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Insider trading potentially reveals proprietary information, allowing rivals to compete more effectively against the insiders' firm. This paper examines whether proprietary costs are associated with insiders' trading decisions and the profitability of their trades. Using a variety of approaches to identify proprietary information risk, I find proprietary costs significantly deter

Insider trading potentially reveals proprietary information, allowing rivals to compete more effectively against the insiders' firm. This paper examines whether proprietary costs are associated with insiders' trading decisions and the profitability of their trades. Using a variety of approaches to identify proprietary information risk, I find proprietary costs significantly deter insiders' trading activities. The deterrence effect is more pronounced when insider trading is likely to be more informative to rivals. Specifically, trades by top executives, non-routine trades, and trades at low complexity firms are curbed to a greater extent by proprietary costs. Examining the mechanisms of this deterrence effect, I find firms with higher proprietary costs are more likely to impose insider trading restrictions, and insiders' trading decisions are more sensitive to proprietary costs when they have higher share ownership of the company. These results suggest insiders reduce trading activities not only due to firm policies, but also due to incentive alignment. Finally, when insiders trade despite higher proprietary costs, they earn significantly higher abnormal profits from their purchase transactions. Overall, this study suggests product market considerations are an important factor associated with insiders' trading decisions and profitability of their trades. These findings are likely to be of interest to regulators and corporate boards in setting insider trading policies, and help investors make investment decisions using insider trading signals.
ContributorsChoi, Lyungmae (Author) / Hillegeist, Stephen (Thesis advisor) / Faurel, Lucile (Thesis advisor) / Hugon, Jean (Committee member) / Huang, Xiaochuan (Committee member) / Arizona State University (Publisher)
Created2017
Description随着经营中赊销占比的增加,对企业的营运资本管理能力要求更高,而如何优化营运资金项目,特别是其中的应收账款和存货等流动资产对企业意义重大。混凝土企业的存货占比很小,应收账款在企业流动性资金中的占比极高,影响营运资本周转期的主要因素为应收账款周转期,如何有效提高营运资本效率,特别是应收账款回收效率,是混凝土企业在市场竞争中求得生存的核心之一。以占用营运资金形成应收帐款促进销售会给企业带来一定的优势,也会为企业带来一定的风险,应收账款增加带来的营运资本管理是财务管理的重要事实,需要准确监视和适当管理,企业必须了解应收帐款的规模、性质以及时限,并深入分析讨论这些因素会对企业的绩效带来的潜在影响,合理发挥应收帐款的作用,确保企业营运资本处于正常、合理水平。本文首先以营运资本周转期为核心被解释变量,查看影响不同企业营运资本周转期差异的原因,并基于对这些原因的分析,建立混凝土企业营运资本风险,特别是应收账款风险的预警机制,其次,探讨营运资本周转期与企业绩效的相关关系,验证不同企业的营运资本周转期差异是否会影响企业绩效,在数据支撑的范围内,对影响大小进行一定程度的探讨,为江苏省混凝土行业公司提供一定的经营指导意见,同时为不同企业探索营运资本周转期与企业绩效的相关关系提供参考。 本文研究发现对于江苏省内不同混凝土企业,营运资本周转期与企业绩效正相关且显著,这表明混凝土企业的贸易特征非常明显,企业绩效更多的来自于降低营运资本效率。同时,研究还发现,企业站点数量、周边站点数量、银行承兑汇票结算、其他结算方式、激励机制、客户付款流程、分类催收、第三方催收、竞争形势、高管交际能力这些变量与营运资本周转期密切相关,这些维度分别属于企业规模、企业竞争、资金结算、激励机制、客户信息、账款催收、高管特征等大类,表明提升混凝土企业营运资本周转效率的方式方法多样,值得行业内企业家们总结与探索。
ContributorsTang, Wenfeng (Author) / Huang, Xiaochuan (Thesis advisor) / Sun, Jianfei (Thesis advisor) / Yan, Hong (Committee member) / Arizona State University (Publisher)
Created2022
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Description2020年的疫情导致全球多数经济体进行史无前例的货币超发,美国的货币超发水平甚至超过二战时期,货币总量快速上升推升资产价格,比特币成为大类资产中的领跑者,越来越受到市场的关注和重视,作为新生事物,全球监管机构对比特币普遍持谨慎态度,主流金融机构虽然对比特币有所关注,但是研究并不深入。本文通过研究加入比特币的资产配置模型投资效果,以及比特币给投资组合带来的边际变化,全面说明比特币在资产配置中的作用,并以一个常见的基础配置模型为出发点,讨论不同参数取值下,资产配置结果的差异,并从统计学角度总结出收益及风险角度的一般化特征。其次,对比特币的金融特性从宏观因素、自回归趋势、格兰杰因果检验等多个角度进行分析,解析比特币的特性。研究发现,比特币具有高风险高收益特征,且其风险收益比优于其它主流金融主权定价锚资产,加入比特币后,组合的收益增益非常明显。且无论投资者怎么选择起始时间、采取何种方式预测收益及风险,在多长时间范围内进行再平衡,投资组合均有80%以上的概率实现收益提升,且收益提升幅度大于风险提升幅度。从最优投资点推广到一般化的投资有效前沿上,以上结论依然成立,且评估投资有效前沿时,重复随机抽样后,投资组合夏普率提升的概率亦达到71%以上。更进一步,为规避收益预测参数不稳定对投资结果的影响,改进收益预测方式,基于波动率大小压缩预测收益后,预测结果能抵御资产短期扰动带来的影响。最后,笔者从比特币的自相关性、格兰杰因果检验与宏观因素影响角度研究了比特币资产的特性,发现比特币具有极强的价格趋势,且资产配置中,其价格趋势是提升组合夏普率的主要贡献来源。在采用工业增加值(IAV)、M2解释比特币的价格变化时,发现比特币与宏观因子之间的关系并不显著,且比特币与其他资产收益的绝大多数格兰杰因果关系检验结果不显著,这说明比特币价格变化受自身特性的影响更大,但是当剔除比特币的极端收益时,比特币与宏观因素之间的相关关系变得局部显著。本文是典型的实践型论文,具有非常强的实战指导价值,可以为资产配置实践者提供借鉴。
ContributorsYun, Zhijie (Author) / Huang, Xiaochuan (Thesis advisor) / Li, Feng (Thesis advisor) / Zhu, Ning (Committee member) / Arizona State University (Publisher)
Created2023