在外协环境中实施RCM的计划(续)--瑞典水电站的一个案例研究

在实施RCM项目时，重点工作放在RCM分析和开发一个适当的RCM模式。但是实施RCM的计划和准备工作开始时，就觉得这项工作过分复杂，并且颇难处理。准备工作屡次被延迟，并且增加了活动次数和资源。工作进度的延迟对这个项目产生了一些影响。例如，项目领导人员有了变动、培训和教育工作要进行多次。在计划和准备阶段可以观察到以下一些问题。     

Sales technology within the salesperson's relationships: A research agenda

The average cost of a sales call has declined approximately 15% over the past decade, while average salesperson compensation has doubled. These statistics illustrate an important benefit of technology: salespeople are more productive. To better understand the impact technology has played on sales and sales management, this paper examines the impact of sales technology from four perspectives: the salesperson, the field sales manager, the sales executive, and the customer. Noting that the needs and evaluations of technology vary, depending upon the functional position and viewpoint, a research agenda is offered that reflects these various perspectives.     

Social networks in the global banking sector

We show that banks with shared social connections partner more often in the global syndicated loan market and that central banks in the network play dominant roles in various interbank transactions, indicating that social connections facilitate business connections. However, more centralized banks in the network also contribute significantly to the global systemic risk. Moreover, we find the soft information generated by social networks is particularly valuable when potential partners operate under different accounting and regulatory standards. Finally, we show that the recent banking crisis significantly limited the positive soft information effects of social networks in the global banking system.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

Short term real earnings management prior to stock repurchases

This study examines whether firms engage in income-decreasing real earnings management before open market stock repurchases to reduce the cost of stock buybacks. In the short run, managers have the ability to underproduce inventory and increase discretionary expenditures, thus decreasing current period earnings. We find that managers engage in both of these activities before repurchasing their firms’ shares, especially the latter. Also, companies increase their discretionary spending before making repurchases to a greater extent following the passage of the Sarbanes–Oxley Act of 2002 as well as when they are financially healthy and have high marginal tax rates. Finally, we document that firms with the most income-decreasing real earnings management experience the largest positive abnormal returns during the subsequent period. Our findings highlight the importance of considering firms’ use of real operating decisions, as opposed to just opportunistic disclosure practices, around significant corporate events, such as the repurchase of their own stock.     

Classical meets modern in the IDEA protocol for structured expert judgement

Expert judgement is pervasive in all forms of risk analysis, yet the development of tools to deal with such judgements in a repeatable and transparent fashion is relatively recent. This work outlines new findings related to an approach to expert elicitation termed the IDEA protocol. IDEA combines psychologically robust interactions among experts with mathematical aggregation of individual estimates. In particular, this research explores whether communication among experts adversely effects the reliability of group estimates. Using data from estimates of the outcomes of geopolitical events, we find that loss of independence is relatively modest and it is compensated by improvements in group accuracy.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.