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

<ul>
<li>Our macro sentiment indicators for key East Asian jurisdictions have all turned above &gt;1.5x STDEVs, highlighting widely-perceived notions of an accelerating synchronized recovery led by China</li>
<li>Beneath the surface, concerns linger on Chinese bond defaults and a tighter regulatory environment, but the magnitude of these trends appears insufficient to derail broader macro views as our reading of Chinese credit sentiment only dropped to -0.5x STDEV</li>
<li>Nonetheless, back-testing suggests that Chinese credit sentiment has been a superior predictor of China Lg-Cap (FXI) performance in recent years, and a recent pullback in relative performance (of both A/H-shares vs. MSCI World) appears to be linked to credit</li>
<li>We suspect the bullish backdrop allows Beijing to opportunistically launch its renewed deleveraging push with relatively limited contagion for the time being</li>
</ul>


Our macro sentiment indicators for key East Asian jurisdictions have all turned above &gt;1.5x STDEVs, highlighting widely-perceived notions of an accelerating synchronized recovery led by China
Beneath the surface, concerns linger on Chinese bond defaults and a tighter regulatory environment, but the magnitude of these trends appears insufficient to derail broader macro views as our reading of Chinese credit sentiment only dropped to -0.5x STDEV
Nonetheless, back-testing suggests that Chinese credit sentiment has been a superior predictor of China Lg-Cap (FXI) performance in recent years, and a recent pullback in relative performance (of both A/H-shares vs. MSCI World) appears to be linked to credit
We suspect the bullish backdrop allows Beijing to opportunistically launch its renewed deleveraging push with relatively limited contagion for the time being


TMI Snapshot: Synchronized Asian Macro Upturn Vs. Chinese Credit Jitters, Via Text Mining

Ping An Digital Economic Research Center

Zheng Zeng

Ping An Digital Economic Research Center (DERC)

Ping An - DERC

Ping An Digital Economic Research Center (DERC) utilizes more than 50 TB of high frequency data points, more than 30 years of historical data and more than 1.5 billion data points to drive research on the “AI + Macro Forecast” and provide insights and methods towards precise macroeconomic trends. 

Ping An DERC is a research center for Ping An Insurance (Group) Company of China, Ltd. (“Ping An”) is a world-leading technology-powered retail financial services group. With over 204 million retail customers and 534 million internet users, Ping An is one of the largest financial services companies in the world.

<blockquote>
ESG Report Series Issue 3
- AI Driven Indicators, Green Washing Detection, and Firm Characteristics
</blockquote>In our previous report, “Where we stand with climate disclosures and why we need them - Natural Language Processing applications in analysis of sustainability reporting,” (Ping An Digital Economic Research Center, 2020), we applied Natural Language Processing (NLP) to climate risk disclosures by US and Chinese firms to gauge the coverage of climate related and financial impact metrics according to Task Force for Climate-related Financial Disclosures (TCFD) recommendations. The results demonstrated that emissions and energy metrics are in general widely covered, whereas water and land use disclosures are limited and sparse. Similarly, disclosure of financial impact metrics shows considerable heterogeneity across sectors, with some important impact dimensions largely under-reported. In this report, we build on these insights and develop NLP based indicators that allow us to better capture the breadth and depth of companies’ climate risk disclosures. The objective is three-fold: first, we leverage AI tools to better assess a firm's climate risk exposure and its compliance with TCFD reporting guidelines; second, we use AI based indicators to detect potential evidence of greenwashing; third, we explore the relationship between climate risk disclosure and firm characteristics, including financial performance. The analysis presented here focuses on US and Chinese firms (constituents of S&amp;P500 and CSI300) that have disclosed climate risks, although parallel work on European firms shows consistent results. A summary of our findings is as follows:AI driven indicators: AI driven transparency indicators are a valuable complement to ESG analytics tools and ratings currently available in the market. <ul style="list-style-type: square;">
<li>We use NLP to develop climate risk disclosure indicators informing us on the scope and depth of disclosures. Of particular importance are climate risk metrics and financial impact disclosure indicators. We also create composite transparency indicators to carry out automated assessment of TCFD reporting compliance.</li>
<li>As opposed to ratings relying on linear aggregation of particular attributes, which currently dominate the industry<a href="#_ftn1" target="_blank" rel="noopener">[1]</a>, our indicators use a mixture of NLP and probabilistic tools to identify and aggregate relevant attributes.</li>
<li>The indicators developed perform better than existing ESG ratings in the market in differentiating between Green and Brown firms. They also offer insights into the relationship between firm characteristics and climate disclosures.</li>
</ul>Green washing detection: we find patterns of disclosure suggesting possible green washing by high emission firms; also, unsophisticated rating tools may implicitly reward the opaqueness of disclosures.<ul style="list-style-type: square;">
<li>Possible evidence of green washing for high emission firms: <ul style="list-style-type: circle;">
<li>Compliance around disclosure of climate related metrics is higher on average for lower emission firms, thus suggesting underreporting by high emission firms.
</li>
<li>High emission firms score better in disclosing financial impact metrics, but with an important exception: information on capital and financing impact is systematically underreported. As the issue of stranded assets and liabilities<a href="#_ftn2" target="_blank" rel="noopener">[2]</a> is of major importance to high emission sectors, such as oil and coal companies, the results indicate that a number of firms selectively leave this impact dimension undisclosed.</li>
<li>
Disclosure of scope 3 emissions is considerably worse for high emission firms, despite the increasing focus of regulators and investors on this dimension.</li>
</ul>
</li>
<li>
Opaqueness may implicitly be rewarded instead of penalized by unsophisticated rating tools, as some companies engaging in climate risk disclosure appear to be penalized by some ESG rating tools. This gives perverse incentives for selective non-disclosure, leading to greenwashing.</li>
<li>Similar to existing literature, we find dispersion between ESG ratings<a href="#_ftn3" target="_blank" rel="noopener">[3]</a> and hence they should be used with caution. Some ESG ratings appear to be positively associated with leverage, after controlling for emissions and other firm characteristics, whereas our AI driven indicators suggest systematically lower leverage for companies engaging in climate risk disclosure.</li>
</ul>Valuations: companies with greater disclosure of metrics on climate risk impact on financial performance have higher valuations and lower leverage, after controlling for carbon emissions and other firm characteristics.<ul style="list-style-type: square;">
<li>Firms with better disclosure of financial impact metrics tend to have higher valuations, lower leverage, and lower cost of capital.</li>
<li>Large cap firms that are TCFD compliant tend to have higher valuations, whereas small and medium cap firms engaging in climate disclosures may still offer considerable opportunities for appreciation.</li>
<li>The results are consistent across a number of specifications and apply after controlling for traditional ESG ratings, which instead appear to have weak or inconclusive bearing.</li>
<li>The results also apply after controlling for carbon emissions, which recent literature has found to be associated with a carbon risk premium and hence lower valuations.<a href="#_ftn4" target="_blank" rel="noopener">[4]</a> Our indicators therefore offer investors and corporates a more granular understanding of valuations beyond carbon emissions.</li>
</ul>Firm value: AI driven indicators suggest that greater disclosure is associated with lower cost of capital, thus boosting firm value. <ul style="list-style-type: square;">
<li>Despite the increasing popularity of ESG criteria,<a href="#_ftn5" target="_blank" rel="noopener">[5]</a> demonstrating whether and how ESG compliance adds value to the firm is notoriously difficult and so far the empirical evidence is mixed or inconclusive.<a href="#_ftn6" target="_blank" rel="noopener">[6]</a>
</li>
<li>We find evidence that companies that are more transparent in disclosing financial impact metrics have lower cost of capital,<a href="#_ftn7" target="_blank" rel="noopener">[7]</a> especially among firms that disclose impact of climate risks on expenditure and assets &amp; liabilities.</li>
<li>Our results on valuations offer a consistent picture in terms of cost of equity, whereas our parallel findings on leverage support consistent implications for the cost of debt. Both these channels represent important components of any corporate valuation framework and hence provide support for climate risk disclosures contributing to firm value. Further research is needed to understand other dimensions, such as revenues and operating margins.</li>
</ul>Summary of AI driven indicators and greenwashing detection <table class="table table-striped"><tbody>
<tr>
<td style="width: 99pt;"> </td>
<td style="width: 6cm;">AI driven indicators</td>
<td style="width: 191.35pt;">Greenwashing detection</td>
</tr>
<tr>
<td style="width: 99pt;">What is it?</td>
<td style="width: 6cm;">AI-based indicators that assess coverage rate of various climate and financial impact related indicators, as well as themes discussed in companies’ climate disclosure reports.</td>
<td style="width: 191.35pt;">Detect anomalies between companies’ disclosures of climate risks and actual exposure.</td>
</tr>
<tr>
<td style="width: 99pt;">How is it done?</td>
<td style="width: 6cm;">
<ul><li>Rule-based matching: For each indicator, construct relevant anchor words or expressions to locate relevant text in disclosure reports and then estimate probability of coverage by assessing sample precision and number of matches found.</li></ul>
<ul><li>Unsupervised learning: extract distinct themes from companies’ disclosure reports and then calculate companies’ relative coverage rate of each theme.</li></ul>
</td>
<td style="width: 191.35pt;"><ul>
<li>Detect anomalies in ESG ratings of companies who engage in climate disclosures vs. those who do not.</li>
<li>Benchmark high vs. low emission firms in terms of disclosure of climate metrics (e.g. emissions, energy use, water use, land use) and financial impact metrics to detect any under-reporting by high emission firms.</li>
<li>When evaluating relationship between disclosures and company performance, detect where traditional ESG ratings and AI driven indicators show inconsistent results.</li>
</ul></td>
</tr>
<tr>
<td style="width: 99pt;">What are the implications?</td>
<td style="width: 6cm;">
Firms with better disclosure of AI driven financial impact indicators have higher valuations, lower leverage, and lower cost of capital. Traditional ESG ratings present either diverging or statistically weaker results.
 
</td>
<td style="width: 191.35pt;">Allows for Automatic detection of under-reporting by high-emission firms, and inconsistency between ESG ratings, which allows investors to use ESG ratings with caution when integrating them in investment analysis.</td>
</tr>
</tbody></table><div>
 <hr align="left" size="1" width="33%">
<div id="ftn1" style="text-align: left;"><a href="#_ftnref1" target="_blank" rel="noopener">[1]</a> See, for example, Berg et al. (2019).</div>
<div id="ftn2" style="text-align: left;"><a href="#_ftnref2" target="_blank" rel="noopener">[2]</a> Stranded assets are assets that have become uneconomic (firms are unable to recover their investment cost) or turned into liabilities, due to transition risks such as taxation of fossil fuel supply or consumption (e.g., Caldecott et al. 2015). Stranded liabilities refer to the cost of retiring uneconomical long-lived assets, such as oil and gas infrastructure.</div>
<div id="ftn3" style="text-align: left;"><a href="#_ftnref3" target="_blank" rel="noopener">[3]</a> See Berg et al. (2019), for example.</div>
<div id="ftn4" style="text-align: left;"><a href="#_ftnref4" target="_blank" rel="noopener">[4]</a> See Bolton and Kacperczyc (2020).</div>
<div id="ftn5" style="text-align: left;"><a href="#_ftnref5" target="_blank" rel="noopener">[5]</a> See Amel-Zadeh and Serafeim (2020).</div>
<div id="ftn6" style="text-align: left;"><a href="#_ftnref6" target="_blank" rel="noopener">[6]</a> See Cornell and Damodaran (2020) for an overview.</div>
<div id="ftn7">
<a href="#_ftnref7" target="_blank" rel="noopener">[7]</a> See Goss and Roberts (2011) for similar evidence on bank loans and corporate social responsibility. Using US data, they find that socially responsible firms pay between 7 and 18 basis points less than other firms, unsecured loans being the most sensitive to social responsibility concerns.
 
</div>
</div>


ESG Report Series Issue 3
- AI Driven Indicators, Green Washing Detection, and Firm Characteristics
In our previous report, “Where we stand with climate disclosures and why we need them - Natural Language Processing applications in analysis of sustainability reporting,” (Ping An Digital Economic Research Center, 2020), we applied Natural Language Processing (NLP) to climate risk disclosures by US and Chinese firms to gauge the coverage of climate related and financial impact metrics according to Task Force for Climate-related Financial Disclosures (TCFD) recommendations. The results demonstrated that emissions and energy metrics are in general widely covered, whereas water and land use disclosures are limited and sparse. Similarly, disclosure of financial impact metrics shows considerable heterogeneity across sectors, with some important impact dimensions largely under-reported. In this report, we build on these insights and develop NLP based indicators that allow us to better capture the breadth and depth of companies’ climate risk disclosures. The objective is three-fold: first, we leverage AI tools to better assess a firm's climate risk exposure and its compliance with TCFD reporting guidelines; second, we use AI based indicators to detect potential evidence of greenwashing; third, we explore the relationship between climate risk disclosure and firm characteristics, including financial performance. The analysis presented here focuses on US and Chinese firms (constituents of S&amp;P500 and CSI300) that have disclosed climate risks, although parallel work on European firms shows consistent results. A summary of our findings is as follows:AI driven indicators: AI driven transparency indicators are a valuable complement to ESG analytics tools and ratings currently available in the market. 
We use NLP to develop climate risk disclosure indicators informing us on the scope and depth of disclosures. Of particular importance are climate risk metrics and financial impact disclosure indicators. We also create composite transparency indicators to carry out automated assessment of TCFD reporting compliance.
As opposed to ratings relying on linear aggregation of particular attributes, which currently dominate the industry[1], our indicators use a mixture of NLP and probabilistic tools to identify and aggregate relevant attributes.
The indicators developed perform better than existing ESG ratings in the market in differentiating between Green and Brown firms. They also offer insights into the relationship between firm characteristics and climate disclosures.
Green washing detection: we find patterns of disclosure suggesting possible green washing by high emission firms; also, unsophisticated rating tools may implicitly reward the opaqueness of disclosures.
Possible evidence of green washing for high emission firms: 
Compliance around disclosure of climate related metrics is higher on average for lower emission firms, thus suggesting underreporting by high emission firms.

High emission firms score better in disclosing financial impact metrics, but with an important exception: information on capital and financing impact is systematically underreported. As the issue of stranded assets and liabilities[2] is of major importance to high emission sectors, such as oil and coal companies, the results indicate that a number of firms selectively leave this impact dimension undisclosed.

Disclosure of scope 3 emissions is considerably worse for high emission firms, despite the increasing focus of regulators and investors on this dimension.



Opaqueness may implicitly be rewarded instead of penalized by unsophisticated rating tools, as some companies engaging in climate risk disclosure appear to be penalized by some ESG rating tools. This gives perverse incentives for selective non-disclosure, leading to greenwashing.
Similar to existing literature, we find dispersion between ESG ratings[3] and hence they should be used with caution. Some ESG ratings appear to be positively associated with leverage, after controlling for emissions and other firm characteristics, whereas our AI driven indicators suggest systematically lower leverage for companies engaging in climate risk disclosure.
Valuations: companies with greater disclosure of metrics on climate risk impact on financial performance have higher valuations and lower leverage, after controlling for carbon emissions and other firm characteristics.
Firms with better disclosure of financial impact metrics tend to have higher valuations, lower leverage, and lower cost of capital.
Large cap firms that are TCFD compliant tend to have higher valuations, whereas small and medium cap firms engaging in climate disclosures may still offer considerable opportunities for appreciation.
The results are consistent across a number of specifications and apply after controlling for traditional ESG ratings, which instead appear to have weak or inconclusive bearing.
The results also apply after controlling for carbon emissions, which recent literature has found to be associated with a carbon risk premium and hence lower valuations.[4] Our indicators therefore offer investors and corporates a more granular understanding of valuations beyond carbon emissions.
Firm value: AI driven indicators suggest that greater disclosure is associated with lower cost of capital, thus boosting firm value. 
Despite the increasing popularity of ESG criteria,[5] demonstrating whether and how ESG compliance adds value to the firm is notoriously difficult and so far the empirical evidence is mixed or inconclusive.[6]

We find evidence that companies that are more transparent in disclosing financial impact metrics have lower cost of capital,[7] especially among firms that disclose impact of climate risks on expenditure and assets &amp; liabilities.
Our results on valuations offer a consistent picture in terms of cost of equity, whereas our parallel findings on leverage support consistent implications for the cost of debt. Both these channels represent important components of any corporate valuation framework and hence provide support for climate risk disclosures contributing to firm value. Further research is needed to understand other dimensions, such as revenues and operating margins.
Summary of AI driven indicators and greenwashing detection 

 
AI driven indicators
Greenwashing detection


What is it?
AI-based indicators that assess coverage rate of various climate and financial impact related indicators, as well as themes discussed in companies’ climate disclosure reports.
Detect anomalies between companies’ disclosures of climate risks and actual exposure.


How is it done?

Rule-based matching: For each indicator, construct relevant anchor words or expressions to locate relevant text in disclosure reports and then estimate probability of coverage by assessing sample precision and number of matches found.
Unsupervised learning: extract distinct themes from companies’ disclosure reports and then calculate companies’ relative coverage rate of each theme.


Detect anomalies in ESG ratings of companies who engage in climate disclosures vs. those who do not.
Benchmark high vs. low emission firms in terms of disclosure of climate metrics (e.g. emissions, energy use, water use, land use) and financial impact metrics to detect any under-reporting by high emission firms.
When evaluating relationship between disclosures and company performance, detect where traditional ESG ratings and AI driven indicators show inconsistent results.



What are the implications?

Firms with better disclosure of AI driven financial impact indicators have higher valuations, lower leverage, and lower cost of capital. Traditional ESG ratings present either diverging or statistically weaker results.
 

Allows for Automatic detection of under-reporting by high-emission firms, and inconsistency between ESG ratings, which allows investors to use ESG ratings with caution when integrating them in investment analysis.



[1] See, for example, Berg et al. (2019).
[2] Stranded assets are assets that have become uneconomic (firms are unable to recover their investment cost) or turned into liabilities, due to transition risks such as taxation of fossil fuel supply or consumption (e.g., Caldecott et al. 2015). Stranded liabilities refer to the cost of retiring uneconomical long-lived assets, such as oil and gas infrastructure.
[3] See Berg et al. (2019), for example.
[4] See Bolton and Kacperczyc (2020).
[5] See Amel-Zadeh and Serafeim (2020).
[6] See Cornell and Damodaran (2020) for an overview.

[7] See Goss and Roberts (2011) for similar evidence on bank loans and corporate social responsibility. Using US data, they find that socially responsible firms pay between 7 and 18 basis points less than other firms, unsecured loans being the most sensitive to social responsibility concerns.
 



Climate Disclosures and Financial Performance

Pan-Asia Index Rebalances/ Events/ Delta One, Periscope Analytics

Brian Freitas

Pan-Asia Index Rebalances/ Events/ Delta One

Event-Driven

Last evening post market close in Hong Kong, Hang Seng Indexes released an <a href="https://www.hsi.com.hk/static/uploads/contents/en/news/indexChgNotice/20201030T100000.pdf" target="_blank" rel="noopener">index notice</a> announcing a new Fast Entry rule for the <a href="https://www.smartkarma.com/entities/hang-seng-china-enterprises-index" target="_blank" data-smartkarma-citation="entities:22900:1" rel="noopener">Hang Seng China Enterprises Index (HSCEI INDEX)</a>.As one can imagine, this is being done solely to ensure the early inclusion of <a href="https://www.smartkarma.com/entities/ant-financial-services-group" target="_blank" data-smartkarma-citation="entities:24240:1" rel="noopener">Ant Group (6688 HK)</a> in the index. Index investors would then look at buying index futures and other derivatives on the HSCEI to play the upside. The early inclusion could also help the HSCEI to catch up with other China focused indices, where it has been a laggard for some time now.<a href="https://www.smartkarma.com/entities/ant-financial-services-group" target="_blank" data-smartkarma-citation="entities:24240:1" rel="noopener">Ant Group (6688 HK)</a>'s Fast Entry into the index will also mean that one stock needs to be deleted, though from the index rules it is not very clear if that would happen at the regular index review in December 2020 or March 2021.In this Insight, we take a look at the impact of <a href="https://www.smartkarma.com/entities/ant-financial-services-group" target="_blank" data-smartkarma-citation="entities:24240:1" rel="noopener">Ant Group (6688 HK)</a>'s inclusion in the HSCEI on the stock and what it means for the other stocks in the index.

Last evening post market close in Hong Kong, Hang Seng Indexes released an index notice announcing a new Fast Entry rule for the Hang Seng China Enterprises Index (HSCEI INDEX).As one can imagine, this is being done solely to ensure the early inclusion of Ant Group (6688 HK) in the index. Index investors would then look at buying index futures and other derivatives on the HSCEI to play the upside. The early inclusion could also help the HSCEI to catch up with other China focused indices, where it has been a laggard for some time now.Ant Group (6688 HK)'s Fast Entry into the index will also mean that one stock needs to be deleted, though from the index rules it is not very clear if that would happen at the regular index review in December 2020 or March 2021.In this Insight, we take a look at the impact of Ant Group (6688 HK)'s inclusion in the HSCEI on the stock and what it means for the other stocks in the index. 

Ant Group: Everyone Wants a Piece of The Action - HSCEI Fast Entry

The <a href="https://www.smartkarma.com/entities/shanghai-shenzhen-csi-300-index" target="_blank" data-smartkarma-citation="entities:22906:1" rel="noopener">Shanghai Shenzhen CSI 300 Index (SHSZ300 INDEX)</a> consists of stocks from the Shanghai Stock Exchange and the Shenzhen Stock Exchange and is one of the most widely tracked A-share indices with passive tracking products listed in multiple markets globally. There are CFFEX listed futures that trade actively as well.There were 21 changes to the index at the June review, and we expect up to 24 changes in the December review. Estimated one-way turnover is 4.06% and there are 4 inclusions and 11 deletions that could have more than 1 day of ADV to trade.With passive tracking products holding around US$28bn of AUM, plus another US$35bn in open interest on the listed futures, there could be significant moves in the inclusion/exclusion names ahead of the rebalance announcement. There will only be 9-10 trading days between the announcement of the changes and the implementation, so having a pre-position should pay off.

The Shanghai Shenzhen CSI 300 Index (SHSZ300 INDEX) consists of stocks from the Shanghai Stock Exchange and the Shenzhen Stock Exchange and is one of the most widely tracked A-share indices with passive tracking products listed in multiple markets globally. There are CFFEX listed futures that trade actively as well.There were 21 changes to the index at the June review, and we expect up to 24 changes in the December review. Estimated one-way turnover is 4.06% and there are 4 inclusions and 11 deletions that could have more than 1 day of ADV to trade.With passive tracking products holding around US$28bn of AUM, plus another US$35bn in open interest on the listed futures, there could be significant moves in the inclusion/exclusion names ahead of the rebalance announcement. There will only be 9-10 trading days between the announcement of the changes and the implementation, so having a pre-position should pay off.

CSI300 Index Rebalance Preview: 21 Changes in June, Could Be 24 in December

Technical Analyst, Vermilion Research

Joe Jasper

Technical Analyst

Technical and Quantitative Research

Vermilion Research

Vermilion’s objective is to deliver timely, actionable, and unique technical research to professional investors through the application of our various technical indicators.

Our research strategists seek to highlight securities which we believe are at major inflection points, based on our various technical and quantitative indicators, and offer asymmetric risk/return profiles. Our unique research methodology, centered on relative strength, enables our strategists to deliver superior investment recommendations.

Our process begins by organizing the universe of actively traded stocks into discrete sectors, then into industry groups. We then apply our technical and quantitative tools to identify developing price trends. Once attractive trends are identified within a selected sector or group, we screen for individual stocks which we believe offer the best risk/reward profile. We provide a historical perspective that often reveals, through our long-term charts, how price patterns often repeat themselves over time.

Technical Analysis

MSCI Emerging Markets Index / Technical Analysis

China (MSCI China) continues to offer many attractive setups and accounts for the vast majority of today’s recommendations. Taiwan (TAIEX) continues to be worthy of an overweight within the EM universe, while Brazil (Bovespa) and India (SENSEX) display improving price and RS trends -

China (MSCI China) continues to offer many attractive setups and accounts for the vast majority of today’s recommendations. Taiwan (TAIEX) continues to be worthy of an overweight within the EM universe, while Brazil (Bovespa) and India (SENSEX) display improving price and RS trends -

US Dollar Weakness Remains A Tailwind For EM

China

The CSI 300 Index is a free-float weighted index that consists of 300 A-share stocks listed on the Shanghai or Shenzhen Stock Exchanges. Index has a base level of 1000 on 12/31/2004. * Due to our agreement with CSI, shares in the index is restricted, please visit SSIS<go> for more information and access. This ticker holds prices fed from Shenzhen Stock Exchange.

Shanghai Shenzhen CSI 300 Index (SHSZ300 INDEX EQUITY)

Price Gauge
52-week Range	0.00 - 0.00
Market Cap (USDm)	0.00
P/E	0.00
Dividend (Yield)	0.00

Shanghai Shenzhen CSI 300 Index (SHSZ300 INDEX EQUITY)

TMI Snapshot: Synchronized Asian Macro Upturn Vs. Chinese Credit Jitters, Via Text Mining

Our macro sentiment indicators have seen a new phase of China-led synchronized upturn in East Asia, despite high-profile SOE credit jitters.

Elan Gore

Climate Disclosures and Financial Performance

How to leverage AI tools toassess a firm's climate risk exposure, detect greenwashing, explore the relationship between climate risk disclosure and...

Ping An - DERC

Ant Group: Everyone Wants a Piece of The Action - HSCEI Fast Entry

Hang Seng Indexes has introduced a Fast Entry rule for the HSCEI which ensures the inclusion of Ant Group in the index after the close of trading...

Brian Freitas

CSI300 Index Rebalance Preview: 21 Changes in June, Could Be 24 in December

There could be 24 changes to the CSI300 index at the December review with an estimated one-way turnover of 4.06%. 4 inclusions and 11 deletions are...

Brian Freitas

US Dollar Weakness Remains A Tailwind For EM

China accounts for the vast majority of today’s recommendations. Taiwan (TAIEX) continues to be worthy of an overweight within EM, while Brazil...

Vermilion Research

Price Gauge

52-week Range

Market Cap (USDm)

P/E

Dividend (Yield)