Valuation Ratios Research Articles

Which Investors Matter for Global Equity Valuations and Expected Returns?

Much work in finance is devoted to identifying characteristics of firms, such as measures of fundamentals and beliefs, that explain differences in asset prices and expected returns. We develop a framework to quantitatively trace the connection between valuations, expected returns, and characteristics back to institutional investors and households. We use it to analyze (i) what information is important to investors in forming their demand beyond prices and (ii) what is the relative importance of different investors—differentiated by type, size, and active share—in the price formation process. We first show that a small set of characteristics explains the majority of variation in a panel of firm-level valuation ratios across countries. We then estimate an asset demand system using investor-level holdings data, allowing for flexible substitution patterns within and across countries. We find that hedge funds and small, active investment advisors are most influential per dollar of assets under management, while long-term investors, such as pension funds and insurance companies are least influential. In terms of pricing characteristics, small, active investment advisors are most important for the pricing of payout policy, cash flows, and the fraction of sales sold abroad. Large, passive investment advisors are most influential in pricing the Lerner index, a measure of markups, and hedge funds for the CAPM beta. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

A note on value investing in the UAE stock market

Value investing is one of the most popular stock-picking strategies employed in financial markets. We investigate its effectiveness in the United Arab Emirates. We examine the performance of 124 firms in the period from January 2004 to March 2019. We analyze portfolios from one-way sorts on several well-known valuation ratios: earnings-to-price, book-to-market, EBITDA-to-EV, and dividend yield. Our results demonstrate a powerful value effect in the UAE stock markets. The long-short strategies based on valuation ratios display high raw and risk-adjusted returns. Our results are robust to many considerations.

The Low-Minus-High Portfolio

Regardless of whether the CAPM is rejected for valid reasons or by mistake, a single long-short portfolio will always explain, together with the market, 100% of the cross- sectional variation in returns. Yet, this portfolio, which we coin the “Low-Minus-High (LMH) portfolio,” need not proxy for fundamental risk. We show theoretically how factors based on valuation ratios (e.g, book-to-market), or on investment rates, can be proxies for the LMH portfolio. More generally, the empiricist can uncover an infinity of proxies for the LMH portfolio, thus unleashing the factor zoo.

Do Valuation (P/E, ROE and P/BV) Ratios Drive Stock Values? A Case of GCC Countries

Do valuation ratios predict the future stock prices? Over the decades, researchers have explored data across various global financial markets and across different timelines to seek its unique answer. The results though were not universal, resulted in generating greater interest in the subject. Using valuation ratios as a stock price predictor gained further momentum after Campbell and Shiller’s seminal work involving a century of data sets. In spite of its practical relevance, not much effort was being made to establish the correlation between valuation ratios and stock price of GCC listed companies. This paper attempts to bridge the existing gap by studying 140 publicly listed companies in the six GCC countries namely Qatar, Kuwait, Bahrain, Saudi Arabia, Oman and United Arab Emirates (UAE) using the multiple regression model. The period of study was between 2013-2017. Correlation is established for each of the countries individually, followed by an integrated approach. The independent variables used in the study are Price Earnings Ratio (P/E), Return on Equity (ROE), Price to Book Ratio (P/BV) and Stock Returns being the dependent variable.

Is the IT revolution over? An asset pricing view

I develop a method that structures financial market data to forecast economic outcomes. I use it to study the IT sector’s transition to its long-run share in the US economy. The method uses a model which links economy-wide growth with IT’s market valuation to match transition data on macroeconomic quantities, the sector’s life cycle patterns, and, importantly, market valuation ratios. My central estimates indicate that the revolution ends between 2028 and 2034 and that future average labor productivity growth will fall to 1.7 percent from the 2.7 recorded over 1974–2015. I show empirically the IT sector’s price-dividend ratio univariately explains over half of the variation in future productivity growth.

Valuing Equities: Discounting Growth Opportunities, Fearing Inflation

Over the last century (Eretained/P)mean ~ constant while D/P has experienced a long secular decline. If investors cannot simultaneously discount both Eretained and D, examination of the relationships between Eretained/P, D/P and the payoff ratio Eretained/D offers support to the hypothesis that Eretained/P is the priced valuation ratio. A linear Empirical Model (EM) identifies two data variables, the trailing earnings growth (EG) and inflation (cpi), that explain R2 ~ 50% of the variance of Eretained/P. The investor’s decision to sell or hold the shares is governed by the investor’s resistance to Intertemporal Substitution, R = γµ, so the MF-Gordon becomes Eretained/P = γµ - µ . A dynamic Model is obtained using Taylor expansions of the functional relationships µ = µ(EG(t)) and γ = γ(cpi(t)). The Model is calibrated using the EM. The MF-Gordon Model is used to explain the quasi-equality of equities and the long bond (1970:2000), the equity/long bond equity premium and the effect of sentiment on equity prices. The Shiller price volatility puzzle is explained by the adaptive growth expectation which is heavily loaded on the highly volatile trailing earnings growth rate.

A Mixed Frequency Approach for Stock Returns and Valuation Ratios

We employ a Mixed-Frequency VAR to study the effect of four valuation ratios (the price-dividend ratio, the price-earnings ratio, the Cyclically Adjusted Price Earnings Ratio and the Total Return Cyclically Adjusted Price Earnings Ratio) on the US stock market. We quantify the interaction between high and low frequency data. We show that all valuation ratios (observed at a monthly frequency) significantly affect stock market returns (observed at a daily frequency) at both long and short horizons.

Predictive modeling of CNX Nifty 200 from a valuation perspective

In the current scenario it has always been a difficult task to predict the value of the share market index. The study is being conducted to predict the direction of the stock price index movement. Accurate forecast of the stock index movement helps the investors to acquire the opportunities for gaining profit in stock exchanges. This paper has examined the relationship between the closing price of NSE 200 and the set of three valuation ratios i.e. Price Earnings ratio (P/E), Price to Book ratio (P/B) and Dividend Yield. Closing price of NSE 200 is the dependent variable and P/E, P/B and Dividend Yield are the independent variables. In this study we have used 1048 observations for the period from April 2015 to June 2018. The values have been collected from the NSE website. The collected data were exploratory data which has been measured through Multiple Regression analysis. The analysis shows that Price Earnings ratio, Price to Book ratio and Dividend Yield are significant determinant factors to predict the closing price of NSE 200.

Predictable risks and returns: further evidence from the UK stock market

This paper examines whether the most cited performance models can explain variation in the UK stock returns. The dataset includes securities of the FTSE 100 from January 2000 to December 2016. Securities are classified based on their market capitalisation and their industry. Also, valuation ratios are put to the test so as to help us retrieve evidence of predictability. Finally, the January effect is included in our analysis as indicated particularly for the UK data. The authors find that during this short time period in which a financial crisis is also evident, all performance models are equally capable of assisting us interpreting UK predictability. Secondly, lagged market's excess returns capture most of the forecasting ability in returns, while the valuation ratios employed manage to partly predict returns in this specific sample. The paper's novelty lies in the fresh evidence presented in the case of the UK returns for the most recent dataset available.

Comment

Comment

First-move advantage in seasoned equity offerings: Evidence from European banks

The financial crisis that started in 2008 has generated significant losses for European banks, forcing them to undertake a series of seasoned equity offerings (SEOs) to reinforce their balance sheets in order to comply with regulatory capital requirements. As a result, they have produced repeated SEO waves in a relatively short time frame, when capital supply was limited due to the economic and financial context. We investigate the conditions at which European banks have been able to raise new equity capital by means of rights issues during the global financial crisis, demonstrating the existence of a first-move advantage: within a SEO wave, banks that acted first were able to complete the capital increase at more favorable conditions than their peers that acted later. We also show that first-movers experienced higher valuation ratios at the final cum-rights date compared to late-comers. As a result, first-movers obtained a double advantage: they could offer a lower discount on a price that embedded an higher valuation ratio.

The Most Important Number Not Printed in the WSJ

We are often asked for our estimate of the long-term return of the equity market. Our framework currently indicates 5.3% above inflation for global equities, which we know strikes many investors as high. This is understandable, given that the most available and frequently cited valuation ratio – the S&P500’s CAPE – currently stands at about 30, which is higher than it’s been 96% of the time since 1900 and far above its average level of about 17. In this note we describe our estimation methodology.

Investor Protection and the Long-Run Performance of Activism

Using a parsimonious measure of investor protection constructed from fund organizational characteristics, this paper documents that companies targeted by activists with better investor protection structures outperform those targeted by those with poor investor protection structures by roughly 10% per year. The outperformance is observed only for active targets for which Schedule 13Ds are filed, not for passive Schedule 13G investments, indicating that the effect is not explained by a superior target-selection ability. The evidence suggests that funds with better investor protection achieve increased profitability and valuation ratio of their targets by reducing agency costs, improving corporate governance, and collaborating with other large institutional investors.

Global factors and equity market valuations: Do country characteristics matter?

AbstractThis paper examines the relationship between equity market valuations (dividend‐price ratio) using a dynamic factor model. The factor model decomposes each country's market valuation into a global, region‐specific, and country‐specific component. We find that the amount of variation explained by the factors is related to economic development indicators. Specifically, the valuations in the most developed (emerging) countries are primarily driven by the global (local) component of valuation ratios.

Investigating Long Short-Term Memory Neural Networks for Financial Time-Series Prediction

Stock prices are co-integrated with fundamental valuation factors (earnings, revenue, cash flow, etc.). This has allowed legendary value investors such as Warren Buffett to enjoy tremendous investment success over the long-term. However, the market is efficient at discounting past information and consensus future expectations. Having the ability to read non-obvious information and to have a better prediction at future valuation factors (surprises from consensus estimates) would likely lead to investment outperformance (generating alpha returns) over the long run. In a recently published work, Alberg and Lipton used various artificial neural networks (ANN) to predict companies’ valuation ratios from their historical fundamental factors and suggested promising results. Aligned with this thought, this research project aims at using Long Short-Term Memory (LSTM) neural network, a type of recurrent neural network (RNN), to predict future fundamental valuation factors of companies and then test investment results by applying active risk-return optimized portfolio strategies. The reasons for choosing LSTM network for this study are the following: (1) like a deep neural network, LSTM is a flexible universal function approximator suited for time-series forecast; (2) LSTM, unlike vanilla version of RNN, does not suffer from “vanishing gradient problem” and is well suited in discovering long-range characteristics, hence its name. Given that LSTM network (or deep neural network in general) enjoys a reputation of being prone to over-fitting to in-sample data, we spend a significant amount of efforts in studying the over-fitting behavior and try to lay out systematic procedures in detecting and mitigating such issues. The current work gives us confidence and excitement that much more can be explored to potentially further improve the prediction performance and investment returns.

Why are REITS Currently So Expensive?

AbstractOver the last several years, the price of listed real estate stocks has been unusually high relative to dividends. I find that neither low interest rates nor low risk premia can account for the high valuation ratios. Lower interest rates have been offset by rising risk premia to keep expected returns close to average. Instead, the market has priced in future income growth on commercial properties far above the growth rates seen in the data. High implied growth rates are less extreme for nontraditional REIT sectors. Income growth expectations are also less extreme for international listed real estate.

Decomposing the predictive power of local and global financial valuation ratios

We examine the predictive power of global financial price-to-fundamental ratios for future stock returns in a panel of major developed countries. By disentangling global and local information, we find the global component to be at least equally important and that its importance has increased in recent decades. We further decompose the variability of valuation ratios into discount rate and cash flow driven components and find that the declining predictive power of local ratios coincides with a diminishing importance of the discount rate component. Our results underscore the relevance of global discount rate news in the time variation of local expected returns.

And the Winner IssA Comparison of Valuation Measures for Country Asset Allocation

The authors evaluate and compare the usefulness of various valuation ratios for country selection. To this end, the performance of 73 national equity indices is investigated for the period 1996 to 2017. The EBITDA-to-EV multiple is the best predictor of performance and outperforms other metrics. An equal-weighted portfolio that is long (short) in the tertile of countries with the highest (lowest) EBITDA-to-EV ratio produces a mean monthly return of 0.69% and a Sharpe ratio of 0.81. These are more than double the Sharpe ratios obtained from using traditional metrics such as the book-to-market ratio or dividend yield. Two major drawbacks of inter-country value strategies are identified: 1) payoffs are derived predominantly from emerging and frontier markets and 2) profitability has significantly declined in the last decade.

Cross-Sectional PEG Ratios, Market Equity Premium, and Macroeconomic Activity

This article explores the information content of PEG ratios (price/earnings to growth ratios) for future aggregate returns and economic fundamentals. We first establish an analytic link between PEG ratios and time-varying expected returns of stocks. We then combine the link with empirical asset pricing models to extract market-wide information from cross-sectional PEG ratios. The resultant cross-section estimates of the risk premiums on stock betas serve as proxies for market-wide information. The proxies contain salient information about future market equity premiums and macroeconomic activity both in-sample and out-of-sample. Moreover, the proxies outperform aggregate PEG ratios and the cross-section beta-premium estimate based on conventional valuation ratios and retain incremental power in forecasting future market equity premiums. The results are robust to using various econometric methods for standard error adjustments.

Regime Shifts in Excess Stock Return Predictability: An Out-of-Sample Portfolio Analysis

The authors analyze the out-of-sample performance of asset allocation decisions based on financial ratio predictability of aggregate stock market returns under linear and regime-switching models. The authors adopt both a statistical perspective to analyze whether models based on valuation ratios can forecast excess equity returns, and an economic approach that turns predictions into portfolio strategies. These consist of a portfolio switching approach, a mean-variance framework, and a long-run dynamic model. The authors find a disconnect between the statistical perspective, whereby the ratios yield a modest forecasting power, and a portfolio approach, by which a moderate predictability is often sufficient to yield significant portfolio outperformance, especially before transaction costs and when regimes are taken into account. However, also when regimes are considered, predictability gives high payoffs only to long horizon, highly risk-averse investors. Moreover, different strategies deliver different performance rankings across predictors.