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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.

Macroeconomics

<ul>
<li>In a New York Times Op-Ed (<a href="https://www.nytimes.com/2021/08/19/opinion/biden-afghanistan-johnson.html" target="_blank" rel="noopener">"To Save His Presidency, Biden Must Tell the Truth About Afghanistan"</a>, August 19) historian and Georgetown University professor Michael Kazin suggests that "the president and his fellow Democrats face a political environment so daunting that even the slightest disruption could derail their domestic agenda"; Kazin draws an analogy to LBJ as "the last time a war blew up in the face of a Democratic president, it derailed his domestic agenda and stalled the most ambitious social reforms of a generation"</li>
<li>We employ text mining to characterize negative media coverage of Biden in the context of the Afghanistan withdrawal, along with other metrics offering clues on potential damage from a week of turbulence in American foreign policy</li>
<li>While the volume of negative coverage in the Conservative media is significantly larger, once we normalize the data it becomes apparent that outrage over the Afghanistan withdrawal is anomalously high on both sides of the aisle</li>
<li>We recognize links between the Afghanistan withdrawal and progress on domestic policy are unlikely to be apparent, as the fate of the budget resolution and infrastructure bill lies solely in the hands of the Democratic Party, and any overt attempt by moderate/centrist Democrats to capitalize on plunging presidential approval in the aftermath of the withdrawal would not be received well</li>
<li>The key indicator of incremental dissent in the Democratic Party remains the actions of Senators Joe Manchin and Kyrsten Sinema, who have already established themselves as thorns in their party's side and are potentially emboldened by recent events to oppose the progressive elements of Biden's agenda more forcefully</li>
<li>Challenges to Biden's domestic agenda could have significant market implications given the inflationary impact of infrastructure and social spending packages, as well as positioning in infrastructure equities; in fact, the quantum of negative Biden coverage in the Liberal media has been quite strongly correlated with a number of cyclical themes as we illustrate below</li>
</ul><div class="content-highlight"><p>TMI Data Science utilizes text mining and Natural Language Processing to build custom leading indicators using unstructured data sourced from the global financial, trade &amp; traditional media. Our proprietary tools text-mine 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>


In a New York Times Op-Ed ("To Save His Presidency, Biden Must Tell the Truth About Afghanistan", August 19) historian and Georgetown University professor Michael Kazin suggests that "the president and his fellow Democrats face a political environment so daunting that even the slightest disruption could derail their domestic agenda"; Kazin draws an analogy to LBJ as "the last time a war blew up in the face of a Democratic president, it derailed his domestic agenda and stalled the most ambitious social reforms of a generation"
We employ text mining to characterize negative media coverage of Biden in the context of the Afghanistan withdrawal, along with other metrics offering clues on potential damage from a week of turbulence in American foreign policy
While the volume of negative coverage in the Conservative media is significantly larger, once we normalize the data it becomes apparent that outrage over the Afghanistan withdrawal is anomalously high on both sides of the aisle
We recognize links between the Afghanistan withdrawal and progress on domestic policy are unlikely to be apparent, as the fate of the budget resolution and infrastructure bill lies solely in the hands of the Democratic Party, and any overt attempt by moderate/centrist Democrats to capitalize on plunging presidential approval in the aftermath of the withdrawal would not be received well
The key indicator of incremental dissent in the Democratic Party remains the actions of Senators Joe Manchin and Kyrsten Sinema, who have already established themselves as thorns in their party's side and are potentially emboldened by recent events to oppose the progressive elements of Biden's agenda more forcefully
Challenges to Biden's domestic agenda could have significant market implications given the inflationary impact of infrastructure and social spending packages, as well as positioning in infrastructure equities; in fact, the quantum of negative Biden coverage in the Liberal media has been quite strongly correlated with a number of cyclical themes as we illustrate below
TMI Data Science utilizes text mining and Natural Language Processing to build custom leading indicators using unstructured data sourced from the global financial, trade &amp; traditional media. Our proprietary tools text-mine 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:  Will the Afghanistan Fiasco Harm Biden's Domestic Agenda?