{"id":37878,"date":"2020-03-11T10:16:11","date_gmt":"2020-03-11T14:16:11","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=37878"},"modified":"2022-11-21T09:45:10","modified_gmt":"2022-11-21T14:45:10","slug":"qmit-realtime-factor-dispersion","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/qmit-realtime-factor-dispersion\/","title":{"rendered":"Weekly Commentary from QMIT by QuantZ – REALTIME Factor Dispersion"},"content":{"rendered":"\n

QMIT by QuantZ presents the March 6, 2020 report. To learn more about QuantZ\/ QMIT<\/a> and to get their factor research + heatmaps daily or even real-time, please get in touch<\/a>! <\/em><\/p>\n\n\n\n

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

Please note that all\nESBs are sorted in descending rank order & show the optimal\ncombination of factors within the Smart Beta cohorts based on the best\nmethodology (defined as highest cumulative return LTD) out of the five below: <\/p>\n\n\n\n

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

    NB heatmap below is as of 2020-03-06

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

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

    QMIT by QuantZ presents the March 6, 2020 report. To learn more about QuantZ\/ QMIT<\/a> and to get their factor research + heatmaps daily or even real-time, please get in touch<\/a>! <\/p>\n\n\n\n

    EXPLANATORY FOOTNOTES:
    \nSector Ranks are aggregated bottom up average ranks for each of the smart beta composites.
    \nFactor portfolios are not sector neutral.
    \nGenerated weekly as of last night\u2019s close this report shows the DTD, MTD, YTD and LTD returns for our smart beta composite spreads.
    \nFactors 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).
    \nCertain industries like Biotechs and REITS are excluded due to event risk or because a generic quant model is not appropriate for those industries.
    \nIndividual 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.
    \nMTD returns\/ spreads are geometrically chain-linked DTD returns\/ spreads where both are based on factor portfolios formed at the prior month end close.
    \nYTD & LTD returns are based on geometric chain-linking of monthlies without transaction costs or fees as is customary in the factor literature.
    \nMulti-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.
    \nBoth 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.
    \nLTD data commences January 2000.
    \nEnhanced Smart Beta Definitions
    \nARS: 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.
    \nART: 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.
    \nCSU: 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.
    \nDividends: 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.
    \nDV: 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.
    \nEfficiency: 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.
    \nEnMOM: 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.
    \nEQ: This smart beta composite shows our Earnings Quality cohort based on a variety of Accrual measures which can outperform traditional metrics like Total Accruals.
    \nGrowth: 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.
    \nLeverage: 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.
    \nPMOM: This smart beta composite shows our PMOM related cohort which can outperform traditional 12 month price momentum using a variety of traditional momentum factors.
    \nProfit: 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.
    \nRV: This smart beta composite shows our Relative Value cohort based on measures of EPS, CFO, EBITDA etc which can outperform traditional Earnings yield.
    \nReversals: 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.
    \nRisk: This smart beta composite shows our Risk\/ Low Vol cohort which is comprised of metrics like Beta, Low volatility etc.
    \nSIRF: 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.
    \nSize: This smart beta composite shows our Size cohort which is comprised of metrics related to firm size including market capitalization.
    \nStability: 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 by QuantZ presents the March 6, 2020 report. REALTIME Factor Dispersion<\/p>\n","protected":false},"author":269,"featured_media":37900,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,338,341,352,344],"tags":[851,5457,4923,4922,6110,6111,6806,5757,4581,852,494,5926,5436],"contributors-categories":[13678],"class_list":{"0":"post-37878","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","14":"tag-computational-finance","15":"tag-econometrics","16":"tag-enhanced-smart-beta","17":"tag-factor-covariance-matrix","18":"tag-factor-dispersion","19":"tag-factor-investing","20":"tag-heatmap","21":"tag-machine-learning","22":"tag-quant","23":"tag-quantamental","24":"tag-smart-beta","25":"contributors-categories-qmit-quantz-machine-intelligence-technologies"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nWeekly Commentary from QMIT by QuantZ – REALTIME Factor Dispersion<\/title>\n<meta name=\"description\" content=\"Weekly Commentary from QMIT by QuantZ - 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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. Milind has an MSCF and an MS in Applied Math from the pioneering financial engineering program at Carnegie Mellon University where he was also in the Doctoral program in Logic (A.I.). Other education includes Wharton, Vassar and Oxford. He has published extensively (JoIM, Risk Books, Wiley etc.) and is a frequent speaker at conferences. Contact Milind by email at Milind.Sharma@QuantzCap.com. 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