A discussion of “Inter-industry network structure and the cross-predictability of earnings and stock returns”

Aobdia et al. (Rev Account Stud, 2014) view the economy as a network of customers and suppliers. Using the 1997 input–output trade flow data from the Bureau of Economic Analysis to model the inter-industry network, they examine whether an industry’s position in the network, in particular, its “network centrality,” affects the transmission of information and economic shocks. They find that, compared to the accounting performance and stock returns of noncentral industries, those of central industries are explained by aggregate risks to a greater extent and are more highly associated with the contemporaneous and future performance of their linked industries. These findings suggest that network centrality matters—it plays an important role in how economic shocks are transmitted within the economy. The question of why network centrality matters, however, remains unanswered. A fruitful avenue for future research is to explore the origin of shocks to shed light on the fundamental question of whether sectoral shocks can aggregate into macro shocks.     

Investor sentiment and the mean–variance relation

This study shows the influence of investor sentiment on the market's mean–variance tradeoff. We find that the stock market's expected excess return is positively related to the market's conditional variance in low-sentiment periods but unrelated to variance in high-sentiment periods. These findings are consistent with sentiment traders who, during the high-sentiment periods, undermine an otherwise positive mean–variance tradeoff. We also find that the negative correlation between returns and contemporaneous volatility innovations is much stronger in the low-sentiment periods. The latter result is consistent with the stronger positive ex ante relation during such periods.     

Discussion of “on the relation between expected returns and implied cost of capital”

This paper discusses the paper “On the relationship between expected returns and implied cost of capital” by Hughes, Liu, and Liu. The discussion focuses on developing the intuition behind the mathematical results and on extensions of the analysis that future research could address.     

Time-varying betas and the cross-sectional return–risk relation: evidence from the UK

The seminal study by Fama and MacBeth in 1973 initiated a stream of papers testing for the cross-sectional relation between return and risk. The debate as to whether beta is a valid measure of risk was reanimated by Fama and French and subsequent studies. Rather than focusing on exogenous variables that have a larger explanatory power than an asset's beta in cross-sectional tests, the matrix of variances-covariances is assumed to follow a time varying ARCH process. Using monthly data from the UK market from February 1975 to December 1996, the cross-sectional return–risk relations obtained with an unconditional specification for assets’ betas are compared to those obtained when the estimated betas are based on an ARCH model. The approach taken by Pettengill, Sundaram and Mathur, which allows a negative cross sectional return–risk relation in periods in which the market portfolio yields a negative return relative to the risk free rate, was also investigated. These tests are also carried out on samples pertaining to a specific month and on samples from which a particular month is removed. Results suggest that the CAPM holds better in downward moving markets than in upward moving markets hence beta is a more appropriate measure of risk in bear markets.     

Do management EPS forecasts allow returns to reflect future earnings? Implications for the continuation of management’s quarterly earnings guidance

Using 18,253 firm-year observations from 1998 through 2003, we build on literature suggesting that more informative disclosures allow returns to better reflect future earnings and test whether management earnings per share forecasts and their characteristics influence the future earnings response coefficient (FERC). We find that FERCs are greater for forecasting firms and when forecasts are more frequent or precise. We suggest that more frequent and more precise forecasts assist investors in better predicting future earnings. Importantly, we find that quarterly and short-term forecasts incrementally increase the association between returns and future earnings beyond annual and long-term forecasts; thus, even short-term, quarterly forecasts allow investors to form better expectations about future earnings. This suggests a benefit of quarterly earnings forecasts possibly overlooked in recommendations from the United States Chamber of Commerce, CFA Institute, Business Roundtable Institute for Corporate Ethics, and The Conference Board to eliminate quarterly earnings guidance.     

Foreign exchange,fractional cointegration and the implied–realized volatility relation

Almost all relevant literature has characterized implied volatility as a biased predictor of realized volatility. In this paper we provide new time series techniques to investigate the validity of this finding in several foreign exchange options markets, including the Euro market. First, we develop a new fractional cointegration test that is shown to be robust to both stationary and non-stationary regions. Second, we employ both intra-day and daily data to measure realized volatility in order to assess the relevance of data frequency in resolving the bias. Third, we use data on implied volatility traded on the market. In contrast to previous studies, we show that the frequency of data used for measuring realized volatility within a fractionally cointegrating framework is important for the results of unbiasedness tests. Significantly, for many popular exchange rates, the use of intra-day rather than daily data affects the emergence of a different bias, as the possibility of a fractionally integrated risk premium admits itself!     

Investor sentiment and the risk–return relation: A two-in-one approach

Traditional finance theory posits a positive risk–return relation, but empirical evidence is inconclusive. Retail investor sentiment has long been viewed as a distorting factor, while more recently institutional investor sentiment is thought to play a role. We examine the separate and joint impacts of retail and institutional investor sentiments on the risk-return relation. We find, at both market and firm levels, the risk-return relation is more likely to be distorted by the two investor-type sentiments jointly, rather than separately. We further find a cross-sectional pattern, with the risk-return relation being more sensitive to investor sentiment for stocks with specific characteristics.     

The relation between earnings and price momentum: Does it vary across regimes?

Review of Quantitative Finance and Accounting - This paper investigates the time-varying relationship between earnings momentum and price momentum. Using a Markov-switching framework, allowing for...     

Liquidity and expected returns—Evidence from 1926–2008

This paper re-examines the liquidity effect on stock expected returns in the NYSE over the period 1926–2008, the pre-1963 period, for which there is a lack of research, and the post-1963 period. The results from the entire sample of 1926–2008 show that expected returns increase with the stock level illiquidity. However, illiquidity level has explanatory power in the cross-sectional variation of stock expected returns only over the post-1963 period, and is, both economically and statistically, insignificant for the whole sample and the pre-1963 period. These findings are robust after taking into account various characteristics such as size and risk controls. On the other hand, evidence from the entire sample and the pre-1963 sample suggests that the systematic liquidity risk plays a significant role in the cross-sectional variation of stock expected returns. The different result for the pre- and post-1963 is explained by the portfolio shifts occurred during the economic downturns.     

An empirical re-examination of the dividend–investment relation

Modigliani and Miller show that, in perfect capital markets, the optimal investment decisions of a firm are not affected by how these investments are financed. Miller and Modigliani further imply that, under the assumption of perfect capital markets, a firm's investment decisions are not affected by its dividend decisions, although dividend decisions may or may not be influenced by investment decisions. Fama and Miller label this result the separation principle. Most recent studies of the separation principle that take into account the existence of market imperfections report sharply contradictory results. This paper tests for linear and nonlinear causality between dividends and investments using both firm-specific and aggregate data for a sample of 417 firms over the 1962 to 2004 period. In general, linear causality tests support the separation principle, whereas nonlinear causality test results contradict the separation principle by revealing strong bi-directional linkages between dividends and investments.     

When does the dividend–price ratio predict stock returns?

If the dividend–price ratio becomes I(1) while stock returns are I(0), the unbalanced predictive regression makes the predictability test more likely to indicate that the dividend–price ratio has no predictive power. This might explain why the dividend–price ratio evidences strong predictive power during one period, while it exhibits weak or no predictive power at other times. Using international data, this paper demonstrates that the dividend–price ratio generally has predictive power for stock returns when both are I(0). However, this paper also shows that the dividend–price ratio loses its predictive power when it becomes I(1). The results are shown to be robust across countries.     

The roles of contextual moderation and personality in relation to the knowledge–risk link in the workplace

While hypothesized, a consistent association between knowledge about a hazard and the estimation of risk associated with that hazard has not been found. Risk communication is premised on the fact that knowledge about a hazard (information) will change people's perceptions of risk and behaviour. In this review methodological and theoretical factors that might account for this lack of consistent association are explored. In this context issues to do with the measurement and assessment of hazard knowledge, knowledge calibration, and the role of context as a moderator are explored. It is argued that contextual factors moderate the knowledge–risk link, with this link being stronger in less controllable, ambiguous contexts. Further this review asks: How can messages be framed (using prospect theory) so as to be more effective in the work place? It is argued that messages framed as gains should be more useful for prevention behaviours (e.g. wearing ear guards) and messages framed as losses should be more effective for detection behaviours (using radiation meters). Finally, the role of personality (e.g. need for cognition, typical intellectual engagement, and self-esteem) in relation to knowledge about a hazard, message framing and risk perception is discussed.     

Revisiting the ICAPM under the distortion of risk–return tradeoff in short-horizon stock returns

We suggest that the distortion of the positive risk–return relation in the ICAPM is a consequence of trading by informed investors to exploit mispricing. We hypothesize and demonstrate that a non-positive (strongly positive) risk–return relation following positive (negative) market returns is attributed to short-selling (purchasing) of overpriced (underpriced) stocks along with optimistic (pessimistic) expectations conditional on good (bad) market news. We verify this asymmetry in the risk–return relation through the indirect risk–return relation conditional on good (bad) market news. We also find that the attenuation (reinforcement) of the positive risk–return relation is more profound in high- (low-) sentiment periods.     

Personal taxes and the time variation of stock returns – evidence from the UK

A potential explanation for the time-series predictability of market returns with dividend yields is the differential tax treatment of capital gains and dividends. This article investigates to what extent the predictability of long-horizon returns can be explained by this tax effect. Assuming that after-tax expected returns are constant, we test the derived relationships between pretax returns and lagged dividend yields with UK data. We also compare the predictability of before-tax long-horizon returns with that of after-tax returns. The results indicate that the tax treatment of dividends does not significantly contribute towards the predictability of stock returns.     

Does the relationship between small and large portfolios’ returns confirm the lead–lag effect? Evidence from the Athens Stock Exchange

This paper investigates whether lead–lag patterns exist between small and large size portfolios constructed from stocks traded in the Athens Stock Exchange (ASE). We examine this relationship in both the short-run (by using the correlation-based approach of Lo and MacKinlay, 1990 and the generalised impulse response analysis by Pesaran and Shin, 1996, Pesaran and Shin, 1998) and the long-run by employing the cointegration-based methodology of Kanas and Kouretas (2005). Furthermore, upon identifying that cointegration exists we then use the estimated error correction models (ECMs) to obtain out-of-sample forecasts of small-firm portfolio returns and it is shown that these ECMs have superior forecasting performance relative to models without the error correction terms. Therefore, we were able to provide a richer exploration of the lead–lag relationships than the one obtained by standard autocorrelation and cross-correlation analysis and vector autoregression analysis. The main finding of our analysis is that a lead–lag effect between small and large size portfolios was established in both the short-run and the long-run for the Athens equity market.     

The bond–stock mix under time-varying interest rates and predictable stock returns

I investigate the allocation of wealth to cash, bonds, and stocks, along with the bond-to-stock ratio (BSR) when interest rates are time-varying and stock returns are predictable via the dividend-price ratio (DPR). The bond–stock mix and the BSR vary with the deviation of the current level of the DPR from its long-run mean and the correlations between all asset classes. The BSR may decrease over time, which contradicts both previously reported results on the matter as well as popular advice. Finally, I show that it is only at the investment horizon that the BSR is independent of risk aversion.     

A behavioral explanation for the negative asymmetric return–volatility relation

We examine the short-term dynamic relation between the S&P 500 (Nasdaq 100) index return and changes in implied volatility at both the daily and intraday level. Neither the leverage hypothesis nor the volatility feedback hypothesis adequately explains the results. Alternatively, we propose that the behavior of traders (from the representativeness, affect, and extrapolation bias concepts of behavioral finance) is consistent with our empirical results of a strong daily and intraday negative return–implied volatility relation. Moreover, both the presence and magnitude of the negative relation and the asymmetry between return and implied volatility are most closely associated with extreme changes in the index returns. We also show that the strength of the relation is consistent with the implied volatility skew.     

The relation between R&;D intensity and future market returns: does expensing versus capitalization matter?

The Australian accounting environment provides an ideal setting for examining the impact of different accounting treatments of firms’ R&D activities on their subsequent returns. Unlike US firms, which can only expense R&D, Australian GAAP permits firms to either expense or capitalize their R&D expenditure. We examine separately the market impact of the R&D intensity of all R&D active firms, ‘capitalizers’ and ‘expensers’. Our results suggest that firms with higher R&D intensity perform better, regardless of the accounting method used, consistent with the resource-based view of the firm. We also find some evidence that firms which expense R&D outperform those which capitalize R&D after controlling for R&D intensity.