金融期货价格波动限制机制探讨

金融期货价格稳定机制延缓了价格发现过程，并造成了流动性干扰，但从降低期货、现货交易总成本来讲，它还是利大于弊，因此设置价格波动限制是一种可行的政策，而且在期货、现货市场同时设定的效果最好。此外，从不同价格波动限制方式的影响来看，选择弹性涨跌幅限制可较好地发挥价格限制的好处，减小价格限制的不利影响。     

权证对投资者投资偏好影响的实证研究

金融衍生产品的出现提高了证券市场信息传递的速度，知情交易者通过对衍生品的投资提高传递的效率。本文通过对我国权证市场和相应标的股票市场交易高频数据的研究，探讨知情交易者在权证市场出现后是否改变了投资的对象。实证结果表明，尽管有部分知情交易者投资了认购权证，但是并没有证据表明知情交易者偏好认沽权证。造成这一结果的原因可能是权证制度自身的缺陷。     

公允价值的困境解析及其对策

公允价值的运用在国外已成为一种趋势,在我国却并不顺利.文章认为公允价值在理论上是可行的,尽管实际运用中,由于使用者的逐利动机、会计准则制度不完善、相关的法律法规不健全及执法不严等,导致在关联交易、非货币交易、债务重组和资产减值中出现了很多问题,但是通过引入全面收益并且建立良好的实施环境,它也就具有了实务上的可行性.     

The Future of Financial Reporting: Insights from Research

Research offers insights that, if heeded and built upon, could improve financial reporting, thereby contributing to a more prosperous society. In particular, research suggests improvement could stem from greater use of fair value; a performance statement designed to reveal the information embedded in changes in fair value; better information to help investors assess the value of intangible assets; more specific information about risk and uncertainty; information that is unbiased, rather than conservative; acceptance of a degree of earnings management together with a determination of how much earnings management is acceptable; and a financial reporting package designed to convey information users need, which presumably is broader than today's financial statements.     

Optimal pair-trading strategy over long/short/square positions—empirical study

Suzuki [Automatica, 2016, 67, 33–45] solves the optimal, finitely iterative, three-regime switching problem for investing in a mean-reverting asset that follows an Ornstein–Uhlenbeck price process and find explicit solutions. The remarkable feature of this model is that the investor can explicitly take either a long, short or square position and can switch the position, with transaction costs, during the investment period. We run empirical simulations of such multiple-regime switching models. There are very few such attempts in the existing literature because it is difficult to find, first, an explicit solution to the problem and second, appropriate financial assets that follow the artificial stochastic process required by the mathematical model. According to the Monte Carlo simulations of the optimal pair-trading strategy, the mean daily Sharp ratio is more than 2.3, whereas the mean Sharp ratio for the historical simulation of the ‘stub’ pairs (combinations of parent/subsidiary companies) is 0.6886. We believe that the results obtained from performing the empirical simulations are remarkable and consider that the optimal switching strategy of the rigorous mathematical model is applicable to businesses in the real world. For the reference many pseudo-program codes are added, which can help to replicate the optimal trading strategies.     

Fair value disclosures and crash risk

In response to the public criticism of the inadequate disclosures mandated by SFAS No. 157, Fair Value Measurements, the FASB issued ASU (Accounting Standards Update) 2010–06, Improving Disclosures about Fair Value Measurements, and ASU 2011–04, Amendments to Achieve Common Fair Value Measurement and Disclosure Requirements, in an effort to increase the reporting transparency. We examine whether the increased fair value disclosures required by these two updates effectively decrease crash risk, defined as the frequency of extreme negative stock returns. In support of the hypothesis, we find that increased transparency from these updates reduces crash risk among U.S. banking firms and that the reduction is greater in banks that have a higher level of Level 3 financial assets.     

Measuring the liquidity part of volume

Based on the concept that the presence of liquidity frictions can increase the daily traded volume, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Tauchen and Pitts (1983) to measure the liquidity portion of volume. Our approach relies on a structural definition of liquidity frictions arising from the theoretical framework of Grossman and Miller (1988), which explains how liquidity shocks affect the way in which information is incorporated into daily trading characteristics. In addition, we propose an econometric setup exploiting the volatility–volume relationship to filter the liquidity portion of volume and infer the presence of liquidity frictions using daily data. Finally, based on FTSE 100 stocks, we show that the extended MDH model proposed here outperforms that of Andersen (1996) and that the liquidity frictions are priced in the cross-section of stock returns.     

Does high frequency algorithmic trading matter for non-AT investors?

The extant literature has typically measured the impact of high frequency algorithmic trading (HFT) on short term outcomes, in seconds or minutes. We focus on outcomes of concern for longer term non-algorithm investors. We find in some cases HFT increases volatility arising from news relating to fundamentals. Furthermore HFT is associated with the transmission of that volatility across industries, and that transmission is based on short term correlations. Finally, we find that the period since the introduction of algorithmic trading (AT) has seen increases in both the variances and covariances of return volatility in most industries. However increases in the variances has not been uniform in that it has fallen sharply in a few industries. The magnitudes are such that, overall, AT has coincided with reduced return volatility variance.     

Performance of Moving Average Trading Rules in a Volatile Stock Market: The Russian Evidence

This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Russian stock market over the 2003–12 period. It contributes to the existing technical analysis (TA) literature by testing, for the first time, the applicability of ordered weighted moving averages (OWMA) as an alternative calculation basis for determining DMACs. In addition, this article provides the first comprehensive performance comparison of DMAC trading rules in the stock market that is known as one of the most volatile markets in the world. The results show that the best trading strategies of the in-sample period can also outperform their benchmark portfolio during the subsequent out-of-sample period. Moreover, the outperformance of the best DMAC strategies is mostly attributable to their superior performance during bearish periods and, particularly, during stock market crashes.     

The composite iteration algorithm for finding efficient and financially fair risk-sharing rules

We consider the problem of finding an efficient and fair ex-ante rule for division of an uncertain monetary outcome among a finite number of von Neumann–Morgenstern agents. Efficiency is understood here, as usual, in the sense of Pareto efficiency subject to the feasibility constraint. Fairness is defined as financial fairness with respect to a predetermined pricing functional. We show that efficient and financially fair allocation rules are in one-to-one correspondence with positive eigenvectors of a nonlinear homogeneous and monotone mapping associated to the risk sharing problem. We establish relevant properties of this mapping. On the basis of this, we obtain a proof of existence and uniqueness of solutions via nonlinear Perron–Frobenius theory, as well as a proof of global convergence of the natural iterative algorithm. We argue that this algorithm is computationally attractive, and discuss its rate of convergence.