Non-tradable share reform, liquidity, and stock returns in China

This article studies the influence of the non-tradable share reform in the cross-section of stock returns in China. Prior research has generally neglected this important development in the Chinese stock market. We find that the firm-specific illiquidity measures that reflect direct transaction costs, price impact and difficulties in trading immediacy, exhibit a positive and significant relationship with stock returns. These effects are particularly pronounced after the non-tradable share reform. Furthermore, in the post-reform era, portfolios with high illiquidity (i.e. high relative bid-ask spread, high Amihud illiquidity, low Amivest liquidity ratio) significantly outperform portfolios with low illiquidity, controlling for size, and book-to-market effects.

Studies of firm efficiency on stock returns

 The thesis provides strong evidence for a negative relationship between firm efficiency and average stock returns in the Australian market. Moreover, the findings indicate that the return anomalies are more likely due to mispricing in which arbitrage costs play an important role in explaining the efficiency effect.

The relationship between stock returns and the foreign exchange rate: the ARDL approach

This study employs the ARDL cointegration approach in order to examine the impact
of financial liberalization on the relationships between the exchange rate and share
market performance in China. We discovered that cointegration has existed between the
Shanghai A Share Index and the exchange rate of the renminbi against the US dollar
and Hong Kong dollar since 2005, when the Chinese exchange rate regime became a
flexible, managed, floating system. We found that both the exchange rate and the money
supply influenced stock price, with a positive correlation. We further show that the
money supply increase was largely caused by a huge ‘hot money’ inflow from other
countries in recent years. After local currency appreciation, hot money, followed by
the money supply increase, pushed the market into a high level, based on expectations
regarding the local currency’s further appreciation.

Do order imbalances predict Chinese stock returns? New evidence from intraday data

In this paper we examine whether order imbalances can predict the Chinese stock market returns. We use intraday data, a panel data predictive regression model that accounts for persistent and endogenous order imbalances and cross-sectional dependence in returns, and show that order imbalances predict stock returns from 1-minute trading to 90-minute trading. On the basis of this predictability evidence using multiple trading strategies we show that profits persist during the day. These results imply that a source of Chinese market inefficiency is order imbalances.

Are Indian stock returns predictable?

In this paper we show that Indian stock returns, based on industry portfolios, portfolios sorted on book-to-market, and on size, are predictable. While we discover that this predictability holds both in in-sample and out-of-sample tests, predictability is not homogenous. Some predictors are important than others and some industries and portfolios of stocks are more predictable and, therefore, more profitable than others. We also discover that a mean combination forecast approach delivers significant out-of-sample performance. Our results survive a battery of robustness tests.

A random coefficient approach to the predictability of stock returns in panels

© The Author, 2014. Most studies of the predictability of returns are based on time series data, and whenever panel data are used, the testing is almost always conducted in an unrestricted unit-by-unit fashion, which makes for a very heavy parametrization of the model. On the other hand, the few panel tests that exist are too restrictive in the sense that they are based on homogeneity assumptions that might not be true. As a response to this, the current study proposes new predictability tests in the context of a random coefficient panel data model, in which the null of no predictability corresponds to the joint restriction that the predictive slope has zero mean and variance. The tests are applied to a large panel of stocks listed at the New York Stock Exchange. The results suggest that while the predictive slopes tend to average to zero, in case of book-to-market and cash flow-to-price the variance of the slopes is positive, which we take as evidence of predictability.

Aggregate volatility risk and the cross-section of stock returns: Australian evidence

This study examines the relation between aggregate volatility risk and the cross-section of stock returns in Australia. We use a stock's sensitivity to innovations in the ASX200 implied volatility (VIX) as a proxy for aggregate volatility risk. Consistent with theoretical predictions, aggregate volatility risk is negatively related to the cross-section of stock returns only when market volatility is rising. The asymmetric volatility effect is persistent throughout the sample period and is robust after controlling for size, book-to-market, momentum, and liquidity issues. There is some evidence that aggregate volatility risk is a priced factor, especially in months with increasing market volatility.

Monetary policy and the cross-section of stock returns: a FAVAR approach

We use a factor-augmented vector autoregression (FAVAR) to estimate the impact of monetary policy shocks on the cross-section of stock returns. Our FAVAR combines unobserved factors extracted from a large set of nancial and macroeconomic indicators with the Federal Funds rate. We nd that monetary policy shocks have heterogeneous e ects on the crosssection of stock returns. These e ects are very well explained by the degree of external nance dependence, as well as by other sectoral characteristics.

Nonparametric tail risk, macroeconomics and stock returns: predictability and risk premia

This paper proposes a new novel to calculate tail risks incorporating risk-neutral information without dependence on options data. Proceeding via a non parametric approach we derive a stochastic discount factor that correctly price a chosen panel of stocks returns. With the assumption that states probabilities are homogeneous we back out the risk neutral distribution and calculate five primitive tail risk measures, all extracted from this risk neutral probability. The final measure is than set as the first principal component of the preliminary measures. Using six Fama-French size and book to market portfolios to calculate our tail risk, we find that it has significant predictive power when forecasting market returns one month ahead, aggregate U.S. consumption and GDP one quarter ahead and also macroeconomic activity indexes. Conditional Fama-Macbeth two-pass cross-sectional regressions reveal that our factor present a positive risk premium when controlling for traditional factors.

Automatic model selection for forecasting brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We nd that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.

Consumption-wealth ratio and expected stock returns: evidence from panel data

This paper investigates the role of consumption-wealth ratio on predicting future stock returns through a panel approach. We follow the theoretical framework proposed by Lettau and Ludvigson (2001), in which a model derived from a nonlinear consumer’s budget constraint is used to settle the link between consumption-wealth ratio and stock returns. Using G7’s quarterly aggregate and financial data ranging from the first quarter of 1981 to the first quarter of 2014, we set an unbalanced panel that we use for both estimating the parameters of the cointegrating residual from the shared trend among consumption, asset wealth and labor income, cay, and performing in and out-of-sample forecasting regressions. Due to the panel structure, we propose different methodologies of estimating cay and making forecasts from the one applied by Lettau and Ludvigson (2001). The results indicate that cay is in fact a strong and robust predictor of future stock return at intermediate and long horizons, but presents a poor performance on predicting one or two-quarter-ahead stock returns.

Consumption-Wealth Ratio and Expected Stock Returns: Evidence from Panel Data on G7 Countries

Using the theoretical framework of Lettau and Ludvigson (2001), we perform an empirical investigation on how widespread is the predictability of cay {a modi ed consumption-wealth ratio { once we consider a set of important countries from a global perspective. We chose to work with the set of G7 countries, which represent more than 64% of net global wealth and 46% of global GDP at market exchange rates. We evaluate the forecasting performance of cay using a panel-data approach, since applying cointegration and other time-series techniques is now standard practice in the panel-data literature. Hence, we generalize Lettau and Ludvigson's tests for a panel of important countries. We employ macroeconomic and nancial quarterly data for the group of G7 countries, forming an unbalanced panel. For most countries, data is available from the early 1990s until 2014Q1, but for the U.S. economy it is available from 1981Q1 through 2014Q1. Results of an exhaustive empirical investigation are overwhelmingly in favor of the predictive power of cay in forecasting future stock returns and excess returns.

Automatic model selection for forecasting Brazilian stock returns

This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We find that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.