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.

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 Inde (SHSZ300 INDEX EQUITY)

<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

<blockquote>ESG Report Series Issue 4</blockquote>ESG investing has become a mainstream phenomenon for capital markets worldwide. Global flows to ESG-themed ETFs hit a record high in 2019, up to 3 times than that of 2018. During the pandemic in 2020, despite the outflow of traditional ETF funds, the market needs for ESG investing remains strong. The opening up of China's capital markets is also driving more international capital with ESG themes into China, promoting ESG and responsible investment philosophies to gain greater recognition in China. In addition, with stronger regulatory requirements for ESG disclosures and a broader guidance to build a "green financial system,” China has witnessed an increase in the quantity and quality of disclosures, as well as rapid growth of ESG investment in recent years. <ol>
<li>By conducting Natural Language Processing based analysis to detect themes discussed in 103 market wide third-party research reports, we find that there has been an increasing focus on ESG in the Chinese capital market, with a gradual transition from the initial conceptual descriptions to specific investment products and their performance.</li>
<li>The number of ESG indices has doubled in 2020 in China, with increased diversity in investment strategies and notable performance of quality factors.</li>
<li>The issuance of ESG fund products is undergoing a booming period, with “pure ESG” fund products that combine the entire dimension of ESG gaining momentum.</li>
<li>ESG fund products have performed well since the start of 2020, with returns above the market average for funds in China.</li>
<li>Recommendations and look ahead for ESG Investing in China:  • ESG ratings and data coverage in the domestic market need to be further enhanced to enhance alignment with the Chinese market to support the enrichment of ESG theme investment products.  • Alternative data and technology can help investors distinguish between companies that are truly ESG compliant and well performing vs. those who are not. • Establishment of ESG ratings and data for bonds and bond issuers is essential to accelerate integration of ESG in fixed income investment.  • Index providers and fund management companies should develop more diversified ESG products such as passive funds, quantitative funds, and investment products for the primary market, providing more investment options for investors.  • As China puts more emphasis on climate change, financial markets should proactively innovate and develop financial products to respond to the need of climate risk mitigation and transition into a low-carbon economy. </li>
</ol><div class="content-highlight">ESG investment trends in China in Charts</div><figure class="editor-figure mceNonEditable"><img src="https://static-prod.smartkarma.com/images/media/000/172/658/original/image_19643538131605767175083.png?1605767176" class="editor-image img-responsive" data-smartkarma-image-id="172658"></figure>

ESG Report Series Issue 4ESG investing has become a mainstream phenomenon for capital markets worldwide. Global flows to ESG-themed ETFs hit a record high in 2019, up to 3 times than that of 2018. During the pandemic in 2020, despite the outflow of traditional ETF funds, the market needs for ESG investing remains strong. The opening up of China's capital markets is also driving more international capital with ESG themes into China, promoting ESG and responsible investment philosophies to gain greater recognition in China. In addition, with stronger regulatory requirements for ESG disclosures and a broader guidance to build a "green financial system,” China has witnessed an increase in the quantity and quality of disclosures, as well as rapid growth of ESG investment in recent years. 
By conducting Natural Language Processing based analysis to detect themes discussed in 103 market wide third-party research reports, we find that there has been an increasing focus on ESG in the Chinese capital market, with a gradual transition from the initial conceptual descriptions to specific investment products and their performance.
The number of ESG indices has doubled in 2020 in China, with increased diversity in investment strategies and notable performance of quality factors.
The issuance of ESG fund products is undergoing a booming period, with “pure ESG” fund products that combine the entire dimension of ESG gaining momentum.
ESG fund products have performed well since the start of 2020, with returns above the market average for funds in China.
Recommendations and look ahead for ESG Investing in China:  • ESG ratings and data coverage in the domestic market need to be further enhanced to enhance alignment with the Chinese market to support the enrichment of ESG theme investment products.  • Alternative data and technology can help investors distinguish between companies that are truly ESG compliant and well performing vs. those who are not. • Establishment of ESG ratings and data for bonds and bond issuers is essential to accelerate integration of ESG in fixed income investment.  • Index providers and fund management companies should develop more diversified ESG products such as passive funds, quantitative funds, and investment products for the primary market, providing more investment options for investors.  • As China puts more emphasis on climate change, financial markets should proactively innovate and develop financial products to respond to the need of climate risk mitigation and transition into a low-carbon economy. 
ESG investment trends in China in Charts

Macroeconomics

<div class="content-highlight">China SME Financing Report Series Report III by Fudan-Ping An Research Institute for Macroeconomy</div>The sudden outbreak of COVID-19 has brought unprecedented challenges to small and medium-sized enterprises (hereinafter SMEs), but it has also provided them with excellent opportunities to go digital. Digital transformation can eliminate information barriers between SMEs and financial institutions, improve information asymmetry and alleviate financing difficulties. In March this year, the Ministry of Industry and Information Technology of China (MIIT) issued a special action plan on digital empowerment of SMEs. Our research results confirm the necessity of this action plan: At present, the digital maturity of SMEs in China is low; high cost and tight budget are the main reasons that hinder their digital transformation. Accordingly, we suggest that Chinese governments at all levels increase their support to large technology companies that are developing standardized digital services which meet the needs of SMEs, and provide tax cuts and financing facilities for SMEs' digital transformation projects.In July this year, through on-site interviews and questionnaires, we surveyed nearly 100 enterprises (86% of them have fewer than 50 employees in 2019) in a national economic development zone in Shanghai, and found that:<ul><li>The digital maturity of the surveyed enterprises is low:</li></ul><ul>
<li>Low penetration of artificial intelligence and big data: more than two-thirds of the surveyed enterprises have not yet used artificial intelligence or big data in their production and business;</li>
<li>The proportion of online procurement and online sales is low: only 17% of the surveyed enterprises has more than 50% of their procurement done online, while only 14% of the surveyed enterprises sell more than 50% online.</li>
<li>The penetration rate of smart manufacturing is low: less than 18% of the surveyed manufacturing enterprises are building smart production lines, smart workshops and smart factories.</li>
<li>Low use of internet finance: over 64% of the surveyed enterprises have never used internet finance to obtain financing; among the 35 surveyed companies that have used internet finance, only 1 company get more than 50% financing through this channel.</li>
</ul><ul><li>Companies surveyed cited high cost and tight budget as major obstacles to digital transformation. 35% of the companies surveyed said the biggest obstacle to digital transformation was the high fees charged by service providers, while 30% said the biggest obstacle was lack of funds. At the same time, 97% of the companies surveyed realized that digitalization can help improve their operating efficiency and development. Therefore, reducing service providers' fees and filling the shortage of funds can effectively promote digital transformation among SMEs.</li></ul>Policy Suggestions: support the development of standardized digital services and provide tax cuts and financing facilities for SMEs’ digital transformation.<ol>
<li>By taking advantage of the large number of SMEs in China, the size disadvantage of a single enterprise is transformed into the quantity advantage of the whole economy. Thus, governments should support large technology companies that are developing standardized digital services which meet the needs of SMEs. In March this year, the Ministry of Industry and Information Technology of China proposed to cultivate and promote a number of digital platforms which meet the needs of SMEs. This survey confirms the necessity of this special action plan.</li>
<li>Provide SMEs with tax cuts for digital transformation.</li>
<li>Guide financial institutions to provide cheap loans to SMEs for digital transformation projects. Digital transformation can eliminate information barriers between enterprises and financial institutions, improve information asymmetry, help SMEs obtain more accurate credit assessment, and alleviate financing difficulties.</li>
</ol>

China SME Financing Report Series Report III by Fudan-Ping An Research Institute for MacroeconomyThe sudden outbreak of COVID-19 has brought unprecedented challenges to small and medium-sized enterprises (hereinafter SMEs), but it has also provided them with excellent opportunities to go digital. Digital transformation can eliminate information barriers between SMEs and financial institutions, improve information asymmetry and alleviate financing difficulties. In March this year, the Ministry of Industry and Information Technology of China (MIIT) issued a special action plan on digital empowerment of SMEs. Our research results confirm the necessity of this action plan: At present, the digital maturity of SMEs in China is low; high cost and tight budget are the main reasons that hinder their digital transformation. Accordingly, we suggest that Chinese governments at all levels increase their support to large technology companies that are developing standardized digital services which meet the needs of SMEs, and provide tax cuts and financing facilities for SMEs' digital transformation projects.In July this year, through on-site interviews and questionnaires, we surveyed nearly 100 enterprises (86% of them have fewer than 50 employees in 2019) in a national economic development zone in Shanghai, and found that:The digital maturity of the surveyed enterprises is low:
Low penetration of artificial intelligence and big data: more than two-thirds of the surveyed enterprises have not yet used artificial intelligence or big data in their production and business;
The proportion of online procurement and online sales is low: only 17% of the surveyed enterprises has more than 50% of their procurement done online, while only 14% of the surveyed enterprises sell more than 50% online.
The penetration rate of smart manufacturing is low: less than 18% of the surveyed manufacturing enterprises are building smart production lines, smart workshops and smart factories.
Low use of internet finance: over 64% of the surveyed enterprises have never used internet finance to obtain financing; among the 35 surveyed companies that have used internet finance, only 1 company get more than 50% financing through this channel.
Companies surveyed cited high cost and tight budget as major obstacles to digital transformation. 35% of the companies surveyed said the biggest obstacle to digital transformation was the high fees charged by service providers, while 30% said the biggest obstacle was lack of funds. At the same time, 97% of the companies surveyed realized that digitalization can help improve their operating efficiency and development. Therefore, reducing service providers' fees and filling the shortage of funds can effectively promote digital transformation among SMEs.Policy Suggestions: support the development of standardized digital services and provide tax cuts and financing facilities for SMEs’ digital transformation.
By taking advantage of the large number of SMEs in China, the size disadvantage of a single enterprise is transformed into the quantity advantage of the whole economy. Thus, governments should support large technology companies that are developing standardized digital services which meet the needs of SMEs. In March this year, the Ministry of Industry and Information Technology of China proposed to cultivate and promote a number of digital platforms which meet the needs of SMEs. This survey confirms the necessity of this special action plan.
Provide SMEs with tax cuts for digital transformation.
Guide financial institutions to provide cheap loans to SMEs for digital transformation projects. Digital transformation can eliminate information barriers between enterprises and financial institutions, improve information asymmetry, help SMEs obtain more accurate credit assessment, and alleviate financing difficulties.
  

Digital Transformation: A Game-Changing Strategy to Alleviate the Plight of SMEs in China

Quantitative Analysis

<blockquote>A follow-up report on our previously weekly report on pork prices from July. </blockquote>African swine fever (ASF) is resurging outside China, but has a relatively small impact on pork prices within China.Domestic supply: We searched livestock sales data for 30 major hog breeding areas in China. The data is quite positive, in the sense that the sales have not been that sensitive to the overseas outbreak. Yet it is worth noting that many farmers in southern China are showing signs of reluctance to sell, which may affect pork supply in the second half of the fourth quarter.Import: Pork supply volatility has flattened as global pork prices marginally weakened due to the COVID-19 pandemic. There has been a change in China’s pork import mix, with the US taking a bigger share. This is affected mainly by international prices and not much by the geopolitical relations between China and the US. China is expected to import more pork from the US as ASF cases are on the rise in Germany.We expect Chinese pork prices to maintain a downward trend in the first half of the fourth quarter, and then stabilize and rise after November.<h3>Looking Back</h3>April forecast: We predicted pork prices in China would rise in April. Pork prices rose by RMB10.2 per 500g in the three months following our April forecast.July forecast: We predicted pork prices in China would plateau and then decline. Pork prices fell by RMB5.2 per 500g in the two months following our July forecast.Figure 1: Domestic pork prices began to fall back after August<figure class="editor-figure mceNonEditable"><img src="https://static-prod.smartkarma.com/images/media/000/166/704/original/image_81251287621603352831835.png?1603352836" class="editor-image img-responsive" data-smartkarma-image-id="166704"></figure><div style="text-align: left;" class="content-caption">Source: Wind, Ping An DERC</div>

A follow-up report on our previously weekly report on pork prices from July. African swine fever (ASF) is resurging outside China, but has a relatively small impact on pork prices within China.Domestic supply: We searched livestock sales data for 30 major hog breeding areas in China. The data is quite positive, in the sense that the sales have not been that sensitive to the overseas outbreak. Yet it is worth noting that many farmers in southern China are showing signs of reluctance to sell, which may affect pork supply in the second half of the fourth quarter.Import: Pork supply volatility has flattened as global pork prices marginally weakened due to the COVID-19 pandemic. There has been a change in China’s pork import mix, with the US taking a bigger share. This is affected mainly by international prices and not much by the geopolitical relations between China and the US. China is expected to import more pork from the US as ASF cases are on the rise in Germany.We expect Chinese pork prices to maintain a downward trend in the first half of the fourth quarter, and then stabilize and rise after November.Looking BackApril forecast: We predicted pork prices in China would rise in April. Pork prices rose by RMB10.2 per 500g in the three months following our April forecast.July forecast: We predicted pork prices in China would plateau and then decline. Pork prices fell by RMB5.2 per 500g in the two months following our July forecast.Figure 1: Domestic pork prices began to fall back after AugustSource: Wind, Ping An DERC

Chinese Pork Prices to Restabilize in Mid to Late Q4

<blockquote>The real estate policy sentiment index effectively matched the past four major real estate policy cycles since 2008.</blockquote>China real estate policy cycles highly correlate with and lead interest rate movements. Note that there will be limited downside for interest rates going forward, as China is on the verge of tightening its real estate policy. The real estate policy sentiment has hit a historic low in China. Under the Chinese government’s “dual circulation” policy, the real estate sector will not become a pillar for "internal circulation" and further relaxation of real estate policy is unlikely.

The real estate policy sentiment index effectively matched the past four major real estate policy cycles since 2008.China real estate policy cycles highly correlate with and lead interest rate movements. Note that there will be limited downside for interest rates going forward, as China is on the verge of tightening its real estate policy. The real estate policy sentiment has hit a historic low in China. Under the Chinese government’s “dual circulation” policy, the real estate sector will not become a pillar for "internal circulation" and further relaxation of real estate policy is unlikely.

Correlation Between China Real Estate Policy Sentiment and Interest Rates

Ping An - DERC
Ping An Digital Economic Research Center (DERC)

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Ping An - DERC

Chinese Pork Prices to Restabilize in Mid to Late Q4

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Ping An - DERC

Correlation Between China Real Estate Policy Sentiment and Interest Rates

China real estate policy cycles highly correlate with and lead interest rate movements. With a limited downside for interest rates going forward,...

Ping An - DERC