Abstract

Purpose – Recent studies on the securities market’s differential pricing of earnings components indicate that cash flows from operations are valued more highly than extreme total accruals. However, no previous study has examined whether cash flows from operations have a higher valuation than moderate total accruals. Therefore, this study examines the securities market’s differential pricing of cash flows from operations and both moderate and extreme total accruals

Design/methodology/approach – The study’s sample is divided into two sub-samples: a moderate total accruals sub-sample; and an extreme total accruals sub-sample. To evaluate whether cash flows have a higher valuation when compared to total accruals, for the entire sample and for each of the two sub-samples, the study examines the statistical significance of the difference between slope coefficients of cash flows and total accruals for regression of returns on both unexpected cash flows from operations and unexpected total accruals

Findings – Consistent with prior research, results from the entire sample show a differential higher valuation of cash flows when compared to total accruals. Another finding, consistent with recent studies, is that cash flows from operations have a higher valuation when compared to extreme total accruals. However, there is no higher differential valuation of cash flows over moderate total accruals. These findings support the decomposition of earnings into the components of cash flows from operations and total accruals only when total accruals are extreme (rather than moderate).

Practical implications – A possible explanation for these results is that since accruals predict cash flows, total accruals – when moderate (i.e. not extreme) – are priced similarly to cash flows. These results reveal that when total accruals are moderate, earnings are a better proxy for the underlying cash flows (over the entire future horizon, not just the current period) than is cash flows. However, since total accruals are unlikely to persist in a permanent way over the years, these results indicate that the decomposition of earnings into the components of cash flows from operations and total accruals is consistent with the information set used to value equity securities. Therefore, separate disclosure of cash flows is value relevant. In addition, users of financial statements certainly need the cash flows information as an ex-post validation of the prior earnings.

Originality/value – This study’s contribution stems from its determination of the preferred level of disaggregation of earnings components (i.e. operating cash flows and total accruals). This is expected to help investors in their attempt to enhance the outcome of their informed investment and credit decisions.

Introduction

Reported numbers can be considered informative if they assist investors in making more informed decisions. When stock prices change in response to reported numbers, it can be presumed that investors’ valuation, buying, and selling decisions are influenced by reported numbers. It follows that reported numbers inform their decisions. Earnings can be divided into total accruals and cash flows from operations [1]. Earnings components (i.e. operating cash flows and total accruals) can be considered incrementally informative in relation to total earnings if they assist investors in making decisions that are more informed compared to those made when only the total is known. For example, if investors think that current cash flows are more or less informative when compared to total accruals with respect to their ability to predict future cash flows, then they are likely to be interested in learning about these earnings components and not simply total earnings. Identifying the preferred level of disaggregation regarding the elements of earnings can assist investors in making more effective investment and lending decisions. With these considerations in mind, the present study seeks to investigate the differential price of earnings components in the securities market.

Results

5.1 Stock returns and earnings components for the entire sample Table 2 presents the results regarding the relationship between stock returns and earnings components (i.e. cash flows from operations and total accruals) for the entire sample. The sum of the coefficients for the level and change in cash flows (α1þα3) is 1.218 (t 5 6.743), which is significant and positive at the 1% level. The sum of the coefficients for the level and change in total accruals (α2þα4) is 0.699 (t 5 11.311), which is significant and positive at the 1% level. These results suggest that both cash flows from operations and total accruals have incremental information content beyond each other.

Abstract

     Using daily credit default swap (CDS) data, we find a positive relation between corporate credit risk and unexpected monetary policy shocks during FOMC announcement days. Positive shocks to interest rates increase the expected loss component of CDS spreads as well as a risk premium component. However, not all firms respond in the same manner. We show that firm-level credit risk is an important driver of the monetary policy response, both in credit and equity markets, and its role is not diminished by the inclusion of other risk proxies. A stylized corporate model of monetary policy, investment, and financing rationalizes our findings.

Introduction

     Understanding the transmission of interest rate shocks to corporations is of paramount importance to policymakers and economic researchers, as corporate borrowing is widely used to fund investment, production, labor, and other real activities. While economic theory suggests that firms obtain credit at a premium that compensates for default risk, it is still widely debated as to how corporate credit risk is affected by monetary policy. This transmission mechanism has played a crucial role in policy responses over the last two decades.

     In this paper, we connect unexpected interest rate movements to fluctuations in credit risk to shed more light on this transmission mechanism. To measure credit risk at the firm-level, we take advantage of daily quoted prices on the corporate, single name, credit default swap (CDS) market. Using CDS rates allows us to conveniently separate the two main components of credit spreads, an expected loss component and a credit risk premium, and independently study their reaction to monetary policy surprises.2

Conclusion

     Monetary policy surprises significantly affect firm-level credit risk. Consistent with recent evidence, we document that both components of firm-level credit risk (i.e., the expected loss and the risk premium components) and equity prices respond more to monetary policy surprises when firms have higher credit risk, as measured by their CDS spreads. Additionally, the importance of firm-level credit risk for the propagation of monetary policy shocks does not diminish when we contemporaneously control for book leverage and market size, two characteristics commonly associated with firm-level risk.

     To rationalize the heterogenous response of credit risk and the noisiness of leverage as a risk indicator, we construct a stylized three-period model that features endogenous financing and investment decisions together with a monetary authority that sets short term interest rates. Central to our results is an intermediary pricing kernel for which larger prices of risk related to interest rate shocks correspond to larger responses of credit spreads and real quantities. We show in the model economy that firms with higher ex-ante spreads, that are closer to default, are those that are more sensitive to interest rate shocks. Meanwhile, firms with higher leverage are able to afford such debt capacity due to their fundamental strength and, as a result, their credit spreads are less sensitive to monetary policy shocks.

     Overall, our work suggests that a simple and intuitive measure such as leverage might not be as effective as a more direct measure of credit risk such as CDS spreads, to understand the pass through of monetary policy at the firm level.

Abstract

     Investors commonly rely on macroeconomic variables to drive capital allocation decisions. But other institutional factors may alter investor returns as well, particularly in emerging market countries. Given these concerns, this paper examines the effects of institutional factors—specifically democracy, transparency and corruption—on emerging market equity returns and flows. We find that institutional quality impacts stock market returns and flows in emerging markets where corruption, transparency, and democracy levels are below average. We also find that government-owned or controlled industries are positively impacted by a deterioration in the corruption and democracy indexes, while highly concentrated sectors, like the financial industry, are negatively impacted by improving transparency.

Introduction

     Investors often rely on macroeconomic factors (e.g., inflation, GDP growth) to drive allocation decisions (Ahlquist, 2006). But a burgeoning body of research suggests that investors should couple macroeconomic factors with market-specific institutional factors such as corruption when deciding where to allocate their capital across international markets. Institutional quality varies significantly across emerging market (EM) countries and within EM countries over time presenting an opportunity to explore the impact of institutional quality on EM stock market performance. As such, this paper examines if, and when, institutional quality impacts investor equity (i.e., stocks) returns and flows in emerging markets. Additionally, we assess if institutional quality has differential effects on investor equity returns in specific EM industries.

     The three institutional factors we focus on are: democracy, transparency, and corruption. The quality of these factors can affect market performance because they impact the reliability of the data in which investors rely upon to allocate capital. In addition, institutional quality can influence the cost of doing business, and affect the stability of markets.1 But how do each of these three institutional factors affect stock market performance in emerging markets.

Conclusions

     This paper examines the effect that varying levels of institutional quality regarding corruption, transparency, and democracy has on annual equity returns, flows, and industry returns in EM countries over an 18-year period. We find that an increasing level of transparency has a positive, significant impact on average annual market returns in each of the below median group of countries relative to corruption, transparency, and democracy. This result suggests that for emerging market countries where corruption, transparency, or democracy are poor, policies that improve transparency will have a positive impact on market returns.

     In addition, changes in institutional indices impact several EM industry returns. Specifically, we see higher democracy scores result in lower returns in the utility, oil and gas, and technology industries, while higher scores for corruption result in lower returns for the consumer services and utility industries. The commonality between these industries is that they have a high level of government ownership or management, particularly in emerging market countries, or are highly concentrated industries. Finally, we see higher corruption scores result in lower net equity flows, a result that may be driven by FDI into China. Less surprising is the finding that net equity flows are positively impacted by an increase in the democracy index in countries with below median democracy and transparency scores.

1- Introduction

2- Data and summary statistics

3- Econometric methodology

4- Out-of-sample forecasting results

5- Extension analysis

6- Robustness analysis

7- Conclusion

References

Abstract

We find that mixing existing forecasting models can significantly improve prediction performance of stock returns. Empirical results suggest that the stock return forecasting by three proposed mixed models are more significant both in statistical and economic terms than the corresponding models in Campbell and Thompson (2008), Wang et al. (2018) and Zhang et al. (2019). This improvement of predictability is also remarkable when we employ the multivariate information to predict stock return. The prediction performance of mixed models is robust to a series of robustness test. Particularly, the three proposed mixed models obtain superior out-of-sample forecasting performance of stock return for business cycles, rolling window predictions and different out-of-sample periods.

Introduction

Stock return prediction is of great significance to asset allocation, risk management and asset pricing. An influential research by Goyal and Welch (2008) indicates that it is difficult to find a predictor or a rational model to accurately forecast out-of-sample stock return. So far, numerous literatures have proposed predictors that could be used to predict stock returns, including interest rates (Ang and Bekaert, 2007; Fama and Schwert, 1977; Campbell, 1987), dividend ratios (Fama and French, 1988, 1989; Goyal and Welch, 2003; Lewellen, 2004), the consumption-wealth ratio (Lettau and Ludvigson, 2001), inflation (Campbell and Vuolteenaho, 2004), stock variances (Guo, 2006; Ludvigson and Ng, 2007), downside variance risk (Feunou et al. 2015; Kilic and Shaliastovich, 2018), the variance risk premium (Bollerslev et al. 2009; Bollerslev Bollerslev et al. 2014), economic policy uncertainty (Chen et al. 2017), investor sentiment (Huang et al. 2015), short interest index (Rapach et al. 2016), news-implied volatility (Manela and Moreira, 2017), technical indicators (Neely et al. 2014; Gao et al. 2018; Zhang et al. 2019), manager sentiment (Jiang et al. 2017), oil-related variables (Chiang and Hughen, 2017; Nonejad, 2018; Wang et al. 2019), and among others.

1. Introduction

2. The data

3. Methodologies: the SVAR models and copula modeling

4. Results

5. Concluding remarks

Appendix A. Supplementary data

References

Abstract

Following the adoption of new techniques of shale and fracking by U.S. oil companies, a structural vector autoregression model (SVAR) complements studies on why Brent and WTI started to diverge around early2011. Using monthly data from 2000 to 2018, we decompose oil supply into: world oil (excluding U.S.), U.S. conventional (non-tight) oil and U.S. tight oil. We examine the variance decomposition of stock returns for the aggregate market (S&P 500), the S&P Energy sector and Chevron and Exxon Mobil oil companies, and we further identify differences between two subsamples from 2000 to 2010 and 2011 to 2018, respectively. We find that supply considerations (especially due to tight oil) become more important in the subsample after 2011, not only for individual oil companies but also for the aggregate market and energy sector: Supply shocks due to tight oil explain in our benchmark model between 29% (S&P 500) and 31% (S&P Energy) of the variance in stock returns after 24 months and between 28% and 29% for oil companies. None of these are statistically significant in the pre-2011 subsample. Among impulse responses, tight oil production responds positively to disruptions in world oil, and U.S. stock returns respond positively to oil price shocks and respond negatively to tight oil shocks which is a further finding while being consistent with the literature. Copula modeling uncovers stronger tail dependences in the second subsample for the interactions during downturns and upturns among global demand, crude oil prices and stock markets.

Introduction

In an influential paper, Kilian (2009) proposes a structural vector autoregression (SVAR) approach to global oil supply, an index of global real demand and real oil prices. The latter are typically calculated by oil prices denominated in U.S. dollars (such as the U.S. refiner cost of oil, crude WTI or Brent oil prices) deflated by the U.S. price index. Its main message is that supply shocks are relatively less important than demand shocks, themselves captured by a global index of commodity prices, which became known as “Kilian index”. Due to its impact in the academic literature, several extensions of Kilian (2009) have been developed, usually adding one series in the SVAR to consider the effects of supply and demand not only on real oil prices but also on other financial markets, including U.S. stock prices by Kilian and Park (2009), U.S. bond prices by Kang et al. (2014), exchange rates by Chen et al. (2016a), economic policy uncertainty by Kang et al. (2017) and U.S. consumer sentiment by Güntner and Linsbauer (2018). Another stream of papers has focused on misspecifications of the basic SVAR model to allow for inventories and speculative trading put forward by Kilian and Murphy (2014).

Some authors have examined the causes of why Brent and WTI started to diverge. This is very important because the two oil prices serve as major benchmarks for the world and the U.S. For example, Scheitrum et al. (2018, p. 463) aim to “explain what happened in 2011 when the spread diverged from historical levels, sending WTI to more than a $20 discount under Brent and resulting in the WTI being viewed as a broken benchmark for a time.” Fig. 1a illustrates the divergence between Brent and WTI using monthly data making clear the shift in the spread after 2011 when advances in oil production became evident, as shown in Fig. 1b which compares the world (excluding U.S., right axis), U.S. non-tight and U.S. tight oil production over our sample period. Interestingly, the oil spread started diverging from historical levels at a time when U.S. tight oil production started rising. In this paper we will therefore refer to the first subsample running from 2000 to 2010 and the second running from 2011 to 2018. This paper merges the SVAR literature on real oil prices and the divergence in crude oil prices. Our contribution is not on the causes of the divergence but on the implications of this divergence for stock returns. We believe this is of interest because both academic studies and the financial press have suggested that the vast increase in U.S. oil production may be responsible for the divergence in oil prices, which itself may impact how oil price shocks are transmitted into the financial markets.

The decision over asset holding is traditionally premised on the double-edge objective of returns maximization and risk minimization. While the class of assets held by a typical investor depends on his attitude towards risks, an optimal investment portfolio requires a strategic combination of alternative assets (commodities, T-bills, stocks etc). To this end, our paper analyzes the role of crude oil prices in predicting stock returns, in addition to the traditional factors, particularly the returns on risk-free assets (such as, T-bills) as enunciated by the Capital Asset Pricing Model (CAPM). We also consider the possibility of nonlinearities in the nexus between crude oil prices and stock returns of nine major oil and gas companies that are currently listed on the Nigerian Stock Exchange over the period of January 2014 to November 2019. Our results show significant in-sample predictability of stock returns using crude oil prices, thereby affirming our argument that oil price matters in the predictability of stock returns for some listed oil and gas firms in Nigeria. We also offer evidence for the role of asymmetries in the predictability of stock returns for the majority of the listed oil and gas companies in Nigeria. By implication, the increasing exposure of the earnings, vis-a-vis, � the share prices of some major oil and gas companies to negative changes in global oil prices suggests the need for diversification of their scope of operations.

Introduction

The Nigerian economy is largely oil dependent, making it highly sensitive to movements in global crude oil prices. Oil price volatility matters for the investment decisions of prospective investors in Nigeria’s oil and gas sector, most especially. This in turn affects the profitability of firms and hence the values of their shares on the domestic stock market (see, for instance, Gupta, 2016 and Soyemi et al., 2017). In the words of Kayalar et al. (2016), changes in crude oil prices are believed to affect stock markets through the channel of expectations. Meanwhile, Basher and Sadorsky (2006) argued that the impact of falling oil prices on stock market differs from country to country depending on whether the country is an oil exporter or an oil importer. In an oil exporting country, an increase in oil prices improves the trade balance, leading to a higher current account surplus and an improving net foreign asset position. At the same time, a rise in oil prices tends to increase private disposable income in oil exporting countries. This in turn enhances corporate profitability, boosts domestic demand and push up stock prices, thereby causing exchange rate to appreciate. In oil importing countries, the process works broadly in reverse: trade deficits are offset by weaker growth and, overtime, real exchange rate depreciates and stock prices decline (Basher and Sadorsky, 2006). The extent to which stock prices are influenced by world oil price changes is explained by the theory of equity valuation, which defines the stock price as the sum of discounted values of expected future cash flows at different investment horizons (Jouini, 2013). Consequently, oil prices affect stock prices directly by impacting future cash flows or indirectly through an impact on the interest rate used to discount the future cash flows. In the absence of complete substitution effects between the factors of production, rising oil prices, for example, increases the cost of doing business, and for non-oil related companies, it reduces profits. Rising oil prices can also be passed on to consumers in form of higher prices, but this will reduce the demand for final goods and services and depress profits.

1- Introduction

2- Data

3- The time-varying relation between inflation and consumption

4- The time-varying inflation risk premium

5- Origins of the time-varying inflation risk premium

6- Model

7- Calibration

8- Robustness checks

9- Conclusion

References

Abstract

We show that inflation risk is priced in stock returns and that inflation risk premia in the cross-section and the aggregate market vary over time, even changing sign as in the early 2000s. This time variation is due to both price and quantities of inflation risk changing over time. Using a consumption-based asset pricing model, we argue that inflation risk is priced because inflation predicts real consumption growth. The historical changes in this predictability and in stocks’ inflation betas can account for the size, variability, predictability, and sign reversals in inflation risk premia.

Introduction

We show that inflation risk is priced in stock returns and that both the price and quantities of inflation risk are strongly time-varying, even changing sign over our sample from 1962 to 2014. We argue that inflation is risky because it predicts real consumption growth in a time-varying way. Positive shocks to inflation sometimes contain bad news for future consumption, whereas at other times they contain good news. We provide new empirical evidence, for both the cross-section of stock returns and the aggregate stock market, and develop an equilibrium model to argue that it is the time-variation in the predictive content of inflation that drives the observed time-variation in the price and quantities of inflation risk. To quantify the predictive content of inflation at a given point in time, we define the nominal-real covariance as the slope coefficient in a conditional (that is, rolling) regression of real consumption growth on lagged inflation. We find that this nominal-real covariance is an economically strong and statistically significant predictor of the inflation risk premium in the cross-section of stocks. We measure the quantity of inflation risk of stocks by their conditional inflation beta, that is, the slope coefficient in a rolling regression of returns on inflation shocks. A one standard deviation increase in the nominal-real covariance predicts an increase in annualized expected return of 5.3% for a high-minus-low portfolio constructed by sorting stocks in deciles according to their inflation beta. Given an unconditional average return for the high-minus-low inflation beta portfolio of -4.2%, this increase implies that the inflation risk premium can switch sign.

Labor input is an important factor in a firm’s production and affects stock return. We use an optimization model to explore the stock return-labor hiring relation with the effects of employment frictions and labor supply. Our model illustrates that labor hiring is negatively related to the expected stock return from the discount rate channel; the negative return-hiring relation becomes steeper when the firm’s employment frictions are higher; positive labor supply shock leads to a flatter return-hiring relation. Using Chinese-listed firm data, we provide evidence for the existence of the return-hiring relation and the impact of employment frictions and labor supply confirming the theoretical predictions.

Introduction

Traditional studies of stock returns mainly focus on the effects of a firm’s financial variables and relevant systematic risks. These studies usually leave no role for the labor input by assuming that labor can be adjusted without costs. However, the labor market does experience frictions. For example, the labor adjustment costs paid by a firm when adjusting the labor force size reduce the income attributable to investors and thus should be reflected in its asset prices. Danthine and Donaldson (2002) suggest the impact of operating leverage on stock returns due to labor contracts. More recent studies have already looked into the effect of the labor force on financial markets and find some crucial labor features significantly affect asset prices. Donangelo (2014) suggests that labor mobility has a positive effect on a firm’s stock returns since it increases the firm’s operating leverage. Favilukis and Lin (2016a,b) report that stock returns increase with wage rigidity because smoother wages make dividends riskier. Donangelo et al. (2019) find the relative size and inflexibility of labor costs result in labor leverage for a firm, and higher labor share increases the risk and equity premium. Labor hiring, as an important labor market characteristic, is closely related to the development of a firm needing competent employees. Thus, some studies investigate the impact of labor hiring.

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our insample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.

Introduction

Stock market volatility is a crucial input for risk management, asset pricing and portfolio management. Therefore, modeling and forecasting stock market volatility remain a hot topic in financial econometrics. To improve the stock market volatility forecasting, some studies have constructed new and powerful predictors or factors. Schwert (1989) finds limited support for links between volatility and macroeconomic predictors, whereas more recent papers such as Christiansen, Schmeling, and Schrimpf (2012), Paye (2012), Engle, Ghysels, and Sohn (2013), Conrad and Loch (2015), and Nonejad (2017), Mohsen and Sujata (2019) arrive at somewhat more encouraging results by constructing the macroeconomic and financial variables. Very recently, Feng, Wang, and Yin (2017) find that oil volatility risk premium (oil VRP) does exhibit statistically and economically significant in-sample and out-of-sample forecasting power for stock market volatility in G7 countries. Wang, Wei, Wu, and Yin (2018) also show that the crude oil volatility is predictive of stock volatility in the short-term from both in-sample and out-ofsample perspectives. In addition, Bašta and Molnár (2018) study the comovement between volatility of the equity market and the oil market, both for implied and realized volatilities by using the wavelets method.

1- Introduction

2- Data and methodology

3- Empirical analysis

4- Robustness check

5- Concluding remarks

References

Abstract 

This paper investigates return and cash flow predictability via the decomposition of VIX. The squared VIX index is decomposed into expected return variations (ERV) and variance risk premium (VRP). Without imposing a strong assumption on the dynamics of the return variations, I examine the predictability via the generalized method of moments (GMM) approach with appropriately chosen instruments. Empirical analysis shows the short-term return predictability of VRP and the short- and long-term cash flow predictability of ERV.

Concluding remarks

This paper shows the short-term return predictability of VRP and the short- and long-term cash flow predictability of ERV. As argued by Bekaert and Hoerova (2014), the return predictability of VRP can potentially be explained in terms of the ‘‘habit’’ advocated by Campbell and Cochrane (1999). Their habit-induced counter-cyclical changes in risk aversion generate variations in risk premiums. On the other hand, one candidate theory for the cash flow predictability of ERV may be the long-run risk model (e.g., Bansal and Yaron, 2004). Economic uncertainty, which can be proxied by ERV, is a key variable in the long-run risk model. In a version of the model in Bollerslev et al. (2015), innovations in time-varying volatility and the long-run risk factor are correlated; thus, the long-run risk factor can have a persistent effect on dividend growth. This specification may drive the long-term cash flow predictability of ERV. It would be interesting to develop a version of the long-run risk model that leads to long-run cash flow predictability of economic uncertainty in future research.

Accounting and finance have a long tradition of studying the relation between accounting earnings and stock market returns. This interest is driven by the importance of earnings to investment decisions and to the prediction of returns. In their asset allocation decisions, investors form expectations about the firm’s future cash flows and the risk associated with these cash flows (Fama 1970). As earnings contain information about the stream of cash flows, investors use earnings information to revise their expectations about future flows and this leads to a revision of stock prices. In other words, earnings are useful for stock price formation. Prior studies have focused on explaining the time series variation or the cross-sectional variation in the earnings-return relation. We propose to study both the temporal and cross-sectional variation in the relation between earnings, earnings changes and returns using an extension of the regime switching methodology introduced by Hamilton (1989): the heterogeneous regime switching methodology. The heterogeneous regime switching method can be summarized as follows. First, we estimate the time series variation in the earnings-returns relation for the sample firms for the period 1997–2010. The estimation method allows us to identify breaks in the time series of earnings-returns and to characterize each regime. As a result, we are able to let the data generating process determine the regime rather than identifying the breaks ex-ante which would be subjective. Second, each firm is assigned to a group (or cluster) based on how long it stays in one regime and the likelihood of switching to the other regime. Thus, the model is dynamic as it allows firms to switch between regimes across time. We identify two regimes. The low volatility regime corresponds to periods of low return volatility and a moderate association between earnings and returns. Both earnings and earnings changes are positively associated with returns but the magnitude of the earnings coefficients is smaller than in the other regime. The high volatility regime represents periods of high volatility in returns with earnings and earnings changes strongly associated with returns. This result is consistent with the idea that in periods of high price instability, such as financial crises, information about earnings is more important to investors than in ‘‘normal’’ periods. During bear market conditions, investors become more risk averse and fly from stocks with high levels of uncertainty about fundamental value (Lang and Maffett 2011). As financial information lessens uncertainty about the firm’s fundamental value and reduces risk perception, earnings become more important for investors (Lang and Maffett 2011). In other words, investors rely more on earnings information during market downturns because other information is more likely to reflect speculation and noise.

Background

Data, variables and model specification

Main empirical tests

Endogeneity issue

Robustness check

Conclusion

References

Abstract

We investigate whether foreign institutional investors facilitate firm-specific information flow in the global market. Specifically, using annual institutional ownership data from firms across 40 countries, we find that foreign institutional ownership is negatively associated with excess stock return comovement. Our results are more pronounced when foreign institutional investors originate from common-law countries and hold a large equity stake in invested firms; and when the invested firms are located in civil-law countries. Overall, the evidence suggests that foreign institutional investors from countries with strong investor protection play an important informational role in mitigating excess stock return comovement around the world.

Background

There has been dramatic growth in foreign institutional investment in global capital markets over the past few decades (Karolyi, 2006). Researchers have so far focused on the monitoring role played by foreign institutional investors in firms in which they invest. For example, foreign institutional investors are credited with promoting domestic firms’ corporate governance (Gillan and Starks, 2003). Foreign institutional investors play a more active monitoring role than domestic institutional investors, because foreign institutional investors are less likely to seek business relationships with local firms (Ferreira and Matos, 2008). Aggarwal et al. (2011) find a positive relation between foreign institutional ownership and firm-level governance efficacy. However, an unexplored but equally important question is whether foreign institutional investors play an informational role in influencing firms’ information environment. To fill this gap, we investigate whether foreign institutional investors facilitate firm-specific information flows in the global market, thereby mitigating excess stock return comovements. A growing body of research has established firm-specific return variation as an effective measure of firm private information impounded into stock price. French and Roll (1986) and Roll (1988) show that neither market returns nor public news explain stock return variation, suggesting that firm-specific return variation captures the impounding of private information into stock price. An influential study by Morck et al. (2000) finds that stock returns are less synchronous in developed markets with relatively strong property rights protection (and thus fewer impediments to informed trading) than in emerging markets with relatively poor protection, supporting the notion that firm-specific return variation is associated with the intensity of information-based trading. Empirical studies have largely supported their conclusions (Brockman and Yan, 2009; Durnev et al., 2003; Gul et al., 2009; Hutton et al., 2009; Kim and Shi, 2012). Piotroski and Roulstone (2004) show that insider trading reduces stock return synchronicity. Fernandes and Ferreira (2009) show that the enforcement of insider trading laws encourages informed risk arbitrage, which in turn facilitates the impounding of firm-specific information into stock prices. Ye (2012) shows that active institutional investors mitigate excessive stock return comovement caused by noise traders