LIN WILLIAM CONG
RESEARCH INTEREST:
AI & Big Data, Applied Theory, China's Economy, Digital Economy, Entrepreneurship, Financial
Economics, FinTech, Information Economics, Innovation/Science/Technology, Sustainability
TOPICS:
AI for Finance & the Economics of AI, Digital Economy, and Financial Technology
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Data Economy and Digital Platforms
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Data as production factor, crowd-based mechanisms, digital payments, business digitization, FinTech ecosystems, financial innovation, learning in digital networks, influencer/creator economy, interactions of technology with development/education/inequality, digital entrepreneurship, etc.
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Distributed Systems and Web3
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Blockchains, (central bank) digital currencies, cryptocurrencies, Decentralized Finance (DeFi), entrepreneurial finance, FinTech regulation, forensic finance, frauds & manipulation, interoperability, competition/IO in Web3 , oracles, secure multi-party computation, tokenomics, etc.
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AI and Big Data: Applications and Implications
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AI-human interaction, alignment & behavior, reinforcement learning, financial marchine learning, heterogeneity modeling, portfolio management, tree-based interpretable AI, language models, (social) science of science/innovation/technology, etc.
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Other Topics in Economics, Finance, and Management
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Asset Pricing, Corporate Finance, Financial Infrastructure & Innovation
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China's Economy, Entrepreneurship, Policy Intervention/Uncertainty, & Sustainability
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Applied Theory, Contracting & Mechanism Design, and Information Economics​​​​
AI, Digital Economy, & FinTech |||| Data Economy & Digital Platforms
Production, Trade, and Cross-Border Data Flows
(with Qing Chang, Liyong Wang, and Longtian Zhang) | 2023, Working Paper
SSRN | Data Economy | General Equilibrium | Growth | International Trade
Small and Medium Enterprises Amidst the Pandemic and Reopening: Digital Edge and Transformation (with Xiaohan Yang and Xiaobo Zhang) | 2024, Management Science, Vol 70/7, (2024), pp. 4564-4582
SSRN | PDF | COVID-19 | Digital Economy | E-Commerce | SMEs
We document significant economic benefits of digitization in increasing SMEs’ resilience against large shocks such as the COVID-19 pandemic, as seen through mitigated demand decline, sustainable cash flow, ability to quickly reopen, and positive outlook for growth. The pandemic also accelerated digitization of firms which does not revert long after the reopening..
Rise of Factor Investing: Security Design and Asset Pricing Implications
(with Shiyang Huang and Douglas Xu) | 2020, Journal of Finance, Revise and Resubmit.
SSRN | Asset Pricing | Financial Innovation | Market Microstructure | Security Design
Conceptually, passive investing is not really passive because the composition of underlying assets and the exposure to the passive indices are both active decisions. We discuss the optimal design and asset pricing implications of composite securities that include index mutual funds, smart beta products, and ETFs. Theory predictions are corroborated by empirical dobservations.
FinTech Platforms and Asymmetric Network Effects: Theory and Evidence from Marketplace Lending
(with Ke Tang, Danxia Xie, and Weiyi Zhao) | 2020, Working Paper.
SSRN | FinTech | Marketplace Lending | Network Effects | Platforms
We theoretically and empirically highlight the distinguishing features of FinTech platforms from those of non-financial platforms. We find platform-level cross-side network effects to play a crucial role in platform survival.
Antitrust and User Union in the Era of Digital Platforms and Big Data
(with Simon Mayer) | 2022, Working Paper.
SSRN | Data Economy | FinTech | Industrial Organization | Platforms
xxx
AI, Digital Economy, & FinTech |||| Distributed Systems & Web3 Economics
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Decentralized Mining in Centralized Pools
(with Zhiguo He and Jiasun Li) | 2021, Review of Financial Studies, 34(3), pp. 1191-1235.
SSRN | PDF | BLockchain | Bitcoin | Sustainability | IO
Contrary to the industry heuristics, mining pools as risk-sharing contracts do not lead to over-concentration. It has to be vertical integration that leads to centralization. That said, mining pools contributed as much as, if not more than, cryptocurrency prices, to the increase in the electricity devoted to cryptocurrency mining, augmenting environmental harms.
Token-based Platform Finance
(with Ye Li and Neng Wang) | 2022, Journal of Financial Economics, 144(3), pp. 972-991.
SSRN | PDF | Tokenomics | Platforms | Corporate Finance | Monetary Policy
In a dynamic tokenomics framework with endogenous adoption, token pricing, and supply policy, entrepreneurs' optimal "monetary" supply follows a double-threshold policy, issuing more token dividend and compensations to reward themselves and stimulate economic activities when token supply - platform productivity ratio is low and burning tokens when it is high.
Blockchain Architecture for Auditing Automation and Trust-building in Public Markets
(with Sean Cao, Meng Han, Qixuan Hou, and Baozhong Yang) | 2020, IEEE Computer, 53(7), pp. 20-28.
arXiv | PDF | Accounting | Blockchain | Privacy-Preserving MPC
This is the CS implementation of the theoretical framework introduced in Cao, Cong, and Yang (2018). We build the architecture for auditing automation and financial disclosure using permissioned blockchains and secure multi-party computation.
Tax-Loss Harvesting with Cryptocurrencies
(with Wayne Landsman, Edward Maydew, & Daniel Rabetti) | 2023, Journal of Accounting and Economics, 76(2-3)
SSRN | PDF | Cryptocurrency | NFTs | Taxation
When crypto taxation tightens, traders wash trade to take advantage of the lack of coordination between tax authorities and cryptocurrency trading regulatory bodies for tax-loss harvesting.
Crypto Wash Trading
(with Xi Li, Ke Tang, and Yang Yang) | 2023, Management Science, Lead Article, Vol 69/11, pp. 6427-6454.
SSRN | PDF | Big Data | Cryptocurrency | Forensic Finance | Regulation
We provide the earliest rigorous and systematic detection and quantification of wash trading on centralized crypto exchanges. Unregulated exchanges on average fake over 70% of transaction volume, but regulation effectives prevents such manipulation.
Advances in Blockchain and Crypto Economics
(with Bruno Biais, Agostino Capponi, Vishal Gaur, and Kay Giesecke) | 2023, Management Science, Featured Article, Vol 69/11, pp. 6417-6426
PDF | Blockchain | Cryptography | DeFi | FinTech
Editorial for the first special issue on the topic at a top journal summarizing recent advances in the literature, highlighting important developments and issues in research and in practice, and proposing promising directions for future research.
Scaling Smart Contracts via Layer-2 Technologies: Some Empirical Evidence
(with Xiang Hui, Catherine Tucker, and Luofeng Zhou) | 2023, Management Science, Vol 69/12, pp. 7151-7882, iii-iv.
SSRN | PDF | Information Economics | Blockchain | Smart Contract Scaling
We provide initial empirical evidence on the economic implications of layer-2 solution for scaling smart contracts. In a parallel-system experiment in a dominant oracle network, we observe significantly reduced operating costs and greater decentralization and improved data accuracy when the system moved from on-chain to P2P networks.
Opportunities and Challenges Associated with the Development of FinTech and Central Bank Digital Currency
(with Stijn Claessens, Fariborz Moshirian, and Cyn-Young Park) | 2024, Journal of Financial Stability, Vol 73, 101280
PDF | CBDC | Crytpocurrencies | Digital Economics | FinTech
Editorial article for the special issue on the topic summarizing recent advances in the literature, highlighting important developments and issues in research and in practice, and proposing promising directions for future research.
Distributed Ledgers and Secure Multi-Party Computation for
Auditing and Financial Reporting
(with Sean Cao and Baozhong Yang) | 2024, Management Science, Forthcoming.
SSRN | Accounting | Blockchain | FinTech | Privacy-Preserving MPC
We analyze firm misreporting, auditor monitoring and competition, and regulatory policy in a setting with endogenous adoption of permissioned/federated blockchains and secure MPC. Multiple equilibria can ensue. Private incentives for firms and first-mover advantages for auditors can create inefficient under-/ partial adoption that favors larger auditors.
An Anatomy of Crypto-Enabled Cybercrimes
(with Campbell Harvey, Daniel Rabetti, and Zong-Yu Wu) | 2024, Management Science, Forthcoming.
SSRN | Blockchain | Cryptocurrency | Cybersecurity | Forensic Finance
Assembling a diverse set of public on-chain and off-chain, proprietary and hand-collected data, we present an initial anatomy of crypto-enabled cybercrimes, highlighting relevant economic issues and proposing areas for future research. We identify ransomware as a most dominant crypto-enabled cybercrime, entails criminal gangs and business-like operations.
Financial and Information Integration Through Oracle Networks
(with Eswar Prasad and Daniel Rabetti) | 2023, Review of Financial Studies, R&R.
SSRN | DeFi | Information Economics | International Economics | Interoperability
Oracle integration enables interoperability and information exchanges between blockchain networks with off-chain economies. Drawing parallels with international integration, and as one of the first empirical studies on oracle networks, our paper reveals symbiotic gains and risk-sharing improvements.
The Coming Battle of Digital Currencies (with Simon Mayer) | 2021, Working Paper.
SSRN | CBDCs | Cryptocurrency | Stablecoin | Tokenomics
We analyze how fiat, cryptocurrencies, private payment systems, and CBDCs in an international and dynamic setting. We find that countries with dominant currencies are less incentivized to introduce CBDC relative to the ones with slightly weaker currencies, whereas small, open economies are better off adopting cryptocurrencies.
Factor Pricing, Categorization, and Market Segmentation of Crypto Assets
(with Andrew Karolyi, Ke Tang, & Weiyi Zhao) | 2021, Working Paper.
SSRN | Asset Pricing | Cryptocurrencies | International Finance
Assembling the most comprehensive cryptocurrency data to date, we construct a 5-factor model for pricing the returns of crypto assets. We systematically categorize crypto-tokens based on their economic functions and then leverage on international finance models to show significant segmentation across the categories, with "local" factor pricing models performing the best.
Inclusion and Democratization Through Web3 and DeFi? Initial Evidence from the Ethereum Ecosystem (with Ke Tang, Yanxin Wang, and Xi Zhao) | 2022, Working Paper. SSRN | FinTech | Blockchains | DeFi | Digital Economy
We offer the first systematic documentation of income, wealth, and transaction distributions in the Ethereum Ecosystem. The fixed component in fees disadvantages small players but recent changes such as the EIP-1559 and airdrop programs as redistributive monetary policies turn out to make the system more equal and democratic.
The Tokenomics of Staking (with Zhiheng He and Ke Tang) | 2023, Working Paper.
SSRN | Asset Pricing | Blockchain | Cryptocurrency | International Finance
We incorporate tokens' platform consumption, investment speculation, and staking functions in a dynamic setting with potentially heterogeneous agents and stochastic aggregate shocks, for which we apply mean-field game and the master equation to solve. We identify staking ratio as the key state variable for predicting asset returns. Empirical data corroborate our model predictions.
Inflation Expectation and Cryptocurrency Investment
(with Pulak Ghosh, Jiasun Li, and Qihong Ruan) | 2023, Working Paper.
SSRN | Cryptocurrency | Household Finance | Inflation Expectation
We provide empirical evidence of households adopting cryptocurrencies for inflation hedging using proprietary data from a dominant crypto exchange in India and the country’s Household Inflation Expectations Inflation expectations prompt more new investors, particularly male investors, to begin purchasing cryptocurrencies. The effects are concentrated in Bitcoin and Tether.
AI, Digital Economy, & FinTech |||| AI and Big Data: Applications and Implications
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Deep Sequence Modeling: Development and Applications in Asset Pricing
(with Ke Tang, Jingyuan Wang, and Yang Zhang) | 2021, Journal of Financial Data Science, Vol 3/1, pp 28-42.
​SSRN | AI | Asset Pricing | Time Series Econometrics | Machine Learning
Textual Factors: A Scalable, Interpretable, and Data-driven Approach to Analyzing Unstructured Information
(with Tengyuan Liang, Xiao Zhang, and Wu Zhu) | 2024, Management Science, Minor Revision.
SSRN | Big Data | Econometrics | Machine Learning | Textual Analysis
AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI (with Ke Tang, Jingyuan Wang, and Yang Zhang) | 2020, Working Paper. ​
SSRN | AI | Asset Pricing | Portfolio Theory | Reinforcement Learning
We build the first "large" model in Finance using Transformer encoder in the time series and attention-based deep learning on the cross section of investible assets. Reinforcement learning allows optimizing flexible portfolio objectives via direct construction while handling financial big data. We demonstrate the efficacy of the framework on U.S. equities with various robustness tests and interpretation exercises.
AlphaManager: A Data-Driven-Robust-Control Approach to Corporate Finance
(with Murillo Campello and Luofeng Zhou) | 2021, Working Paper.
SSRN | AI | Corporate Finance | Reinforcement Learning | Robust Control
Corporate decision-making is high-dimensional, non-linear stochastic control under managerial learning and dynamic interactions with the economic environment. We build a "world model" of the corporate environment using robust control techniques and deep learning, while deriving optimal managerial policies using RL The resulting AlphaManager complements reduced-form models and structural estimations to explain and predict firm outcomes and improve managerial decision-making.
Growing Panel Trees to Harvest Basis Portfolios and Pricing Kernels
(with Gavin Feng, Jingyu He, & Xin He) | 2023, Journal of Financial Economics, R&R.
SSRN | Macine Learning | AI | Asset Pricing
We introduce a new class of tree-based AI models to generalize security sorting for constructing effective test assets and latent asset pricing factors. Our Panel Trees achieve comparable performance as deep learning models but are more interpretable.
Sparse Modeling Under Grouped Heterogeneity with an Application to Asset Pricing (with Gavin Feng, Jingyu He, & Junye Li) | 2023, Working Paper.
SSRN | Asset Pricing | Heterogeneity Modeling | Interpretable AI | Panel Data
We introduce Bayesian Clustering Model (BCM) to model grouped heterogeneity in a data-driven panel tree framework. We derive analytical marginal likelihoods to incorporate economic guidance, address parameter/model uncertainties, and prevent overfitting. We apply BCM to estimating uncommon- factor-asset-pricing models for asset clusters and macroeconomic regimes.
Mosaics of Predictability
(with Gavin Feng, Jingyu He, and Yuanzhi Wang) | 2024, Working Paper.
SSRN | AI | Asset Pricing | Econometrics | Heterogeneity Modeling
We apply panel tree to partition the panel of asset-return observations by return predictability, using high-dimensional asset characteristics and aggregate time-series predictors. Some characteristics-managed and/or macro-based asset clusters are more predictable. We empirically establish that less predictability leads to lower trading profits.
An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States
(with Ke Tang, Bing Wang, and Jingyuan Wang) | 2021, Working Paper. ​
SSRN | AI | COVID-19 | Dynamic Systems | Epidemiology
We build a deep-learning-based framework integrating the classical SEIR epidemiology model with forecast modules of infection, community mobility, and unemployment, allowing endogenous responses to economic incentives and health risks. Long-term effective reproduction number of COVID-19 equilibrates around one and reopening schools and workplaces appear to be the most effective. We also document that the public health impact of BLM is negligible in the data.
Financial Economics |||| Corporate Finance, Policy Interventions, Sustainability
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Policy Uncertainty and Innovation: Evidence from IPO Interventions in China
(with Sabrina Howell) | 2021, Management Science, Finance Best Paper, Vol 67/11, pp. 7238-7261.
SSRN | PDF | China | Corporate Finance | Innovation | Policy Intervention
We show that IPO-suspension-induced delay reduces corporate innovation both during the delay and for years after listing. Our findings imply that corporate innovation is cumulative and is negatively affected by policy uncertainty. Predictable, well-functioning IPO markets are important for firm value creation.
Token-based Platform Finance
(with Ye Li and Neng Wang) | 2022, Journal of Financial Economics, 144(3), pp. 972-991.
SSRN | PDF | Tokenomics | Platforms | Corporate Finance | Monetary Policy
In a dynamic tokenomics framework with endogenous adoption, token pricing, and supply policy, entrepreneurs' optimal "monetary" supply follows a double-threshold policy, issuing more token dividend and compensations to reward themselves and stimulate economic activities when token supply - platform productivity ratio is low and burning tokens when it is high.
AlphaManager: A Data-Driven-Robust-Control Approach to Corporate Finance
(with Murillo Campello and Luofeng Zhou) | 2021, Working Paper.
SSRN | AI | Corporate Finance | Reinforcement Learning | Robust Control
Corporate decision-making is high-dimensional, non-linear stochastic control under managerial learning and dynamic interactions with the economic environment. We build a "world model" of the corporate environment using robust control techniques and deep learning, while deriving optimal managerial policies using RL The resulting AlphaManager complements reduced-form models and structural estimations to explain and predict firm outcomes and improve managerial decision-making.
Pricing the Priceless: The Cost of Biodiversity Conservation
(with Fukang Chen, Minhao Chen, Haoyu Gao, and Jacopo Ponticelli) | 2024, Working Paper.
SSRN | Biodiversity | Bond Market | China | Policy Intervention
Policy intervention in China for biodiversity conservation reveals that while improving local biodiversity, its associated costs raise local governments' cost of capital as investors focus on near-term financial returns.
Financial Economics |||| Asset Pricing, Machine Learning, and Investment
Tokenomics: Dynamic Adoption and Valuation
(with Ye Li and Neng Wang) | 2021, Review of Financial Studies, Editor's Choice, 34(3), pp. 1105-1155​SSRN | PDF | Tokenomics | Blockchain | Platforms | Asset Pricing
We provide one of the earliest dynamic asset pricing framework for cryptocurrencies and tokens, recognizing their hybrid nature as digital money and investible assets. Tokens can accelerate platform adoption and stabilizes the userbase, but endogenous user adoption amplifies token return volatility.
Deep Sequence Modeling: Development and Applications in Asset Pricing
(with Ke Tang, Jingyuan Wang, and Yang Zhang) | 2021, Journal of Financial Data Science, Vol 3/1, pp 28-42.
​SSRN | PDF | AI | Asset Pricing | Time Series Econometrics | Machine Learning
AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI (with Ke Tang, Jingyuan Wang, and Yang Zhang) | 2020, Working Paper. ​
SSRN | AI | Asset Pricing | Portfolio Theory | Reinforcement Learning
We build the first "large" model in Finance using Transformer encoder in the time series and attention-based deep learning on the cross section of investible assets. Reinforcement learning allows optimizing flexible portfolio objectives via direct construction while handling financial big data. We demonstrate the efficacy of the framework on U.S. equities with various robustness tests and interpretation exercises.
Growing Panel Trees to Harvest Basis Portfolios and Pricing Kernels
(with Gavin Feng, Jingyu He, & Xin He) | 2023, Journal of Financial Economics, R&R.
SSRN | Macine Learning | AI | Asset Pricing
We introduce a new class of tree-based AI models to generalize security sorting for constructing effective test assets and latent asset pricing factors. Our Panel Trees achieve comparable performance as deep learning models but are more interpretable.
Sparse Modeling Under Grouped Heterogeneity with an Application to Asset Pricing (with Gavin Feng, Jingyu He, & Junye Li) | 2023, Working Paper.
SSRN | Asset Pricing | Heterogeneity Modeling | Interpretable AI | Panel Data
We introduce Bayesian Clustering Model (BCM) to model grouped heterogeneity in a data-driven panel tree framework. We derive analytical marginal likelihoods to incorporate economic guidance, address parameter/model uncertainties, and prevent overfitting. We apply BCM to estimating uncommon- factor-asset-pricing models for asset clusters and macroeconomic regimes.
Mosaics of Predictability
(with Gavin Feng, Jingyu He, and Yuanzhi Wang) | 2024, Working Paper.
SSRN | AI | Asset Pricing | Econometrics | Heterogeneity Modeling
We apply panel tree to partition the panel of asset-return observations by return predictability, using high-dimensional asset characteristics and aggregate time-series predictors. Some characteristics-managed and/or macro-based asset clusters are more predictable. We empirically establish that less predictability leads to lower trading profits.
Factor Pricing, Categorization, and Market Segmentation of Crypto Assets
(with Andrew Karolyi, Ke Tang, & Weiyi Zhao) | 2021, Working Paper.
SSRN | Asset Pricing | Cryptocurrencies | International Finance
Assembling the most comprehensive cryptocurrency data to date, we construct a 5-factor model for pricing the returns of crypto assets. We systematically categorize crypto-tokens based on their economic functions and then leverage on international finance models to show significant segmentation across the categories, with "local" factor pricing models performing the best.
The Tokenomics of Staking (with Zhiheng He and Ke Tang) | 2023, Working Paper. SSRN | Asset Pricing | Blockchain | Cryptocurrency | International Finance
We incorporate tokens' platform consumption, investment speculation, and staking functions in a dynamic setting with potentially heterogeneous agents and stochastic aggregate shocks, for which we apply mean-field game and the master equation to solve. We identify staking ratio as the key state variable for predicting asset returns. Empirical data corroborate our model predictions.
Applied Economic Theory ||||
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Influencer Marketing and Product Competition
(with Siguang Li) | 2024, Journal of Economic Theory, Vol 220, Sept., pp. 105867.
SSRN | Digital Economics | Industrial Organization | Marketing | Platforms
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Information Economics |||| Financial Reporting, Information Design, Learning/Info Aggregation
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Information Cascades and Threshold Implementation: Theory and An Application to Crowdfunding (with Yizhou Xiao) | 2024, Journal of Finance, Vol 79/1, pp. 579-629. ​
SSRN | PDF | Crowdfunding | Digital Platforms | Entrepreneurial Finance | Learning
We incorporate all-or-nothing thresholds in a canonical model of information cascades. Uni-directional cascades ensue without herding on rejections. Proposal feasibility, project selection, and information aggregation all improve, even when agents can wait. Project implementation and information aggregation achieve efficiency in the large-crowd limit.
Firm Disclosure Under Relationship Lending: Theory and Evidence from PPP Loans
(with Daniel Rabetti) | 2023, Working Paper.
SSRN | Accounting | Disclosure | Intervention
We derive a novel double-threshold disclosure strategy under which a manager discloses sufficiently good or sufficiently bad news in the unique equilibrium. We bring the insights to data by assessing the Paycheck Protection Program, and document early disclosure of bailout loans for firms in longer and more intense relationship lending, which corroborates model predictions.
Cournot Competition, Informational Feedback, and Real Efficiency
(with Xiaohong Huang, Siguang Li, and Jian Ni) | 2024, Working Paper.
SSRN | Feedback | Industrial Organization | Market Microstructure | Merger & Acquisition
Although intensified competition can decrease market concentration in production, it reduces the value of proprietary information for speculators and discourages information production and price discovery in financial markets. When information reflected in stock prices is sufficiently valuable for production decisions, competition can harm both consumer welfare and real efficiency.