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The purpose of this paper is to determine the impact level of parameters affecting wing design at low speeds using Taguchi method.

Using brain storming approach airfoil shape, wing angle of attack and Reynolds number are determined as important wing design parameters. Most important parameters over these parameters are determined using Taguchi method. The lift-to-drag ratio (CL/CD ratio) is chosen as the performance criterion and L8 orthogonal index is chosen as experimental study scheme for this study.

Experimental results are examined using Taguchi method. After making experiments and also analyses, Reynolds number is found as the most important and identifier parameter for aircraft wing design.

Taguchi method makes the experimental design for experimental studies. This method reduces the number of experiments substantially using orthogonal indices while keeping effects of uncontrolled parameters to a minimum. Reduction in number of experiments helps save time and also cost.

In this study, with less number of experiments, the most important parameter for aircraft wing design is determined. Moreover, with less number of experiments, not only is time saved but the design stage is also made faster.

The purpose of this paper is to contrast market risk exposure and diversification of single-listed American depository receipts (“ADRs”) with those of dual-listed ADRs from the same geographical region during 2004-2012.

The study uses orthogonal returns in two-factor models to infer exposure to the US and ADRs’ home markets.

The authors found that both ADR types provide no diversification and are significantly exposed to US market risk. The authors also found that portfolios of both single- and dual-listed ADRs behave significantly differently than their home markets.

Only several academic papers discuss single-listed ADRs, and to the best of the knowledge, this study is the first to assess their diversification value.

With growing interdependence between financial markets, the goal of this paper is to examine the dynamic interdependence between corporate equity and public real estate markets for the USA and a select group of seven European developed economies under a cross-country framework in crisis and boom market conditions. Dynamic interdependence is related to four measures of market linkages of “correlation, spillover, connectedness and causality”.

This study adopts a four-step investigation. The authors first estimate “time-varying variance–covariance spillovers and implied correlations” modeled with the bivariate BEKK-MGARCH methods. Second, the methods of Diebold and Yilmaz (2012, 2014) measure the conditional volatility spillover-connectedness effects across the corporate equity and public real estate markets based on a decomposition of the forecast error variance. Third, the authors implement nonlinear bivariate and multivariate causality tests to understand the lead-lag dynamics of the two asset markets' returns, volatilities and net directional volatility connectedness across different sample periods. Finally, the authors conclude the study by providing a portfolio hedging analysis.

The authors find that corporate equity and public real estate are moderately interdependent to the extent that their diversification benefits increases in the longer term. Moreover, the authors find increased corporate equity-public real estate causal dependence of the market groups of the European and international portfolios during the GFC and INTERCRISIS periods. The nonlinear causality test findings indicate that the joint information of asset markets can be a useful source of prediction for future innovation of market risks. Additionally, policy makers may also be able to employ conditional volatility and volatility connectedness as two other measures to manage market stability in the cross-asset market dependence during highly volatile periods.

One major take away from this academic research is since international portfolio investors are not only concerned the long-term price relationship but also the correlation structure and volatility spillover-connectedness, the conditional BEKK modeling, generalized risk connectedness analysis and nonlinear causal dependence explorations from this multi-country study can shed fresh light on the nature of market interdependence and magnitude of volatility connectedness effects in a multi-portfolio framework.

The hedging performance analysis for portfolio diversification and risk management indicates that industrial stocks (“pure” equities) are valuable assets that can improve the hedging performance of a well-diversified corporate equity-public real estate portfolio during crisis periods. For policymakers, the findings provide important information about the nature of causal links and predictability during the crisis and asset-market boom periods. They can then equip with this information to manage and coordinate market stability in cross corporate equity-real estate relationships effectively.

Although traditional research has in general reported at least a moderate degree of relationship between the two asset markets, investors' knowledge of stock-public real estate market linkage is somewhat inadequate and confine mostly to broad stocks (i.e. stocks that are exposed to public real estate influence) in a single-country context. In this paper, the authors examine the interdependence dynamics in a multi-country (multi-portfolio) context. A clear understanding their changing market relationships in a multi-country context is of crucial importance for portfolio investors, financial institutions and policy makers. Moreover, since the authors use an orthogonal stock market index, the authors allow global investors to understand the potential diversification benefits from stock markets that are beyond the public real estate market under different market conditions.

One technique for searching a Co‐ordinate Index is to compare each reference with a Boolean expression of index terms. This divides the file into retrieved and not‐retrieved references. An alternative is to assign each reference a score calculated from its index terms and to retrieve the N highest scoring references in the file. This scoring technique has several advantages in theory, and it performed slightly better in a retrieval test with N equal to the number of references retrieved by the corresponding Boolean search. In the test a minimum value of N = 10 was used, and when less than this number of references matched the Boolean search requirement, the Scoring technique successfully widened the scope of the search and retrieved twice as many relevant references as the Boolean searches.

The purpose of this paper is to evaluate the pricing implication of aggregate market wide investor sentiment risk for cross sectional return variation in the presence of other market wide risk factors.

The paper employs the Fama and French time series regression approach to examine the impact of market risk premium, size, book‐to‐market equity, momentum and liquidity as risk factors on stock return. Given the importance of inherent imperfect rationality or sentiment risk, the paper further investigates the impact of investor sentiment on the cross section of stock return.

The choice of a five factor model is apparently persuasive for consideration in investment decisions. Stocks are hard to value and difficult to arbitrage with characteristics which are significantly influenced with the sentiment risk. It is naïve to argue for the universal pricing implication of sentiment risk in a multifactor model framework.

The test assets portfolios are not segregated as per any industry criteria.

Investment managers can use a contrarian investment strategy, for the stocks that are hard to value and riskier to arbitrage to gain excess return when the market follows a downward trend.

This makes the first attempt towards the investigation of the impact of the sentiment risk on cross sectional return variation from an emerging market perspective on such a diversified and large test asset portfolios. The paper has extended the available literature by investigating the impact of sentiment risk after controlling the liquidity risk factor in a multifactor specification. This measure of market wide irrational sentiment index is more comprehensive.

To provide a good insight into solving a multi‐response optimization problem using neuro‐fuzzy model and Taguchi method of experimental design.

Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision‐making process. In this paper, a four‐step procedure is proposed to resolve the parameter design problem involving multiple responses. This approach employs the advantage of both artificial intelligence tool (neuro‐fuzzy model) and Taguchi method of experimental design to tackle problems involving multiple responses optimization.

The proposed methodology is validated by revisiting a case study to optimize the three responses for a double‐sided surface mount technology of an electronic assembly. Multiple signal‐to‐noise ratios are mapped into a single performance statistic through neuro‐fuzzy based model, to identify the optimal level settings for each parameter. Analysis of variance is finally performed to identify parameters significant to the process.

The proposed model will be validated in future by conducting a real life case study, where multiple responses need to be optimized simultaneously.

It is believed that the proposed procedure in this study can resolve a complex parameter design problem with multiple responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready‐made neural and statistical software like Neuro Work II professional and Minitab.

This study adds to the literature of multi‐optimization problem, where a combination of the neuro‐fuzzy model and Taguchi method is utilized hand‐in‐hand.

There is evidence that corporate social responsibility (CSR) practices predict higher firm value, but little evidence on which specific aspects of CSR drive this relationship. The purpose of this paper is to study this question in a sample drawn from 35 countries over 2003-2016.

The authors employ a research design that analyzes observational data with panel data methods including ordinary least squares, firm-random effects, and firm-fixed effects.

The authors find in a sample drawn from 35 countries over 2003-2016 an economically significant relationship between an overall CSR measure and firm value. The overall CSR score builds on data from Asset4 and is comprised of three indices for environmental, social, and corporate governance aspects of CSR. The authors find that the social index consistently predicts higher market value. The authors also show that the use of particular elements of CSR can lead to substantial omitted variables bias when predicting firm value. The results also suggest a similar bias in studies that focus on a single index, which captures a specific aspect of CSR, but omits the remaining aspects.

The study is subject to limitations common to observational studies.

The authors find robust evidence that CSR predicts market value using a country-benchmarked overall CSR index. The power to predict firm value comes solely from the social dimension of this measure, which captures firm-level practices related to treatment of employees and stakeholder relations including those with customers and the broader community. Three elements drive the social index: customer/product responsibility, human rights, and employment quality. None of the remaining 12 elements significantly predicts firm vale in an empirical setting with firm-FE and extensive covariates. The authors also show that omitted aspects of CSR can easily lead to an omitted variable bias and that the magnitude of this bias is potentially greater with an OLS specification.

Among the many dimensions of CSR, only a subset drives firm value. Policies that target to improve the CSR performance of firms adopt a broader definition of CSR.

The authors provide first-hand evidence on which specific aspects of CSR drive firm market value.