學術論文

本文結合新加坡最大的公立大學的學生記錄與收入調查數據，估算擁有學士學位的畢業生若多接受一年高等本科教育的回報。部份學院第四（榮譽）年的資格取決於第三年末成績是否達標，我們通過斷點迴歸設計對此進行研究。相對於成績差一點就達標的學生，第三年末平均成績略高於門檻的學生明顯更有可能完成第四年課程，並且於畢業後有更高的收入。額外一年的本科教育為學生在畢業六個月後帶來的收入回報率約為 12%。結合稅收數據亦顯示學生在畢業後至少四年仍受惠於此持續回報。此外，即使大學降低門檻以鼓勵更多學生完成第四年本科教育，额外一年高等教育带来的收入回報仍然可觀，證明此現象並不完全源於教育的信号机制。

New products are highly valued by manufacturers and retailers due to their vital role in revenue generation. Product life cycle curves often vary by their shapes and are complicated by promotional activities that induce spiky and irregular behaviors. We collaborate with JD.com to develop a flexible product life cycle curve forecasting framework based on Bayesian functional regression that accounts for useful covariate information, including product attributes and promotion. The functional model treats product life cycle curves as target variables and includes both scalar and functional predictors, capturing time-varying promotional activities. Harnessing the power of basis function transformation, the developed model can effectively characterize the local features and temporal evolution of sales curves. Our Bayesian framework can generate initial curve forecasts before the product launch and update the forecasts dynamically as new sales data is collected. We validate the superior performance of our method through extensive numerical experiments using three real-world data sets. Our forecasting framework reduces the forecasting error by 5.35%-30.76% over JD.com's current model and outperforms alternative models significantly. Furthermore, the estimated promotion effect function provides useful insights into how promotional activities interact with sales curves.

The Chinese Communist Party began decoupling its policies and practices from Maoist communist ideology more than four decades ago. Yet, why does Maoism continue to have an impact on the behavior of Party members? In this study, we argue that, although the influence of Maoist ideology has become weaker among younger Party members and Party members with higher educational attainment, such ideological decay is countered by a process of secondhand ideological imprinting. Based on data from 1,298 non-state-owned Chinese listed firms for 2000–2017, we find that firms with Party-member board chairs file fewer patent applications and are more likely to commit patent infringement. These effects are weaker if a board chair is younger or has higher educational attainment. Importantly, the moderating effect of young age is reduced as the presence of older Party-member corporate directors in a region becomes more prominent. However, the moderating effect of education appears to be unaffected by the presence of older Party-member directors. These findings generate fresh insights on the dynamics of ideological decay and persistence.

We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD’s current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a (potentially misspecified) stand-alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification—if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors and find that the BMA-SDF outperforms existing models in- and out-of-sample.

A university uses both early-stage selection outcome (high school affiliation) and late-stage admission test outcome (standardized test scores) to select students. We use this model to study policies that have been proposed to combat inefficient gaming in college admissions. Increasing university enrollment size can exacerbate gaming and worsen the selection outcome. Abolishing standardized tests for university admissions increases gaming targeting high school admissions and worsens the selection outcome, while eliminating high-school ability sorting may improve the university selection outcome under some cost conditions of gaming. Committing to a lower-powered selection scheme can improve the selection outcome by reducing gaming behaviors.

Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.

我們研究外匯市場中擇時交易的經濟價值，即是利用與有條件夏普比率相關的資訊，來調整條件下均值方差最優的貨幣組合的名義價值。當條件下風險收益權衡更有利（不利）時，我們的策略會更激進（保守）地進行交易。這樣可以顯著改善樣本外無條件夏普比率、偏度和每百分之一預期超額收益率的最大回撤。該策略中的擇時交易預測了外匯市場的回報、波動性和偏度。普遍的貨幣定價因子無法解釋該策略的高平均超額收益率。我們的研究結果顯示，在建構貨幣交易策略時，採用槓桿、風險（即條件波動性）限製或其他次等擇時交易策略的代價高昂。

消費者之間（P2P）的共享市場有助於實現社會閒置資源的共享。當租客對物主的資源發出共享請求時，物主則需要決定是否接受該請求：接受請求可以馬上填補資源的閒置期，給物主帶來即刻回報，但卻降低了物主滿足未來對資源有更長時間的請求（更高回報）的靈活性。本文建立了一個模型，來揭示這些共享平台上物主在決定接受租客請求時面臨的權衡。該模型可以優化物主的決策，並改善平台的運營。該模型明確容納了兩種類型的物主：一類物主具有前瞻性，會考慮其資源可供租用的狀態，而另一類物主則只考慮眼前的請求，短視地做出接受決定。我們將該模型應用於中國一家領先的共享汽車租賃平台的數據集上。結果顯示，數據中前瞻型和短視型車主人數各半；女性、經驗豐富及年輕的車主更具備前瞻性。結果還顯示兩類車主對租客有不同的偏好。根據模型的估計結果，我們計算了未來每一天對前瞻型車主的期權價值（即資源一天可用的價值），發現期權價值隨時間的推移先增後減。我們進行了兩個反事實推論分析。第一項分析表明，如果平台規定最短租期，即便當前的租用條件可帶來更高的預期回報，前瞻型物主仍會傾向拒絕請求。第二項分析則表明，隨著平台對物主更深入的了解，平台可以通過對租賃請求的優化分配或再分配，大幅提高匹配效率，此舉可令絕大部分平台參與者收益。