Does the choice of estimator matter when forecasting returns?

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.     

Infrastructure spillovers and strategic interaction: does the size matter?

We set up a model in which the residents of two neighboring municipalities use the services provided by public infrastructures located in both jurisdictions. The outcome is that municipalities strategically interact when investing in infrastructures, with the small municipality reacting more to the expenditure of its neighbor than the big one. This theoretical prediction is tested by estimating the determinants of the stock of public infrastructures of the municipalities belonging to the Autonomous Province of Trento in Italy. By introducing the classical spatial lag-error component, we find that municipalities positively react to an increase in infrastructures by their neighbors, but the effect vanishes above a given population threshold. Such a result is confirmed when we exploit the exogenous variation in the neighbors’ stock of infrastructures induced by a strong flood that occurred in the Province of Trento in 2000.     

Developed markets’ business cycle dynamics and time-variation in emerging markets’ asset returns

This paper empirically studies the predictability of emerging markets’ stock returns by business cycle variables and the role of developed markets’ business cycle dynamics in this respect. The evidence shows that the link between business cycles and future stock market returns among emerging markets is considerably weaker than among developed markets. By contrast, I find strong evidence of stock return predictability by the respective country’s dividend-price ratio. This latter finding could reflect that variation in dividend-price ratios potentially reflects both the temporary impact of “hot money” inflows on emerging markets’ asset prices and rational expectations of future returns.     

Why does shareholder protection matter for abnormal returns after reported insider purchases and sales?

We use a unique data set of more than 240,000 reported insider transactions across 15 European countries and the USA to analyze the link between country-level shareholder protection and abnormal returns following insider trades. We show that abnormal returns after insider transactions are positively correlated with country-level shareholder protection against expropriation by corporate insiders, which supports the information-content hypothesis. Market reaction to insider purchases increases with shareholder protection because shareholder protection enhances the transparency and trustworthiness of insiders’ actions, and limits possibilities for direct profit diversion, so that more information is eventually reflected in stock prices. For insider sales, shareholder protection decreases their negative information content. We conjecture that this is due to the effect of greater transparency and trustworthiness strengthening the diversification and liquidity reasons for selling in better shareholder protection countries. We find limited support for the rent-extraction hypothesis that conjectures that shareholder protection is associated with insider trading dollar profits.     

How does the choice of Value-at-Risk estimator influence asset allocation decisions?

Considering the growing need for managing financial risk, Value-at-Risk (VaR) prediction and portfolio optimisation with a focus on VaR have taken up an important role in banking and finance. Motivated by recent results showing that the choice of VaR estimator does not crucially influence decision-making in certain practical applications (e.g. in investment rankings), this study analyses the important question of how asset allocation decisions are affected when alternative VaR estimation methodologies are used. Focusing on the most popular, successful and conceptually different conditional VaR estimation techniques (i.e. historical simulation, peak over threshold method and quantile regression) and the flexible portfolio model of Campbell et al. [J. Banking Finance. 2001, 25(9), 1789–1804], we show in an empirical example and in a simulation study that these methods tend to deliver similar asset weights. In other words, optimal portfolio allocations appear to be not very sensitive to the choice of VaR estimator. This finding, which is robust in a variety of distributional environments and pre-whitening settings, supports the notion that, depending on the specific application, simple standard methods (i.e. historical simulation) used by many commercial banks do not necessarily have to be replaced by more complex approaches (based on, e.g. extreme value theory).     

The size and scope of government in the US states: does party ideology matter?

We investigate empirically how party ideology influences size and scope of government as measured by the size of government, tax structure and labor market regulation. Our dataset comprises 49 US states over the 1993–2009 period. We employ the new data on the ideological mapping of US legislatures by Shor and McCarty (Am. Polit. Sci. Rev. 105(3):530–551, 2011) that considers spatial and temporal differences in Democratic and Republican Party ideology. We distinguish between three types of divided government: overall divided government, proposal division and approval division. The main result suggests that Republican governors have been more active in deregulating labor markets. We find that ideology-induced policies were counteracted under overall divided government and proposal division.     

Do asset impairments and the associated disclosures resolve uncertainty about future returns and reduce information asymmetry?

The objective of this study is to provide insights into how Australian listed firms are implementing AASB 136 Impairments of Assets. Our first concern is whether uncertainty about future returns and information asymmetry motivates the recognition of asset impairments. We find no evidence that the recognition of asset impairments is associated with higher uncertainty about future returns. Furthermore, we find no evidence that the recognition of asset impairments is associated with higher information asymmetry. Our second concern is whether asset impairments and the associated disclosures provide information that reduces uncertainty about future returns and information asymmetry. While we find some evidence that asset impairments are associated with decreases in information asymmetry before the financial crisis, during the financial crisis, asset impairments are associated with increases in both measurement uncertainty and information asymmetry.     

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.

When does the tone of earnings press releases matter?

The tone of a firm's financial disclosure is increasingly used as a variable in panel data regressions to predict future performance and explain investors' reaction at earnings announcement. We investigate when tone is informative, and argue that the informativeness of tone increases with the information asymmetry between firms and investors. Using a sample of over 50,000 earnings press releases of about 1800 U.S. public firms between 2004 and 2015, we find that firm growth, size, age, complexity and forecast inaccuracy are key drivers of tone informativeness. The effect is economically significant, since, compared to the reference case of a transparent firm, we find that the slope coefficient of tone doubles or even quadruples in panel data regressions when the firm operates in an environment with high information asymmetry.     

On the risk situation of financial conglomerates: does diversification matter?

In general, conglomeration leads to diversification of risk (the diversification benefit) and a decrease in shareholder value (the conglomerate discount). Diversification benefits in financial conglomerates are typically derived without explicitly accounting for reduced shareholder value. However, a comprehensive analysis requires competitive conditions within the conglomerate, i.e., shareholders and debt holders should receive risk-adequate returns on their investment. In this paper, we contribute to the literature on this topic by comparing the diversification effect in conglomerates with and without accounting for altered shareholder value. We derive results for a holding company, a parent-subsidiary structure, and an integrated model. In addition, we consider different types of capital and risk transfer instruments in the parent-subsidiary model, including intragroup retrocession and guarantees. We conclude that under competitive conditions, diversification does not matter to the extent frequently emphasized in the literature. The analysis contributes to the ongoing discussion on group solvency regulation and enterprise risk management, which is of relevance to insurance groups and other financial conglomerates.     

What does futures market interest tell us about the macroeconomy and asset prices?

Economists have traditionally viewed futures prices as fully informative about future economic activity and asset prices. We argue that open interest could be more informative than futures prices in the presence of hedging demand and limited risk absorption capacity in futures markets. We find that movements in open interest are highly pro-cyclical, correlated with both macroeconomic activity and movements in asset prices. Movements in commodity market interest predict commodity returns, bond returns, and movements in the short rate even after controlling for other known predictors. To a lesser degree, movements in open interest predict returns in currency, bond, and stock markets.     

Do industries matter in explaining stock returns and asset-pricing anomalies?

Industry returns cannot be explained fully by well-known asset pricing models. This study reveals that common factors extracted from industry returns carry significant risk premiums that go beyond the explanatory power of size, book-to-market (BM) ratios, and momentum. In particular, this study shows that (1) the small-firm effect is significant only for firms whose market capitalization is below their industry average; (2) the BM effect is an intra-industry phenomenon; (3) a one-year momentum effect is significant only for firms whose BM ratio is smaller than the industry average and limited to non-January months; and (4) there is seasonality in all effects that cannot be explained by risk-based asset-pricing models. Neither rational nor behavioral theories alone can explain industry returns, and it is perhaps too hasty to attribute asset pricing anomalies to a single driving force.     

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.     

Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models

The objective of this paper is to select the most optimum model or set of models useful for modeling sixteen of the most popular crypto-currencies associated volatility. Five GARCH models, with different error distributions, are fitted to each of these crypto-currencies. The most effectively fit model or superior set of models is then selected through maximizing the likelihood and minimizing the AIC and BIC information criteria. The reached results prove that the majority of crypto-currencies turn out to be rather effectively modulated via the TGARCH with double exponential distribution. Indeed, the attained findings report an asymmetric effect whereby volatility turns out to increase rather by response to positive shocks than by response to negative shocks, implying an asymmetric effect that differs from that generally observed in stock markets. The increase in volatility, as emanating in response to positive shocks may well have its justification in the uninformed investors’ undertaken herding strategies.     

Mergers in the bond rating industry: does rating provider matter?

I examine whether investors favour bond ratings from established global agencies by analyzing the market response to Standard and Poor’s (S&P) acquisition of the Canadian Bond Rating Service (CBRS). As a result of the acquisition, CBRS ratings were completely eliminated and replaced with ratings from S&P. While little reaction was apparent for bonds, the stocks of firms with CBRS ratings responded positively to the acquisition announcement. Small firms and those with little institutional ownership experienced the greatest benefit. The findings suggest that ratings from S&P may increase the exposure of foreign firms to international investors.     

UK brokers' characteristics: does size matter?

This study provides one of the first insights into how UK brokers' institutional characteristics may impact on the forecasting performance of their financial analysts. The study focuses on brokerage house size and finds it to be a significant factor explaining cross sectional variation in forecasting performance. This is consistent with evidence from several recent US studies (Jacob et al. 1997; Clement, 1999). It is likely that this broker-size effect reflects the resources (human, IT) available to brokers' analysts to support them in their activities. It may also reflect larger brokers' superior access to company managers and information. However, this broker-size effect appears to be significant only for forecasts made at horizons of one year or less. The sign of the earnings change being predicted also has a significant impact: for observations where earnings changes are negative, the broker-size effect is larger than for positive changes, though the effect is significant for both cases. In addition, the form of the model employed here suggests diminishing marginal returns to broker size. More generally, this study reiterates the importance of controlling for the most commonly cited explanatory variables for forecast accuracy, and there is evidence that the heavy industry sectors may be more difficult to forecast, echoing the conclusions of UK studies from the 1980s.     

Do firm characteristics matter in explaining the herding effect on returns?

This paper explores whether firm characteristics matter in determining the effect of investor herding on asset returns. We find that the level of herding alone does not command a significant effect on industry returns, implied by insignificant return spreads between industries that experience high and low degrees of herding. On the other hand, we observe that herding has a significant interaction with size and past returns. We find that small firms with high level of herding significantly underperform small firms that experience low herding. Similarly, past loser industries with high level of herding significantly outperform loser industries with low herding. No significant interactions between book‐to‐market and market beta with herding are observed. Overall, the findings suggest that the herding effect presents itself via size and momentum channels with significant investment implications.