betas) Heatmaps<\/h4>\n\n\n\n

The Sector ranks table (based on bottom up aggregation of QMIT Enhanced Smart Betas within sectors) allows for sector rotation based on factors. The cross-sectional factor rank correlations tell us how correlated the factors are at this juncture vs recent 3y return correlations vs LTD (20y) return correlations. It\u2019s worth noting that cross sectional factor rank correlations are based on today\u2019s alphas across the entire universe while the historical return correlations are only based on the information in the tails (i.e., the 5%-tile spread returns). Further, as the astute may surmise, one can extract a risk model from our factor covariance matrix which should better align one\u2019s alphas with the risk optimization.

Please find below heatmaps with the DTD, MTD, YTD, 5 year, Post-07 & LTD returns for our ESBs as of last night\u2019s close. Stay tuned for more composite signals on our ESBs which will continue to be added. These spreads are based on the best methodology (defined as highest cumulative return LTD) out of five that are available to clients for each of the ESBs as regards aggregation of factors within the Smart Beta cohorts. Customized heatmaps may be available based on all five methodologies: <\/p>\n\n\n\n

    \n
  1. Equal Weighted <\/li>\n\n\n\n
  2. Max Sharpe Ratio optimization (on an expanding window\n to prevent look ahead bias) <\/li>\n\n\n\n
  3. Risk Parity optimization (on an expanding window to\n prevent look ahead bias) <\/li>\n\n\n\n
  4. Top 3 factors based on cumulative return but Equal\n Weighted (on an expanding window to prevent look ahead bias) <\/li>\n\n\n\n
  5. Top 3 factors based on Sharpe ratio but Equal Weighted\n (based on cumulative return on an expanding window to prevent look ahead\n bias)\n <\/li>\n<\/ol>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \"\"<\/figure>\n\n\n\n

    Sector ranks based on QMIT Enhanced Smart Betas:<\/strong><\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    C-S Rank correlations for QMIT Enhanced Smart Betas:<\/strong><\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    3y Return correlations for QMIT Enhanced Smart Betas:<\/strong><\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    20y Return correlations for QMIT Enhanced Smart Betas:<\/strong><\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    EXPLANATORY FOOTNOTES:<\/em><\/p>\n\n\n\n

    • Sector Ranks are aggregated bottom up average ranks for each of the smart beta composites.<\/span><\/em><\/li>
    • Factor portfolios are not sector neutral.<\/span><\/em><\/li>
    • Generated weekly as of last night\u2019s close this report shows the DTD, MTD, YTD and LTD returns for our smart beta composite spreads.<\/span><\/em><\/li>
    • 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).<\/span><\/em><\/li>
    • Certain industries like Biotechs and REITS are excluded due to event risk or because a generic quant model is not appropriate for those industries.<\/span><\/em><\/li>
    • 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.<\/span><\/em><\/li>
    • MTD returns\/ spreads are geometrically chain-linked DTD returns\/ spreads where both are based on factor portfolios formed at the prior month end close.<\/span><\/em><\/li>
    • YTD & LTD returns are based on geometric chain-linking of monthlies without transaction costs or fees as is customary in the factor literature.<\/span><\/em><\/li>
    • 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 \u201cactive return\u201d differential of the top vs bottom portfolios which is a more realistic representation.<\/span><\/em><\/li>
    • 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\/ \u201cmixing\u201d but 2] subsequently run the combined ESB spreads outsample on a fully \u201cintegrated\u201d basis not just as the linear combination of factor returns.<\/span><\/em><\/li>
    • LTD data commences January 2000.<\/span><\/em><\/li><\/ul>\n\n\n\n

      Enhanced Smart Beta\nDefinitions<\/strong> <\/p>\n\n\n\n

      ARS: <\/strong>\nThis smart beta composite shows our Analyst Revisions<\/strong> cohort based on\nmeasures of estimate revisions, dispersion, Standardized Unexpected Earnings\nsurprise (SUE score) & consensus change in both earnings as well as\nrevenues which can outperform traditional metrics like a 1mo consensus change.\n
      \nART: <\/strong>\nThis smart beta composite shows our Analyst Ratings & Targets<\/strong>\ncohort based on measures of analyst recommendations, target price, changes\n& diffusion which can outperform traditional metrics like a 1mo consensus\nchange.
      \nCSU: <\/strong>\nThis smart beta composite shows our Capital Structure\/Usage<\/strong> cohort\nbased on measures including Buybacks, Total yield, Capex, capital usage ratios\netc which can outperform traditional metrics like Cash\/MC.
      \nDividends: <\/strong>\nThis smart beta composite shows our Dividends<\/strong> related cohort based on\nmeasures including Yield, payout, growth, forward yield etc which can\noutperform traditional metrics like Dividend Yield.
      \nDV: <\/strong>\nThis smart beta composite shows our Deep Value<\/strong> (or intrinsic value)\ncohort based on measures including tangible book & sales which can\noutperform traditional Book yield.
      \nEfficiency: <\/strong>\nThis smart beta composite shows our Efficiency<\/strong> cohort based on\nmeasures including Asset Turnover, Current Liabilities, Receivables etc which\ncan outperform traditional metrics like Asset Turnover.
      \nEnMOM: <\/strong>\nThis smart beta composite shows our Enhanced Momentum<\/strong> cohort which\ncan outperform traditional 12 month price momentum in both return & risk\nadjusted terms particularly at market inflection points.
      \nEQ: <\/strong>\nThis smart beta composite shows our Earnings Quality<\/strong> cohort based on\na variety of Accrual measures which can outperform traditional metrics like\nTotal Accruals.
      \nGrowth: <\/strong>\nThis smart beta composite shows our Historical Growth<\/strong> cohort based on\na variety of Earnings, Sales, Margins & CF related growth measures which\ncan outperform traditional metrics like 3yr Sales growth.
      \nLeverage: <\/strong>\nThis smart beta composite shows our Leverage<\/strong> related cohort based on\nmeasures of Balance Sheet leverage which can outperform traditional metrics\nlike Debt To Equity.
      \nPMOM: <\/strong>\nThis smart beta composite shows our PMOM<\/strong> related cohort which can\noutperform traditional 12 month price momentum using a variety of traditional\nmomentum factors.
      \nProfit: <\/strong>\nThis smart beta composite shows our Profitability<\/strong> cohort based on\nmeasures like ROA, ROE, ROCE, ROTC, Margins etc which can outperform\ntraditional metrics like ROE.
      \nRV: <\/strong>\nThis smart beta composite shows our Relative Value<\/strong> cohort based on\nmeasures of EPS, CFO, EBITDA etc which can outperform traditional Earnings\nyield.
      \nReversals: <\/strong>\nThis smart beta composite shows our Reversals<\/strong> cohort which is\ncomprised of metrics like short term reversals, RSI, DMA & other technical\nfactors which can outperform traditional metrics like a 1 month total return.\n
      \nRisk: <\/strong>\nThis smart beta composite shows our Risk\/ Low Vol<\/strong> cohort which is\ncomprised of metrics like Beta, Low volatility etc.
      \nSIRF: <\/strong>\nThis smart beta composite shows our Short Interest<\/strong> cohort which is\ncomprised of metrics related to Short Interest and its normalization by Float,\ntrading volume etc.
      \nSize: <\/strong>\nThis smart beta composite shows our Size<\/strong> cohort which is comprised of\nmetrics related to firm size including market capitalization.
      \nStability: <\/strong>\nThis smart beta composite shows our Stability <\/strong>cohort which is\ncomprised of metrics like Dispersion of EPS\/ SPS estimates as well as the\nstability of Margins, EPS & CFs etc. <\/p>\n","protected":false},"excerpt":{"rendered":"

      The Sector ranks table (based on bottom up aggregation of QMIT Enhanced Smart Betas within sectors) allows for sector rotation based on factors. The cross-sectional factor rank correlations tell us how correlated the factors are at this juncture vs recent 3y return correlations vs LTD (20y) return correlations.<\/p>\n","protected":false},"author":269,"featured_media":25937,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,338,341,352,344],"tags":[851,4923,4922,494,4921],"contributors-categories":[13678],"class_list":{"0":"post-25899","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-ibkr-quant-news","9":"category-quant-development","10":"category-quant-north-america","11":"category-quant-regions","12":"tag-algo-trading","13":"tag-computational-finance","14":"tag-econometrics","15":"tag-quant","16":"tag-quantitative-finance","17":"contributors-categories-qmit-quantz-machine-intelligence-technologies"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nQMIT by QuantZ Presents the Weekly Smart Beta Book<\/title>\n<meta name=\"description\" content=\"The Sector ranks table - 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