What Moves Retail Property Returns at the Metropolitan Level?

In this article the determinants of metropolitan-level appraisal-based retail property returns are examined by estimating a six-equation model of retail construction starts, retail sales, stock-market returns, commercial mortgage rates, inflation, and the logarithm of stock-market volatility. Residuals from these equations are then used to explain actual movements in retail real estate returns. Our empirical procedure looks at both unadjusted and unsmoothed appraisal-based retail real estate returns. The general finding is that unsmoothed appraisal-based retail real estate returns lag significantly behind market conditions. Furthermore, the results suggest that very little of the variation in metropolitan-level appraisal-based retail real estate returns can be explained by macroeconomic news events.     

Why Do Price Limits Exist in Stock Markets? A Manipulation‐Based Explanation

Numerous stock market regulators around the world impose daily price limits on individual stock price movements. We derive a simple model that shows that price limits may deter stock market manipulators. Based on our model's implications, we predict that regulators impose price limit rules for markets where the likelihood of manipulation is high. We present empirical evidence consistent with this hypothesis. Our study is the first to formally propose a manipulation‐based rationale for the existence of price limits in stock markets.     

Do emerging markets with consistent returns have better future performance?

Using monthly data for 25 emerging markets around the world, it is found that emerging markets with recently consistent stock returns tend to have future returns that continue in the same direction. The effects are long-lived for negative consistency, and imply that capital flows are much more sensitive to market downturns than market upturns. Additionally, the longer a market has had consistently negative (positive) stock returns, the more negative (positive) are future returns. These results serve as confirmation that the consistency effects of Grinblatt and Moskowitz [J. Finan. Econ., 2004 Grinblatt, M and Moskowitz, T. 2004. Predicting stock price movements from the pattern of past returns. J. Finan. Econ., 71: 541–579. [Crossref], [Web of Science ®] , [Google Scholar], forthcoming] and Watkins [J. Behav. Finan., 2003 Watkins, B. 2003. Riding the wave of investor sentiment: an analysis of consistency as a predictor of future stock returns. J. Behav. Finan., 4: 1–32.  [Google Scholar], 4, 1–32] exist in emerging markets around the world.     

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.     

Profitability,Value, and Stock Returns in Production‐Based Asset Pricing without Frictions

In a production‐based asset pricing model without adjustment costs and with decreasing returns to scale following Brock (1982), stock returns at the firm level are determined by profitability, the book‐to‐market ratio, and the change in future profitability prospects. Although firms with low book‐to‐market ratios are normally more profitable and profitable firms are predicted to have higher returns, the stylized fact that book‐to‐market ratios positively forecast returns still holds theoretically, but with specific predicted exceptions. These implications are confirmed empirically.     

Non-linear Dependence in Stock Returns: Does Trading Frequency Matter?

The UK has a quote-driven pure dealer market structure that is very different from order driven markets such as the NYSE and Japanese markets. This paper investigates non-linear dependence in stock returns for an exhaustive sample of UK stocks for a 21 year period. The results are analysed on the basis of trading frequency. It is found that non-linear dependence is highly significant in all cases for both individual stocks and stock portfolios formed on the basis of trading frequency. The non-linear dependence is primarily over a one day interval, although statistically significant non-linear dependence exists consistently even up to five trading days. Most of the non-linear dependence is in the form of ARCH-type conditional heteroskedasticity. However, statistically significant non-linearity in addition to an EGARCH(1,1) dependence also appears to be present. This additional non-linearity is greater for individual stocks than for portfolios and greater for smaller, less-liquid portfolios. Non-linear dependence does not appear to be caused by non-stationarity in underlying economic fundamentals or by non-linearity in the conditional mean. However, low dimensional chaos is not generally supported. The limited evidence on chaotic behaviour is stronger for portfolios with long price adjustment delays across component stocks. The main results are consistent with US studies on stock indices, suggesting that the process generating non-linear dependence is not dependent on market microstructure characteristics.     

The impact of option introduction on the conditional return distribution of underlying securities

This paper combines several interesting econometric techniques to examine changes in the conditional return distribution of security returns following option introduction. An EGARCH model is used to characterize the return generating process. An intervention analysis is performed to determine whether the parameters of the EGARCH model shift following initial options listing. This paper finds that the conditional distribution of security returns is unaffected by option introduction. Estimation of a transfer function-noise model also shows that option introduction has no effect on conditional volatility.