Decision trees unearth return sign predictability in the S&P 500

Technical trading rules and linear regression models are often used by practitioners to find trends in asset returns. However, these models typically neglect interaction terms between the lagged daily directional movements. We propose a decision tree forecasting model that has the flexibility to capture arbitrary interaction patterns. To study the importance of interaction terms, we construct a binary Markov process with a deterministic component that cannot be predicted without interaction terms between the lagged directional movements. We show that some tree based strategies achieve trading performance significant at the 99% confidence level on the S&P 500 over the past 20 years, after adjusting for multiple testing. The best strategy breaks even with the buy-and-hold strategy at 21 bps in transaction costs per round trip. A four-factor regression analysis shows significant intercept, and correlation with the market. The directional predictability is strongest during the bursts of the dotcom bubble, financial crisis, and European debt crisis. The return sign predictability during these periods confirms the necessity of interaction terms to model daily returns.     

Asset pricing for inefficient markets: Evidence from China and India

Over last four decades, evidence of market inefficiencies has been widely documented by several scholars for all major stock markets in the globe. Chinese and Indian markets are not exempt. Inefficiencies in these markets are described by many authors as roots of all mispricing. Mispricing might be the outcome of application of familiar asset pricing models which may mislead an investor into adopting inappropriate policies for his new investments or for reallocating his old investments. In an alternative approach, we propose a transformation on original market returns in the objective of relaxing the strong assumption of market efficiency behind application of an asset pricing model. This modification will widen the scope of rational models on asset pricing ranging from an efficient to an inefficient market.     

Firm growth and efficiency in the banking industry: A new test of the efficient structure hypothesis

In this paper we propose a new test of the efficient structure (ES) hypothesis, which predicts that efficient firms come out ahead in competition and grow as a result. Our test has significant advantages over existing ones, because it is more direct, and can jointly test the so-called quiet-life hypothesis, which predicts that in a concentrated market firms do not minimize costs. We then apply this test to large banks in Japan. Consistent with the ES hypothesis, we find that more efficient banks become larger. We also find that market concentration reduces banks’ efficiency, which supports the quiet-life hypothesis. These findings imply that there is an intriguing growth–efficiency dynamic throughout banks’ life cycle, although our findings also suggest that the ES hypothesis dominates the quiet-life hypothesis in terms of economic impact.     

职业道德建设存在的问题以及强化职工责任心有效途径

在进入二十一世纪的新时期,中国的社会经济领域发生了深刻而复杂的变化,国有企业的改革改制使职工的职业道德出现了一些新情况、新问题,如何适应变化了的客观情况,有针对性地加强职业道德建设,是一个非常紧迫的重大课题。本文就这一问题进行了深入的调查研究,对当前职业道德建设的一些问题提出自己的一点对策性的建议。     

Testing efficiency of the ruble-sterling foreign-exchange market under the gold standard

This paper tests an efficient market hypothesis for the Russian ruble–UK sterling exchange rates in the gold standard period 1897–1913. Using Bayesian Markov Chain Monte Carlo methods it is shown how to test a weak-form market efficiency in a doubly truncated regression model with ARMA-GARCH error. The suggested model accounts for time series characteristics of the data and bounds of exchange rates caused by the gold points and government intervention. We find that the weak-form efficiency hypothesis can not be rejected for the gold standard ruble exchange rates in both St.Petersburg and London markets.

The Investment Opportunity Set and Acquired Goodwill*

This paper examines how managers in New Zealand allocate the cost of firms' investments in subsidiaries between net tangible assets and acquired goodwill. We find a negative relation between acquired goodwill and leverage. This could be interpreted as the result of managers of highly leveraged acquiring firms opportunistically allocating a lower portion of the acquisition price to acquired goodwill. However, this analysis, like much of the research on accounting choice, suffers from an omitted variables problem. We present evidence that the observed negative relation between acquired goodwill and leverage may stem from each variable's relation to the investment opportunity set. Further, we find no evidence that acquired goodwill is related to the existence of debt covenants. Together, these results suggest an endogenous relation between the firm's asset structure, its financing policy, and the allocation of acquisition price to acquired goodwill.     

中国市场短期利率动态研究-基于有效矩方法的实证分析

本文以上海证券交易所国债回购市场的7天期回购利率为分析对象,选择从2000年1月4日到2005年12月13日的每日数据为样本,采用有效矩估计对三个在国外文献中流行的利率模型:CKLS模型、SV2模型、SV3模型进行实证分析.结果表明单因子CKLS模型不能很好的刻画我国短期利率的动态特征,SV2模型和SV3模型则可以较好的描述短期利率的动态变化过程.同时,受制于利率市场化问题,我国短期利率的水平效应较之于美国弱很多.     

Will the frog change into a prince? Predicting future customer profitability

More and more companies have customer databases that enable them to analyze customer profitability over time. These companies often seek to determine the most important customers as indicated by their current or historical profitability and focus attention on them. Focusing on profitable customers can result in more efficient use of marketing resources, but this approach neglects the fact that customers can evolve over time. Some customers begin as low-profit customers but eventually develop into high-profit customers. Others may start out as high-profit customers but become unprofitable over time. Previous efforts to predict future profitability have been relatively unsuccessful, with relatively simple, naïve models often performing just as well as or better than more sophisticated ones. Our paper presents a new approach to predicting customer profitability in future periods that performs significantly better than naïve models. We estimate the models on data from a high-tech company in a business-to-business context and validate the models' predictive ability on a holdout sample.We show that a model based on simulation of customer futures provides large improvements over naïve extrapolation of average profits. By using the simulation model to select customers, ROI from marketing efforts is projected to increase by 58%.