Here's how you can revolutionize economic research and analysis.

Revolucionar é um pouco demais. Como aprendemos em Economia, grandes mudan?as se fazem na margem. Tenho um ex-aluno que seguiu esta trilha. Formou-se, foi para o mestrado e deu um passo na dire??o do uso da econometria. Depois, no doutorado, come?ou a pensar em novos métodos e hoje é citado como referência em vários artigos da área. Publicou na Econometrica e na American Economic Review. O segredo é a persistência e foco. Nem sempre é seguir os modismos.

La incorporación de inteligencia artificial (IA) en la investigación y el análisis económicos está transformando radicalmente el panorama financiero. Al automatizar el procesamiento de grandes volúmenes de datos, la IA ofrece análisis más rápidos y precisos, permitiendo a las empresas y a los analistas tomar decisiones informadas en tiempo real. Además, facilita la identificación de tendencias emergentes y la detección de riesgos potenciales con una precisión sin precedentes. Así, la IA no solo optimiza la eficiencia operativa, sino que también abre nuevas posibilidades para el desarrollo de estrategias financieras más innovadoras y robustas.

In the current era of big data, data assets have become greatly enriched. However, more critical is the need to delve into the economic implications behind this data and extract valuable insights through creative modeling to support decision-making and promote sustainable development. Caixin Data, with roots in financial analysis, has expanded its data sources beyond finance, collaborating with entities across industries. By helping partners structure their data, Caixin has amassed a valuable trove of alternative data, which will serve as essential raw material for developing innovative economic models.

Focus on Real-World Applications: Prioritize research that addresses real-world challenges and issues facing societies, businesses, and policymakers. Emphasize the practical relevance and impact of economic research by addressing pressing issues such as inequality, climate change, poverty, and technological disruption. Emphasize Long-Term Sustainability: Shift towards a more sustainable and long-term-oriented approach to economic research and analysis. Consider the environmental, social, and ethical implications of economic policies and practices, and strive to create inclusive, equitable, and resilient economic systems.

The application of AI is a necessity. For Caixin Data, the 25-year article archive of Caixin, the largest non-English media outlet globally, has provided the basis for structuring and machine learning, forming an AI system like Know-Link. This serves as the underlying annotation system for all AI applications. Based on this system, Caixin can interpret real-time news and develop more tools to assist reading and trading decisions. In the realm of macroeconomic research, it also provides the underlying support for generating more factors. Through this module, Caixin has developed the world's first big data-based indicator for researching the macro economy - the Caixin China New Economy Index, using their Caixin China PMI as a foundation.

Interdisciplinary collaboration is indeed a necessity. But more importantly, the journey from data to insights is primarily a data science endeavor, where various disciplines contribute their principles to the modeling process - and economics is no exception. For example, when utilizing climate data for agricultural yield forecasting, the foundational disciplines are naturally climate science and agriculture, and the relevant data sources stem from these domains. However, once the basic analysis of the fundamentals is complete, the development of economic models becomes crucial before the insights can be provided to financial institutions for their reference and decision-making.

We acknowledge the challenges of the monolithic database model for applying alternative data in finance. To address this, we have established a "pay-as-you-go" database structure at yun.ccxe.com.cn. We have aggregated alternative data sources, reviewed them for compliance, and packaged the datasets to deliver them to financial practitioners, including individual investors. This differs from monolithic database sales or B2B API delivery models. The goal is to make data applications people-oriented, making alternative data accessible and affordable for those who need to use it. We aim to lower barriers to alternative data adoption and empower more users to gain valuable insights.

Open collaboration encourages researchers from different disciplines, backgrounds, and locations to share ideas. This can lead to innovative approaches and solutions, making research more thorough and rigorous. By allowing rapid sharing of findings and promoting real-time feedback and collaboration, open collaboration can speed up the pace of research. This can lead to quicker identification of key trends, patterns, and insights, ultimately driving new discoveries and advancements in the field of economics. In summary, open collaboration has the potential to revolutionize the way economic research is conducted, resulting in more impactful and relevant findings that can inform policy and drive economic growth.

The government is highly interested in researching and monitoring macroeconomic innovation. Our New Economy Index has since expanded to cover multiple categories, such as a Digital Economy Index. The government is keen to understand the performance of their local economies, and each month their investment promotion departments will refer to our economic index reports to gauge the situation. If the results are positive, they may even choose to publicly release the data. At the same time, we also utilize our macroeconomic indicators to design investable indices. This provides the government with additional tools to stimulate the economy, using more long-term and strategic approaches, rather than simply relying on short-term subsidies.

Revolucionar a pesquisa e a análise econ?mica com aprendizado contínuo envolve usar IA e machine learning para previs?es precisas e análises em tempo real, coletar e integrar dados massivos de diversas fontes, utilizar plataformas de aprendizado contínuo para manter-se atualizado, criar simula??es e modelos dinamicos para testar políticas econ?micas, colaborar com especialistas de outras disciplinas, adotar tecnologias de visualiza??o de dados interativas, explorar blockchain e fintech para maior transparência e seguran?a, e implementar sistemas de feedback para melhorias contínuas. Isso torna a análise econ?mica mais eficiente, precisa e adaptável a mudan?as rápidas.

Turn everyday data analysis into a treasure hunt. Use social media sentiment analysis to measure public reaction to economic events in real time. Correlate those sentiments with market movements and discover patterns. It's like finding hidden clues in the chatter of the crowd that could predict the next big economic shift.