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TMI Snapshot:  Revisiting Cyclical Sector Sentiment - Industrials Showing Signs of Life

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TMI Thematics: Cyclical Rotation Through The Lens of Text Mining

TMI Snapshots: Quantifying Relative Sector Sentiment in the Recent Rotation Via Text Mining

TMI Snapshot: Update on Text-Mined Inflation Indicators & How They Line Up With the Value Rotation

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Managing Director

Elan Gore

TMI Data Science

TMI Data Science utilizes Natural Language Processing to build custom leading indicators using unstructured data sourced from the global financial, trade & traditional media.  Our proprietary software, TMI Machine, is a text-mining platform designed with the buy-side & capital markets professional in mind, text-mining the global media to discern and quantify nuanced qualitative shifts in press coverage as they apply to macroeconomics, equity indexes/ETFs, commodities, currencies, fixed income & individual equities.  TMI provides custom leading indicators and systematic trading strategies, operating at the nexus of linguistics & statistics drawing on vast experience in investment banking and hedge fund settings in senior quantitative and fundamental roles.  

TMI Data Science was founded by Elan Gore, an award-winning Senior Global Basic Materials Equity Analyst & China Economist with 15 years of experience in North American hedge funds & institutional managers; and Alessandro Porrino, Ph.D., an Algorithmic Trading Analyst and Programmer with 10 years of front-office experience ranging from complex FX option pricing to auto-hedging strategies at Bank of America Merrill Lynch and Standard Chartered.  Mr. Gore holds an MBA in Finance from New York University Stern School of Business, and Mr. Porrino holds a Ph.D. in Systems Engineering from Brunel University London.

Cross Asset Strategy

Multiple Countries / Cross Asset Strategy

<ul>
<li>Our text-mining finds cyclicals sentiment momentum now up +3.3x STDEVs, highest in the history of our series and slightly above the highs we last saw in December '17</li>
<li>Defensives sentiment momentum bottomed in the first half of December at -1x STDEV, lowest reading since May, but has since recovered to marginally positive territory potentially aided by recent tech outperformance</li>
<li>In the past year, cyclicals sentiment has been &gt;80% correlated with Global Metals &amp; Materials, 68% correlated with Global Industrials, 35% correlated with Global Financials and uncorrelated with Global Energy</li>
<li>In December, only Global Metals appear to be strongly correlated with the surge in cyclicals sentiment as energy, financials and industrials performance has been flat; one explanation is the stark divergence in '21 estimate revision trends by sector, where materials analysts are struggling to catch up with surging metals prices vs. tepid industrials and energy revisions</li>
<li>Ironically, our copper sentiment indicator registered a +3x STDEV spike in November and remains elevated, preceding the final 2020 melt-up in cyclicals sentiment (as well as the move in copper); back-testing suggests trading MSCI Global Metals based on cyclicals sentiment outperforms a passive position, but trading based on copper sentiment would have generated superior returns - implying that getting the copper trade right is key to getting the overall cyclical trade right at this stage of the economic cycle</li>
<li>Do other cyclical sectors simply catch up with metals &amp; materials in Q1?  Industrials may hold the key to the next leg of cyclical recovery as the market looks through winter seasonality to improved operating leverage starting in Q2; investors may not be as forgiving in the short-term with energy, as long as mobility is depressed; but the promise of post-vaccine normalization remains compelling across the board</li>
</ul><div class="content-highlight"><p>TMI Data Science utilizes Natural Language Processing to build custom leading indicators using unstructured data sourced from the global financial, trade &amp; traditional media. Our proprietary software text-mines the global media to discern and quantify nuanced qualitative shifts in press coverage as they apply to macroeconomics, equity indexes/ETFs, commodities, currencies, fixed income &amp; individual equities. </p></div>


Our text-mining finds cyclicals sentiment momentum now up +3.3x STDEVs, highest in the history of our series and slightly above the highs we last saw in December '17
Defensives sentiment momentum bottomed in the first half of December at -1x STDEV, lowest reading since May, but has since recovered to marginally positive territory potentially aided by recent tech outperformance
In the past year, cyclicals sentiment has been &gt;80% correlated with Global Metals &amp; Materials, 68% correlated with Global Industrials, 35% correlated with Global Financials and uncorrelated with Global Energy
In December, only Global Metals appear to be strongly correlated with the surge in cyclicals sentiment as energy, financials and industrials performance has been flat; one explanation is the stark divergence in '21 estimate revision trends by sector, where materials analysts are struggling to catch up with surging metals prices vs. tepid industrials and energy revisions
Ironically, our copper sentiment indicator registered a +3x STDEV spike in November and remains elevated, preceding the final 2020 melt-up in cyclicals sentiment (as well as the move in copper); back-testing suggests trading MSCI Global Metals based on cyclicals sentiment outperforms a passive position, but trading based on copper sentiment would have generated superior returns - implying that getting the copper trade right is key to getting the overall cyclical trade right at this stage of the economic cycle
Do other cyclical sectors simply catch up with metals &amp; materials in Q1?  Industrials may hold the key to the next leg of cyclical recovery as the market looks through winter seasonality to improved operating leverage starting in Q2; investors may not be as forgiving in the short-term with energy, as long as mobility is depressed; but the promise of post-vaccine normalization remains compelling across the board
TMI Data Science utilizes Natural Language Processing to build custom leading indicators using unstructured data sourced from the global financial, trade &amp; traditional media. Our proprietary software text-mines the global media to discern and quantify nuanced qualitative shifts in press coverage as they apply to macroeconomics, equity indexes/ETFs, commodities, currencies, fixed income &amp; individual equities. 

TMI Snapshot:  Cyclicals Sentiment Melts Up into Year-End, Impact Concentrated in Materials Sector