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>Consensus now sees a virtuous new EM &amp; Asia growth cycle centering on China, where outstanding growth prospects are said to coexist with structural CNY appreciation supporting continued strength in commodities and cyclicals</li>
<li>Ironically, Chinese equities sharply underperformed the rest of APAC in November just as these bullish views reached critical mass </li>
<li>Coincidentally, our text-mining finds that financial media coverage of CNY strength has recently taken an increasingly negative tone, while coverage of US-China tensions has turned neutral after a short post-election honeymoon period </li>
<li>If Chinese equities are not resilient to outgoing Trump administration flare-ups, is the CNY immune?  Should we still view the strong CNY as an indisputable positive?</li>
</ul><div class="content-highlight">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. </div>


Consensus now sees a virtuous new EM &amp; Asia growth cycle centering on China, where outstanding growth prospects are said to coexist with structural CNY appreciation supporting continued strength in commodities and cyclicals
Ironically, Chinese equities sharply underperformed the rest of APAC in November just as these bullish views reached critical mass 
Coincidentally, our text-mining finds that financial media coverage of CNY strength has recently taken an increasingly negative tone, while coverage of US-China tensions has turned neutral after a short post-election honeymoon period 
If Chinese equities are not resilient to outgoing Trump administration flare-ups, is the CNY immune?  Should we still view the strong CNY as an indisputable positive?
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:  Is a Strong CNY Still Viewed Positively?

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

Brian Freitas

Pan-Asia Index Rebalances/ Events/ Delta One

Index Rebalance

China CSSC Holdings Ltd A / Index Rebalance

Post market close on Friday, the China Securities Index Company announced the changes to 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> that will be effective after the close of trading on 11 December.
There are 26 inclusions and exclusions in the review with an estimated one-way turnover of 3.87%.
The inclusions are <a href="https://www.smartkarma.com/entities/tongfang-guoxin-electronics-co-ltd" target="_blank" data-smartkarma-citation="entities:15513:1" rel="noopener">Unigroup Guoxin (002049 CH)</a>, <a href="https://www.smartkarma.com/entities/suzhou-dongshan-precision-a" target="_blank" data-smartkarma-citation="entities:8796:1" rel="noopener">Suzhou Dongshan Precision A (002384 CH)</a>, <a href="https://www.smartkarma.com/entities/wuhan-guide-infrared-co-ltd" target="_blank" data-smartkarma-citation="entities:20330:1" rel="noopener">Wuhan Guide Infrared Co,Ltd. (002414 CH)</a>, <a href="https://www.smartkarma.com/entities/jpmf-guangdong-co-ltd-a" target="_blank" data-smartkarma-citation="entities:19050:1" rel="noopener">Jpmf Guangdong Co Ltd A (002600 CH)</a>, <a href="https://www.smartkarma.com/entities/yunnan-chuangxin-new-material" target="_blank" data-smartkarma-citation="entities:27135:1" rel="noopener">Yunnan Chuangxin New Material (002812 CH)</a>, <a href="https://www.smartkarma.com/entities/asymchem-laboratories-tianjin-co-ltd" target="_blank" data-smartkarma-citation="entities:28265:1" rel="noopener">Asymchem Laboratories (002821 CH)</a>, <a href="https://www.smartkarma.com/entities/jafron-biomedical-co-ltd" target="_blank" data-smartkarma-citation="entities:29575:1" rel="noopener">Jafron Biomedical Co Ltd (300529 CH)</a>, <a href="https://www.smartkarma.com/entities/bgi-genomics-co-ltd" target="_blank" data-smartkarma-citation="entities:25890:1" rel="noopener">Bgi Genomics (300676 CH)</a>, <a href="https://www.smartkarma.com/entities/china-cssc-holdings-ltd-a" target="_blank" data-smartkarma-citation="entities:2464:1" rel="noopener">China Cssc Holdings Ltd A (600150 CH)</a>, <a href="https://www.smartkarma.com/entities/beijing-tiantan-biological-products-corp" target="_blank" data-smartkarma-citation="entities:5190:1" rel="noopener">Beijing Tiantan Biological Products (600161 CH)</a>, <a href="https://www.smartkarma.com/entities/jcet-group-co-ltd" target="_blank" data-smartkarma-citation="entities:8628:1" rel="noopener">JCET Group (600584 CH)</a>, <a href="https://www.smartkarma.com/entities/tsingtao-brewery-co-ltd-a" target="_blank" data-smartkarma-citation="entities:846:1" rel="noopener">Tsingtao Brewery Co Ltd A (600600 CH)</a>, <a href="https://www.smartkarma.com/entities/topchoice-medical-corporation" target="_blank" data-smartkarma-citation="entities:1102:1" rel="noopener">Topchoice Medical (600763 CH)</a>, <a href="https://www.smartkarma.com/entities/shanghai-baosight-software-co" target="_blank" data-smartkarma-citation="entities:28228:1" rel="noopener">Shanghai Baosight Software Co (600845 CH)</a>, <a href="https://www.smartkarma.com/entities/jonjee-high-tech-industrial-and-commercial-holding-co-ltd" target="_blank" data-smartkarma-citation="entities:14622:1" rel="noopener">Jonjee High-Tech Ind.&amp; Com. (600872 CH)</a>, <a href="https://www.smartkarma.com/entities/zhongtai-securities-co-ltd" target="_blank" data-smartkarma-citation="entities:29935:1" rel="noopener">Zhongtai Securities Co Ltd (600918 CH)</a>, <a href="https://www.smartkarma.com/entities/boc-international-china-co-a" target="_blank" data-smartkarma-citation="entities:29972:1" rel="noopener">Boc International China Co-A (601696 CH)</a>, <a href="https://www.smartkarma.com/entities/china-merchants-energy-a" target="_blank" data-smartkarma-citation="entities:14450:1" rel="noopener">China Merchants Energy A (601872 CH)</a>, <a href="https://www.smartkarma.com/entities/nanjing-securities-co-ltd" target="_blank" data-smartkarma-citation="entities:27192:1" rel="noopener">Nanjing Securities Co Ltd (601990 CH)</a>, Gan&amp;Lee Pharma (603087 CH), <a href="https://www.smartkarma.com/entities/gongniu-group-co-ltd" target="_blank" data-smartkarma-citation="entities:30060:1" rel="noopener">Gongniu Group Co Ltd (603195 CH)</a>, <a href="https://www.smartkarma.com/entities/beijing-wantai-biological-a" target="_blank" data-smartkarma-citation="entities:30129:1" rel="noopener">Beijing Wantai Biological-A (603392 CH)</a>, <a href="https://www.smartkarma.com/entities/montage-technology-co-ltd" target="_blank" data-smartkarma-citation="entities:28236:1" rel="noopener">Montage Technology Co Ltd (688008 CH)</a>, <a href="https://www.smartkarma.com/entities/china-railway-signal-com-a" target="_blank" data-smartkarma-citation="entities:28237:1" rel="noopener">China Railway Signal &amp; Com-A (688009 CH)</a>, <a href="https://www.smartkarma.com/entities/advanced-micro-fabrication-a" target="_blank" data-smartkarma-citation="entities:30308:1" rel="noopener">Advanced Micro-Fabrication-A (688012 CH)</a> and <a href="https://www.smartkarma.com/entities/shenzhen-transsion-holding-a" target="_blank" data-smartkarma-citation="entities:30323:1" rel="noopener">Shenzhen Transsion Holding-A (688036 CH).</a>
The deletions are <a href="https://www.smartkarma.com/entities/hebei-iron-steel-co-ltd-a" target="_blank" data-smartkarma-citation="entities:11538:1" rel="noopener">Hebei Iron &amp; Steel Co Ltd A (000709 CH)</a>, <a href="https://www.smartkarma.com/entities/sichuan-tianqi-lithium-industries-inc" target="_blank" data-smartkarma-citation="entities:5698:1" rel="noopener">Sichuan Tianqi Lithium Industries, Inc (002466 CH)</a>, <a href="https://www.smartkarma.com/entities/zhejiang-idc-fluid-control-co-ltd" target="_blank" data-smartkarma-citation="entities:18695:1" rel="noopener">STO Express (002468 CH)</a>, <a href="https://www.smartkarma.com/entities/avic-helicopter-co-ltd-a" target="_blank" data-smartkarma-citation="entities:14380:1" rel="noopener">Avic Helicopter Co Ltd A (600038 CH)</a>, <a href="https://www.smartkarma.com/entities/tbea-co-ltd-a" target="_blank" data-smartkarma-citation="entities:20303:1" rel="noopener">Tbea Co Ltd A (600089 CH)</a>, <a href="https://www.smartkarma.com/entities/shanghai-construction-group-co-ltd" target="_blank" data-smartkarma-citation="entities:17136:1" rel="noopener">Shanghai Construction Group Co, Ltd. (600170 CH)</a>, <a href="https://www.smartkarma.com/entities/yanzhou-coal-mining-co-ltd" target="_blank" data-smartkarma-citation="entities:24592:1" rel="noopener">Yanzhou Coal Mining (600188 CH)</a>, <a href="https://www.smartkarma.com/entities/shandong-nanshan-aluminum-a" target="_blank" data-smartkarma-citation="entities:3920:1" rel="noopener">Shandong Nanshan Aluminum A (600219 CH)</a>, <a href="https://www.smartkarma.com/entities/hainan-airlines-co" target="_blank" data-smartkarma-citation="entities:24023:1" rel="noopener">Hainan Airlines Co (600221 CH)</a>, <a href="https://www.smartkarma.com/entities/china-avionics-systems-co-ltd" target="_blank" data-smartkarma-citation="entities:11393:1" rel="noopener">China Avionics Systems Co., Ltd. (600372 CH)</a>, <a href="https://www.smartkarma.com/entities/hla-corp-ltd" target="_blank" data-smartkarma-citation="entities:2586:1" rel="noopener">HLA CORP., Ltd. (600398 CH)</a>, <a href="https://www.smartkarma.com/entities/fangda-carbon-new-material-co-ltd" target="_blank" data-smartkarma-citation="entities:14878:1" rel="noopener">Fangda Carbon New Material Co, Ltd. (600516 CH)</a>, <a href="https://www.smartkarma.com/entities/offshore-oil-engineering-co-ltd-a" target="_blank" data-smartkarma-citation="entities:4968:1" rel="noopener">Offshore Oil Engineering A (600583 CH)</a>, <a href="https://www.smartkarma.com/entities/sichuan-chuantou-energy-stock-co-ltd" target="_blank" data-smartkarma-citation="entities:2557:1" rel="noopener">Sichuan Chuantou Energy Stock (600674 CH)</a>, <a href="https://www.smartkarma.com/entities/tonghua-dongbao-pharmaceutical-co-ltd" target="_blank" data-smartkarma-citation="entities:10358:1" rel="noopener">Tonghua Dongbao Pharmaceutical (600867 CH)</a>, <a href="https://www.smartkarma.com/entities/bank-of-xi-an-co-ltd" target="_blank" data-smartkarma-citation="entities:29936:1" rel="noopener">Bank of Xi'an Co Ltd (600928 CH)</a>, <a href="https://www.smartkarma.com/entities/cnooc-energy-technology-a" target="_blank" data-smartkarma-citation="entities:29941:1" rel="noopener">Cnooc Energy Technology &amp; -A (600968 CH)</a>, <a href="https://www.smartkarma.com/entities/china-film-co-ltd" target="_blank" data-smartkarma-citation="entities:29943:1" rel="noopener">China Film Co Ltd (600977 CH)</a>, <a href="https://www.smartkarma.com/entities/ningbo-port-co-ltd-a" target="_blank" data-smartkarma-citation="entities:9969:1" rel="noopener">Ningbo Port (601018 CH)</a>, <a href="https://www.smartkarma.com/entities/baiyin-nonferrous-group-co-a" target="_blank" data-smartkarma-citation="entities:29955:1" rel="noopener">Baiyin Nonferrous Group Co-A (601212 CH)</a>, <a href="https://www.smartkarma.com/entities/qingdao-port-international-co" target="_blank" data-smartkarma-citation="entities:29958:1" rel="noopener">Qingdao Port International Co (601298 CH)</a>, <a href="https://www.smartkarma.com/entities/red-star-macalline-group-corp" target="_blank" data-smartkarma-citation="entities:26732:1" rel="noopener">Red Star Macalline Group (601828 CH)</a>, <a href="https://www.smartkarma.com/entities/china-coal-energy-co-ltd" target="_blank" data-smartkarma-citation="entities:24607:1" rel="noopener">China Coal Energy (601898 CH)</a>, <a href="https://www.smartkarma.com/entities/bbmg-corporation" target="_blank" data-smartkarma-citation="entities:22769:1" rel="noopener">BBMG Corporation (601992 CH)</a>, <a href="https://www.smartkarma.com/entities/bank-of-guiyang-co-ltd" target="_blank" data-smartkarma-citation="entities:27479:1" rel="noopener">Bank of Guiyang Co Ltd (601997 CH)</a> and <a href="https://www.smartkarma.com/entities/hoshine-silicon-industry-c-a" target="_blank" data-smartkarma-citation="entities:26646:1" rel="noopener">Hoshine Silicon Industry C-A (603260 CH)</a>.
Only 3 of the inclusions have over 1 day of ADV to buy, while there are a lot more deletions that have over 1 day of ADV to sell.
The index universe has also been expanded to include STAR Board stocks that have been listed for over 1 year. The change was implemented at this review (with no advance notice) and will see the inclusion of <a href="https://www.smartkarma.com/entities/montage-technology-co-ltd" target="_blank" data-smartkarma-citation="entities:28236:1" rel="noopener">Montage Technology Co Ltd (688008 CH)</a>, <a href="https://www.smartkarma.com/entities/china-railway-signal-com-a" target="_blank" data-smartkarma-citation="entities:28237:1" rel="noopener">China Railway Signal &amp; Com-A (688009 CH)</a>, <a href="https://www.smartkarma.com/entities/advanced-micro-fabrication-a" target="_blank" data-smartkarma-citation="entities:30308:1" rel="noopener">Advanced Micro-Fabrication-A (688012 CH)</a> and <a href="https://www.smartkarma.com/entities/shenzhen-transsion-holding-a" target="_blank" data-smartkarma-citation="entities:30323:1" rel="noopener">Shenzhen Transsion Holding-A (688036 CH)</a> into 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>.

Post market close on Friday, the China Securities Index Company announced the changes to the Shanghai Shenzhen CSI 300 Index (SHSZ300 INDEX) that will be effective after the close of trading on 11 December.
There are 26 inclusions and exclusions in the review with an estimated one-way turnover of 3.87%.
The inclusions are Unigroup Guoxin (002049 CH), Suzhou Dongshan Precision A (002384 CH), Wuhan Guide Infrared Co,Ltd. (002414 CH), Jpmf Guangdong Co Ltd A (002600 CH), Yunnan Chuangxin New Material (002812 CH), Asymchem Laboratories (002821 CH), Jafron Biomedical Co Ltd (300529 CH), Bgi Genomics (300676 CH), China Cssc Holdings Ltd A (600150 CH), Beijing Tiantan Biological Products (600161 CH), JCET Group (600584 CH), Tsingtao Brewery Co Ltd A (600600 CH), Topchoice Medical (600763 CH), Shanghai Baosight Software Co (600845 CH), Jonjee High-Tech Ind.&amp; Com. (600872 CH), Zhongtai Securities Co Ltd (600918 CH), Boc International China Co-A (601696 CH), China Merchants Energy A (601872 CH), Nanjing Securities Co Ltd (601990 CH), Gan&amp;Lee Pharma (603087 CH), Gongniu Group Co Ltd (603195 CH), Beijing Wantai Biological-A (603392 CH), Montage Technology Co Ltd (688008 CH), China Railway Signal &amp; Com-A (688009 CH), Advanced Micro-Fabrication-A (688012 CH) and Shenzhen Transsion Holding-A (688036 CH).
The deletions are Hebei Iron &amp; Steel Co Ltd A (000709 CH), Sichuan Tianqi Lithium Industries, Inc (002466 CH), STO Express (002468 CH), Avic Helicopter Co Ltd A (600038 CH), Tbea Co Ltd A (600089 CH), Shanghai Construction Group Co, Ltd. (600170 CH), Yanzhou Coal Mining (600188 CH), Shandong Nanshan Aluminum A (600219 CH), Hainan Airlines Co (600221 CH), China Avionics Systems Co., Ltd. (600372 CH), HLA CORP., Ltd. (600398 CH), Fangda Carbon New Material Co, Ltd. (600516 CH), Offshore Oil Engineering A (600583 CH), Sichuan Chuantou Energy Stock (600674 CH), Tonghua Dongbao Pharmaceutical (600867 CH), Bank of Xi'an Co Ltd (600928 CH), Cnooc Energy Technology &amp; -A (600968 CH), China Film Co Ltd (600977 CH), Ningbo Port (601018 CH), Baiyin Nonferrous Group Co-A (601212 CH), Qingdao Port International Co (601298 CH), Red Star Macalline Group (601828 CH), China Coal Energy (601898 CH), BBMG Corporation (601992 CH), Bank of Guiyang Co Ltd (601997 CH) and Hoshine Silicon Industry C-A (603260 CH).
Only 3 of the inclusions have over 1 day of ADV to buy, while there are a lot more deletions that have over 1 day of ADV to sell.
The index universe has also been expanded to include STAR Board stocks that have been listed for over 1 year. The change was implemented at this review (with no advance notice) and will see the inclusion of Montage Technology Co Ltd (688008 CH), China Railway Signal &amp; Com-A (688009 CH), Advanced Micro-Fabrication-A (688012 CH) and Shenzhen Transsion Holding-A (688036 CH) into the Shanghai Shenzhen CSI 300 Index (SHSZ300 INDEX). 

STAR Board: CSI300 Index Rebalance: Rule Change Sees STAR Board Stocks Included

Beijing Roborock Technology  / Index Rebalance

Yesterday evening post market close, the Shanghai Stock Exchange and China Securities Index Company announced the changes to the <a href="https://www.smartkarma.com/entities/sse-science-and-technology-innovation-board-50-index" target="_blank" data-smartkarma-citation="entities:29279:1" rel="noopener">SSE STAR 50 (STAR50 INDEX)</a>. The changes will be effective after the close of trading on 11 December.The  inclusions are <a href="https://www.smartkarma.com/entities/national-silicon-industry-a" target="_blank" data-smartkarma-citation="entities:30349:1" rel="noopener">National Silicon Industry -A (688126 CH)</a>, <a href="https://www.smartkarma.com/entities/beijing-roborock-technolog-a" target="_blank" data-smartkarma-citation="entities:30358:1" rel="noopener">Beijing Roborock Technolog-A (688169 CH)</a>, <a href="https://www.smartkarma.com/entities/autel-intelligent-technolo-a" target="_blank" data-smartkarma-citation="entities:30369:1" rel="noopener">Autel Intelligent Technolo-A (688208 CH)</a>, <a href="https://www.smartkarma.com/entities/china-resources-microelect-a" target="_blank" data-smartkarma-citation="entities:30396:1" rel="noopener">China Resources Microelect-A (688396 CH)</a> and SMIC (688981 CH).The deletions are <a href="https://www.smartkarma.com/entities/suzhou-harmontronics-autom-a" target="_blank" data-smartkarma-citation="entities:30315:1" rel="noopener">Suzhou Harmontronics Autom-A (688022 CH)</a>, <a href="https://www.smartkarma.com/entities/beijing-worldia-diamond-tools" target="_blank" data-smartkarma-citation="entities:30319:1" rel="noopener">Beijing Worldia Diamond Tools (688028 CH)</a>, <a href="https://www.smartkarma.com/entities/beijing-tianyishangjia-new-a" target="_blank" data-smartkarma-citation="entities:30322:1" rel="noopener">Beijing Tianyishangjia New-A (688033 CH)</a>, <a href="https://www.smartkarma.com/entities/beijing-baolande-software-a" target="_blank" data-smartkarma-citation="entities:30327:1" rel="noopener">Beijing Baolande Software-A (688058 CH)</a> and <a href="https://www.smartkarma.com/entities/beijing-abt-networks-co-ltd" target="_blank" data-smartkarma-citation="entities:30357:1" rel="noopener">Beijing ABT Networks Co Ltd (688168 CH)</a>.With a little more than CNY 21bn benchmarked to the <a href="https://www.smartkarma.com/entities/sse-science-and-technology-innovation-board-50-index" target="_blank" data-smartkarma-citation="entities:29279:1" rel="noopener">SSE STAR 50 (STAR50 INDEX)</a>, there is quite an impact expected on a few stocks. One-way turnover is close to 30% and the funding flow will impact quite a few index constituents with over 1.5 days of ADV to sell. There are only 11 trading days to implementation and we do not expect a lot of pre-positions have been built up on the stocks.

Yesterday evening post market close, the Shanghai Stock Exchange and China Securities Index Company announced the changes to the SSE STAR 50 (STAR50 INDEX). The changes will be effective after the close of trading on 11 December.The  inclusions are National Silicon Industry -A (688126 CH), Beijing Roborock Technolog-A (688169 CH), Autel Intelligent Technolo-A (688208 CH), China Resources Microelect-A (688396 CH) and SMIC (688981 CH).The deletions are Suzhou Harmontronics Autom-A (688022 CH), Beijing Worldia Diamond Tools (688028 CH), Beijing Tianyishangjia New-A (688033 CH), Beijing Baolande Software-A (688058 CH) and Beijing ABT Networks Co Ltd (688168 CH).With a little more than CNY 21bn benchmarked to the SSE STAR 50 (STAR50 INDEX), there is quite an impact expected on a few stocks. One-way turnover is close to 30% and the funding flow will impact quite a few index constituents with over 1.5 days of ADV to sell. There are only 11 trading days to implementation and we do not expect a lot of pre-positions have been built up on the stocks.

STAR 50 Index Rebalance: We Thought BIG, Turns Out Its BIGGER

<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

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