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 This study employs a comprehensive set of 207 fundamental and volume-price factors from the United States equity market spanning from January 1985 to October 2022. Utilizing an array of 10 machine learning techniques—including linear regression, penalized linear regression, tree-based methods, and neural networks—the research synthesizes factor signals and constructs investment portfolios. The empirical results indicate that machine learning algorithms effectively capture the relationship between anomalies (factors) and portfolio returns. With a 1-year training window and monthly rebalancing, the resulting long-short portfolios generate average annualized returns ranging from 16.5% to 22.8%, with Sharpe ratios varying between 0.69 and 1.43. Modifying the training window to 3 months and 24 months produces annualized returns and Sharpe ratios that range from 11.9% to 19.6% and 0.57 to 1.18, and from 4.96% to 21.2% and 0.59 to 1.54, respectively. 

网络众筹平台作为数字化浪潮的新兴产物，极大地拓宽了个人救助与慈善事业的路径，为公众提供了更为便捷参与公益活动的途径。然而，近年来频发的负面事件已引发公众对众筹机制的广泛质疑，信任危机逐渐显现。在此背景下，公众对网络众筹平台的参与程度、情感态度如何？哪些因素在影响着公众的捐款意愿？各大网络众筹平台当前面临着哪些问题？这些问题不仅关乎着公众的利益，更直接关系到网络众筹平台的未来发展走向。鉴于此，本文深入探究了网络众筹平台的发展现状与公众捐款意愿，旨在为其可持续发展提供有力支撑。

调查结果显示：（1）社会各类群体对于网络众筹平台均较为关注，其中项目透明度和平台公信力是影响各类群体捐款偏好和意愿的关键因素。（2）当前公众对网络众筹平台的信任度普遍较低，网友对平台持消极态度的主要原因为担心不法分子利用网络众筹平台进行诈捐牟利。（3）目前各代表性平台在筹款案例真实性上表现较好，但项目透明度及平台手续费满意度情况亟须改进。根据定性和定量分析结论，本文针对公众、网络众筹平台及政府三方提出可行性建议，以期能够改善网络众筹平台的发展环境，解决公众的“信任危机”，营造“我为人人，人人为我”的良好社会氛围。