Tong Wang

Research

My research focuses on Digital Platforms and Fintech using economics theory and empirical methods. I leverage Game Theory and Machine Learning to study platform dynamics and financial innovations.

Publications

  1. Xintong Han, Zimin Liu, and Tong Wang. “Nonlinear pricing in multidimensional context: An empirical analysis of energy consumption.” International Journal of Industrial Organization 91 (2023): 103034.
    Abstract

    Modern business practices frequently employ a blend of pricing strategies to segment markets effectively. As a result, consumers may encounter pricing schedules that are nonlinear and multidimensional. This paper presents a structural approach for estimating multidimensional nonlinear pricing models involving multiple decision variables in an energy market. Using a unique, rich panel dataset of Chinese household electricity consumption, we structurally estimate consumer preferences under the influence of an Increasing Block Price (IBP) and a Time-of-Use (ToU) system. Our structural approach allows us to distinguish and evaluate household-level price elasticities of demand, presenting a novel explanation for consumer's feedback on marginal price changes. Through model-based simulations, we demonstrate that a 1% increase in price corresponds to a 0.7% reduction in total electricity demand. However, our analysis indicates that practical opportunities for optimization within multi-dimensional pricing systems are limited. Our findings offer distinct insights into the complex interplay between intricate pricing structures and energy consumption behavior, thereby providing valuable guidance for policymakers and regulators.

  2. Renhe Wang, Tong Wang, Zhiyong Qian, and Shulan Hu. “A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast.” Finance Research Letters 58 (2023): 104525.
    Abstract

    We introduce an efficient Markov Chain Monte Carlo sampler in precision-based algorithms for the estimation of the Random Switching Exponential Smoothing model, a versatile forecasting mechanism for time series data characterized with changing trends. Through a series of simulation experiments, RC-MCMC exhibits superior parameter estimation accuracy, particularly for datasets featuring low persistence trends. Furthermore, an empirical evaluation using the Bank for International Settlements' quarterly time series data on the non-financial sector's total credit relative to GDP validates the findings. The out-of-sample results indicate that the proposed approach outperforms its counterparts in estimating and forecasting accuracy for trending time series data.

  3. Xintong Han, Yushen Li, and Tong Wang. “Peer recognition, badge policies, and content contribution: An empirical study.” Journal of Economic Behavior & Organization 214 (2023): 691–707.
  4. Tong Wang, Sheng Zhao, and Mengqiu Zhou. “Does soft information in expert ratings curb information asymmetry? Evidence from crowdfunding and early transaction phases of Initial Coin Offerings.” Journal of International Financial Markets, Institutions and Money 81 (2022): 101661.
    Abstract

    This paper examines the role of soft information in reducing information asymmetry in ICO markets. Expert ratings normally contain two types of information, hard information, a summary of public, numerically-measured information, and soft information, containing expert insights, rumours and private conversations. Taking advantage of the special dual-rating system of a leading ICO rating platform, we successfully isolate soft information from expert ratings. We find that soft information reduces information asymmetry between ICO management teams and investors, measured by the amount of raised funds in the crowdfunding phase and the underpricing levels in the early transaction phase, removing soft information from expert ratings significantly undermines their predictability on ICO performances. We also test the long-term survival rates and market price trends of ICOs and conclude that it is soft information rather than hard information that plays a crucial role for investors to predict the long-term performance of ICO tokens.

  5. Tong Wang, Sheng Zhao, and Xin Shen. “Why Does Regional Information Matter? Evidence from Peer-to-Peer Lending.” The European Journal of Finance 27, no. 4–5 (2021): 346–366.
    Abstract

    In this paper, we study regional discrimination in a peer-to-peer lending scenario and provide novel empirical evidence for theories of soft information collection and information cost. We find that the regional information matters for borrowers' funding probabilities and that discrimination is profit-oriented or taste-oriented depending on the specific region. Moreover, using borrowers' birthplace as an instrumental variable, we find no evidence of genuine discrimination based purely on region in the peer-to-peer lending market.

  6. Sheng Zhao, Fernando Moreira, and Tong Wang. “Is solicitation status related to rating conservatism and rating quality?” Journal of International Financial Markets, Institutions and Money 72 (2021): 101341.
    Abstract

    This paper discusses the association between rating solicitation status and rating quality. Our model shows that if firms with a declining trend of performance are less likely to solicit ratings from credit rating agencies, these firms are more conservatively rated, as their self-selection behaviour is seen as a signal of high credit risk. Our empirical results support the predictions of the model proposed in this study and show that, first, when controlling for fundamental factors, unsolicited ratings are lower than solicited ones, and second, the rating qualities of both types of ratings are not significantly different from each other.

  7. Sanxi Li, Hailin Sun, Tong Wang, and Jun Yu. “Assortative matching and risk sharing.” Journal of Economic Theory 163 (2016): 248–275.
    Abstract

    This paper explores the sorting patterns in a two-sided matching market where agents facing different risks match to share them. When preference belongs to the class of harmonic absolute risk aversion (HARA), the risk premium is perfectly transferable within each partnership; thus a stable match minimizes the social cost of risk. In the systematic risk model, where agents are ranked by their holdings of a common risky asset, the convexity of the joint risk premium in joint risk size leads to negative assortative matching (NAM). In the idiosyncratic risk model, where agents are ranked by their independent riskiness in the sense of second-order stochastic dominance (SSD), NAM arises when preference exhibits decreasing absolute risk aversion (DARA) in the sense of Ross and riskier background risk leads to more risk-averse behavior. However, NAM may fail to arise when riskier background risk leads to more risk-tolerant behavior.

  8. Zonglai Kou, Patrick Rey, and Tong Wang. “Non-Obviousness and Screening.” The Journal of Industrial Economics 61, no. 3 (2013): 700–732.
    Abstract

    The paper offers a novel justification for the non-obviousness patentability requirement. An innovation involves two stages: research results in a technology blueprint, which development transforms into a profitable activity. An innovator, who is either efficient or inefficient, must rely on outside finance for the development. Only patented technologies are developed. Strengthening the non-obviousness requirement alleviates adverse selection by discouraging inefficient innovators from doing research, but creates inefficiencies by excluding marginal innovations. We show that it is socially optimal to raise the non-obviousness requirement so as to exclude bad innovators; we also provide several robustness checks and discuss the policy implications.

Books, chapters and reports

In Chinese.

Research grants

Refereeing

RAND Journal of Economics; Journal of Economic Theory; Journal of Industrial Economics; International Journal of Industrial Organization; Review of Network Economics; Journal of International Financial Markets, Institutions and Money; European Journal of Finance; Regional Studies; Journal of the Operational Research Society; Journal of Optimization Theory and Applications; European Journal of Operational Research.