Information diffusion and the predictability of New Zealand stock market returns

This paper examines the impact of international predictors from liquid markets on the predictability of excess returns in the New Zealand stock market using data from May 1992 to February 2011. We find that US stock market return and VIX contribute significantly to the out‐of‐sample forecasts at short horizons even after controlling for the effect of local predictors, while the contribution by Australian stock market return is not significant. We further demonstrate that the predictability of New Zealand stock market returns using US market predictors could be explained by the information diffusion between these two countries.     

Nonlinear mean reversion in stock prices

This paper provides new evidence on the time-series predictability of stock market returns by introducing a test of nonlinear mean reversion. The performance of extreme daily returns is evaluated in terms of their power to predict short- and long-horizon returns on various stock market indices and size portfolios. The paper shows that the speed of mean reversion is significantly higher during the large falls of the market. The parameter estimates indicate a negative and significant relation between the monthly portfolio returns and the extreme daily returns observed over the past one to eight months. Specifically, in a quarter in which the minimum daily return is −2% the expected excess return is 37 basis points higher than in a month in which the minimum return is only −1%. This result holds for the value-weighted and equal-weighted stock market indices and for each of the size decile portfolios. The findings are also robust to different sample periods, different indices, and investment horizons.     

The decomposition of US and Euro area stock and bond returns and their sensitivity to economic state variables

This paper decomposes US and Euro area excess stock and bond return innovations into news factors using the Campbell–Schiller methodology. The results indicate that stock return volatility is mostly due to volatility of future excess return news. Inflation news plays a minor role although it is significantly correlated with excess return innovations. For the bond market too, it is future return news—not inflation news—that moves bond returns most. For finite investment horizons, however, asset market movements give a differential importance to the various news components. Results are comparable for the US and the Euro area, but differ in terms of magnitudes. In addition, sensitivities (‘betas’) to a set of state variables are estimated, yielding high interest rate betas and low money growth betas. Generally, inflation, unemployment and leading indicator betas are significant. Asset market exposures to oil and exchange rate changes are more significant for the Euro area than in the US.     

Market Efficiency and the Returns to Simple Technical Trading Rules: New Evidence from U.S. Equity Market and Chinese Equity Markets

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. This study is an attempt to explore whether some forms of technical analysis can predict stock price movement and make excess profits based on certain trading rules in markets with different efficiency level. To avoid using arbitrarily selected 26 trading rules as did by Brock, Lakonishok and LeBaron (1992) and later by Bessembinder and Chan (1998), this paper examines predictive power and profitability of simple trading rules by expanding their universe of 26 rules to 412 rules. In order to find out the relationship between market efficiency and excess return by applying trading rules, we examine excess return over periods in U.S. markets and also compare the excess returns between U.S. market and Chinese markets. Our results found that there is no evidence at all supporting technical forecast power by these trading rules in U.S. equity index after 1975. During the 1990s break-even costs turned to be negative, –0.06%, even failing to beat a buy-holding strategyin U.S. equity market. In comparison, our results provide support for the technical strategies even in the presence of trading cost in Chinese stock markets.     

Which time-frequency domain dominates spillover in the Chinese energy stock market?

Previous studies detected the spillover relations among stocks and identified the spillover roles of stocks. However, due to the participants with different dealing frequencies, the spillover effects in the stock market present multiscale features, then which time-frequency domain dominates the spillover in the stock market? Take Chinese energy stocks as an example, this paper examines the return spillover effects of the energy stock market under each time-frequency domain. We find significant return spillover in the Chinese energy stock market under different time scales, and the spillover effect under the time scale of 32–64 days contributes the most to the spillover in the whole energy stock market. Then we take further research on the directional spillovers, spillovers between energy stocks and spillovers between energy industries to detect who plays leading positions under each time scale. We divide the stocks into four roles, and find that it is different role that plays a leading position under each time scale. Furthermore, a small number of spillover relationships between energy stocks carry a large part of the total spillover quantities, and coal and consumable fuel-related stocks play an important role in the spillover of Chinese energy stocks. The robustness of our results is proved by additional tests with different forecast horizons. Our paper contributes to the literature by examining the multiscale spillover effect in the Chinese energy stock market, which provides references for market participants on investment horizons choosing, stocks selection and risk aversion.     

Causality in volatility and volatility spillover effects between US, Japan and four equity markets in the South China Growth Triangular

In examining co-movement across international stock markets, previous researchers usually pre-determine the direction of causation and neglect the Chinese equity markets. In this study, we examine the spillover effects of volatility among the two developed markets and four emerging markets in the South China Growth Triangular using Chueng and Ng's causality-in-variance test. Several findings deserve mention: (1) the Japanese stock market affects the US stock market and there is a feedback relationship between the Hong Kong and US stock market. (2) Markets of the SCGT are contemporaneously correlated with the return volatility of the US market. (3) Econometric models constructed according to the results of variance-in-causality tests have greater explanatory power than the conventional GARCH(1,1) model. (4) Using the return volatility of foreign exchange as a proxy for informational arrival can explain excess kurtosis of a stock return series, especially for the less open emerging market. (5) Geographic proximity and economic ties do not necessarily lead to a strong relationship in volatility across markets.     

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.     

Time-varying risk premia and the cost of capital: An alternative implication of the Q theory of investment

Evidence suggests that expected excess stock market returns vary over time, and that this variation is much larger than that of expected real interest rates. It follows that a large fraction of the movement in the cost of capital in standard investment models must be attributable to movements in equity risk-premia. In this paper we emphasize that such movements in equity risk premia should have implications not merely for investment today, but also for future investment over long horizons. In this case, predictive variables for excess stock returns over long-horizons are also likely to forecast long-horizon fluctuations in the growth of marginal Q, and therefore investment. We test this implication directly by performing long-horizon forecasting regressions of aggregate investment growth using a variety of predictive variables shown elsewhere to have forecasting power for excess stock market returns.     

Valuation Ratios and Stock Return Predictability in South Africa: Is It There?

Using monthly South African data for January 1990 through October 2009, this paper, to the best of our knowledge, is the first to examine the predictability of real stock return based on valuation ratios, namely, price-dividend and price-earnings ratios. We cannot detect either short-horizon or long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at both short and long horizons based on bootstrapped critical values constructed from linear representations of the data. We find, via Monte Carlo simulations, that the power to detect predictability in finite samples tends to decrease at long horizons in a linear framework. Although Monte Carlo simulations applied to exponential smooth-transition autoregressive models of the price-dividend and price-earnings ratios show increased power, the ability of the nonlinear framework in explaining the pattern of stock return predictability in the data does not show any promise at either short or long horizons, just as in the linear predictive regressions.     

Is There a Size Effect in the Pricing of Stocks in the Chinese Stock Markets?: The Case of Bull Versus Bear Markets

Size effect studies generally suggest that a return premium exists for small firms. While the size effect has mostly disappeared in recent years in mature markets (e.g., US and UK), it remains mostly strong in developing markets. The purpose of this paper is to examine the relationship between firm size and excess stock returns in the Chinese stock markets, and to examine this effect in both a bull and bear market. No studies have previously examined these relationships in the Chinese markets. The results of the study indicate that a size effect exists in the Chinese stock markets over the 6-year period from 1998 to 2003. We find small firms have significantly greater excess returns than large firms. Moreover, small firms are found to have a stronger reaction to the direction of the market than large firms. Small firms have significantly greater positive excess returns than large firms during the bull market. However, small firms have significantly greater negative returns (using total market value), or no significant difference in returns (using float market value) during the bear market period.     

南橘北枳:A股市场的经济关联与股票回报

本文系统检验并比较了中国A股市场中行业动量、区域动量、供应链动量以及科技关联动量等经济关联动量的显著性及预测周期。本文发现,中国股票市场中经济关联因子呈现出与美国股票市场不同的规律,在月度层面行业动量显著,而科技关联因子只在周度上具有显著的预测能力。进一步分析科技关联动量发现,中国股票市场中科技关联因子能预测目标公司未来1-3周的股票收益和未来基本面的变化,据此构建的多空策略能够产生周度0.16%的超额收益(年化8.67%);机制检验发现,科技关联因子预测期短的原因是由于中国股票市场中存在较多具有博彩倾向的散户投资者;有限注意和市场摩擦两个机制检验证明科技关联动量源自错误定价。进一步检验发现,科技关联动量在国有企业和创新政策颁布后更加显著。本文补充了现有A股市场的动量研究,有助于理解中国股票市场规律、提升资本市场有效性。     

Term structure of return correlations and international diversification: evidence from European stock markets

The paper examines the term structure of correlations of weekly returns for the stock market in the US, Japan and nine European countries between 1988 and 1994. Stock indices are decomposed into permanent and temporary components using a canonical correlation analysis and then short- and long-horizon return correlations are calculated from these two price components. The empirical results reveal that the relationships of return correlations among these stock markets are not stable across return horizons. While correlations, in general, tend to increase with return horizons, there are several cases showing that correlations decline when investment horizons increase.     

The dynamics of individual and institutional trading on the Shanghai Stock Exchange

We investigate the daily dynamic relation between returns and institutional and individual trades in the emerging Chinese stock market. Consistent with the hypotheses of trend-chasing and attention-grabbing trading, we find that the response of individual trading to return shocks is much stronger than that of institutional trading, and individuals are net buyers following return shocks. Second, we find that past individual buys and sells have predictive power, whereas past institutional buys and sells have predictive power for market returns in longer horizons. However, both institutional and individual trading activities are more strongly related to past trades than past returns, and individual trading is also influenced by institutional trading. Moreover, we find that institutional trading in the largest quintile leads the trading in the smallest quintile, but no such lead–lag relation is found for individual trades. Finally, we find that the average cumulative abnormal trading volume of individuals is much larger than that of institutions around the firms' earnings announcement, suggesting that less-informed individual investors are more heavily influenced by firm-specific information disclosures and attention-grabbing events.     

Forecasting stock market returns over multiple time horizons

In this paper, we seek to demonstrate the predictability of stock market returns and explain the nature of this return predictability. To this end, we introduce investors with different investment horizons into the news-driven, analytic, agent-based market model developed in Gusev et al. [Algo. Finance, 2015, 4, 5–51]. This heterogeneous framework enables us to capture dynamics at multiple timescales, expanding the model’s applications and improving precision. We study the heterogeneous model theoretically and empirically to highlight essential mechanisms underlying certain market behaviours, such as transitions between bull and bear markets and the self-similar behaviour of price changes. Most importantly, we apply this model to show that the stock market is nearly efficient on intraday timescales, adjusting quickly to incoming news, but becomes inefficient on longer timescales, where news may have a long-lasting nonlinear impact on dynamics, attributable to a feedback mechanism acting over these horizons. Then, using the model, we design algorithmic strategies that utilize news flow, quantified and measured, as the only input to trade on market return forecasts over multiple horizons, from days to months. The backtested results suggest that the return is predictable to the extent that successful trading strategies can be constructed to harness this predictability.     

A comprehensive investigation on the predictive power of economic policy uncertainty from non-U.S. countries for U.S. stock market returns

This study uses economic policy uncertainty (EPU) indices for ten developed countries, three diffusion models, and five combination methods to forecast excess returns in the U.S. stock market. It shows empirically that, over the period January 1997 to January 2022, non-U.S. EPU indices have better predictive power for U.S. equity market excess returns than the U.S. EPU index itself. This illustrates how economic information from international markets can affect the U.S. stock market. This finding challenges the extensively recognized view that the U.S. is where important market signals are initially transmitted to other markets, suggesting that this belief is incomplete. Our outcomes are robust to a battery of tests covering model selection, model specification, forecast horizons, and the pandemic period, and their economic values are assessed. The findings are essential for the financial field to confront future fierce situations and crises.     

Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method

This study combines the variational mode decomposition (VMD) method and static and time-varying symmetric and asymmetric copula functions to examine the dependence structure between crude oil prices and major regional developed stock markets (S&P500, stoxx600, DJPI and TSX indexes) during bear, normal and bull markets under different investment horizons. Furthermore, it analyzes the upside and downside short- and long-run risk spillovers between oil and stock markets by quantifying three market risk measures, namely the value at risk (VaR), conditional VaR (CoVaR) and the delta CoVaR (∆CoVaR). The results show that there is a tail dependence between oil and all stock markets for the raw return series. By considering time horizons, we show that there is an average dependence between the considered markets for the short-run horizons. However, the tail dependence is also found for the long-run horizons between the oil and stock markets, with the exception of the S&P500 index which exhibits average dependence with the oil market. Moreover, we find strong evidence of up and down risk asymmetric spillovers from oil to stock markets and vice versa in the short-and long run horizons. Finally, the market risk spillovers are asymmetric over the time and investment horizons.     

Does an intertemporal tradeoff between risk and return explain mean reversion in stock prices?

When volatility feedback is taken into account, there is strong evidence of a positive tradeoff between stock market volatility and expected returns on a market portfolio. In this paper, we ask whether this intertemporal tradeoff between risk and return is responsible for the reported evidence of mean reversion in stock prices. There are two relevant findings. First, price movements not related to the effects of Markov-switching market volatility are largely unpredictable over long horizons. Second, time-varying parameter estimates of the long-horizon predictability of stock returns reject any systematic mean reversion in favour of behaviour implicit in the historical timing of the tradeoff between risk and return.     

Asset Pricing with a Bank Risk Factor

This paper studies how the state of the banking sector influences stock returns of nonfinancial firms. We consider a two‐factor pricing model, where the first factor is the traditional market excess return and the second factor is the change in the average distance to default of commercial banks. We find that this bank factor is priced in the cross section of U.S. nonfinancial firms. Controlling for market beta, the expected excess return for a stock in the top quintile of bank risk exposure is on average 2.83% higher than for a stock in the bottom quintile.     

信息不确定、坏消息与投资者交易行为

本文以2000年至2010年的A股上市公司违法违规事件为样本,分析该类事件中信息不确定性的影响,以及市场反应中的投资者交易行为。研究发现:上市公司市场价值在事件日呈显著下跌;然而,与直觉有些相悖的是,信息不确定程度与超额累积收益呈显著正相关,这意味着在坏消息到来时,不确定性反而提高了股票的市场价值;最后,通过对各类投资者在此类事件中的净买入情况分析,我们发现不同投资者的交易行为有明显差异。机构投资者在坏消息中采用了反向交易策略,并且知情交易促进机构投资者的买入。     

A Time-Series Model of Stock Returns with a Positive Short-Term Correlation and a Negative Long-Term Correlation

We study portfolio stock return behavior that exhibits both a positive autocorrelation over short horizons and a negative autocorrelation over long horizons. These autocorrelations are more significant in small size portfolios. Among various forms of temporary components in stock prices, an AR(2) component is the simplest model compatible with this pattern of returns, which yields an ARMA(2,2) model of stock returns. We show that the significance of this model is that it requires the presence of feedback trading, which is a form of irrational trades, and the market's slow adjustment to the market fundamentals, which is consistent with recent modelings of stock prices. We find that the variation of the temporary component becomes greater as the firm size gets smaller. This implies that the deviation from the market fundamentals is larger in small size portfolios than in large size portfolios.