基于BG/NBD模型的电信客户终身价值测度研究

在技术水平不断提升以及产品服务趋于同质化的环境下,我国移动通信企业开始由基础网络建设向专业化的增值业务转型,在激烈的市场竞争中,及时准确的挖掘高价值客户,提供个性化的功能业务,提升客户价值,成为各通信企业获取新竞争优势的有效途径之一。在BG/NBD模型的基础上,通过捕捉客户随机交易行为,构建了客户终身价值测度模型,结合电信运营商的客户数据进行客户终身价值预测研究,结果发现,模型在客户整体层面和个体层面均能有效且准确的预测客户终身价值。     

试论虚拟网上证券交易场所的法律监管

虚拟网上证券交易场所作为一种智能化的交易系统,允许投资者直接进入交易系统交易,一方面为我国大量未能上市的各类证券提供了流通场所,另一方面也对传统的法律监管构成了挑战。对之无论是按证券公司监管还是按证券交易所监管,都存在一定的困惑。就我国实际来看,从保护投资者利益出发应以证券交易所监管较好,为此我国现行法律需要作出相应的调整。     

规则的背后:美国对华实施首次反补贴调查剖析

美国在2005年颁布《美国贸易权利执行法案》后,将反补贴的国内立法适用到包括中国在内的所谓非市场经济体的输美产品。贸易救济权是平等赋予世贸组织所有成员的,仅仅从规则本身来看,美国对华实施反补贴调查于法有据。但它是在美国对华贸易保护主义定势思维的惯性下启动的,是与《补贴与反补贴措施协议》倡导的公平贸易的精神背道而驰的。规则背后隐藏着贸易保护主义动机的对华贸易壁垒。     

Pairs trading strategies in a cointegration framework: back-tested on CFD and optimized by profit factor

Statistical arbitrage is based on pairs trading of mean-reverting returns. We used cointegration approach and ECM-DCC-GARCH to construct 98 pairs of 152 stocks of 3 currencies. Stocks trading is done by Contract for Difference (CFD), a financial derivative product which facilitates short selling and provides a leverage up to 25 times. To measure the performance of a leveraged strategy, we introduced the profit factor which is the annualized return rate per unit risk. And the historical risk is measured by maximum drawdown. We compared three main strategies: percentage, standard deviation of cointegration long-term residuals and Bollinger Bands (dynamic standard deviation), with and without double confirmation of short-term standard deviation modelled by ECM-DCC-GARCH. Each of the three main strategies is optimized by two optimizers: absolute profit and profit factor. The optimization period goes from 2012–01-01 to 2014–12-31, and validation period is from 2015–01-01 to 2016–06-01. Our results showed that the USD Bollinger Bands strategy without double confirmation and optimized by profit factor, outperformed other strategies and provided the highest annualized return rate per unit risk; 32% of our sample pairs ended up in loss, and 94% of which are explained by a cointegration break during the testing period.     

钢贸企业经营模式创新和流动资金管理问题探讨

实施经营模式创新,已成为当前钢贸企业走出困境的必由之路。应收款、预付款和库存三个方面是当前钢贸企业日常经营风险的重点之所在。本文对创新钢贸企业经营模式和改进流动资金(应收款、预付款和库存)管理进行探讨。     

Does insider trading explain price run‐up ahead of takeover announcements?

This study empirically examines the impact of changes in substantial shareholdings ahead of 450 Australian takeover offers between the years 2000 and 2009. Previous studies have attributed a significant proportion of the price run‐up effect in takeover targets to insider‐trading behaviour. This study examines the contribution of a broad range of public information sources that are known to typically generate market anticipation, including the acquisition of toeholds ahead of takeover announcements. Our findings show no significant pre‐bid run‐up for takeover targets after considering these sources. We conclude from these results that previous findings attributing pre‐bid share price run‐up to illegal insider trading may overstate the existence of such conduct.     

Trade-size clustering and price efficiency

Using a sample of 26 markets, this paper investigates if trade-size clustering affects price efficiency. Our results suggest that more clustering trades are associated with greater resemblance of a random walk, less pricing errors, and shorter price delays. Moreover, we examine three underlying mechanisms to explain how clustering improves efficiency. First, we show that clustering trades are informative, consistent with the idea that stealth traders leverage such tactics to convey private information to prices. Second, we discover that clustering trades are positively related to investor attention (stock liquidity), implying that informed clustering trades happen at the presence of enormous uninformed investors. High attention and liquid markets help reduce the trading friction, thereby prompting quick price adjustments to private information released by the stealth trading.     

Does stardom affect the informativeness of a CEO's insider trades?

This study examines whether the celebrity or star status of a chief executive officer (CEO) affects the informativeness of his insider trades. Using three different measures to identify star CEOs in a sample of S&P 1500 firms, we find that trades of non‐star CEOs predict future abnormal returns and earnings innovations and that trades of star CEOs do not. The predictive power of non‐star CEO trades is mostly attributable to opportunistic trades, not routine trades. We also find evidence suggesting that the abnormal returns associated with non‐star CEO insider trades are due to the lower visibility and consequently less scrutiny of non‐star CEOs compared with star CEOs.     

Trade Size Preference of Informed Traders in Indian Equity Markets

This study utilized high frequency transactions data to analyze the trade size preference of informed traders in Indian equity markets. It is observed that informed traders at an aggregate level adopt stealth trading strategy, wherein they prefer medium sized trades over large sized trades in order to camouflage their private information. However, the stealth trading behavior varies across stocks, wherein informed traders prefer more large sized trades on firms that are part of an index compared to non-index firms. Trading behavior also varies across other market conditions. It has been noted that informed traders prefer large sized trades during periods of high market thickness, negative returns, and low volatility. This study also provides a rationale for such varied behavior of informed traders.     

Optimal order display in limit order markets with liquidity competition

Order display is associated with benefits and costs. Benefits arise from increased execution-priority, while costs are due to adverse market impact. We analyze a structural model of optimal order placement that captures trade-off between the costs and benefits of order display. For a benchmark model of pure liquidity competition, we give a closed-form solution for optimal display sizes. We show that competition in liquidity supply incentivizes the use of hidden orders to prevent losses due to over-bidding. Thus, because aggressive liquidity competition is more prevalent in liquid stocks, our model predicts that the proportion of hidden liquidity is higher in liquid markets. Our theoretical considerations ares supported by an empirical analysis using high-frequency order-message data from NASDAQ. We find that there are no benefits in hiding orders in il-liquid stocks, whereas the performance gains can be significant in liquid stocks.