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We know that estimates of terminal value of long‐term investment horizons are biased. Unbiased estimates exist only for investment horizon of one time‐period. The purpose of this paper is to suggest a method based on the arithmetic mean in order to obtain unbiased estimates for the terminal value of long‐term investment horizons.

The method used for the investigation was to employ loss functions or error statistics. Namely, the mean error, the mean absolute error, the root mean squared error, and the mean absolute percentage error was used.

The suggested method produced the closest values to the actual ones than any other suggested averaging method when the authors examined ten‐year investment horizons for Standard & Poor's 500 index and on Dow Jones Industrial index.

Portfolio managers and individual investors may use this paper's suggestion if they wish to obtain unbiased estimates for investment horizons greater than one time‐period.

The suggestion to equate the time‐period of the observed data to the time‐period of the investment horizons is novel and useful to practitioners since it produces unbiased estimates.

Suggests that the use of the geometric mean as a measure of average return on investment presents problems for estimating the variance as a measure of risk. Notes that the use of a measure based on the arithmetic mean seems an uncomfortable compromise. Shows that measures based on the geometric mean are also systematically biased in the case of log normal returns. Concludes that this can have major consequences for investment decision‐making and portfolio selection.

Most investors' retirement portfolios have inter‐period cash inflows. The standard time‐weighted mean return (or geometric mean return) is generally used to report returns on investors' retirement portfolios. The purpose of this paper is to examine the standard time‐weighted mean return and point out additional deficiencies in the time‐weighted mean in this situation, which have not been addressed in the literature.

The paper provides examples that point out additional deficiencies that arise using geometric mean returns as estimates of an individual investor's performance.

With inter‐period cash flows the dollar‐weighted return can be affected by both timing and the sequence of the asset return series even if the investor has constant inflows or outflows of capital. In contrast for these same asset return arrays, the time‐weighted mean return measure may be unaffected by these important variations in the return arrays, and thus may misrepresent actual investor results. This is an important point that has not been addressed in the literature.

With inter‐period cash flows the dollar‐weighted return can be affected by both timing and the sequence of the asset return series even if the investor has constant inflows or outflows of capital. In contrast for these same asset return arrays, the time‐weighted mean return measure may be unaffected by these important variations in the return arrays, and thus may misrepresent actual investor results. This is an important point that has not been addressed in the literature.

One popular method to assess discriminant validity in structural equation modeling is the heterotrait-monotrait ratio of correlations (HTMT). However, the HTMT assumes tau-equivalent measurement models, which are unlikely to hold for most empirical studies. To relax this assumption, the authors modify the original HTMT and introduce a new consistent measure for congeneric measurement models: the HTMT2.

The HTMT2 is designed in analogy to the HTMT but relies on the geometric mean instead of the arithmetic mean. A Monte Carlo simulation compares the performance of the HTMT and the HTMT2. In the simulation, several design factors are varied such as loading patterns, sample sizes and inter-construct correlations in order to compare the estimation bias of the two criteria.

The HTMT2 provides less biased estimations of the correlations among the latent variables compared to the HTMT, in particular if indicators loading patterns are heterogeneous. Consequently, the HTMT2 should be preferred over the HTMT to assess discriminant validity in case of congeneric measurement models.

However, the HTMT2 can only be determined if all correlations between involved observable variables are positive.

This paper introduces the HTMT2 as an improved version of the traditional HTMT. Compared to other approaches assessing discriminant validity, the HTMT2 provides two advantages: (1) the ease of its computation, since HTMT2 is only based on the indicator correlations, and (2) the relaxed assumption of tau-equivalence. The authors highly recommend the HTMT2 criterion over the traditional HTMT for assessing discriminant validity in empirical studies.

The purpose of this paper is to develop a means of the kinematic calibration of a parallel manipulator with full-circle rotation.

An error-mapping model based on the space vector chain is formulated and parameter identification is proposed based on double ball-bar (DBB) measurements. The measurement trajectory is determined by the motion characteristics of this mechanism and whether the error sources can be identified. Error compensation is proposed by modifying the inputs, and a two-step kinematic calibration method is implemented.

The simulation and experiment results show that this kinematic calibration method is effective. The DBB length errors and the position errors in the end-effector of the parallel manipulator with full-circle rotation are greatly reduced after error compensation.

By establishing the mapping relationship between measured error data and geometric error sources, the error parameters of this mechanism are identified; thus, the pose errors are unnecessary to be measured directly. The effectiveness of the kinematic calibration method is verified by computer simulation and experiment. This proposed calibration method can help the novel parallel manipulator with full-circle rotation and other similar parallel mechanisms to improve their accuracy.

This paper aims to understand how aversion to interest income in Islam may influence the demand for real estate and gold when inflation is rampant.

According to Markowitz’s mean-variance model, an optimal portfolio is one that blends maximum return with minimum variance. In investment portfolios, real estate and gold serve as inflation hedges. For religious reasons, many Muslims exclude interest-earning assets from their portfolios, however. This paper explores how this attitude influences the hedging role of real estate and gold when inflation is rampant. This paper compares optimal portfolios that include and do not include interest-earning assets. In the calculations, this study uses monthly Turkish data from 1997 until 2018.

The analysis shows that the best hedging instrument against inflation is an interest-earning asset. In its absence, the role of real estate and gold as inflation hedges markedly increases: For a medium-return and medium-risk portfolio, for instance, the portfolio share of gold holdings increases from 3.16% to 58.43% and that for real estate increases from 14.97% to 24.06%.

This paper is a pioneering work on the influence of Islam on the roles of real estate and gold as inflation hedges when inflation is rampant. It provides an explanation from financial theory for the strong real estate and gold demand in Turkey in the past two decades.

This paper explores the relationship between integrative potential, information exchange, and behavioral and perceptual indicators of negotiation outcome. A measure of integrative potential is introduced that allows the researcher to quantify how much potential for integrativeness is contained in various bargaining scenarios. An experiment using a variant of Pruitt's (1981) bargaining scenario was conducted to investigate the usefulness of the measure. In particular, competitiveness interacted with information exchange to affect joint benefit. It is concluded that integrative potential can help develop useful theories of integrative bargaining.

The aim of this paper is to construct a historical index for the New Zealand stock markets going back to 1899. From these historical returns, the authors can extract the average capital gains and dividend yield. It also allows them to provide an estimate for the equity risk premium (ERP).

The authors collect stock-level data (prices, dividends, etc.) from quote records that are kept at the National Library in Wellington. From the stock-level data, the authors compute a value-weighted market index over the period 1899-2013.

Over the period 1899-2013, the arithmetic mean of equity returns is 10.82 per cent p.a., with a standard deviation of 20.09 per cent. The New Zealand equity market had 92 years of positive returns and 23 years of negative returns during the sample period. The 10-year government bond yield, over the entire period, has an arithmetic mean return of 5.75 per cent. The ERP, on average, is 5.07 per cent.

The authors collect the longest available historical data series for the New Zealand equity market. They document statistical properties as well as the long-term ERP over the entire sample period of 115 years and several subperiods. The ERP is a key input in corporate/project valuation.

This chapter introduces four research methods that are not covered in the previous chapters. They are (1) non-parametric statistics, (2) interpretive structural modeling, (3) analytic hierarchy process, and (4) data envelopment analysis. The methods are discussed with examples. The discussion, however, is introductory; so we urge the reader to go through the pertinent references for details.

Present 3D electromagnetic simulators have high accuracy but they are time and memory expensive. Owing to a fast and simple expression for inductance is also necessary for initial inductor design. In this paper, new efficient methods for total inductance calculation of meander inductor, are given. By using an algorithm, it is possible to predict correctly all inductance variations introduced by varying geometry parameters such as number of turns, width of conductor or spacing between conductors.

The starting point for the derivation of the recurrent formula is Greenhouse theory. Greenhouse decomposed inductor into its constituent segments. Meander inductor is divided into straight conductive segments. Then the total inductance of the meander inductor is a sum of self‐inductances of all segments and the negative and positive mutual inductances between all combinations of straight segments. The monomial equation for the total inductance of meander inductor has been obtained by fitting procedure. The fitting technique, using the method of least squares, finds the parameters of the monomial equation that minimize the sum of squares of the error between the accurate data and fitted equation. The paper presents new expression for inductance of meander inductor, in the monomial form, which is suitable for optimization via geometric programming. The computed inductances are compared with measured data from the literature.

The first, recurrent, expression has the advantage that it indicates to the designer how the relative contributions of self, positive, and negative mutual inductance are related to the geometrical parameters. The second expression presents the inductance of the meander inductor in the monomial form, so that the optimization of the inductor can be done by procedure of the geometric programming. Simplicity and relatively good accuracy are the advantages of this expression, but on the other hand the physical sense of the expression is being lost. Thus, the effects of various geometry parameters on inductance are analyzed using two expressions and the software tool INDCAL.

Applied flexible efficient methods for inductance calculation of meander inductor are able to significantly increase the speed of RF and sensor integrated circuit design.

For the first time a simple expression for fast inductance calculation for meander inductor in monomial form is presented. It is explained how such an expression is generated, which can be directly implemented in circuit simulators.