房价对城市全要素生产率的影响机制研究——基于Tobit模型实证分析

基于2008—2016年全国35个大中型城市的面板数据,利用Tobit模型和投入导向型的数据包络分析(DEA)方法探究城市房屋销售价格对城市全要素生产率的影响。研究发现：在整体维度城市房价与城市生产率呈现先增加后减少的倒U型曲线,且城市的房价水平超过了“房住不炒”的最高临界值,房价上升的抑制效应显著。房价对于不同人口规模城市的生产率影响具有异质性,在人口规模达到特大城市时,房价与城市生产率呈现倒U型特征,人口规模达到超大城市后,房价与城市生产率倒U型特征消失。不同类型城市房价与城市生产率之间的倒U型关系和城市的异质性特征表明,我国大中型城市应进行房价的“一城一策”精细化调节,“房住不炒”的政策定位具有坚实理论依据和现实必然。     

我国股市涨跌幅限制日内效应的实证研究

本文利用我国沪市ST股和普通股的5分钟高频数据对我国股市涨跌幅限制的效应进行分析。结果表明,虽然普通股和ST股在接近涨跌幅限制时均存在趋势反转现象,但普通股10％的涨跌幅限制存在一定的磁吸效应,而ST股5％的涨跌幅限制不存在磁吸效应。这与国外其他研究以及国内基于其他方法得出的研究结论存在差异。本文的分析还说明,国内所谓涨跌幅限制的冷却效应只是股价正常的均值回复,与涨跌幅限制制度无关。     

How does the diversity of investors’ beliefs affect stock price informativeness?

A growing number of studies have investigated the role of stock prices in aggregating private information and guiding resource reallocation. However, this article may be the first attempt to study how the diversity of beliefs affects stock price informativeness. The framework of the noisy rational expectations model shows that stock informativeness is determined by both the precision and use of private information in trading. If private beliefs about the value are highly diverse, the aggregate average opinion revealed in a stock’s price will be more accurate and, thus, more informative. As the price becomes more informative, however, individual investors will rely less on their private information. When this occurs, less private information will be absorbed in price, which, in turn, reduces price informativeness. Our model shows that the relationship between belief diversity and price informativeness is U-shaped in equilibrium.     

Time-varying synchronization of European stock markets

We study intraday comovements among three developed (France, Germany, and the United Kingdom) and three emerging (the Czech Republic, Hungary, and Poland) European stock markets. When applying a Dynamic Conditional Correlation GARCH model to 5- min tick intraday stock price data (2003–2006), we find a strong correlation between the German and French markets and also between these two markets and the UK stock market. However, very little systematic positive correlation during a trading day can be detected between the developed and emerging stock markets, or within the emerging group itself. Hungary exhibits higher correlation with the developing markets and the emerging markets and its dynamics show an increasing trend; Poland and the Czech Republic produce less clear-cut results.     

Market Underreaction and Overreaction of Technology Stocks

Several studies have assessed stock market under- or overreaction of stocks and there is some agreement among them. However, there is much disagreement about what constitutes market underreaction or overreaction, and the conditions that cause it. The substantial variation in results among studies may be partially attributed to the types of firms that are contained in any sample. We investigate this premise by focusing on a sample of technology stocks that experienced an extreme change in stock price, along with a corresponding control sample of non-technology stocks that experienced a similar extreme change in stock price on the same day.

Based on the subsequent stock price behavior of each sample, we find a greater degree of overreaction within extreme positive changes in technology stock prices (winners) than in non-technology stock prices. In addition, we find a greater degree of underreaction within extreme negative changes in technology stock prices (losers) than in non-technology stock prices. When considering winners and losers collectively for technology and non-technology firms, it appears the market is overoptimistic when it initially revalues technology stock prices relative to non-technology stock prices.

The degree of under- or overreaction of technology stocks varies within the sample of technology stocks, and is conditioned on firm-specific characteristics. Overall, our results suggest that technology stocks exhibit unique stock price behavior subsequent to an extreme change in price, and that this unique behavior can even vary among technology firms according to firm-specific characteristics.     

Exploring the influence of industries and randomness in stock prices

This study explores the behavior of time series of historical prices and makes two additional contributions to the literature. In summarized form, we present an overview of each of the financial theories that discuss the movements of stock prices and their connection with industry trends. Within this theoretical framework, we first propose that prices be distinguished by following stock prices and a random-walk approach, and second, that the analysis of historical prices be broken down by industries. Similarities among price series are extracted through a clustering methodology based on an approach to non-computable Kolmogorov complexity. We model price series by following geometric Brownian motion and compare them to historical series of stock prices. Our first contribution confirms the existence of hidden common patterns in time series of historical prices that are clearly distinguishable from simulated series. The second contribution claims strong connections among firms carrying out similar industrial activities. The results confirm that stock prices belonging to the same industry behave similarly, whereas they behave differently from those of firms in other industries. Our research sheds new light on the stylized feature of the non-randomness of stock prices by pointing at fundamental aspects related to the industry as partial explanatory factors behind price movements.     

A Study of Longterm Memory in Stock Market Prices Using Rescaled Range Analysis

Most of the studies on the behaviourof the Indian stock market using the autocorrelation function have revealed that the stock market is weakly efficient and the time series of stock prices and stock indices are random walks. The autocorrelation function assumes Gaussian or near-Gaussian properties in the underlying distribution. The distribution function is assumed to have the normal bellshaped curve. Mandelbrot [1972] has proved that the autocorrelation function works well in determining short-term dependence only. But it tends to underestimate long-run correlation for non-Gaussian series. Alternatively the Rescaled Range Analysis is used to study the long-term dépendance in the time series. The Rescaled Range Analysis (R/S Analysis) is a nonparametric methodology developed by H. E. Hurst, a British hydrologist in 1951. Originally this methodology was applied to study the long-term storage capacity of reservoirs and later it was extended to study many natural systems. This statistical methodology is used for distinguishing random time series from biased random time series (Fractal time series) and to study the persistence of trends and also the presence of periodic and nonperiodic cycles in a time series. In this paper a study of the Indian stock market is carried out using the method of Rescaled Range Analysis and Hurst Coefficient. We conclude that the series of stock prices have persistent behaviour. Nearly 18% of the stock prices are influenced by the past. This ‘memory effect’ in the case of stock indices is found to be 23%. The stock market has shown persistent trends and that the series of prices and indices are biased random walks. The present prices are influenced by the past prices and this influence goes across time scales, one period influencing all the subsequent periods.

     

Oil prices and UK industry-level stock returns

In this article, we study whether the behaviour of oil prices can be used as a reliable predictor for the disaggregated industry-level stock market indices. We find strong evidence for the relevance of changes in oil price as a predictor for the returns of UK industry portfolios, while this relevance is heterogeneous across industries. In an out-of-sample framework, we find that both the contemporaneous and lagged oil price changes do predict UK industry stock market returns. The predictive power is more transient for the latter case, and mostly appearing after allowing for time variation in the relative performance. In addition, we find some evidence of asymmetry in the oil–stock price relationships.     

Deregulation,news releases,and price discovery

We hypothesize that the firm’s regulatory environment influences the sensitivity of its equity value to information. Using intraday stock price data of utilities operating in differing regulatory environments we test for systematic differences between the responsiveness of stock prices of utility firms operating in deregulated and regulated environments to a common information set. Our findings reveal sharp differences in responses, with those of utilities operating in deregulated environments the strongest, and the responses of utilities in highly regulated environments the weakest. While the evidence supports our hypothesis, in a broader sense, the evidence suggests that deregulation aids in the process of price discovery. We also find evidence that suggests that deregulation, per se, does not lead to higher stock price volatility.      

Asset bubbles,monetary policy and bank lending in Japan: an empirical investigation

Asset prices rose rapidly in Japan during the latter half of the 1980s, and then declined as quickly in the early 1990s. Their behaviour is consistent with the existence of speculative ‘bubbles’ in these markets. This paper investigates the dynamic relationships among stock and land prices in Japan, output, and monetary and bank lending variables. The results of causality tests and variance decompositions are reported for two time periods, 1972#1501985 and 1986#1501991. The price bubbles affected each other in the first period, although the size of this impact is dependent on the choice of variables in the VARs. In the bubble period, there is strong evidence that the stock market bubble was determined by its own past and also influenced the land market bubble, accounting for a significant proportion of the variance of the land market bubble. However, neither output, the money supply nor the lending variables were significant in the causality tests or in explaining the variation of the two assset bubbles.     

Price adjustment in an atomistic market

This paper considers the price adjustment process in a market which retains the characteristics of a perfectly competitive market except that individual firms are price-setters. Buyers, unaware initially of what prices which firms are charging, indulge in search by contacting a sample of firms and buy (according to a demand curve) from the lowest-price firm encountered. Firms set prices to maximise profits over their perceived (or estimated) demand curve, and update their estimated demand curve in accordance with the observed change in demand between successive time periods. It is shown that the price distribution converges to a degenerate distribution centred on the monopoly price.     

Rationing rule, imperfect information and equilibrium

Summary. The impact of imperfect information on the price setting behaviour of firms is analysed. Specifically, consumers support an information cost to become informed about prices. Firms are endowed with U-shaped average cost curves. If a firm does not supply more than its competitive supply as determined by its marginal cost schedule, then we show that the existence of a pure strategy equilibrium is conditional on the rationing rule employed. If uninformed consumers are served first then the monopoly price is the sole equilibrium whenever consumers' information costs are high enough. Otherwise, a pure strategy equilibrium fails to exist contrary to the results of Salop and Stiglitz (1977) or Braverman (1980) who implicitly suppose that firms supply all the demand at a given price. Received: May 17, 1999; revised version: September 15, 2000     

Overreaction of Exchange-Traded Funds During the Bubble of 1998-2002

This study of overreaction is motivated by the unique characteristics of exchange-traded funds (ETFs), which should contribute to market efficiency. Since ETFs represent portfolios of stocks, they may not be as susceptible to short-term overreaction as individual stocks. In addition, they can be traded throughout the day and can be sold short, which might further limit potential overreaction. Yet, the tradability of ETFs may allow unusual pressure on ETF prices that is not initiated by price movements of all the component stocks. We find substantial overreaction of ETFs during normal trading hours (9:30 a.m. to 4 p.m.) and after hours, which presents opportunities for feedback traders. Extreme price movements of ETFs occur more frequently after hours. Yet, the after-hours correction of extreme price movements that occurred that day is more pronounced than the day correction of extreme stock price movements that occurred in the previous after-hours period, even after controlling for ETF type and other potential confounding effects. The degree of overreaction is also more pronounced for international ETFs.     

Dynamic relationships between crude oil prices and socially responsible investing in Brazil: evidence for linear and non-linear causality

Recent years have witnessed an increasing interest in socially responsible investing (SRI), reflecting investors’ growing awareness of social, environmental, ethical and corporate governance issues. At the same time, the effect of oil price shocks on stock price returns has become a prominent issue due to surges in energy prices. Using the Brazilian corporate sustainability index (ISE) as a benchmark for socially responsible investments in the Brazilian stock market, the present study extends the understandings on the impact of oil prices on stock price behaviour, focusing on a new class of assets: those from socially responsible firms. To this end, apart from conventional linear causality approaches, we apply a nonparametric test by Diks and Panchenko (DP) on daily data spanning from January 2008 to December 2015 to test for non-linear causality, before and after controlling for conditional heteroscedasticity. Our findings show that, in spite of their efforts to become more socially responsible, firms that have adhered to the ISE in recent years are influenced by crude oil spot prices, especially the WTI crude. In line with previous studies, we also provide consistent evidence that the Brazilian stock market, as a whole, is associated with the international crude oil market.     

Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach

Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging (DMA) and dynamic model selection (DMS) are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.     

OIL PRICE SHOCKS AND STOCK MARKET BOOMS IN AN OIL EXPORTING COUNTRY

This paper analyses the effects of oil price shocks on stock returns in Norway, an oil-exporting country, highlighting the transmission channels of oil prices for macroeconomic behaviour. To capture the interaction between the different variables, stock returns are incorporated into a structural VAR model. I find that following a 10% increase in oil prices, stock returns increase by 2.5%, after which the effect gradually dies out. The results are robust to different (linear and non-linear) transformations of oil prices. The effects on the other variables are more modest. However, all variables indicate that the Norwegian economy responds to higher oil prices by increasing aggregate wealth and demand. The results also emphasize the role of other shocks; monetary policy shocks in particular, as important driving forces behind stock price variability in the short term.     

Dynamic modelling of real estate investment trusts and stock markets

Taiwan launched the first case of real estate securitization in 2005. The interrelationship between Taiwan Real Estate Investment Trusts (T-REITs) and the aggregate equity markets and segmented industries has drawn the interests of both investors and academia. This paper employs Toda and Yamamoto's (1995) procedure and the generalized impulse response approach to uncover the extent and the magnitude of the relationship between T-REITs and aggregate and segmented stock prices. We collected daily data of the first two issued T-REITs, Fubon No.1 and Cathay No. 1, from March 2005 to March 2010 and October 2005 to March 2010, respectively, to examine their causal relationships with aggregate stock markets, the financial sector, and the construction sector. The empirical results indicate that all variables have break points, reflecting shocks from the Subprime Mortgage Crisis or deregulation of the Qualified Domestic Institutional Investors (QDII) for Mainland Chinese to invest in Taiwan. We also discover that an individual T-REIT may lead or lag behind stock price indices due to its capitalization scale or business type. The transitory initial impacts of innovations in T-REITs on stock price indices are observed herein.     

A股市场上的“中石油魔咒”现象及其解释

文章针对A股市场上流传甚广的"中石油魔咒"现象,首次从基本面角度进行了解释,认为造成这一现象的根本原因是国际原油价格对我国股票市场存在显著的负向溢出效应,即当国际原油价格上涨时,对中石油个股产生利好,但对整体宏观经济却构成利空,因而中石油股票价格上涨,整个股票市场却会下跌。进一步研究还发现,这种负向溢出效应具有非线性特征,表现为国际原油价格上涨对股票市场的打压力度要大于其下跌对股票市场的提升力度。这提醒A股投资者相对于国际原油价格下跌,要更加关注国际原油价格上涨带来的投资风险。     

The effect of quantitative easing on stock prices: a structural time series approach

A structural time series model is estimated and tested to examine the effect of quantitative easing (QE) on US stock prices. The model is estimated by maximum likelihood in a Time-varying parametric (TVP) framework, using the S&P 500 index as the dependent variable and the Fed’s balance as an explanatory variable in addition to the unobserved components accounting for the behaviour of variables that do not appear explicitly in the equation. The results show that QE had a sizeable, but not exclusive, effect on stock prices and that stock prices were also affected by other missing variables and cyclical movements. Several explanations are presented for the rise of the US stock market in the post-QE period, particularly since the election of Donald Trump.     

股票价格、实体经济与货币政策研究——基于我国1997-2011年的经验证据

本文运用格兰杰因果关系检验和向量自回归方法分析了1997年1月至2011年6月我国股票价格、GDP、通货膨胀率和货币政策的关系,实证结果表明,在中国,股票价格对通货膨胀的效应为正向,即股市上涨能带动通货膨胀水平的上涨。股票市场对GDP的影响短期内主要表现为替代效应,长期来看,则是财富效应和投资效应占主导;同时,货币供应量和利率对股票价格均有影响,但影响均不显著。通过格兰杰因果关系检验发现,利率变动导致货币供应量和股票价格发生变化。而货币供应量的变化影响着通货膨胀,也一定程度影响利率和股票价格。通过广义脉冲响应发现,中国人民银行紧缩性的利率政策并不能抑制股票价格上涨。增加货币供给短期内能够推动股市上涨,但长期对股市仍没有效果。