學術論文

當員工對工作的多方面產生佔有欲時，就會出現工作心理所有權。借鑒資源保存理論和調節定向理論，這研究採用以資源為基礎的觀點，提出標記、捍衛及拓展這三種形式的領域感，以及信息交換和個人調節定向是工作績效的雙面刃。作者在研究一通過多步驟分析，制定並驗證了領域擴張的規模。研究二以358組員工與主管的配對來測試這個模型；發現工作心理所有權會促使員工標記、捍衛和拓展工作領域。捍衛領域會窒礙信息交換、拓展領域對信息交換則有正面影響，而標記領域與信息交換無關。信息交換與工作績效有正向關係。當員工加強對工作領域的防衛，信息交流便會減少，窒礙其工作表現，這影響對保守型的員工尤甚。當員工拓展其工作領域及加強信息交流，其工作績效便會提高，這對較進取員工的影響尤為明顯。以上的發現對研究和實踐有顯著影響。

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.

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.

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.

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.

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。