earticle

영어

In recent years, digital commerce platforms have grown at an unprecedented pace, fundamentally reshaping retail and service industries worldwide. While these platforms prioritize rapid user acquisition and market share expansion, often at the expense of immediate profitability, their business models inherently carry heightened financial vulnerability. Aggressive marketing expenditures, heavy investments in logistics and technology, and extended deferred payment arrangements with vendors contribute to elevated cash burn rates and liquidity risk. Traditional insolvency prediction models, designed for asset-intensive or manufacturing firms, fail to capture the nuanced risk dynamics of digitally mediated, two-sided marketplaces. Against this backdrop, this study develops and empirically validates a hybrid risk-diagnosis framework tailored to digital commerce platforms, combining conventional insolvency scores with liquidity-specific indicators. Specifically, we integrate Altman’s Z″-Score, Ohlson’s O-Score, and Piotroski’s F-Score with burn rate and cash runway metrics to holistically assess structural solvency, operational health, and short-term survival capacity. Our empirical analysis covers eight major Korean platform firms—Homeplus, Woowa Brothers (Baemin), Coupang, TMON, Wemakeprice, Balan, skplanet, and Kurly—over the 2020–2024 period. Using publicly disclosed financial statements and, where necessary, reputable media reports to supplement missing data, we first employ logistic regression to identify the most predictive variables for default occurrence. The results reveal that the Ohlson O-Score (β≈+0.84, p≈0.10) and Cash Runway (β≈–0.60, p≈0.12) exert statistically meaningful effects on default odds, with one-unit increases in O-Score and one-month increases in runway corresponding to roughly 2.3× higher and 0.56× lower default odds, respectively. Conversely, Z″-Score and F-Score showed limited predictive power in this binary setting. To extend beyond binary classification and capture the temporal dimension of default risk, this study applies both frequentist and Bayesian Cox proportional hazards models. Consistent with prior logistic regression results, both models identify Ohlson O-Score and Cash Runway as statistically significant predictors of time-to-default. The frequentist model estimates a hazard ratio of approximately 2.22 for O-Score and 0.56 for Runway, indicating a higher default hazard with increased financial stress and a protective effect from greater liquidity. To enhance robustness under small-sample constraints and quantify uncertainty, we implement a Bayesian Cox model using Markov Chain Monte Carlo (MCMC) sampling via the No-U-Turn Sampler (NUTS). Posterior estimates confirm the predictive power of O-Score and Runway, with 95% credible intervals excluding zero and posterior probabilities exceeding 99%. This Bayesian approach provides not only directional validation but also interpretable uncertainty bounds critical for risk-sensitive policy decisions. Building on these insights, we propose a three-stage Early Warning System (EWS) and a complementary risk-based regulatory framework (“pinpoint regulation”). Stage 1 real-time monitoring flags acute liquidity stress via burn rate spikes or runway collapse. Stage 2 quantitative diagnosis confirms structural weakness using O-Score and Z″-Score thresholds. Stage 3 synthesizes signals to classify platforms into high-risk, recovering, and stable cohorts. For high-risk firms, we recommend emergency liquidity support (conditional lending, escrow mandate), shortened settlement cycles, and external management oversight. Recovering firms receive buffer instruments (guarantees, tax incentives) and quarterly indicator reviews, while stable firms benefit from annual stress tests and disclosure expansion to foster transparent, sustainable growth. Internationally, we compare EU, US, and Japanese regulatory approaches: the EU’s PSD2 and Late Payment Directive mandate client fund segregation and penalize payment delays; the US relies on private escrow services, trust-account rules under Money Transmitter Laws, and trade-credit insurance; Japan’s Transparency Law enforces upfront disclosure and administrative guidance. These case studies underscore the effectiveness of segmented regulation—combining pre-emptive oversight for high-risk entities with marketfriendly self-regulation for sound firms. Academically, our research enriches platform finance literature by adapting legacy credit risk models to the liquiditysensitive, digitally mediated economy. Practically, it delivers a replicable methodology and policy toolbox for regulators, investors, and platform operators to preempt financial distress, safeguard transactional ecosystems, and promote resilient, inclusive growth in the digital commerce sector.

한국어

본 연구는 국내 주요 유통·커머스 플랫폼 8개사를 대상으로 2020~2024년 재무제표 와 부도 사건 데이터를 활용하여 구조적·유동성 리스크를 조기에 진단하는 정량적 프레임워크를 제시한다. Altman Z″-Score, Ohlson O-Score, Piotroski F-Score 와 더불어 Burn Rate, Cash Runway를 주요 변수로 설정하고, 부도 발생 여부 예측에는 로지스틱 회귀와 베이지안 회귀, 발생 시점 분석에는 Cox 비례위험모형을 적용하였다. 분석 결과, Ohlson O-Score와 Cash Runway가 부도 가능성과 발생 시점 모두에서 통계적으로 유의한 핵심 변수로 확인되었으며, 베이지안 분석은 소표본 에서도 높은 예측 신뢰도를 제공함을 보여주었다. 이를 토대로 ‘긴급 모니터링→정량 진단→위험군 분류’ 3단계 조기경보체계(EWS)와 리스크 기반 핀포인트 규제를 설계 하였다. 또한 EU·미국·일본 사례 분석을 통해 플랫폼 거래 투명성 강화와 선제적 감독체계를 위한 정책 방향을 도출함으로써, 유통·디지털 커머스 생태계의 지속가능 성과 거래 신뢰성 제고에 기여하고자 한다.