Futures markets and fundamentals of base metals

According to the theory of storage, the interest-adjusted basis equals the warehousing cost minus the convenience yield (i.e., benefit of holding a physical commodity) per time unit. By assuming that warehousing costs relatively constant at alternative stock levels, the interest-adjusted basis will be inversely associated with the convenience yield.In this article, we explore whether the sign of the interest-adjusted basis determines the degree of association between spot and futures returns on the six London Metal Exchange base metals—aluminum, copper, lead, nickel, tin, and zinc. In addition, we study to what extent the sign of the interest-adjusted basis correlates with the business cycle of industrial production of various countries (e.g., US, G7, OECD, Russia, and China), and with the business cycle of consumption/production of the aforementioned six base metals.We conclude that a negative interest-adjusted basis is generally associated with booming industrial production, a negative or small metal surplus (i. e., production minus consumption), and low metal stocks. To our knowledge, this is the first study to link metal market fundamentals to futures markets dynamics.     

Oil commodity returns and macroeconomic factors: A time-varying approach

This paper analyses the dynamic influence of macroeconomic factors on oil commodity returns (crude oil and heating oil) shown in monthly data over the period of 1990–2013. Using a time-varying parameter model via the Kalman filter, we find that macroeconomic factors are relevant for explaining oil commodity returns. We find that multilateral exchange rates have a negative effect on commodity returns. We confirm the existence of a strong linkage between energy and non-energy commodities. More importantly, we find shifts in global demand and SP500 effects that are not identified through the constant parameter model. These variables have had a progressively positive effect on oil commodity returns, especially since 2008.     

Consumer confidence or the business cycle: What matters more for European expected returns?

Answer: The business cycle.We show that consumer confidence and the output gap both affect excess returns on stocks in many European countries: When the output gap is positive (the economy is doing well), expected returns are low, and when consumer confidence is high, expected returns are also low. Consumer confidence and the output gap are also highly positively correlated. In fact, we find that consumer confidence does not contain independent information (i.e. information over and above that contained by the output gap) about expected returns. Our use of European data allows us to examine both aggregate European and local-country data on consumer confidence and output gaps. We find that even local-country consumer confidence does not contain independent information about expected returns. Our findings have asset pricing implications: We show that the cross-country distribution of expected returns is better captured when using the European output gap as a risk factor.     

Stock Returns and the Business Cycle

This article examines whether movements in economic factors dictated by the dividend discount model can explain broad movements in stock returns over the business cycle. As anticipated, stock returns decrease throughout economic expansions and become negative during the first half of recessions. Returns are largest during the second half of recessions, suggesting an important role for expected earnings. These results are consistent with the notion that expected stock returns vary inversely with economic conditions, yet suggest that realized returns are especially poor indicators of expected returns prior to turning points in the business cycle.     

Order Flows and Stock Returns: Compensation for Market Makers with Inventory Concerns

We investigate whether market makers with inventory concerns are compensated with subsequent monthly returns in the cross‐section. We find a significant negative relation between order flows and monthly returns, “the order flow effect,” suggesting that market makers lower prices for stocks with sell order flows and demand a reward in the form of higher expected returns. Further, the order flow effect is stronger for high‐volatility or high‐volume stocks for which market makers have serious inventory concerns. Funding liquidity of market makers also affects the order flow effect. Finally, our finding is independent of existing regularities and robust to the decimalization.     

Inventory investment and the cost of capital

We examine the relation between inventory investment and the cost of capital in the time series and the cross section. We find consistent evidence that risk premiums, rather than real interest rates, are strongly negatively related to future inventory growth at the aggregate, industry, and firm levels. The effect is stronger for firms in industries that produce durables rather than nondurables, exhibit greater cyclicality in sales, require longer lead times, and are subject to more technological innovation. We then construct a production-based asset pricing model with two types of capital, fixed capital and inventories, to explain these empirical findings. Convex adjustment costs and a countercyclical price of risk lead to negative time series and cross-sectional relations between expected returns and inventory growth.     

Corporate life cycle and M&A activity

We investigate the impact of corporate life cycle on takeover activity from the perspective of acquiring firms. Using the earned/contributed capital mix as the proxy for firm life cycle, we find a highly significant and positive relation between firm life cycle and the likelihood of becoming a bidder. This finding is, however, driven by the mature rather than the old acquirers in the sample. Further we find that, whilst firm life cycle has a positive effect on the probability that a deal will be negotiated, it is negatively related to tender offers. In addition, the likelihood of making both cash and mixed deals are positively related to the corporate life cycle. Finally, we find that life cycle has a negative impact on the abnormal returns generated on the announcement of a deal although it is unable to distinguish between the returns received by firms at different stages in their life cycle.     

The impact of jumps on carry trade returns

This paper investigates how jump risks are priced in currency markets. We find that currencies whose changes are more sensitive to negative market jumps provide significantly higher expected returns. The positive risk premium constitutes compensation for the extreme losses during periods of market turmoil. Using the empirical findings, we propose a jump modified carry trade strategy, which has approximately two-percentage-point (per annum) higher returns than the regular carry trade strategy. These findings result from the fact that negative jump betas are significantly related to the riskiness of currencies and business conditions.     

Real Activity, Inflation, Stock Returns, and Monetary Policy

We find that contractionary monetary policy shocks generate statistically significant movements in inflation and expected real stock returns, and that these movements go in opposite directions. Since positive shocks to output precipitate monetary tightening, we argue that the countercyclical monetary policy process is important in explaining the negative correlation between inflation and stock returns. Examining the 1979–1982 period, we find that monetary policy tightens significantly in response to positive shocks to inflation, and that the impact of monetary policy shocks on stock returns is negative and volatile. Therefore, we see evidence that an “anticipated policy” hypothesis is at work.     

Extreme connectedness of agri-commodities with stock markets and its determinants

In this paper, we quantify the extreme connectedness between agricultural commodity prices with food and beverage stock market returns. We find that the connectedness of returns relies on the degree of the inverse shock, as suggested by the larger impact of the anticipated shock on the upper and lower tails than the estimated shock on the conditional mean. Additionally, the dynamics of the connectedness of returns monitored in the tail differ from the conditional mean. These two outcomes recommend that using conditional averages is limited and imprecise to analyze returns connected with extreme positive/negative events in agricultural commodities and food & beverage indices. Next, we find the determinants of the extent of the connectedness by employing firm level statistics. We find that some of the determinants driving the return spillovers at upper and lower quantiles are quite different from those driving the return spillovers at the middle quantile.     

The return impact of realized and expected idiosyncratic volatility

We show that the negative relation between realized idiosyncratic volatility, measured over the prior month, and returns is robust in non-January months. Controlling for realized idiosyncratic volatility, we show that the relation between returns and expected idiosyncratic volatility is positive and robust. Realized and expected idiosyncratic volatility are separate and important effects describing the cross-section of returns. We find the negative return on a zero-investment portfolio that is long high realized idiosyncratic volatility stocks and short low realized idiosyncratic volatility stocks is dependent on aggregate investor sentiment. In cross-sectional tests, we find the negative relation is weaker for stocks with a large analyst following and stronger for stocks with high dispersion of analyst forecasts. The positive relation between expected idiosyncratic volatility and returns is not due to mispricing.     

Return dispersion and expected returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.     

Stock prices, news, and business conditions

Previous research finds that fundamental macroeconomic newshas little effect on stock prices. We show that after allowingfor different stages of the business cycle, a stronger relationshipbetween stock prices and news is evident. In addition to stockprices, we examine the effect of real activity news on proxiesfor expected cashflows and equity discount rates. We find thatwhen the economy is strong the stock market responds negativelyto news about bigger real economic activity. This negative relationis caused by the larger increase in discount rates relativeto expected cashflows.     

Predicting market returns using aggregate implied cost of capital

Theoretically, the implied cost of capital (ICC) is a good proxy for time-varying expected returns. We find that aggregate ICC strongly predicts future excess market returns at horizons ranging from one month to four years. This predictive power persists even in the presence of popular valuation ratios and business cycle variables, both in-sample and out-of-sample, and is robust to alternative implementations. We also find that ICCs of size and book-to-market portfolios predict corresponding portfolio returns.     

Weekend Effect, 'Reverse' Weekend Effect, and Investor Trading Activities

Abstract:   In this study, we document evidence of a 'reverse' weekend effect – whereby Monday returns are significantly positive and they are higher than the returns on other days of the week – over an extended period of eleven years (from 1988 to 1998). We also find that the 'traditional' weekend effect and the 'reverse' effect are related to firm size in that the 'traditional' weekend effect tends to be associated with small firms while the 'reverse' weekend effect tends to be associated with large firms. In addition, we find that during the period in which the 'reverse' weekend effect is observed, Monday returns for large firms tend to follow previous Friday returns when previous Friday returns are positive , but they do not follow the previous Friday returns when Friday returns are negative . Furthermore, we find that during the period in which the 'reverse' weekend effect is observed, Monday returns are positively related to the volume of medium‐size and block transactions, but negatively related to the volume of odd‐lot transactions.     

GOOD NEWS, BAD NEWS, VOLUME, AND THE MONDAY EFFECT

New evidence is presented on the nature of the Monday effect in stock market returns. Using stock returns for the years 1962-1986, the Monday effect is found to be confined to periods of negative market returns. Monday's returns are no different from other weekday returns in periods of positive returns. In addition, trading volume and the Monday effect are related. Monday's volume is lower than the other weekdays. When returns are compared controlling for trading volume, we find that the Monday effect is confined to negative return periods with above normal volume, which represent only two per cent of the sample period.     

Is presidential cycle in security returns merely a reflection of business conditions?

We confirm previous findings that both large-cap and small-cap stock returns in the US exhibit a presidential cycle pattern, i.e. returns are significantly higher in the last 2 years than in the first 2 years of the presidential term. We attempt to examine if this presidential cycle pattern can be explained away by the traditional business cycle proxies, namely the term spread (TERM), dividend yield (D/P), and default spread (DEF). Our motivation arises from the political business cycle theory that monetary and fiscal measures undertaken by presidents are usually translated into the business cycle. We find that the presidential cycle has explanatory power beyond business conditions proxies shown to be important in explaining stock returns. Tests of slope parameters show that stock returns are less sensitive to only the D/P during the last 2 years of the presidential term. The presidential cycle effect prevails even after controlling for the party in power and the incumbent versus nonincumbent presidents.     

Accounting Conservatism and the Relation Between Returns and Accounting Data

This study adds change in cash investments and change in lagged operating assets to the regression of returns on earnings levels and earnings changes examined in Easton and Harris (1991). We argue that a positive coefficient on change in cash investments captures conservatism associated with investments in positive net present value projects the effects of which will not flow into the accounting statements until the expected future benefits are realized. A positive coefficient on change in lagged operating assets implies accounting conservatism associated with the application of accounting rules to operating assets in place. Our empirical results are, in general, consistent with these arguments. We examine differences in conservatism across samples with different market to book ratios, we compare firms with non-negative returns with firms with negative returns, we compare firms reporting losses with firms reporting profits, and we examine firms in different industries, firms with different levels of research and development expenditure, different amounts of depreciation, different amounts of advertising expense, and firms that adopt LIFO inventory valuation compared with those that adopt an alternative to LIFO.JEL Classification: M41     

Commodity financialization and sector ETFs: Evidence from crude oil futures

We investigate the diversification benefits of energy assets in the setting of commodity financialization using data on crude oil futures and Sector ETFs (SPDRs). Correlations between commodities and financial assets increased during the post-Commodity Futures Modernization Act (CFMA)/commodity bull cycle period, resulting in lower benefits of diversification. However, we find that conditional correlations between crude oil futures and sector ETFs meaningfully increased only since the 2008–09 financial crisis. The results therefore suggest that the financial crisis, rather than CFMA regulation, explains changes in the diversification benefits of commodities. Moreover, we find that oil futures returns are less correlated with SPDRs than with the S&P index. Thus, energy futures, and crude oil in particular, offer the potential for diversification benefits in sector-style investing.     

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.