Optimal hedge ration with moving least squares - an empirical study using Indian single stock futures data

The use of commodity, currency and stock index futures to hedge risky exposures in the underlying assets is well documented in financial literature. However single stock futures are a relatively new addition to the family of futures and as such, academic research on its use as a hedging tool is relatively thin. In this study we have explored the efficacy of two different methodological approaches that may be applied when hedging a long position in the underlying stock with a single stock future. We use daily trading data covering years 2002 to 2007 from the Indian market, where single stock futures have been really thriving in terms of volume of trade, to extract the optimal hedge ratios using both static OLS as well as 30-day, 60-day and 90-day moving least squares. The method of moving least squares has been in use by market practitioners for some time primarily as a trend analysis and charting tool. Our results indicate that the moving least squares approach outperforms the static OLS in terms of the hedging efficiency, which has been measured by the root mean square hedging error.

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.

The productivity and performance of Australia’s major banks since deregulation

The aim of this study is to present some measures of the performance of banks operating in Australia since the deregulation of the Australian financial system in early 1980s; including the periods of financial market instability (the early 1990s and mid to late 2000s). In undertaking this measurement two approaches will be used. The first simply applies standard financial indicators. The second approach applies data envelopment analysis (DEA), to determine Malmquist indices of the levels of and the changes in the efficiency and productivity of Australian banks. The empirical results demonstrate the effect of deregulation and periodic financial crisis’s on the performance of individual banks, and the major part of the Australian banking sector. Overall the productivity performance of the Australian banks tended to improve considerably in those periods of strongest economic growth (i.e. the mid 1980s and 2000s).

Derivative-free optimization and neural networks for robust regression

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares (LTS) criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks (ANNs) to contaminated data using LTS criterion. We introduce a penalized LTS criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression.

Does implicit motor imagery ability predict reaching correction efficiency? A test of recent models of human motor control

Neurocomputational models of reaching indicate that efficient purposive correction of movement midflight (e.g., online control) depends on one's ability to generate and monitor an accurate internal (neural) movement representation. In the first study to test this empirically, the authors investigated the relationship between healthy young adults’ implicit motor imagery performance and their capacity to correct their reaching trajectory. As expected, after controlling for general reaching speed, hierarchical regression demonstrated that imagery ability was a significant predictor of hand correction speed; that is, faster and more accurate imagery performance associated with faster corrections to reaching following target displacement at movement onset. They argue that these findings provide preliminary support for the view that a link exists between an individual's ability to represent movement mentally and correct movement online efficiently.

Impact of policy changes on the efficiency and returns-to-scale of Japanese financial institutions: an evaluation

This paper examines the impact of FSA's (Financial Services Agency) recent policy changes on the efficiency and returns-to-scale (RTS) of Japanese financial institutions including banks, securities companies and bank holding companies. Three kinds of efficiency are investigated namely, technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) using the non-parametric methodology named data envelopment analysis (DEA). The DEA analysis shows a substantial improvement in the overall efficiency of Japanese banks, albeit a significant difference of efficiency scores between the major/city banks and the regional banks. Results are robust to alternative specifications of efficiency and scale changes.

Developmental improvements in reaching correction efficiency are associated with an increased ability to represent action mentally

We investigated the purported association between developmental changes in the efficiency of online reaching corrections and improved action representation. Younger children (6-7years), older children (8-12years), adolescents (13-17years), and young adults (18-24years) completed a double-step reaching paradigm and a motor imagery task. Results showed similar nonlinear performance improvements across both tasks, typified by substantial changes in efficiency after 6 or 7years followed by incremental improvements. Regression showed that imagery ability significantly predicted reaching efficiency and that this association stayed constant across age. Findings provide the first empirical evidence that more efficient online control through development is predicted, partly, by improved action representation.

Reduced motor imagery efficiency is associated with online control difficulties in children with probable developmental coordination disorder

Recent evidence indicates that the ability to correct reaching movements in response to unexpected target changes (i.e., online control) is reduced in children with developmental coordination disorder (DCD). Recent computational modeling of human reaching suggests that these inefficiencies may result from difficulties generating and/or monitoring internal representations of movement. This study was the first to test this putative relationship empirically. We did so by investigating the degree to which the capacity to correct reaching mid-flight could be predicted by motor imagery (MI) proficiency in a sample of children with probable DCD (pDCD). Thirty-four children aged 8 to 12 years (17 children with pDCD and 17 age-matched controls) completed the hand rotation task, a well-validated measure of MI, and a double-step reaching task (DSRT), a protocol commonly adopted to infer one's capacity for correcting reaching online. As per previous research, children with pDCD demonstrated inefficiencies in their ability to generate internal action representations and correct their reaching online, demonstrated by inefficient hand rotation performance and slower correction to the reach trajectory following unexpected target perturbation during the DSRT compared to age-matched controls. Critically, hierarchical moderating regression demonstrated that even after general reaching ability was controlled for, MI efficiency was a significant predictor of reaching correction efficiency, a relationship that was constant across groups. Ours is the first study to provide direct pilot evidence in support of the view that a decreased capacity for online control of reaching typical of DCD may be associated with inefficiencies generating and/or using internal representations of action.

Estimating Forward Pricing Function: How Efficient is Indian Stock Index Futures Market?

This paper uses Indian stock futures data to explore unbiased expectations and efficient market hypothesis. Having experienced voluminous transactions within a short time span after its establishment, the Indian stock futures market provides an unparalleled case for exploring these issues involving expectation and efficiency. Besides analyzing market efficiency between cash and futures prices using cointegration and error correction frameworks, the efficiency hypothesis is also investigated after explicitly modeling the underlying state of the market (expansion or contraction) through the first-order Markov switching set-up. The results based on Markov switching analysis show that relatively longer time horizon is more effective in eliminating arbitrage opportunities than the short run.

Panel bootstrap tests of slope homogeneity

This paper proposes two bootstrap-based tests that can be used to infer whether the individual slopes in a panel regression model are homogenous. The first test is suitable when wanting to infer the null of homogeneity versus the general alternative, while the second is suitable when wanting to infer the units of the panel that can be pooled. Both approaches are shown to be asymptotically valid, a property that is verified in small samples using Monte Carlo simulation.

Does banks’ dual holding affect bank lending and firms’ investment decisions? Evidence from China

This study investigates the effect of banks’ dual holding on bank lending and firms’ investment decisions using a sample of listed firms in China. We find that dual holding leads to easier access to bank loans, a result that is more pronounced for non-state-owned enterprises (non-SOEs) than SOEs. We also find that dual holding distorts banks’ lending decisions and harms the investment efficiency for SOEs, while resulting in optimal lending decisions and enhanced investment efficiency for non-SOEs. For non-SOEs, further analysis suggests that optimal lending decisions and efficient investment can be achieved for firms with higher ownership concentration, and firms in which the family and foreign investors are the controlling shareholders. We argue that, in emerging markets, whether a bank plays a monitoring role by directly holding the debt and equity claims of companies relies heavily on whether the potential collusion between firm executives and bank managers can be averted, which in turn is determined by the firms’ governance framework and ownership structure.