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This paper aims to examine the effect of both inflation rate and interest rate on stock prices using quarterly data on non-financial firms listed in DJIA30 and NASDAQ100 for the period 1999-2016. The stock duration model is used to measure the sensitivity in variations in inflation rates and interest rates on stock prices.

The authors use standard statistical tools that include Johansen cointegration test, linearity, normality tests, cointegration regression, Granger causality and vector error correction model.

The results of panel Johansen cointegration analysis show that cointegration exists between the stock prices, the changes in stock prices due to inflation rates and the changes in stock prices due to real interest rates. The results of cointegration regression show that inflation rates are negatively associated with stock prices, the real interest rates and stock prices are positively associated, changes in real interest rates and inflation rates Granger cause significant changes in stock prices, significant speed of adjustment to long run equilibrium between observed stock prices and real interest rates and significant speed of adjustment to long run equilibrium between changes in stock prices due to real interest rates and changes in inflation rates.

This paper contributes to the empirical literature in three ways. The paper examines the effects of inflation and interest rates on stock prices differently from other related studies by separating inflation from real interest rates. The paper examines the causality between stock prices, interest and inflation rates. This paper offers significant updated validity to extended literature that a negative association exists between stock prices and inflation rates. This validity can be considered as an existence a theory of stock prices, inflation rates and interest rates.

The purpose of this paper is to explore the potential benefits of an optimal vs observed working capital; the latter being measured by cash conversion cycle (CCC). Optimal CCC is defined and measured as the CCC that maximizes sales in the last four quarters. The initial exploratory results show that optimal CCC has been shorter than the observed. In addition, shorter CCC is accompanied by higher return on investment.

The authors use various statistical tools to analyze the differences between determinants of observed and optimal CCC. These statistical tools include Johansen cointegration test, linearity, normality tests, cointegration regression and Granger causality. The authors also use the benefits of discriminant analysis in order to reach a Z-score model that can be used for monitoring the move from an observed to optimal working capital.

The results show that: significant association exists between volatility of sales and CCC; sales volatility and lagged growth of sales carry relatively the highest weights when a firm moves from observed to optimal CCC; shorter CCC is associated significantly with higher profitability; the observed CCC adjusts to an optimal level; as inflation rises causing potential rise in cost of goods sold, firms prefer staying away from optimal levels of working capital; as economic growth slows down, firms stay at the current level of observed working capital; the results are subject to industry and size effects; and the DJIA and NASDAQ listed firms adjust observed CCC to optimal level slowly.

This paper offers three advances in the literature. The first advance is that the paper determines an optimal level of working capital empirically. To the best of the authors’ knowledge up to the date of submission, other related studies did not include an empirical solution to determine optimal working capital. The second advance is that the paper develops an empirical discriminant model that can be used for monitoring firms’ move from an observed to optimal working capital. The third advance is that optimal working capital shows the empirical integration between short-term and long-term investments that results in an improvement to firm’s liquidity and profitability.

The purpose of this paper is to examine the effect of economic policy uncertainty (EPU) of China on investment opportunities in five ASEAN economies.

This paper employs advanced empirical approaches, such as Multivariate DCC-GARCH and Continuous Wavelet Transform (CWT) to test the research objective. The period of analysis involved monthly data from 2003 until 2019.

This paper provides evidence where the Malaysian stock market to be the least exposed to risks emanating from Chinese EPU, followed by Singapore, the Philippines, Thailand and Indonesia. Results for investment opportunities based on time horizon suggest, for a short-term holding period, investors are better off investing in Singapore and Indonesia, while, for medium-term holding periods, all ASEAN markets appear lucrative except for the Philippines.

From a managerial perspective, the outcome or findings of this study are expected to aid the retail and institutional investors in designing better strategies on diversifying a stock portfolio with different holding periods.

Theoretically, the findings of this study contribute fresh insights into an emerging strand of literature focusing on the transmission of regional policy. Methodologically as well, this study is a novel venture to the best of authors' knowledge.

The literature on the relation between dividends and stock risks include mixed results. The related studies have reached either insignificant, or positive, or negative results. The authors offer a mathematical structure that addresses potential mutual benefits of dividends signaling under conditions of stock risks (systematic and unsystematic). The mathematical structure demonstrates explicitly a case of risk transfer. The purpose of this paper is to examine the potential benefits to firms and stockholders when financial managers adjust dividends per share (DPS) using percentage change in the explanatory power of systematic and unsystematic risks. This perspective is derived from a practical consideration that dividends are part of stock returns that can be adjusted to take stock risks into account.

The paper utilizes the specifications of the two-stage (simultaneous) regression and partial adjustment model. The sample includes quarterly data for firms listed in the Dow Jones Industrial Average and NASDAQ for the period December 31, 1989-March 31, 2011.

The authors have reached general results based on hypotheses developed from related literature. The results show that: first, benefits of risk transfer can be realized. That is, firms as well as stockholders achieve benefits when the DPS are adjusted using percentage change in the explanatory power of systematic risk only; second, dividend growth rates are affected positively by changes in systematic risks; third, the highest stock returns in the market are reached with sharp decreases in dividend growth rates; fourth, in the highest returns quartile, firm size and time do not matter but the industry type does; and fifth, the associations between dividend growth rates, systematic, unsystematic risks, and stock returns are intrinsically nonlinear.

The study contributes to the literature in terms of first, providing practical insights on the financial strategies that help in the use of dividends to convey the right signals to stockholders, and second, empirically show the potential benefits of adjusting dividends growth rates according to systematic and unsystematic stock risks in a unified mathematical structure that adds to the current literature.