Do sunspot numbers cause global temperatures? Evidence from a frequency domain causality test

This article applies the causality test in the frequency domain, developed by Breitung and Candelon (2006), to analyse whether sunspot numbers (used as a partial approximation to solar irradiance) cause global temperatures, using monthly data covering the time period 1880:1–2013:9. While standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers do not cause global temperatures for both full and sub-samples (identified based on tests of structural breaks), the frequency domain causality test detects predictability for both the full-sample and the last sub-sample at short (2–2.6 months) and long (10.3 months and above) cycle lengths, respectively. Our results highlight the importance of analysing causality using the frequency domain test, which, unlike the time domain Granger causality test, allows us to decompose causality by different time horizons, and hence, could detect predictability at certain cycle lengths even when the time domain causality test might fail to pick up any causality. Further, given the widespread discussion in the literature, those results for the full-sample causality, irrespective of whether it is in time or frequency domains, cannot be relied upon when there are structural breaks present, and one needs to draw inference regarding causality from the sub-samples, we can conclude that there has been an emergence of causality running from sunspot numbers to global temperatures only recently at cycle length of 10.3 months and above.     

中国各省份农业效率和全要素生产率增长——基于SBM方向性距离函数的实证分析

本文运用SBM方向性距离函数和Luenberger生产率指标测度了中国31个省份1995－2008年的农业效率和农业全要素生产率,并对影响农业效率和全要素生产率的因素进行了分析。研究结果发现：东部地区的农业无效率水平显著低于中西部地区,产出无效率、役畜投入无效率和播种面积无效率是中国农业无效率的主要来源。1995－2008年中国农业的全要素生产率增长率为5.58%,主要体现于技术进步和技术规模变化。东部地区的农业全要素生产率最高。农业从业人员教育水平的提高对中国农业效率和全要素生产率的提高起到推动作用,机械化的普及有利于中国农业全要素生产率的增长。     

Four centuries of return predictability

We combine annual stock market data for the most important equity markets of the last four centuries: the Netherlands and UK (1629–1812), UK (1813–1870), and US (1871–2015). We show that dividend yields are stationary and consistently forecast returns. The documented predictability holds for annual and multi-annual horizons and works both in- and out-of-sample, providing strong evidence that expected returns in stock markets are time-varying. In part, this variation is related to the business cycle, with expected returns increasing in recessions. We also find that, except for the period after 1945, dividend yields predict dividend growth rates.     

The dual contributions of information instruments in return models: magnitude and direction predictability

By partitioning asset return prediction errors, we show explicitly the dual role of magnitude and sign prediction of return instruments. We demonstrate analytically that sign prediction directly affects heteroskedasticity in asset returns; increases in precision attenuate the heteroskedasticity. Our findings with monthly asset returns are consistent with earlier evidence and indicate that our proposed analytical model captures the sign predictive component of returns. Our results are supportive of a nonlinear return generating model that can be thought of as the product of a model, perhaps linear, for forecasting return signs and a model for forecasting return magnitudes.     

Testing the value of directional forecasts in the presence of serial correlation

Common approaches to testing the economic value of directional forecasts are based on the classical χ²${\chi }^2$-test for independence, Fisher’s exact test or the Pesaran and Timmermann test for market timing. These tests are asymptotically valid for serially independent observations, but in the presence of serial correlation they are markedly oversized, as has been confirmed in a simulation study. We therefore summarize robust test procedures for serial correlation and propose a bootstrap approach, the relative merits of which we illustrate by means of a Monte Carlo study. Our evaluations of directional predictions of stock returns and changes in Euribor rates demonstrate the importance of accounting for serial correlation in economic time series when making such predictions.     

Predictability of equity returns during a financial crisis

An occurrence of a market crash or a financial crisis has long been considered a cause of market inefficiency. An inefficient market commonly implies return predictability and the existence of profitable opportunities for traders and speculators. Technical analysis has been a popular tool to identify predictable patterns in asset prices. The usefulness of a large universe of technical trading rules popularized in the existing literature on technical analysis is tested when they are applied to a set of equity markets that are generally considered developed and efficient during the two most recent periods of major financial turmoil: the 1997 Asian financial crisis and the 2008 global financial crisis. Three major statistical deficiencies that existing studies on return predictability are commonly criticized for – data snooping bias, nonsynchronicity bias and transaction costs – have been incorporated in the analysis. Technical trading rules are largely unable to yield abnormal excess returns over the passive benchmark after data snooping bias, nonsynchronous pricing and transaction costs are accounted for. Chaotic price movements typical for a volatile market during a financial crisis are likely to have an adverse effect on the performance of active trend chasing trading strategies.     

An Analysis of Energy and Environment Efficiency of China’s Iron and Steel Industry

Due to heavy energy consumption and low technical efficiency, China’s iron and steel industry is trapped in the dilemma “large but not strong”. This situation not only exerts enormous pressure on energy security but also on increased carbon emission and environmental pollution. The contribution of this study is to calculate the energy and environment efficiency of China’s iron and steel industry and to analyze the factors affecting this efficiency. An index of energy and environment efficiency is introduced based on Directional Slacks-based Distance Measure Model. This index is adopted to measure the energy and environment efficiency of China’s iron and steel industry using 2,382 firm observations during 2001 to 2005. In addition, Hierarchy Linear Model (HLM) is applied to analyze the factors which can influence the efficiency with both firm-level and province-level data. The conclusions are as follows: The energy and environment efficiency of China’s iron and steel industry did not have a significant change during the research period. A firm’s age, size, ownership, product category and the economy of its province have significant influence on its energy and environment efficiency.     

Revisiting dividend yield dynamics and returns predictability: Evidence from a time-varying ESTR model

Since the bubble of the late 1990s the dividend yield appears non-stationary indicating the breakdown of the equilibrium relationship between prices and dividends. Two lines of research have developed in order to explain this apparent breakdown. First, that the dividend yield is better characterised as a non-linear process and second, that it is subject to mean level shifts. This paper jointly models both of these characteristics by allowing non-linear reversion to a changing mean level. Results support stationarity of this model for eight international dividend yield series. This model is than applied to the forecast of monthly stock returns. Evidence supports our time-varying non-linear model over linear alternatives, particularly so on the basis of an out-of-sample R-squared measure and a trading rule exercise. More detailed examination of the trading rule measure suggests that investors could obtain positive returns, as the model forecasts do not imply excessive trading such that costs would not outweigh returns. Finally, the superior performance of the non-linear model largely arises from its ability to forecast negative returns, whereas linear models are unable to do.     

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.     

Return predictability in emerging equity market sectors

This article investigates the predictive power of technical trading rules in the emerging equity market sector portfolios and finds that trading strategies based on technical indicators significantly outperform the buy-and-hold benchmark. Combination of data snooping bias, data measurement errors in the form of non-synchronicity bias and fluctuations in currency exchange rates is unable to explain the observed outperformance. The introduction of transaction costs tempers the results but technical analysis still possesses significant predictive power for a number of sectors. The performance of technical analysis in the emerging equity market sectors does not conform to historical trends observed in the developed equity markets as well as in the emerging equity markets when broadly diversified portfolios are considered, where predictive power of technical trading rules has been shown to decline over time.