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The purpose of this paper is to explore the impact of S&P 500 index fund money flow on the valuations of companies that are constituents of the index and those that are not.

To examine the impact of passive investing on corporate valuations, the authors run panel regressions of price‐to‐earnings ratio on aggregate money flow into S&P 500 index funds and control for various accounting variables that impact price‐to‐earnings ratio. These regressions involve two samples of stocks. The first sample consists of S&P 500 constituents. The second consists of large‐cap stocks that are not constituents of the S&P 500. The authors also run a set of separate regressions with price‐to‐book ratio rather than price‐to‐earnings ratio as the dependent variable.

It is found that the valuations of S&P 500 constituents increased by 139 to 167 basis points relative to nonconstituents, depending on valuation metric, due to S&P 500 index fund money flow when evaluated at mean values of money flow and valuation metrics. The valuations of firms within the S&P 500 index respond positively to changes in S&P 500 index fund money flow while the valuations of firms outside the index do not. Additionally, the impact of money flow on valuations persists the month after the flow occurs, suggesting that the impact does not dissipate over time.

Mispricings among individual stocks arising from index fund investing may reduce the allocative efficiency of the stock market and distort investors' performance evaluations of actively managed funds.

The paper is the first to explore the long‐run relationship between S&P 500 index fund money flow and corporate valuations.

The purpose of this paper is to investigate the dynamic effect of Tobin's q ratio on price-to-earnings (PE) ratio.

The objective of this study is to investigate the dynamic effect of Tobin's q on PE ratio. To achieve this objective, a vector autoregressive analysis (Equation (1)) is employed to analyze the quarterly data from 1951Q4 to 2012Q4 to determine the generalized impulse response functions and perform the variance decomposition of Tobin's q ratio on PE ratio. The Granger causality Wald test is performed to determine if Tobin's q ratio causes PE ratio and vice versa.

Based on the analysis of the quarterly market-level data from 1951Q4 to 2012Q4, the results show that PE ratio significantly drops immediately following the shock to the change in Tobin's q ratio. The results from the Granger-causality test indicate that Tobin's q ratio change causes PE ratio to drop. There is not a reverse causation from PE ratio to Tobin's q ratio change. The variance decomposition results reveal that Tobin's q ratio change forecasts about 67.53 to 67.78 percent of PE ratio at the two-quarter to eight-quarter horizons.

Up to this point, there is not a single study in the literature investigating the relationship between Tobin's q and PE ratios. Consequently, the current study is set up to investigate the dynamic effect of Tobin's q ratio on PE ratio. A major contribution of this study is to provide empirical evidence of the dynamic effect of Tobin's q ratio on PE ratio.

This paper extends previous research by reexamining the difference in cross‐sectional variability of Japanese and U.S. price‐to‐earnings (PE) ratios. A simple model is developed to decompose the variance of the PE ratio into three components: the variance of the price‐to‐book (PB) ratio, the variance of the book‐to‐earnings (BE) ratio and the covariance of the PB and BE ratios. We analyze the behavior of the cross‐sectional variability of the PE ratio and its components and compare the behavior of these ratios across the U.S. and Japanese markets. We find that the cross‐sectional variability of the PE ratio in the Japanese market is consistently lower than that of the PB ratio and the converse is true for the U.S. market. The cross‐sectional variability of PE ratios in Japan is lower than that in the U.S. and the converse is true for the PB ratio. Our results are inconsistent with those reported by Bildersee et al. and indicate that the main factor causing the differences between the cross‐sectional variability of PE ratios and PB ratios is the high negative covariance of the PB and BE ratios.

This study aims to investigate the variation in overvaluation proxies and volatility across industry sectors and time.

Using industry sector data from the S&P Capital IQ database, this study applies traditional cross-sectional regressions to investigate the relationship between overvaluation and volatility over the 2001–2020 time period.

This study finds that the most volatile industry sectors generally do not coincide with overvalued industry sectors in the cross-section, implying that there are limitations to price-multiple methods for forecasting future volatility. Rather, this study finds that historical volatility significantly increases the goodness-of-fit when modeling volatility in the cross section of industry sectors. The findings of this study imply that firms should increase disclosures and transparency about corporate practices to decrease downside risk that stems from bad news. In addition, the findings underline the consistency between market efficiency and high levels of volatility in periods of significant uncertainty.

This study proposes a novel approach to examining the cross section of volatility across time for industry sectors.

This study aims to understand and document the impact of market-based – market returns and momentum – as well as firm-specific – size, book-to-market (B/M) ratio, price-to-earnings ratio (PER) and cash flow (CF) – factors on pricing of Shari’ah-compliant securities as explanation of variations in stock returns in an emerging market – Pakistan’s Karachi Stock Exchange.

Initially, the authors test Fama and French (FF) three-factor model – market risk premium, size and B/M – followed by modified FF model by including additional risk factors (PER, CF and momentum) over a 10-year period (2001-2010).

Our results support superiority of FF three-factor model over single-factor capital asset pricing model. However, addition of further risk factors – including PER, CF and momentum – improves explanatory power of the model, as well as refines the selection of risk factors. In this study, CF, B/M and momentum factors remain insignificant. Traditional B/M factor in FF model is replaced by PER.

Based on the modified FF model, the authors propose a stock valuation model for Shari’ah-compliant securities consisting of three factors: market returns, size and earnings, which explains 76per cent variations in cross sectional stock returns.

To the best of the authors’ knowledge, this is the first study (which combines market-based as well as fundamental factors) on pricing of Islamic securities and identification of risk factors in an emerging market – Karachi Stock Exchange.

This study examines the determinants of idiosyncratic risk (IR) or unsystematic risk. The study also examines the determinants of IR by dividing the firms into different categories: beta-based firms, liquid and illiquid firms and financially constrained (FC) and unconstrained (FUC) firms.

The fixed effects static panel data model specifications are formulated based on Hausman (1978) test for BRICS (Brazil, Russia, India, China, and South Africa) member countries over the period 2000–2019. Moreover, the t-test is applied to see whether the returns of different types of portfolios are significantly different.

The portfolio analysis results show that, on average, high IR firms tend to be small in size, highly leveraged, have low competitiveness, low profitability, less dividend yield and low returns for all the sampled countries. The sample paired t-test also confirms that a significant difference exists between extreme portfolios: small and large size and low IR and high IR portfolios. The panel regression results show that firm size, market power, price-to-earnings ratio, return on equity (ROE) and dividend yield negatively relates to IR. Yet, both leverage and liquidity are positively related to IR. However, the sign of momentum returns is mostly positive for the entire sample. The coefficient values for high-beta, FC and illiquid firms are more significant and large than the firms' counterparts for all BRICS member countries. These results support the hypothesis of an under-diversified portfolio and suggest that the above-mentioned firm-specific variables are the significant determinants of unsystematic risk.

The securities exchange commission, as the supervisor of the public limited companies, needs to increase its role in investor protection related to the uncertainty of investment in the capital market. Accordingly, in making investment decisions in a stock exchange, investors can use the information that captures unsystematic risk for investment decision-making.

This study is the first to explore the determinants of IR in top emerging countries. Second, none of the existing studies has focused on the determinants of the IR based on different categories of firms.

This study aims to suggest that firms and stock market investors are more sensitive about inventory leanness when industry information technology (IT) usage is high. First, when industry IT usage is high, a firm's inventory leanness is more responsive to information inputs (cash holding and sales efficiency). Second, when industry IT usage is high, the price-to-earnings ratio (indicative of stock market investors' willingness to pay a premium) is more sensitive to the firm's inventory leanness.

This study highlights the contextual role of industry IT usage during the 1998–2009 lost decade (wherein the steepest falls in manufacturing jobs happened in the USA).

The results highlight the significant contextual role of industry IT usage. In manufacturing industry sectors with high IT usage, (1) inventory levels of firms are more responsive to information inputs and (2) stock market investors have greater appreciation for inventory leanness.

The lost decade, 1998–2009, was a difficult period for the manufacturing industry. Nonetheless, there was variation in stock market valuations of manufacturing firms, with many firms outperforming others. Stock market investors were sensitive to inventory leanness. Firms that positively impressed stock market investors were strategically positioned in high IT usage industry sectors and prioritized inventory leanness. Further, their inventories were sensitive to information inputs – their inventories were leaner in response to improved sales-efficiency and/or shortage in cash.

Refers to previous research on the reasons for takeovers, the characteristics of bidders/targets and methods of payment. Uses 1987‐1998 New Zealand data on a sample of 28 successful bidder/target pairs to analyse their growth/value relationships and methods of payment. Shows that target firms have significantly higher book‐to‐market ratios and lower price‐earnings and price to cash flow ratios than bidders, who appeared to overpay for targets’ shares. Finds bidders with the highest growth and market value tend to use shares or mixed payments while those with lower growth use cash. Describes some less conclusive results and considers consistency with other research and theories.

Investor sentiment (optimism or pessimism) may influence investors to follow others (herding) while taking their investment decisions. Herding may result in bubbles and crashes in the financial markets. The purpose of the study is to examine the presence of herding and the effects of investor sentiment on herding in China and Pakistan.

The investor sentiment is captured by five variables (trading volume, advance/decline ratio, weighted price-to-earnings ratio, relative strength index and interest rates) and a sentiment index developed through principal component analysis (PCA). The study uses daily prices of 2,184 firms from China and 568 firms from Pakistan for the period 2005 to 2018.

The study finds that herding prevails in China while reverse herding prevails in Pakistan. Interestingly, as investors become optimistic, herding in China and reverse herding in Pakistan decrease. This indicates that herding and reverse herding are greater during pessimistic periods. Further, the increase in herding in one market reduces herding in the other market. Moreover, optimistic sentiment in the Chinese market increases herding in the Pakistani market but the reverse is not true.

Considering the greater global financial liberalization, and better opportunities for emotion sharing, this study has important implications for regulators and investors. Market participants need to understand the prevalent irrational behavior before trading in the markets.

Since individual proxies may depict different picture of the relationship between sentiment and herding therefore the study also develops a sentiment index through PCA and incorporates this index in the analysis. Further, this study examines cross-country effects of herding and investor sentiment.

Introduction: Turkish cement industry, which sustains growth trend between the years 2015 and 2018, is the biggest cement producer of Europe besides the growth success. Production trend in cement industry reversed after the decrease in the value of Turkish Lira and increased inflation in 2018. The data of this industry, which contributes to Turkish economy directly and indirectly, have become one of the leading indicators.

Aim: From this point of view, 17 cement industry firms which are traded in Borsa İstanbul equity market continuously are examined in terms of their Earnings Before Interest, Taxes, Depreciation, and Amortization, Cash Conversion Cycle (CCC), Export Rate Ratio and Gross Sales. These variables are analyzed between the period 2013:Q1 and 2019:Q2.

Method: Independent variables in the models are Industry Production Index (IPI), CBRT Dollar/TL selling rate of exchange, Tangible Asset Ratio, Growth Rate, Financial Leverage Ratio, Current Ratio, Market-to-Book Ratio (MB), Price-to-Earnings Ratio (PE), and Return on Equity (ROE).

Findings: According to panel regression results, Dollar/TL exchange rate is the unique independent variable that affects four dependent variables. While Dollar/TL exchange rate negatively affects Earnings Before Interest and Taxes and Gross Sales, it positively affects CCC and Export Rate. MB ratio positively affects CCC. In contrast, IPI, Tangible Asset Ratio, and Financial Leverage Ratio negatively affect CCC. Export ratio is negatively affected both by IPI and PE ratio. While MB ratio negatively affects Gross Sales ratio, IPI, Tangible Asset Ratio and Growth Rate positively affect it.