Equity-Linked Note(From Wikipedia, the free encyclopedia)

股权挂钩票据(ELN)是一种债务工具,通常为债券形式,其最终支付基于标的股票的表现,可以是单一股票、股票组合或股票指数。典型的ELN产品会保护本金,即投资者到期至少能收回全部初始投资,但期间不支付利息。最终支付金额通常是投资额乘以标的资产的收益再乘以特定的参与率。

Equity-Linked Note

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An Equity-Linked Note (ELN) is a debt instrument, usually a bond, that differs from a standard fixed-income security in that the final payout is based on the return of the underlying equity, which can be a single stock, basket of stocks, or an equity index. A typical ELN is principal-protected, i.e. the investor is guaranteed to receive 100% of the original amount invested at maturity but receives no interest.

Usually, the final payout is the amount invested, times the gain in the underlying stock or index times a note-specific participation rate, which can be more or less than 100%. For example, if the underlying equity gains 50% during the investment period and the participation rate is 80%, the investor receives 1.40 dollars for each dollar invested. If the equity remains unchanged or declines, the investor still receives one dollar per dollar invested (as long as the issuer does not default). Generally, the participation rate is better in longer maturity notes, since the total amount of interest given up by the investor is higher.

Equity-linked note can be thought of as a combination of a zero-coupon bond and an equity option. Indeed, the issuer of the note usually covers the equity payout liability by purchasing an identical option. In some equity-linked notes, the payout structure is more complicated, resembling an exotic option.

Most equity-linked notes are not actively traded on the secondary market and are designed to be kept to maturity. However, the issuer or arranger of the notes may offer to buy back the notes. Unlike the maturity payout, the buy-back price before maturity may be below the amount invested in first place.

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【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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