FFEA Research Presentations

Time

Title

Abstract

Author

Affiliation

Session No.

Room No 

9:30 am

Do mutual fund investors care about climate transition risk?

President Biden campaigned and came to office on a bold ‘clean energy revolution’ platform, which drastically reversed expectations about US climate policy. We use the 2020 US presidential election as a natural experiment to study if mutual fund investors care about climate transition risk. We find that low-carbon mutual funds attract more inflows (or suffer less outflow) by between 25 and 51 basis points a month than high-carbon funds during the three months after the 2020 election compared to three months before. We also find that low-carbon mutual funds underperform high-carbon funds by about 0.47% to 0.90% a month during the same period. These results support the narrative that Biden’s election significantly increases the market’s concerns about climate transition risk. Investors hedge this risk by investing more in low-carbon funds and receive lower returns. We also observe a clientele effect - the low-carbon fund investors reward funds with lower carbon risk and fossil fuel involvement. In contrast, high-carbon fund investors are indifferent to them.

Parida Sitikantha &
Fang Fei

Clark University, School of Management

1

Unity 500

10:00 am

Unraveling the enigma of decentralization in FinTech: Impact of U.S. Policy rate changes

This study examines the impact of rising interest rates and economic uncertainty on the fintech sector. While traditional financial markets are typically influenced by central bank rates, the same does not hold true for fintech assets. We uncover that this distinction can result in a lack of liquidity within the fintech sector. Our findings demonstrate that, surprisingly, during the recent cycle of tightening central bank rates, the lending rates for Defi actually decreased. This unexpected trend contributed to a decline in fintech growth as it heightened levels of market illiquidity. This situation poses a significant challenge for fintech companies that heavily rely on structured funding products and are highly sensitive to price fluctuations. Moreover, our research also reveals a noteworthy correlation between the central bank's policy rate and the level of illiquidity in the fintech sector.

Dr Johnson Owusu-Amoako

Fayetteville State University

1

Unity 500

10:30 am

Country-Level Corruption and ESG Valuation

This study examines whether country-level corruptions influence the market valuation of firms’ environmental, social, and governance (ESG) performance. Using the global firms, we find that ESG performance has positive impacts on firms’ short and long-term values, and country-level corruptions significantly influence this positive association. Specifically, we find that the positive association between ESG performance and firm value is stronger for the firms operating in low-corruption countries, implying a catalyst role of corruption in ESG valuation. Our findings are robust using different measures of firm value and corruption indices, fixed effect models, and after controlling firm-specific and country-specific factors.

Dr Khawaja Saeed Mamun (Session Chair)

Sacred Heart University

1

Unity 500

9:30 am

Policy Experiments in the Interbank Market

We construct an evidence-based simulation framework that incorporates agent behavior to explain observed variance in transactional patterns among heterogeneous institutional agentsin the interbank market. We apply this simulation framework to conduct macroprudential policy experiments to design strategies of controlling systemic risk and increasing financial system resilience. Keywords: collateralized interbank market; evidence-based policy simulation; heterogeneous institutional agents, macroprudential policy JEL classification: C30, E44, G10, G18, G21, D85

Ms. Jiajia Wu

Northeastern University

2

Unity 400

10:00 am

Liquidity Premium, Liquidity-Adjusted Return and Volatility

We establish innovative measures of liquidity premium Beta on both asset and portfolio levels, and corresponding liquidity-adjusted return and volatility, for selected crypto assets. We develop a liquidity-adjusted ARMA-GARCH/EGARCH representation to model the liquidity-adjusted return for individual assets, and a liquidity-adjusted VECM/VAR-DCC/ADCC structure to model the liquidity-adjusted variance for portfolios. Both models exhibit improved predictability at high liquidity, which enables a liquidity-adjusted mean-variance (LAMV) framework a clear advantage over its traditional mean variance (TMV) counterpart in portfolio performance. Collectively, they extend the return/volatility-based Modern Portfolio Theory (MPT) to a Unified Modern Portfolio Theory (UMPT) with built-in treatments on liquidity risk.

Prof. Qi Deng

Hubei University of Automotive Technology
College of Artificial Intelligence

2

Unity 400

10:30 am

Assessing the Volatility of Green Firms

Environmentally responsible (`green') firms have important asset pricing implications. Whilst green firms' performance has been formally studied in terms of returns and pricing (Bolton and Kacperczyk (2022), Pastor et al. (2022)), far less is known about their volatility. We analyze the volatility of green and brown firms from 2012 to 2021, through the multiple lens of GARCH, machine learning, and historical volatility. The unconditional volatilities of brown and green firms are similar. The forecasting of volatility, however, differs sharply between green and brown firms: it is much harder to forecast green firms' volatility.

Prof. Weijia Peng (Session Chair)

Sacred Heart University

2

Unity 400

9:30 am

Does Bitcoin Still Enhance an Investment Portfolio in a Post-COVID World?

We investigate the ability of bitcoin to enhance investment portfolios of traditional assets. We find evidence of a structural break in the correlation and volatility processes around the onset of the COVID-19 pandemic. The correlations between bitcoin and traditional assets have increased, thereby reducing bitcoin's diversification benefits. As a result prior studies using data from pre-2020 may have inadvertently overestimated the value of bitcoin in improving Sharpe ratios and certainty equivalent returns in the post-pandemic era.

Dr Keener Hughen

Sacred Heart University

3

Unity 520

10:00 am

Financial Instability through the Eyes of the Federal Reserve Bank Supervisors

Recent profound disturbances to the global financial system highlight the need for a systems approach to analyzing financial instability. The latest financial crisis provided many examples of significantly destabilizing dynamic processes affecting the behavior of the financial system that indicate that the fragility of the financial system is structural. Therefore, to understand financial fragility, it is critical to elicit the structure of the financial sector. A common practice in economics is to create theoretical models without the practitioners’ input. However, the professionals who work in the financial sector possess in-depth first-hand knowledge of the system. The objective of this project is to contribute to the effort of constructing a unifying theoretical framework of systemic feedbacks within financial systems. This project builds a shared mental model of the Federal Reserve Bank supervisors that captures their understanding of financial instability. To the best of our knowledge, their views have never been explicitly documented before. This research gives a voice to the expert group outside the academic conversation about financial stability. Keywords: financial fragility; financial instability; bank supervision; The Federal Reserve; mental models; structural debriefing; causal diagrams JEL codes: G01, G18, G28, G32, D85, C91, C93

Dr. Oleg Pavlov

Northeastern University

3

Unity 520

10:30 am

Credit Risk Evaluation for Financial Inclusion using ML based optimization

Machine learning (ML) holds substantial potential for transforming the lending process, specifically by converting soft, judgment-based information into hard, numerical information. This paper comprehensively investigates this potential, as well as the challenges and limitations faced in ML-based lending. ML can render soft information more reliable, improving the pricing of assets and thus addressing issues of credit and financial exclusion. However, the conversion of soft information into hard information involves complex processes that require careful attention, especially with respect to ensuring that the data accurately reflects the borrower's creditworthiness. The paper delves into ML's capabilities in capturing nonlinear relationships between risk indicators and credit risk outcomes, significantly enhancing the credit assessment of borrowers with poor credit history, a task that traditional models often struggle with.While the benefits are immense, ML-based lending is not without its challenges. This research critically examines the limitations and weaknesses associated with ML-driven lending, including the potential risks of financial exclusion, challenges in addressing structural changes, and potential borrower manipulation of indicators. In addition, the paper scrutinizes ethical, data privacy, and consumer protection concerns linked to ML-based credit assessment, highlighting the need for regulatory scrutiny and safeguards.In order to mitigate these challenges, the research recommends the continuous monitoring and selection of input features, ensuring that the dataset is representative and free from biases. The paper also emphasizes the importance of a robust technological infrastructure, high-quality digitized data, and cybersecurity measures. It concludes by discussing the potential of FinTech credit in promoting financial inclusion in emerging economies, further justifying the relevance and importance of ML in lending.Despite certain weaknesses, ML-based lending could transform the financial landscape, offering lower loan rates and improving financial inclusion. However, it is crucial to understand and navigate the potential risks and limitations to ensure sustainable, inclusive, and ethical lending practices. Keywords: Financial Inclusion, Credit risk, Alternative Lending, Machine learning evaluation, Fintech

Mr Aryyama Kumar Jana (Session Chair)

Arizona State University

3

Unity 520

9:30 am

Digital Financing: Filling a Gap for Entrepreneurs Across the Country

Digital Financing: Filling a Gap for Entrepreneurs Across the Country PayPal Public Affairs has published a new white paper that evaluates the impact of digital small business financing on underserved borrowers. Rising inflation and the turmoil in the banking sector are causing a contraction in lending from traditional financial institutions. Our research analyzes PayPal Working Capital and PayPal Business Loans to demonstrate how innovative lending solutions can help to fill that gap for borrowers in underserved areas and in communities that have faced systemic barriers when trying to access capital.

Mr. Paul Disselkoen

PayPal

4

Unity 405

10:00 am

The Optimal Size of Supply Chains with Blockchain-enabled Supply Chain Finance

This research studies the effect of adopting blockchain technology (BCT) in a multi-tier supply chain, especially where small and medium-sized enterprises (SMEs) are subject to capital constraints due to limited access to financial systems with the presence of asymmetric information. Contrary to the previous literature, it is the first attempt to model how BCT adoption affects individual members' optimal decision-making process and the overall effectiveness of the supply chain. We consider a three-tier supply chain and use a game theoretical approach where adopting blockchain technology (BCT) affects two decision-making processes; financing and supply chain management operations and the optimal size of a supply chain. Firstly, within the supply chain financing (SCF) framework, the optimal financing and management decisions regarding information asymmetry for the multi-tier supply chain. Secondly, we use a circular city model where the subsuppliers are located along the city. Blockchain technology (BCT) affects the transparency of transactions, improving the visibility of every transaction stage. We use a supply chain financing model where capital-abundant manufacturers use advance trade credit to suppliers, determining the optimal number of sub-suppliers given the number of loans available. With the study, we find interesting and important results are derived. When information asymmetry is relatively high, adopting BCT technology improves subsuppliers' participation and fewer suppliers within a supply chain since suppliers can benefit from more subsuppliers but face a more competitive market structure.

Prof Sang Hoo Bae

Clark University

4

Unity 405

10:30 am

Global Value Chain for FinTech in the Arctic

This project assesses the implementation of Fintech in countries in the Arctic Council. These countries include the United States, Canada, Sweden, the Russia Federation, the Kingdom of Denmark, Finland, Norway and Iceland. Besides these nations, there are six Arctic Indigenous organizations that hold Permanent Participation status in the Arctic Council. In the wake of technological advancements, banks have incorporated internet applications via which customers can access their services remotely. This trend has been noted to improve economic activity, promote environmental sustainability, and provide convenience. As a result, more people continue to subscribe to these services. FinTech ecosystems encompass efforts to incorporate technology into the finance sectors. This practice often draws benefits like improved decision-making, increased speed, and ease of access. Recommendations: The subject countries should invest in contemporary technologies, pushing them to be better and more capable of supporting operations in the finance sector more effectively. The countries should also focus on strategic implementation, which calls for patience and careful selection of technologies to optimize their FinTech ecosystems. These nations should also deal with building a cohesive and strong legal framework to fight against financial crimes and protect residents’ property. Keywords: Arctic Council, FinTech, Online Payment, Exchange markets, Crowdfunding, Pollution.

Dr. Mikhail Oet (Session Chair)

Northeastern University

4

Unity 405

1:30 pm

The Online Payday Loan Premium

Using data from a subprime credit bureau with nationwide coverage in the United States, we investigate the potential for online technology to lower fixed costs and increase lending efficiency in theexpensive payday loan market. We find that prices for online loans are about 100% APR higher than storefront loans. Customers with both types of loans are much more likely to default on onlineloans. This premium is not explained by loan or customer characteristics, differences in pricing models, or traditional measures of credit risk. While part of the online payday loan premium seemsto be due to default rates that are double that for storefront loans, information asymmetry is a crucial candidate to explain this equilibrium

Dr Filipe Correia

University of Georgia

5

Salisbury 104

2:00 pm

Friend or Foe? Disentangling a Network of Unregulated Informal Lending

We collect a comprehensive dataset on an online informal lending forum to study access to credit, how lenders learn from experience, and the impact of social media connections on loan terms. The loans in this forum are small, short duration, and high cost to the borrowers. Evidence from this community shows these loans are profitable in aggregate, but very risky individually. Despite it’s widespread use and importance for access to liquidity, informal lending is understudied in the literature. It remains unclear what information lenders use to select loans and what determines credit access. We study this marketplace from the point of view of lenders, because for borrowers to have access to credit, lenders must be willing to supply it. Our results indicate lenders with more experience have better loan outcomes and provide more lenient loan terms. Additionally, requests are more likely to be converted into a loan when borrowers and lenders are more connected online.

Mr Anthony Waikel

University of Georgia

5

Salisbury 104

2:30 pm

From Whales to Waves: Surfing the Social Media Sentiment Swells in Cryptocurrency Markets

This study delves into the complex relationships between cryptocurrency dynamics and investor sentiment, employing time-varying granger causality and asymmetric TVP-VAR frequency connectedness methodology. To refine our examination of social media sentiment, we create custom sentiment analysis algorithms and establish a dedicated cryptocurrency sentiment lexicon. This enables a nuanced analysis of textual discussions related to the cryptocurrency domain. Empirical analyses underscore a distinct and evolving link between sentiment and cryptocurrency behaviors. Moreover, fluctuations in shock transmissions correlate with significant market activities, particularly those of major stakeholders or 'whales'. The results suggest that, consistently across short and long-term perspectives, market sentiment is predominantly influenced by cryptocurrency volatility.

Ms. Suwan Long/Prof. Brian Lucey (Session Chair)

University of Cambridge

5

Salisbury 104

1:30 pm

Executives vs. Chatbots: Unmasking Insights Through Human-AI Differences

A significant portion of information shared in earnings calls is conveyed through verbal communication by corporate managers. However, quantifying the extent of new information provided by managers poses challenges due to the unstructured nature of human language and the difficulty in gauging the market’s existing knowledge. In this study, we introduce a novel measure of information content (Human-AI Differences, HAID) by exploiting the discrepancy between answers to questions at earnings calls provided by corporate executives and those given by several context-preserving Large Language Models (LLM) such as ChatGPT, Google Bard, and an open source LLM. HAID strongly predicts stock liquidity, abnormal returns, number of analysts’ forecast revisions, analyst forecast accuracy following these calls, and propensity of managers to provide management guidance, consistent with HAID capturing new information conveyed by managers. Overall, our results highlight the importance of using LLM as a tool to help investors unveil the veiled – penetrating the information layers and unearthing hidden insights.

Dr. Jianqiu Bai

Northeastern University

6

Salisbury 105

2:00 pm

The Distribution Builder - A tool for financial decision making in the FinTech era

The era of FinTech heralds personalized financial decision making through tools such as robo-advising. Alas, the input of personal preferences needed to personalize decision making is difficult and existing methods lack robustness. Sharpe, Goldstein, Blythe and Johnson introduced with the distribution builder a powerful tool to directly solicit user preferences on the outcomes of investments that can be used as base from decision making. In this presentation we explain how the methodology of the distribution builder can be leveraged successfully from the original setting - portfolio optimization in complete markets - to a wide array of other situations: consumption, incomplete markets and the timing of asset sales. 

Dr Stephan Strum

WPI

6

Salisbury 105

2:30 pm

Unveiling the Nexus: Carbon Market Activities, Climate Technology Advancements, and the Green Bond Market

Climate Technology disrupts conventional business models, particularly those aligned with the pursuit of sustainable development goals. Anecdotal evidence suggests that the convergence of climate technology and carbon-offset trading plays a pivotal role in amplifying sustainability efforts. Therefore, it is imperative to assess the interconnectedness of these factors in the context of innovation in green financial markets. Consequently, this study investigates the intricate relationship among carbon credit offsets, advancements in climate technology, and their impact on the U.S. green bond market. Through our investigation, we illuminate how emissions trading systems exert influence over the trajectory of climate technology, thereby catalyzing the expansion of the green bond market. Remarkably, a discernible correlation emerged, revealing that heightened activity in emissions trading corresponded to a proportional increase in climate technology advancements. The robustness of this correlation is underscored by the coefficient estimates, which range 2.28–3.58. Further, the profound impact of climate technology has emerged as a pivotal driving force behind observed growth in the green bond market. This influence is a key contributor to overall market expansion.

Dr. Daniel N. Treku (Session Chair)

WPI

6

Salisbury 105

1:30 pm

How Crypto Trading Works and Potentially Takes Over Traditional Markets

This paper explains the mechanics of fully automated market making known to be prevalent in crypto trading and contrasts those principles with operation of traditional markets. This paper further presents a framework for quantifying automated market-making (AMM) from available trading data. Using concrete examples, the study quantifies AMM curves for various financial instruments, including traditional equities. We show that AMMs are possibly already widely used in the traditional markets and that the modern microstructure may have changed as a result.

Ms. Irene Aldridge

Cornell University, Cambridge University

7

Unity 405

2:00 pm

An Agent-Based Model of Country Performance and Competition in Arctic FinTech

This paper proposes an Agent-Based Model (ABM) study of the country actors competing in the Arctic FinTech market. We investigate the possible motivations and risks associated with the emergence of the FinTech sector, explore the game-theoretic problems manifested in the FinTech market, and study the mechanisms for the emergence of country alliances. We simulate the behavior of the Arctic Council’s eight member countries and 13 observer countries in their investment decisions and deployment of resources in the FinTech sector across two investment horizons: short-term (twenty years) and long-term (forty years).The short-term simulation experiments provide insights into the patterns of the countries’ investment strategies, such as market over-saturation or under-saturation, and help identify potential risks associated with FinTech firms' emergence. We observed the emergence of four regional alliances. Country actors from the Nordic region invested more heavily in InsurTech and RegTech, with a significant shift towards sustainable investment choices in response to the changing climate. In contrast, actors from the Russian region invested more heavily in WealthTech and Real EstateTech, focusing on energy and resource extraction investments. The actors from the American region, influenced by the changing climate, shifted their investment focus towards Blockchain and Cryptocurrency, driven by the need for transparent and secure transactions. Actors from the Indigenous Arctic region invested more heavily in Online Lending and Digital Payments, emphasizing financial inclusion and accessibility for their communities. However, in the long term, alliances shifted from four to two. The countries' geopolitical concerns, wealth, and energy resources exploitation significantly moderated the alliance memberships. This shift can be attributed to the projected effects of climate change and energy on the Arctic region, leading to a realignment of geopolitical interests. The two emerging trading blocks—the “Green Alliance” and the “Energy Exporters"—specialized in different FinTech sectors. The “Green Alliance” concentrated on Digital Payments, RegTech, and WealthTech, while the “Energy Exporters” concentrated on Blockchain, Cryptocurrency, and Real EstateTech. Throughout the long-term horizon, the "Green Alliance" expanded its specialization to InsurTech, while the "Energy Exporters" retained its focus on Blockchain and Cryptocurrency.Our study provides insights into the range of outcomes of the countries' short-term and long-term investment strategies in the FinTech market. It helps identify potential risks associated with global competition. The findings can inform policymakers and regulators about the strategic implications of FinTech's emergence and help manage potential financial and geopolitical stability risks. Keywords: agent-based modeling, data envelopment analysis, FinTech, Arctic, country strategies, trade alliances, migration, comparative advantage. JEL codes: C61, C63, O11, O16, O41, O43, O47, F14, F15, F22, F62, F63

Dr. Mikhail Oet

Northeastern University

7

Unity 405

2:30 pm

The Impact of Fractional Trading on Order Book Dynamics.

We study the impact of fractional trading on the price levels and order book dynamics observed in stock markets. Fractional trading has recently been introduced on multiple trading platforms in equities markets, allowing individuals to buy a fraction of a share of stocks or ETFs (exchange-traded funds). Fractional trading, along with direct and easier access to the markets through commission-less trading apps, can potentially modify the risk appetite of non-professional investors (who are generally myopic and risk-averse) and create opportunities for portfolio creation and diversification, hence increasing the demand for stocks. It can also impact non-professional investors’ investment behavior, price levels, and market volatility. Using Nasdaq data feed at a minute frequency, we show that there has been a significant increase in the slope of the price-volume structure. In some cases, there is an increase in the number of steps required to place a limit order after the introduction of fractional trading. Our results suggest that increased demand for stocks led by easiness to trade stocks and fractional trading impacts the order book’s price formation process and price-volume structure.

Dr. Janhavi Shankar Tripathi (Session Chair)

St. Bonaventure University and Fordham University

7

Unity 405

1:30 pm

Foreign Signal Radar

We apply machine learning algorithms to detect signals from foreign markets that predict daily U.S. stock returns. Over 100,000 models are built to capture the stock-specific time-varying relationships between foreign signals and stock returns. Foreign signals exhibit return predictability for 56% of the S&P 500 firms. The return predictability is widespread across multinational and domestic firms and firms in different industries. A portfolio formed on return forecasts by foreign signals generates abnormal returns of 5.77 basis points per day. Foreign signals identified by algorithms are economically meaningful. Signals from foreign markets with more stable business environments and a higher volume of international trade with the U.S. carry greater importance. U.S. stock prices take at least four weeks to incorporate the information in foreign signals. The speed is much slower for signals from non-English speaking and emerging markets and among stocks with lower foreign institutional ownership.

Dr. Wei Jiao

Rutgers

8

Unity 400

2:00 pm

Predictive Patentomics: Forecasting Innovation Success and Valuation with ChatGPT

Analysis of innovation has been fundamentally limited by conventional approaches to broad, structural variables. This paper pushes the boundaries, taking an LLM approach to patent analysis with the groundbreaking ChatGPT technology. OpenAI’s state-of-the-art textual embedding accesses complex information about the quality and impact of each invention to power deep learning predictive models. The nuanced embedding drives a 24% incremental improvement in R-squared predicting patent value and clearly isolates the worst and best applications. These models enable a revision of the contemporary Kogan, Papanikolaou, Seru, and Stoffman (2017) valuation of patents by a median deviation of 1.5 times, accounting for potential institutional predictions. Furthermore, the market fails to incorporate timely information about applications; a long-short portfolio based on predicted acceptance rates achieves significant abnormal returns of 3.3% annually. The models provide an opportunity to revolutionize startup and small-firm corporate policy vis-à-vis patenting.

Mr Stephen Yang

Pace Academy

8

Unity 400

2:30 pm

Fund Investors' Response to Climate Activism

We use the first ‘global climate strike’ on March 15, 2019 (around 1.4 million people participated, according to the Fridays for Future movement) as a natural experiment to study if climate activism matters to mutual fund investors. We find that low-carbon funds attract more (or lose less) investments by 0.46% a month than high-carbon funds during the first two months after the climate strike in March 2019 than before. This effect fades over time, and we do not find a strong response to another series of international climate strikes called in the last week of September 2019 under the name “Global Week for Future.” Our results support the claim that fund investors care about climate transition risks. The unanticipated strike in March 2019 increases the salience of climate risks, and investors respond to it by investing more in (or redeeming less from) low-carbon funds. The second series of climate strikes in September 2019 does not come as a surprise and does not lead to a significant response from the investors. We also find a similar but weaker effect for the sustainable funds denoted by higher Morningstar globe ratings around the strike in March 2019.

Parida Sitikantha &
Fang Fei (Session Chairs)

Clark University, School of Management

8

Unity 400