{"id":36206,"date":"2020-02-26T10:02:07","date_gmt":"2020-02-26T15:02:07","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=36206"},"modified":"2023-02-27T16:59:51","modified_gmt":"2023-02-27T21:59:51","slug":"smart-beta-book-feb24-2020-qmit","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/smart-beta-book-feb24-2020-qmit\/","title":{"rendered":"Smart Beta Book – The Val-Mo rotation – Heatmaps – QMIT by QuantZ"},"content":{"rendered":"\n

See Part I for The Val-Mo rotation<\/a> charts. <\/p>\n\n\n\n

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

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

    Beta neutral – Daily heatmap YTD:<\/strong><\/p>\n\n\n\n

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

    $ neutral – Daily heatmap YTD:<\/strong><\/p>\n\n\n\n

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

    Beta-neutral – 19y Montly +1y Daily heatmap LTD:<\/strong><\/p>\n\n\n\n

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

    $ neutral  – 19y Montly +1y Daily heatmap LTD:<\/strong><\/p>\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:
    Sector Ranks are aggregated bottom up average ranks for each of the smart beta composites.
    Factor portfolios are not sector neutral.
    Generated weekly as of last night\u2019s 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\u00d7125) 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 \u201cactive return\u201d differential of the top vs bottom portfolios which is a more realistic representation.
    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.
    LTD data commences January 2000.
    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. <\/p>\n","protected":false},"excerpt":{"rendered":"

    QMIT sums up the weekly heatmaps based on Equal Weighted, Max Sharpe Ratio optimization, Risk Parity optimization, Top 3 factors based on cumulative return but Equal Weighted, Top 3 factors based on Sharpe ratio but Equal Weighted methodologies.<\/p>\n","protected":false},"author":269,"featured_media":27741,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,338,341,352,344],"tags":[851,6185,4923,4922,6110,6111,6584,494,5926],"contributors-categories":[13678],"class_list":{"0":"post-36206","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-composite-signal-monitor-performance","14":"tag-computational-finance","15":"tag-econometrics","16":"tag-enhanced-smart-beta","17":"tag-factor-covariance-matrix","18":"tag-heatmap-machine-learning","19":"tag-quant","20":"tag-quantamental","21":"contributors-categories-qmit-quantz-machine-intelligence-technologies"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nSmart Beta Book – The Val-Mo rotation – Heatmaps – QMIT by QuantZ<\/title>\n<meta name=\"description\" content=\"QMIT sums up the weekly heatmaps based on Equal Weighted, Max Sharpe Ratio optimization, Risk Parity optimization, Top 3 factors based on cumulative...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/36206\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Smart Beta Book - 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For the 3 years prior, he managed a Prop Trading desk at RBC where he served as Portfolio Manager for Quant EMN, Short Term & Event Driven portfolios. Prior to that, he served as Director and Senior Proprietary Trader at Deutsche Bank (now SABA) where he managed Quant EMN portfolios of significant size with input in Event Driven and the larger Capital Structure Arbitrage desk mandates. Prior to that he was a co-founder of Quant Strategies at Merrill Lynch IM (now BlackRock), where his investment role spanned a dozen quantitatively managed funds with up to $30 Billion in AUM. The ML Large Cap Series funds (with MLIM President & CIO as Senior PM) were 5* rated, in the Lipper top 5% & won several WSJ + Morningstar awards by the time of his departure. In addition to being a founding member of Risk at MLIM, he was also a Manager of the Risk Analytics & Research Group at Ernst & Young where he co-created Raven TM. He also created the AIRAP methodology for hedge funds. 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