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August 2023 - Academy of Management Conference Boston by Ephie Wang

Exploring the Influence of AI tools on Academic Research

AI Tools Nowadays: Pervasive and Convenient

AI tools have seamlessly integrated into the lives of individuals, contributing significantly to various aspects of daily routines. Essentially, an AI tool is a software application designed to leverage artificial intelligence algorithms, effectively addressing specific tasks and problem-solving endeavours.

These tools, ranging from AI chatbots to note-taking and transcription utilities, have become instrumental in enhancing efficiency and productivity. In the academic realm, the pervasive presence of AI tools is becoming increasingly evident, with innovations like the recently introduced ChatGPT making substantial contributions.

For students, ChatGPT serves as a knowledge repository to address queries, while for non-native English-speaking researchers, it aids in refining language usage. Undoubtedly, the allure of convenience offered by AI tools is undeniable. However, the question lingers: does this convenience lead to a future as promising as it seems?


AI Tools in Academia: A Paradigm Shift?

The year 2023 witnessed an insightful event at the Academy of Management conference. During this event, Professor Hila Lifshitz-Assaf from Warwick Business School, Dr. Sarah Lebovitz from the University of Virginia, and PhD student Steven Randazzo from Warwick Business School orchestrated a thought-provoking professional development workshop.

Titled "AI Tools for Academic Research: A Revolutionary Shift or the Beginning of the End?" the workshop delved into the merits and concerns surrounding the integration of AI tools into research endeavours.

These esteemed organisers and facilitators deliberated on the possibilities AI tools present and the reservations they evoke, offering insights into their personal usage patterns.


The Promise and Challenge of AI Tools

The advent of AI tools presents a dual-sided coin – a prospect teeming with opportunities yet accompanied by challenges. Based on the insights gleaned from the workshop, the discourse surrounding the promise and challenge of AI tools gains further depth.

While the advantages of AI tools are evident in time-saving endeavours, such as automating coding tasks and condensing literature summaries for readers, the query arises: does this convenience undermine researchers' fundamental capabilities, rendering them dependent on these tools?

Likewise, the innovation potential of AI tools is evident, generating novel ideas that stimulate intellectual growth. But could this lead to a diminution in human creativity and motivation? Intriguingly, AI tools elicit contrasting outcomes from differing perspectives.

Predicting the long-term or short-term effects of these tools on academic pursuits remains an intricate challenge. Moreover, deciphering whether the impact is contingent on the manner of utilization necessitates more comprehensive exploration and observation.


In Conclusion

As AI tools continue their penetration into academic research, they usher in a transformative juncture – one replete with both promises and perils. The allure of convenience intertwines with potential ramifications on researchers' intrinsic skills and creative faculties. The discourse prompted by events like the Academy of Management conference compels us to reflect on the true implications of these tools in shaping the future of academic inquiry. Only through careful exploration, nuanced observation, and purposeful application can we unravel the intricate tapestry that AI tools weave within the realm of academia.

July 2023 - Unleashing the Power of Large Language Models in Finance: A Deep Dive 

In the rapidly evolving world of fintech, the potential of artificial intelligence (AI) is being harnessed more than ever. A recent webinar hosted by the Gillmore Centre offered a fascinating exploration of how large language models (LLMs) like GPT-3 and GPT-4 revolutionise financial analysis. Here's a detailed look at the key takeaways from this insightful session. 
Demystifying Large Language Models 
LLMs are AI models trained on vast amounts of text data, capable of generating human-like text. They've been making waves across various sectors, including finance, for their ability to perform tasks like sentiment analysis, price prediction, and business lead generation. 
However, it's not all smooth sailing. While LLMs are fluent in language generation, their reliability and reasoning can sometimes be questionable. This makes them less suitable for tasks requiring high accuracy, such as mortgage approvals or writing critical code with real-world implications. 
The Role of LLMs in Financial Analysis 
The webinar highlighted several exciting applications of LLMs in financial analysis. For instance, they can be used to analyze financial news, predict stock prices, and even generate financial reports. AI tools for these applications can be identified using the "there's a for that" plugin, a handy tool that finds AI tools for specific use cases. 
Moreover, LLMs can be used to analyze scientific literature relevant to financial analysis. The "scholarai" plugin can be used to find relevant papers based on keywords, retrieve the full text of a paper, and even save citations to a reference manager. 
Overcoming Challenges with Innovative Solutions 
Despite their potential, LLMs face significant challenges in financial analysis. One of the main hurdles is their inability to handle long texts, such as ESG reports, due to their length restrictions. 
To tackle this issue, Xinyu Wang presented her innovative method, "Orange," based on reading order and font size. This method divides the document based on a tree structure rather than pages, allowing for a more global understanding. In her experiments, Wang's method outperformed other non-large language model-based methods. For instance, her method correctly identified and organized the table of contents of an ESG report, while GPT-4 failed to do so. 
Looking Ahead 
The webinar concluded with a discussion on the future of LLMs in financial analysis. Wang mentioned that she plans to integrate large language models into her method to enable them to understand long documents. She also plans to release her source code on GitHub, which could be a valuable resource for students and researchers. 
The Gillmore Centre webinar offered valuable insights into the potential and challenges of using large language models in financial analysis. While these models have significant potential, they face challenges, particularly in handling long documents. Innovative approaches like Wang's "Orange" method offer promising solutions to these challenges, paving the way for more effective use of LLMs in financial analysis. The future of LLMs in this field looks promising, with ongoing research and development to overcome their current limitations. Stay tuned for more exciting developments in this space! 

June 2023 - GillmoreAI by Ashkan Eshghi


We are thrilled to introduce the alpha version of GillmoreAI, a cutting-edge fintech expert chatbot developed by the Gillmore Centre for Financial Technology. GillmoreAI, in its current form, is powered by the insights gleaned from 650 academic papers authored by our esteemed scholars. As we gather feedback and insights from users like you, we are committed to continuously enhancing GillmoreAI's capabilities and expanding its knowledge base. We invite you to engage with GillmoreAI and witness its evolution firsthand. Explore its functionalities, ask questions, and provide feedback to help us shape the future iterations of this remarkable AI system. Your input is invaluable as we work towards delivering an unparalleled fintech expertise and research tool. Together, we can revolutionize the world of finance and technology.

How AI will accelerate Link opens in a new windowfinancial democratisationLink opens in a new window

Ram Gopal provides insight: Since the pandemic fintech has boomed with World BankLink opens in a new window data showing that digital financial services, such as peer-to-peer lending and mobile payments, now cover more than 80 countries. It means people borrowing, lending, investing and saving themselves, without having to go through intermediaries like banks and financial services companies. It’s already happening, and AI will speed it up, but most leaders don’t fully understand the implications.

Polygon vs Ethereum – which platform is the future for the rise of DEX?Link opens in a new window

Olga Klein discusses the platform in the context of the collapse of cryptocurrency exchange FTX that saw at least $1 billion of customers’ money lost has brought the benefits of decentralised exchanges (DEX) sharply into the spotlight.

May 2023 - The AI Race: who really wins?

Artificial Intelligence in Fintech: The Opportunities, Risks, and Future

Artificial Intelligence (AI) is revolutionizing the world, and no sector is immune to its transformative effects. The financial technology, or fintech, industry is experiencing groundbreaking innovations with the incorporation of AI. However, with these advancements come critical questions around ethics, data privacy, job security, and more.

Several AI experts discussed these pressing matters in a recent webinar titled "The AI race: who really wins?" led by Pro-Vice-Chancellor Professor Carsten Maple. The panel included Raj Balasundaram, VP at Verint, Helena Quinn, Principal Policy Adviser - AI and Data Science at the Information Commissioner's Office, and Dr Shweta Singh, Assistant Professor of Information Systems and Management at Warwick Business School.

AI in Fintech: The Opportunities

AI is making banking and financial services more efficient and accessible. For instance, AI-powered chatbots provide real-time customer assistance, reducing waiting times and streamlining customer service. Predictive analytics, fueled by AI, are helping institutions make informed decisions by accurately predicting market trends and customer behaviour.

Raj Balasundaram, a seasoned executive in the AI sphere, has seen firsthand how AI can transform businesses. The possibilities seem endless, from improving customer engagement through personalized marketing to optimizing business operations with automation.

Dr Shweta Singh, an AI researcher, emphasizes the potential of AI in combating societal injustice. Her work on responsible AI projects, such as protecting children online, underscores the transformative power of AI when it's designed and used ethically.

The Risks and Ethical Considerations

While AI holds tremendous potential, it also brings significant risks and ethical considerations. For instance, the misuse of AI can lead to the creation of deepfakes or the propagation of misinformation. Moreover, AI's ability to mine vast amounts of data can infringe on individual privacy, a concern Helena Quinn, a policy advisor on AI and Data Science, addresses in her role.

Bias is another issue that AI can inadvertently perpetuate. If the data used to train these models contain biases, the decisions made by the AI could be unfairly skewed. Therefore, it is paramount to focus on responsible AI, including Explainable AI (XAI), that can clarify its decision-making process.

Intellectual Property Rights and AI

A less-discussed yet vital aspect of AI is its implications for intellectual property rights. Data, often sourced from the internet, form the backbone of AI training. Balancing the benefits of AI and the rights of content creators is a pressing issue in need of adequate solutions.

The Future of AI in Fintech

The panel was optimistic about the future of AI, anticipating next-generation models that are more conscious about data use, respect intellectual property rights, and incorporate technological solutions like tagging systems for better data management.

AI is here to stay, and its integration in fintech will continue to deepen. However, it is crucial to foster ongoing dialogues about its potential and the ethical considerations it raises. As we chart this new frontier, the guiding principle should be to augment human capabilities, not replace them.

In conclusion, the AI race is not about who wins but how we can leverage this technology to create a more efficient, inclusive, and ethical financial landscape.


The panel discussion covered various aspects of AI, particularly large language models like ChatGPT. The topics include the implications of AI in education, the balance between AI and human roles, the importance of data privacy, ethical considerations, concerns about misuse, and intellectual property rights.


1. AI in Education: AI models like ChatGPT have been effectively used in education. AI can be an efficient tool for self-paced learning and personalized education, providing students with instant feedback and resources.

2. The balance between AI and Humans: AI should be a tool that augments human capabilities rather than replacing them. The human touch is essential in emotional intelligence, creativity, and complex decision-making.

3. Data Privacy: Large language models use enormous amounts of data, raising concerns about personal data use and potential privacy breaches. Even publicly available data can be classified as confidential if the individual can be identified.

4. Ethical Considerations: Using AI and large language models raises significant ethical questions. They must be designed and used responsibly to prevent misuse, avoid bias, and ensure fair decision-making.

5. Misuse of AI: AI can be misused to generate misinformation, deepfakes, and other deceptive content. Having regulations and safeguards to prevent and mitigate these risks is crucial.

6. Intellectual Property Rights: The data used to train these models often comes from the internet, raising questions about intellectual property rights. There needs to be a balance between AI's benefits and respecting the content creators' intellectual property rights.

7. Evolving Technology: Technology continues to evolve rapidly. Ongoing dialogue, exploration, and discussion about AI, its potential, and the ethical considerations it raises are crucial.

8. AI and Art: AI's impact on the field of art is notable, with AI-generated art winning awards. However, it's argued that true artistry involves the artist's philosophy and character, elements that AI cannot replicate.

9. Future of AI: Future models are expected to be more conscious about the data they use for training, ensuring it respects people's rights, including intellectual property rights. There's also anticipation for technological solutions, such as tagging systems, to manage data used in AI training.

April 2023 The Power of Social Networks - Blog by Dr Shu Zhang

The Influence of Social Networks on Financial Markets and Investor Behaviour

The impact of social networks on financial markets has become increasingly significant as investing is, at its core, a social activity. As online social networks transmit information to a broader audience at virtually zero marginal cost, they have the potential to shape economic and political outcomes. This report delves into the role of social networks in disseminating earnings news and the subsequent stock market reactions, revealing both the benefits and unintended consequences for investors.

The Role of Social Networks in Financial Markets

Traditional finance models often ignore the social network dimension, assuming individuals form beliefs and make decisions in a social vacuum. However, social learning can positively impact investor decisions by disseminating valuable information, improving decision-making, and increasing market efficiency. Conversely, social networks can propagate incorrect beliefs, naive trading strategies, information cascades, and biases through social network transmission. The role of social networks in finance is context-specific and warrants further research.

Utilizing Social Media Data for Research

To understand the impact of social networks on financial markets, researchers have used Facebook data as a proxy for the social network of investors in the US. Facebook is the largest social media platform with a representative user base, is convenient and publicly available, and allows for comparison and compatibility with existing literature. Social ties influence various economic outcomes, such as asset allocation choices, dissemination of innovation, and financial decision-making processes.

Impact on Market Reactions to Earnings News

This paper on news diffusion in social networks and stock market reactions focuses on disseminating earnings news and the post-earnings announcement drift anomaly. The study shows significant price reactions, volatility, and volume responses for earnings announced by firms in high centrality locations, with weaker price drift and faster volatility decline in the post-announcement window. The results reveal that, as measured by centrality, social networks play a significant role in facilitating information dissemination and improving price efficiency in response to earnings news.

Investor Behaviour and Trading Activities:

Using stock tweets messages and household trading data, researchers analyse the role of social networks in information dissemination, investor attention, and the influence of social ties on trading activities and outcomes. Stronger social ties lead to faster price adjustments, more efficient price discovery, increased trading volume, and more persistent investor attention. However, they also result in more significant trading losses, primarily due to transaction costs, highlighting the potential for both beneficial and unintended consequences for investors.

Regulation and Future Research:

Given the influence of social networks on stock market reactions, the question of government regulation of social media platforms arises. Regulation is essential but should be done carefully and tailored to specific situations, as there is no one-size-fits-all solution. Future research could explore other social media platforms, news sources, economic outcomes, and policy implications or platform design choices to harness the power of social networks while mitigating potential drawbacks.

Social networks play a crucial role in financial markets, shaping investor behaviour and market reactions to earnings news. Understanding their impact is essential in today's widespread online social media usage. Researchers should continue exploring social networks' role in finance, aiming to harness their power for beneficial purposes while being cautious of potential unintended consequences.


This seminar discusses the impact of social networks on financial markets, focusing on the role of social networks in disseminating earnings news and the subsequent stock market reactions. Utilizing Facebook data and stock tweets messages, the researchers examine the influence of social networks on price efficiency, trading volume, and investor behaviour. The study reveals that social networks can have both beneficial and unintended consequences for investors, improving price efficiency and amplifying behavioural biases. The presenter also addresses questions about the potential need to regulate social media platforms due to their influence on stock market reactions, suggesting that regulation should be done carefully and tailored to specific situations.

Key Takeaways:

1. Social networks play a significant role in financial markets and can influence economic and political outcomes.

2. Researchers utilize Facebook data and stock tweet messages to study social networks' role in information dissemination and investor attention.

3. Social networks can improve price efficiency and facilitate information dissemination but may also amplify behavioural biases and propagate incorrect beliefs.

4. The influence of social networks on decision-making is context-specific and warrants further research.

5. Regulation of social media platforms is essential but should be carefully considered and tailored to individual situations.