Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?

We investigate the prediction of excess returns and fundamentals by financial ratios, which include dividend‐price ratios, earnings‐price ratios, and book‐to‐market ratios, by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess returns and fundamentals. Cyclical components predict increases in future stock returns, while stochastic trend components predict declines in future stock returns in long horizons. This helps explain previous findings that financial ratios in the absence of decomposition find weak predictive power in short horizons and some predictive power in long horizons. We also find both components predict fundamentals.     

Fundamental indexation via smoothed cap weights

If prices of individual stocks are unbiased but noisy approximations to fundamental values, there will be a gap in returns between the standard cap-weighted market portfolio and the one based on fundamentals. The discrepancy occurs because, relative to fundamentals, cap-weights are too large (small) for stocks with positive (negative) deviations from fundamental values. It follows that the usual cap-weighted portfolio will underperform relative to the fundamental-based portfolio as long as prices revert to fundamental values. This has led Arnott et al. to propose new market indices based on a firm’s fundamental size as measured by its revenues, number of employees, and so on. In this paper we follow the same principle but propose to estimate fundamental weights using a smoothed average of standard cap-weights. Since the putative excess returns of a fundamentals-weighted portfolio requires reversion to fundamental values, and because fundamental values are likely to change slowly, we can estimate current fundamentals by smoothing the time series of a stock’s noisy prices. The determination of fundamental size in terms of accounting data is thereby replaced by a simple estimate based on price history. We derive expressions for expected returns of the market capitalization-based and fundamentals-based portfolios under various assumptions about (i) the random deviations from fundamental values and (ii) the change in fundamentals over time. We present empirical comparisons between portfolios and find the returns of the fundamentals-based portfolios exceed the standard indices by an amount comparable to the prior estimates that used accounting data to determine size.     

High-frequency information content in end-user foreign exchange order flows

This article considers the impact of foreign exchange (FX) order flows on contemporaneous and future stock market returns using a new database of customer order flows in the euro-dollar exchange rate market as seen by a leading European bank. We do not find clear contemporaneous relationships between FX order flows and stock market changes at high frequencies, but FX flows do appear to have significant power to forecast stock index returns over 1–30 min horizons, after controlling for lagged exchange rate and stock market returns. The effects of order flows from financial customers on future stock market changes are negative, while the effects of corporate orders are positive. The latter results are consistent with the premise that corporate order flows contain dispersed, passively acquired information about fundamentals. Thus, purchases of the dollar by corporate customers represent good news about the state of the US economy. Importantly, though, there also appears to be extra information in corporate flows which is directly relevant to equity prices over and above the impact derived from stock prices reacting to (predicted) exchange rate changes. Our findings suggest that financial customer flows only affect stock prices through their impact on the value of the dollar.     

The impact of portfolio diversification on mean reverting components of stock indices

Positive autocorrelations are introduced into stock index portfolios when they are formed from individual stock indices while negative autocorrelations are induced in returns by increasing the investment horizon. Using monthly data of six international stock indices, this paper examines the diversification effect with different investment horizons on autocorrelations of stock index portfolios. The results show that portfolio diversification does not alter the impact of the investment horizon on autocorrelations. Different investment horizons, however, have great impact on the diversification effect on autocorrelations. With short (long) horizons, the average autocorrelation coefficient increases (decreases) with an increase in the portfolio size, suggesting that mean-reverting component dominates the delayed adjustment effect in long horizons and vice versa in short horizons. Our results are robust across two 10-year sub-periods.The author would like to thank an anonymous referee of this Journal for the comments on an earlier version of this paper and the Research Committee of Hong Kong Baptist University for the financial support in this research.     

Nonlinear mean reversion in stock prices

This paper provides new evidence on the time-series predictability of stock market returns by introducing a test of nonlinear mean reversion. The performance of extreme daily returns is evaluated in terms of their power to predict short- and long-horizon returns on various stock market indices and size portfolios. The paper shows that the speed of mean reversion is significantly higher during the large falls of the market. The parameter estimates indicate a negative and significant relation between the monthly portfolio returns and the extreme daily returns observed over the past one to eight months. Specifically, in a quarter in which the minimum daily return is −2% the expected excess return is 37 basis points higher than in a month in which the minimum return is only −1%. This result holds for the value-weighted and equal-weighted stock market indices and for each of the size decile portfolios. The findings are also robust to different sample periods, different indices, and investment horizons.     

The roles of past returns and firm fundamentals in driving US stock price movements

This paper provides new empirical evidence that incorporating past stock returns from different time horizons can enhance the ability of firm fundamentals to better explain stock price movements but this benefit dissipates under uncertainty. We apply both OLS and state-space modeling to US firms' stock price movements over the period from 1999 to 2012 to compare the roles of the two main types of information typically used by equity investors. Empirical results reveal the importance of firm fundamentals over longer term horizons for particularly, small-cap stocks with greater information uncertainty. Furthermore, when market uncertainty is high, fundamentals unambiguously dominate in driving stock price movements of smaller sized firms indicating that uncertainty at the firm and market level both create attention bias on firm fundamentals.     

A Bayesian view of temporary components in asset prices

This paper studies models in which the a stock price contains a random walk and a stationary component, as in Fama and French [Fama, Eugene F., and Kenneth R. French, 1988, Permanent and Temporary Components of Stock Returns, Journal of Political Economy 96, 246–273.] and Poterba and Summers [Poterba, James, and Lawrence Summers, 1988, Mean Reversion in Stock Prices: Evidence and Implications, Journal of Financial Economics 22, 27–59.]. We extend this model to allow for two latent factors which generate short term and long term autocorrelations, respectively. To facilitate econometric identification, we assume that these factors are common across multiple asset returns, and we estimate the factor loadings. In an application to size and book/market sorted portfolios, we find the short term factor economically and statistically insignificant. Estimates of parameters relating to the long range component suggest that portfolios of small firm stock display about three times the amount of mean reversion than for large firm stocks. Overall, the evidence suggests that mean reversion is largely a small firm phenomenon. The evidence is consistent with dynamic equilibrium models in which asset prices co-integrate with aggregate consumption or dividends.     

Firm profitability and expected stock returns: Evidence from Latin America

Despite their higher valuation ratios, larger size, and higher investment needs, profitable firms outperform, in both raw and risk-adjusted returns, unprofitable firms in Latin America. The positive effect of firm profitability on stock returns is pervasive in univariate and bivariate sorts, panel regressions, across sub-regional markets, and among small and large stocks. A five-factor model that includes market, size, distress, profitability, and investment factors prices profitability portfolios better than other popular factor models. Five-factor alphas of profitability portfolios tend to be lower and less statistically significant, both individually and collectively, than alphas from other three widely-used pricing models.     

Portfolio Returns,Market Volatility,and Seasonality

This paper examines three important issues related to the relationship between stock returns and volatility. First, are Duffee's (1995) findings of the relationship between individual stock returns and volatility valid at the portfolio level? Second, is there a seasonality of the market return volatility? Lastly, do size portfolio returns react symmetrically to the market volatility during business cycles? We find that the market volatility exhibits strong autocorrelation and small size portfolio returns exhibit seasonality. However, this phenomenon is not present in large size portfolios. For the entire sample period of 1962–1995, the highest average monthly volatility occurred in October, followed by November, and then January. Examining the two sub-sample periods, we find that the average market volatility increases by 15.4% in the second sample period of 1980–1995 compared to the first sample period of 1962–1979. During the contraction period, the average market volatility is 60.9% higher than that during the expansion period. Using a binary regression model, we find that size portfolio returns react asymmetrically with the market volatility during business cycles. This paper documents a strongly negative contemporaneous relationship between the size portfolio returns and the market volatility that is consistent with the previous findings at the aggregate level, but is inconsistent with the findings at the individual firm level. In contrast with the previous findings, however, we find an ambiguous relationship between the percentage change in the market volatility and the contemporaneous stock portfolio returns. This ambiguity is attributed to strongly negative contemporaneous and one-month ahead relationships between the market volatility and portfolio returns.

     

Identifying Permanent and Temporary Components in Daily and Monthly Japanese Stock Prices

The purpose of this study is to analyze time series of daily and monthly values for the Tokyo Stock Price Index (TOPIX) and stock price values for 15 companies listed on the Tokyo Stock Exchange, Section 1 (TSE-I), to determine the contribution of permanent and temporary components to Japanese stock prices. The existence of temporary components in the price series would imply that Japanese stock returns are partially predictable. The method of canonical correlation is used to determine components common to each series and the persistence of each component series is evaluated by estimating the amount of dependence in the series. The results suggest that Japanese stock prices contain a small temporary component. The fractionally integrated ARIMA (ARFIMA) model is used to characterize both the component series and an estimate of the temporary component for each original price series. The contribution of the temporary component to the total variation of the price series estimated. We find that, in general, the temporary component accounts for less than 8% of the variation in the daily price series and from 5% to 15% of variation in the monthly price series, indicating that there may be a small amount of predictability in Japanese stock prices.     

A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests

This paper examines the return predictability of the US stock market using portfolios sorted by size, book-to-market ratio and industry. We use novel panel variance ratio tests, based on the wild bootstrap proposed in this paper, which exhibit desirable size and power properties in small samples. We have found evidence that stock returns have been highly predictable from 1964 to 1996, except for a period leading to the 1987 crash and its aftermath. After 1997, stock returns have been unpredictable overall. At a disaggregated level, we find evidence that large-cap portfolios have been priced more efficiently than small- or medium-cap portfolios; and that the stock returns from high-tech industries are far less predictable than those from non-high-tech industries.     

Fundamentalists,chartists and asset pricing anomalies

This paper investigates whether excess volatility of asset prices and serial correlations of stock monthly returns may be explained by the interactions between fundamentalists and chartists. Fundamentalists forecast future prices cum dividends through an adaptive learning rule. In contrast, chartists forecast future prices based on the observation of past price movements. Numerical simulations reveal that the interplay of fundamentalists and chartists robustly generates excess volatility of asset prices, volatility clustering, trends in prices (i.e. positive serial correlations of returns) over short horizons and oscillations in prices (i.e. negative serial correlations of returns) over long horizons, often observed in financial data. Moreover, we find that the memory of the learning rule plays a key role in explaining the above-mentioned stylized facts. In particular, we establish that excess volatility of asset prices; volatility clustering and autocorrelation of returns at different horizons emerge when fundamentalists have short memory. However, volatility clustering as well as short-run and long-run dependencies, observed in financial time series, are more pronounced when fundamentalists have longer memory.     

Do industries lead stock markets?

We investigate whether the returns of industry portfolios predict stock market movements. In the US, a significant number of industry returns, including retail, services, commercial real estate, metal, and petroleum, forecast the stock market by up to two months. Moreover, the propensity of an industry to predict the market is correlated with its propensity to forecast various indicators of economic activity. The eight largest non-US stock markets show remarkably similar patterns. These findings suggest that stock markets react with a delay to information contained in industry returns about their fundamentals and that information diffuses only gradually across markets.     

A risk-return explanation of the momentum-reversal “anomaly”

This study investigates the nature of the momentum-reversal phenomenon exhibited by U.S. stock returns from 1962 to 2013. We use cumulative future returns of long–short portfolios, which are formed using prior returns as benchmarks, after portfolio formation to analyze the well-documented momentum-reversal pattern. Contrary to many previous studies our results demonstrate that there is no momentum-reversal anomaly. We show that size (market capitalization), which is often considered a proxy for risk, eventually dominates momentum's initial effect, causing stock prices and, hence, returns to move in the opposite direction. We demonstrate that this latter price movement is likely to be related to institutional trading.     

Stock market liquidity and macro-liquidity shocks: Evidence from the 2007–2009 financial crisis

We develop an empirical framework that links micro-liquidity, macro-liquidity and stock prices. We provide evidence of a strong link between macro-liquidity shocks and the returns of UK stock portfolios constructed on the basis of micro-liquidity measures between 1999 and 2012. Specifically, macro-liquidity shocks, which are extracted on the meeting days of the Bank of England Monetary Policy Committee (MPC) relative to market expectations embedded in 3-month LIBOR futures prices, are transmitted in a differential manner to the cross-section of liquidity-sorted portfolios, with liquid stocks playing the most active role. We also find that there is a significant increase in shares' trading activity and a rather small increase in their trading cost on MPC meeting days. Finally, our results emphatically document that during the recent financial crisis the shocks–returns relationship has reversed its sign. Interest rate cuts during the crisis were perceived by market participants as a signal of deteriorating economic prospects and reinforced “flight to safety” trading.     

The Empirical Relationship Between Earnings Information and Stock Returns

Abstract:

The objective of this study was to examine, using a vector autoregressive model, whether the difference in earnings growth rates caused different reaction speeds in stock prices. Monthly returns of stocks listed in the Taiwan stock market from May 2003 to April 2013 were used as empirical data in this study. The analytical results showed that the returns of portfolios with higher earnings growth rates significantly led those portfolios with lower earnings growth rates when size, trading volume, institutional ownership ratio, and revenue factors were controlled, respectively. This paper finds that the earnings growth rate is a significant determinant of the lead-lag patterns observed in monthly stock returns.     

Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.     

线上销售与未来股票收益

本文通过分析相关上市公司在电商平台的线上销售数据,发现线上销售增长可以预测未来股票收益。根据线上销售增长率构建投资组合可以获得月均1.27%的超额收益,经三因子、五因子模型调整后收益率分别为1.40%和1.35%,并且该超额收益在较长时间内不会逆转。横截面回归结果显示,线上销售增长与未来股票收益显著正相关,并在控制其他市场异象因子后仍然显著。此外,本文还发现线上销售数据的预测能力主要集中在投资者关注有限、线上销售占比高以及套利成本高的公司,其投资价值来源于对公司未来基本面信息的预测能力。进一步研究表明,同时利用线上销售指标和营业收入指标进行投资可以获得更高的超额收益。在考虑业绩预告和业绩快报对线上销售指标预测能力的潜在影响后,结果依然稳健。     

Linkages between the ADR market and home country macroeconomic fundamentals: Evidence in the context of the BRICs

This study investigates the long-run and short-run lead–lag linkages between American Depositary Receipt (ADR) prices and home country economic fundamentals in the context of the BRICs (Brazil, Russia, India and China). In order to obtain an indication of the segmentation or integration between the ADR market and its underlying stock market, the same investigation is also undertaken in relation to the latter. We find that in the long run, economic growth positively drives ADR returns in the cases of Brazil and China but negatively in the cases of Russia and India. In the short-run, economic growth and money supply lead ADR prices but ADR prices predict inflation and oil prices with regard to Brazil while in Russia, oil prices predict ADR returns but the ADR market leads monetary policies and real economic activity. As regards India, in the short run, oil prices and economic growth lead ADR prices but ADR prices predict money supply changes. Finally, with respect to China, the ADR index lead economic growth and inflation but economic variables do not predict ADR prices in the short-run. In the long run, with the exception of China, we find the same kind of linkages between these economic fundamentals and the underlying stock market although the linkages are somewhat stronger. The short run dynamics for ADRs with respect to economic fundamentals are, however, different for that of the respective home country stock market. This would imply that the ADR market and its underlying stock market, as far as the BRICs are concerned, are integrated in the long-run but not in the short-run.     

Computerized stock screening rules for portfolio selection

Recent studies have uncovered several systematic patterns that increase the probability that individual investors can select stock portfolios with excess returns. This study tests the feasibility of using a commercially available computerized stock screening program for investors to take advantage of these patterns. The screening program searches the three major exchanges and selects stocks on both fundamental and technical indicators: low price-to-sales ratio, small firm size, accelerating stock prices above their 50 day moving average, high trading volume, and high earnings growth. Of the 18 models tested between 1994 and 1998, those that allow for selection between exchanges yield portfolio returns that significantly exceed the average market indices.