The predictability of stock market volatility in emerging economies: Relative roles of local,regional, and global business cycles

This paper explores the role of business cycle proxies, measured by the output gap at the global, regional, and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic output gap is found to capture significant predictive information for India and China particularly, the business cycles associated with emerging economies and the world in general are strongly important for the BRIC countries and weakly for South Africa, especially in the postglobal financial crisis era. The findings suggest that despite the increase in the financial integration of world capital markets, emerging economies can still bear significant exposures to idiosyncratic risk factors, an issue of high importance for the profitability of global diversification strategies.     

Assessing inefficiency in the s&p 500 futures market

We analyse the price movement of the S&P 500 futures market for violations of the efficient market hypothesis on a short-term basis. To assess market inefficiency we construct a model and find that the returns, i.e. the difference in the logarithm of closing prices on consecutive days, exhibit the usual conditional heteroscedasticity behaviour typical of long series of financial data. To account for this non-linear behaviour we scale the returns by a volatility factor which depends on the daily high, low, and closing price. The rescaled series, which may be interpreted as the trend-countertrend component of the time series, is modelled using Box and Jenkins techniques. The resulting model is an ARMA(1,1). The scale factors are assumed to form a time series and are modelled using a semi-non-parametric method which avoids the restrictive assumptions of most ARCH or GARCH models. Using the combined model we perform 1000 simulations of market data, each simulation comprising 250 days (approximately one year). We then formulate a naive trading strategy which is based on the ratio of the one-day-ahead expected return to its one-day-ahead expected conditional standard deviation. The trading strategy has four adjustable parameters which are set to maximize profits for the simulation data. Next, we apply the trading strategy to one year of recent out-of-sample data. Our conclusion is that the S&P 500 futures market exhibits only slight inefficiencies, but that there exist, in principle, better trading strategies which take account of risk than the benchmark strategy of buy-and-hold. We have also constructed a linear model for the return series. Using the linear model, we have simulated returns and determined the optimum values for the adjustable parameters of the trading strategy. In this case, the optimum trading strategy is the same as the benchmark strategy, buy-and-hold. Finally, we have compared the profitability of the optimized trading strategy, based on the non-linear model, to three ad hoc trading strategies using the out-of-sample data. The three ad hoc strategies are more profitable than the optimized strategy.     

我国中小企业板上市公司资本结构与企业价值关系研究

企业资本结构理论关于企业价值与资本结构的论述是以成熟资本市场为前提,而能在公开资本市场上融资的一般为大型企业：我国于2004年开辟了中小企业板块,使得部分中小企业可以在资本市场上融资,资本结构理论是否适用于这类企业,本文以中小企业板上市公司为对象对中小企业资本结构与企业价值关系进行实证研究.     

Forecasting interest rates through Vasicek and CIR models: A partitioning approach

The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates based on rolling windows from observed financial market data. The novelty, apart from the use of those models not for pricing but for forecasting the expected rates at a given maturity, consists in an appropriate partitioning of the data sample. This allows capturing all the statistically significant time changes in volatility of interest rates, thus giving an account of jumps in market dynamics. The new approach is applied to different term structures and is tested for both models. It is shown how the proposed methodology overcomes both the usual challenges (e.g., simulating regime switching, volatility clustering, skewed tails) as well as the new ones added by the current market environment characterized by low to negative interest rates.     

中小企业员工心理契约与离职倾向关系研究

以国内外现有心理契约测量量表为参考,开发出针对我国中小企业员工心理契约的测量量表。在此基础上,以辽宁省中小企业的员工为例,通过对问卷调查获得的有效数据进行分析,发现中小企业员工心理契约的具体结构分为交易维度、关系维度和发展维度,各个维度与离职倾向均存在不同程度的负相关关系。最后,从心理契约的视角对中小企业如何降低离职率提出了相应的对策和建议。     

Mixed-frequency forecasting of crude oil volatility based on the information content of global economic conditions

This paper subjects six alternative indicators of global economic activity to empirically examine their relative predictive powers in the forecast of crude oil market volatility. GARCH-MIDAS approach is constructed to accommodate all the relevant series at their available data frequencies, thereby circumventing information loss and any associated bias. We find evidence in support of global economic activity as a good predictor of energy market volatility. Our forecast evaluation of the various indicators places a higher weight on the newly developed indicator of global economic activity which is based on a set of 16 variables covering multiple dimensions of the global economy, whereas other indicators do not seem to capture. Furthermore, we find that accounting for any inherent asymmetry in the global economic activity proxies improves the forecast accuracy of the GARCH-MIDAS-X model for oil volatility. The results leading to these conclusions are robust to multiple forecast horizons and consistent across alternative energy sources.     

The Role of Survey Data in Nowcasting Euro Area GDP Growth

This paper evaluates the impact of new releases of financial, real activity and survey data on nowcasting euro area gross domestic product (GDP). We show that all three data categories positively impact on the accuracy of GDP nowcasts, whereby the effect is largest in the case of real activity data. When treating variables as if they were all published at the same time and without any time lag, financial series lose all their significance, while survey data remain an important ingredient for the nowcasting exercise. The subsequent analysis shows that the sectoral coverage of survey data, which is broader than that of timely available real activity data, as well as their information content stemming from questions focusing on agents' expectations, are the main sources of the ‘genuine’ predictive power of survey data. When the forecast period is restricted to the 2008–09 financial crisis, the main change is an enhanced forecasting role for financial data. Copyright © 2015 John Wiley & Sons, Ltd.     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.     

Does Information Help Intra‐Day Volatility Forecasts?

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra‐day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH‐type models with these proxies lead to improved forecasts across a range of intra‐day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2013 John Wiley & Sons, Ltd.     

Direction‐of‐change forecasting using a volatility‐based recurrent neural network

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction‐of‐change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub‐period 8 April 1998 to 5 February 2002 has been reserved for out‐of‐sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability, after the inclusion of transaction costs, during bear market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy‐and‐hold strategy. We suggest that our findings can be justified by invoking either the ‘volatility feedback’ theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence. Copyright © 2008 John Wiley & Sons, Ltd.     

The versatility of spectrum analysis for forecasting financial time series

The versatility of the one‐dimensional discrete wavelet analysis combined with wavelet and Burg extensions for forecasting financial times series with distinctive properties is illustrated with market data. Any time series of financial assets may be decomposed into simpler signals called approximations and details in the framework of the one‐dimensional discrete wavelet analysis. The simplified signals are recomposed after extension. The final output is the forecasted time series which is compared to observed data. Results show the pertinence of adding spectrum analysis to the battery of tools used by econometricians and quantitative analysts for the forecast of economic or financial time series.     

The impact of economic growth in mortality modelling for selected OECD countries

The health of a population is affected by social, environmental, and economic factors. Pension providers and consultants, insurance companies, government agencies and individuals in the developed world have a vested interest in understanding how the economic growth will impact on the life expectancy of their population. Therefore, changes in death rates may occur due to climate and economic changes. In this study, we extend a previous study into excess deaths as a result of climate change to also provide a comprehensive investigation of the impact of economic changes using annual female and male data for 5 developed OECD countries. We find that there is strong negative relationship between mortality index, and climate and economic proxies. This model shows to provide better fitting and forecasting results both for females and males, and for all countries studied.     

Evaluation of current research on stock return predictability

The results of recent replication studies suggest that false positive findings are a big problem in empirical finance. We contribute to this debate by reviewing a sample of articles dealing with the short-term directional forecasting of the prices of stocks, commodities, and currencies. Screening all relevant articles published in 2016 by one of the 96 journals covered by the Social Sciences Citation Index in the category “Business, Finance,” we select only those studies that use easily accessible data of daily or higher frequency. We examine each study in detail, from the selection of the dataset to the interpretation of the results. We also include empirical analyses to illustrate the shortcomings of certain approaches. There are three main findings from our review. First, the number of selected papers is very low, which is surprising even when the strict selection criteria are taken into account. Second, there are hardly any relevant studies that use high-frequency data—despite the hype about financial big data and machine learning. Third, the economic significance of the findings—for example, their usefulness for trading purposes—is questionable. In general, apparently good forecasting performance does not translate into profitability once realistic transaction costs and the effect of data snooping are taken into account. Other typical problems include unsuitable benchmarks, short evaluation periods, and nonoperational trading strategies.     

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.     

Forecasting new product trial in a controlled test market environment

A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e. shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model ‘components’ on forecasting performance. Furthermore, we also explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data. Copyright © 2003 John Wiley & Sons, Ltd.     

Forecasting outcomes in spread betting markets: can bettors use ‘quarbs’ to beat the book?

In this paper, we examine a relatively novel form of gambling, spread (or index) betting that overlaps with practices in conventional financial markets. In this form of betting, a number of bookmakers quote bid–offer spreads about the result of some future event. Bettors may buy (sell) at the top (bottom) end of a spread. We hypothesize that the existence of an outlying spread may provide uninformed traders with forecasting information that can be used to develop improved trading strategies. Using data from a popular spread betting market in the United Kingdom, we find that the price obtaining at the market mid‐point does indeed provide a better forecast of asset values than that implied in the outlying spread. We further show that this information can be used to develop trading strategies leading to returns that are consistently positive and superior to those from noise trading. Copyright © 2005 John Wiley & Sons, Ltd.     

Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy

The aim of this study was to forecast the Singapore gross domestic product (GDP) growth rate by employing the mixed‐data sampling (MIDAS) approach using mixed and high‐frequency financial market data from Singapore, and to examine whether the high‐frequency financial variables could better predict the macroeconomic variables. We adopt different time‐aggregating methods to handle the high‐frequency data in order to match the sampling rate of lower‐frequency data in our regression models. Our results showed that MIDAS regression using high‐frequency stock return data produced a better forecast of GDP growth rate than the other models, and the best forecasting performance was achieved by using weekly stock returns. The forecasting result was further improved by performing intra‐period forecasting.     

How efficient is the European football betting market? Evidence from arbitrage and trading strategies

This paper assesses the international efficiency of the European football betting market by examining the forecastability of match outcomes on the basis of the information contained in different sets of online and fixed odds quoted by six major bookmakers. The paper also investigates the profitability of strategies based on: combined betting, simple heuristic rules, regression models and prediction encompassing. The empirical results show that combined betting across different bookmakers can lead to limited but highly profitable arbitrage opportunities. Simple trading rules and betting strategies based on forecast encompassing are found capable of also producing significant positive returns. Despite the deregulation, globalization and increased competition in the betting industry over recent years, the predictabilities and profits reported in this paper are not fully consistent with weak-form market efficiency. Copyright © 2008 John Wiley & Sons, Ltd.     

我国股市与房地产市场的协整分析

本文以2004年至2009年间上证A股月流通市值和房地产月销售额作二元时间序列，进行协整分析，得出两者之间呈正向相关性，并且股市相较于房地产市场投资倾向约晚一个月。计算表明，近年来二者联动性十分强烈。在宏观调控时，应注意对金融风险的防范，从全局的角度思考政策对资本市场的影响。对个人投资者而言，应当重视房地产市场对股市的预警作用。     

Trading volume and prediction of stock return reversals: Conditioning on investor types' trading

We show that contrasting results on trading volume's predictive role for short‐horizon reversals in stock returns can be reconciled by conditioning on different investor types' trading. Using unique trading data by investor type from Korea, we provide explicit evidence of three distinct mechanisms leading to contrasting outcomes: (i) informed buying—price increases accompanied by high institutional buying volume are less likely to reverse; (ii) liquidity selling—price declines accompanied by high institutional selling volume in institutional investor habitat are more likely to reverse; (iii) attention‐driven speculative buying—price increases accompanied by high individual buying‐volume in individual investor habitat are more likely to reverse. Our approach to predict which mechanism will prevail improves reversal forecasts following return shocks: An augmented contrarian strategy utilizing our ex ante formulation increases short‐horizon reversal strategy profitability by 40–70% in the US and Korean stock markets.