When Do Opening Stock Returns Tend to be Higher?

Abstract

In this study, I make an effort to formulate a trading rule that would make use of some systematic interday patterns in individual stocks’ opening returns. I analyze intraday price data on all the stocks that were S&P 500 Index constituents during the period from 1993 to 2012. I document that if the general market direction of the previous day's opening session is controlled for, then a stock's opening return tends to be higher if, on the previous trading day, its opening return was relatively high (either positive, or higher than the same day's opening market return) and its open-to-close return was relatively low (either non-positive, or lower than or equal to the same day's open-to-close market return). Finally, for the sampling period, I construct two different investment portfolios involving a long position in the stocks on the days when, according to the findings, their opening returns are expected to be high and a short position in the stocks on the days when, according to the findings, their opening returns are expected to be low. Both portfolios are found to yield significantly positive returns, providing evidence for the practical applicability of the documented patterns in opening stock prices.     

A note on the estimated GARCH coefficients from the S&P1500 universe

ABSTRACT

We employ 1440 stocks listed in the S&P Composite 1500 Index of the NYSE. Three benchmark GARCH models are estimated for the returns of each individual stock under three alternative distributions (Normal, t and GED). We provide summary statistics for all the GARCH coefficients derived from 11,520 regressions. The EGARCH model with GED errors emerges as the preferred choice for the individual stocks in the S&P 1500 universe when non-negativity and stationarity constraints in the conditional variance are imposed. 57% of the constraint’s violations are taking place in the S&P small cap stocks.     

Investment horizon effect on asset allocation between value and growth strategies

How does the optimal risk exposure of assets change as their investment horizons increase? Does this impact investment portfolio decision-making, in particular, optimal asset allocation between value and growth strategies over various investment horizons? This paper adopts a new approach to address these questions by examining portfolio allocation between value and growth stocks over various investment horizons. This new approach is based on wavelet analysis, which decomposes the returns of a particular investment strategy across multiple investment horizons. The key empirical results show that the success of pursuing the value strategy (short-selling growth stocks and going long on value stocks) is impacted by the approach used to classify value and growth stock returns. We explore two common alternatives: Fama-French versus Standard & Poor's (S&P) 500/Barra portfolios. The results using Fama-French portfolios show that as the investment horizon increases, the optimal mean allocation of investors tilts heavily away from growth stocks, particularly for lower and moderate levels of risk aversion. Interestingly, for S&P 500/Barra portfolios the allocation weights between value and growth do not vary much.     

On the correlation between stocks and art market returns

We estimate the correlation between the returns of an S&P 500-based portfolio and Renoir paintings. Unlike previous studies that relied on single-point estimates of the correlation to explore the merits of adding art assets to a portfolio of stocks, we rely on a wild bootstrap algorithm to determine confidence intervals for the correlation estimates. We find that these confidence intervals are so wide (a situation not peculiar to our example) that it seems impossible to make absolute remarks about the merits of adding art-related assets to stocks portfolios. Moreover, our results suggest that previous conclusions regarding the correlation between art and stocks should be taken with some scepticism.     

QARMA-Beta-t-EGARCH versus ARMA-GARCH: an application to S&P 500

Statistical performance and out-of-sample forecast precision of ARMA-GARCH and QARMA-Beta-t-EGARCH are compared. We study daily returns on the Standard and Poor’s 500 (S&P 500) index and a random sample of 50 stocks from the S&P 500 for period May 2006 to July 2010. Competing models are estimated for periods before and during the US financial crisis of 2008. Out-of-sample point and density forecasts are performed for periods during and after the US financial crisis. The results provide evidence of the superior in-sample statistical and out-of-sample predictive performance of QARMA-Beta-t-EGARCH.     

Asset pricing with time-varying betas for stocks traded on S&P 500

This study uses a novel approach for capturing time variation in betas whose pattern is treated as a function of market returns. A two-factor model (TFM) is constructed using estimated coefficients of a nonlinear regression. The model is tested against the CAPM and the Fama and French three-factor model in the context of time series regressions. The used stocks are traded on S&P 500. The period spans from 1993 to 2011. The time series regression results depict the superiority of the TFM in explaining portfolio returns including momentum ones. We also provide evidence that the particular portfolios employed at the construction of the new model accommodate different fundamental characteristics and different risk levels.     

Score-driven Markov-switching EGARCH models: an application to systematic risk analysis

We introduce new Markov-switching (MS) dynamic conditional score (DCS) exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models, to be used by practitioners for forecasting value-at-risk (VaR) and expected shortfall (ES) in systematic risk analysis. We use daily log-return data from the Standard & Poor’s 500 (S&P 500) index for the period 1950–2016. The analysis of the S&P 500 is useful, for example, for investors of (i) well-diversified US equity portfolios; (ii) S&P 500 futures and options traded at Chicago Mercantile Exchange Globex; (iii) exchange traded funds (ETFs) related to the S&P 500. The new MS DCS-EGARCH models are alternatives to of the recent MS Beta-t-EGARCH model that uses the symmetric Student’s t distribution for the error term. For the new models, we use more flexible asymmetric probability distributions for the error term: Skew-Gen-t (skewed generalized t), EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverse Gaussian) distributions. For all MS DCS-EGARCH models, we identify high- and low-volatility periods for the S&P 500. We find that the statistical performance of the new MS DCS-EGARCH models is superior to that of the MS Beta-t-EGARCH model. As a practical application, we perform systematic risk analysis by forecasting VaR and ES.

Abbreviation Single regime (SR); Markov-switching (MS); dynamic conditional score (DCS); exponential generalized autoregressive conditional heteroscedasticity (EGARCH); value-at-risk (VaR); expected shortfall (ES); Standard & Poor's 500 (S&P 500); exchange traded funds (ETFs); Skew-Gen-t (skewed generalized t); EGB2 (exponential generalized beta of the second kind); NIG (normal-inverse Gaussian); log-likelihood (LL); standard deviation (SD); partial autocorrelation function (PACF); likelihood-ratio (LR); ordinary least squares (OLS); heteroscedasticity and autocorrelation consistent (HAC); Akaike information criterion (AIC); Bayesian information criterion (BIC); Hannan-Quinn criterion (HQC).     

中国股市价值反转投资策略有效性实证研究

本文以中国深沪A股股票市场为考察对象 ,分析了价值反转投资策略的有效性。作者通过实证分析发现 :在中国深沪A股股票市场上 ,以帐面价值与市场价值比 (B M)、B M GS等指标构造的价值反转投资策略可以产生显著的超额收益率 ,并且其显著程度因持有期不同而不同。接着 ,作者利用CAPM模型、Fama French三因素模型并引入了协偏度 (coskewness)和协峰度 (cokurtosis) ,构造出多风险因子模型来解释价值反转投资策略超额收益率。我们发现 :在经过传统风险因素调整后 ,价值反转投资策略效果依然明显 ;CAPM模型无法解释价值反转投资策略超额收益率 ;Fama French三因素模型对价值反转投资策略超额收益率的解释能力最为显著 ,但对于有些价值投资策略 ,在Fama French三因素基础上加上协偏度和协峰度因子后 ,模型的解释能力有所提高     

Product market competition and productivity shocks

I investigate the interaction effects of competition and productivity shocks on stocks’ earnings and returns. I find that the sensitivities of earnings and returns to productivity shocks are negatively associated with competition intensity. I also find that the excess returns of productivity shocks-sorted portfolios are lower when competition intensity is high, even after controlling for known return predictors. Overall, the empirical evidence shows firms are less exposed to productivity shocks when competition is high. As such, this study provides a possible mechanism through which the structure of product markets affects stock returns.     

Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach

We use regular vine (r-vine), canonical vine (c-vine) and drawable vine (d-vine) copulas to examine the dependence risk characteristics of three 20-stock portfolios from the retail, manufacturing and gold-mining equity sectors of the Australian market in periods before, during and after the 2008–2009 global financial crisis (GFC). Our results indicate that the retail portfolio is less risky than the manufacturing counterpart in the crisis period, while the gold-mining portfolio is less risky than both the retail and manufacturing sector portfolios. Both the retail and gold stocks display a higher propensity to yield positively skewed returns in the crisis periods, contrary to the manufacturing stocks. The r-vine is found to best capture the multivariate dependence structure of the stocks in the retail and gold-mining portfolios, while the d-vine does it for the manufacturing stock portfolio. These findings could be used to develop dependence risk- and investment risk-adjusted strategies for investment, rebalancing and hedging which more adequately account for the downside risk in various market conditions.     

The Role of Industry Effects in Simultaneous Reversal and Momentum Patterns in One-Month Stock Returns

When stocks are ranked by returns in one month, the portfolio of loser stocks tends to outperform the portfolio of winner stocks in the subsequent month. Yet industry portfolios tend to display momentum. We develop a model of information diffusion among agents with constrained information processing ability that reconciles these well-documented phenomena. We test whether this model or the overreaction hypothesis is consistent with the data. Additionally, a trading strategy based on the model outperforms strategies based on overreaction and on industry momentum. The strategy produces abnormal returns while controlling for market–risk and the size, book value, January, momentum, and liquidity effects.     

Optimal trading strategies for Lévy-driven Ornstein–Uhlenbeck processes

This study derives an optimal pairs trading strategy based on a Lévy-driven Ornstein–Uhlenbeck process and applies it to high-frequency data of the S&P 500 constituents from 1998 to 2015. Our model provides optimal entry and exit signals by maximizing the expected return expressed in terms of the first-passage time of the spread process. An explicit representation of the strategy’s objective function allows for direct optimization without Monte Carlo methods. Categorizing the data sample into 10 economic sectors, we depict both the performance of each sector and the efficiency of the strategy in general. Results from empirical back-testing show strong support for the profitability of the model with returns after transaction costs ranging from 31.90% p.a. for the sector ‘Consumer Staples’ to 278.61% p.a. for the sector ‘Financials’. We find that the remarkable returns across all economic sectors are strongly driven by model parameters and sector size. Jump intensity decreases over time with strong outliers in times of high market turmoil. The value-add of our Lévy-based model is demonstrated by benchmarking it with quantitative strategies based on Brownian motion-driven processes.     

Markov regime-switching Beta-t-EGARCH

We suggest a Markov regime-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model for U.S. stock returns. We compare the in-sample statistical performance of the MS Beta-t-EGARCH model with that of the single-regime Beta-t-EGARCH model. For both models we consider leverage effects for conditional volatility. We use data from the Standard Poor’s 500 (S&P 500) index and also a random sample that includes 50 components of the S&P 500. We study the outlier-discounting property of the single-regime Beta-t-EGARCH and MS Beta-t-EGARCH models. For the S&P 500, we show that for the MS Beta-t-EGARCH model extreme observations are discounted more for the low-volatility regime than for the high-volatility regime. The conditions of consistency and asymptotic normality of the maximum likelihood estimator are satisfied for both the single-regime and MS Beta-t-EGARCH models. All likelihood-based in-sample statistical performance metrics suggest that the MS Beta-t-EGARCH model is superior to the single-regime Beta-t-EGARCH model. We present an application to the out-of-sample density forecast performance of both models. The results show that the density forecast performance of the MS Beta-t-EGARCH model is superior to that of the single-regime Beta-t-EGARCH model.     

Performance of value- and size-based strategies in the Italian stock market

This paper investigates the performance of size- and value-based strategies in the Italian stock market in the period 2000–2018. Previous research argued the impossibility to define properly value-sorted portfolios due to the inaccuracy of book-to-market ratios available for Italian listed stocks. Using more accurate data, we implement portfolios sorting based on value and growth stocks, to assess the relevance of the value factor in the Italian stock market. We find that the capital asset pricing model fails to explain the cross-section of returns on the different strategies while the Fama and French three-factor model provides a better fit. The results show that all three factors are significant in explaining Italian stock returns during the sample period. Unlike previous studies, which either found no value effect at all or no clear-cut results when testing the book-to-market variable, we find that the value factor is statistically significant and the associated risk premium is of a considerable size.     

Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility

We show that historical volatility from high frequency returns outperforms implied volatility when standardized returns by historical volatility tends to be normally distributed. For the FTSE 100 futures, we find that historical volatility using high frequency returns outperforms implied volatility in forecasting future volatility. However, we find that implied volatility outperforms historical volatility in forecasting future volatility for the S&P 500 futures. The results also indicate that historical volatility using high frequency returns could be an unbiased forecast for the FTSE 100 futures.     

Skillful hiding: evaluating hedge fund managers’ performance based on what they hide

Mandatory disclosure of hedge fund portfolios has been a hotly debated topic. This article studies asset returns of ‘confidential holdings’ (confidentiality treatment [CT]) or those assets that were not voluntarily disclosed by US-based hedge funds in their original 13F filings to the Securities and Exchange Commission. After analysing returns from 1999 to 2013, we find that in aggregate, the CT position size, ownership share and returns are statistically different to non-CT positions. We provide a mechanism for regulators and investors to rank fund managers based on what they hide in positions.     

Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&P 100

We develop a copula-based pairs trading framework and apply it to the S&P 100 index constituents from 1990 to 2014. We propose an integrated approach, relying on copulas for pairs selection and trading. Essentially, we fit t-copulas to all possible combinations of pairs in a formation period. Next, we trade these pairs in-sample to assess the profitability of mispricing signals derived from t-copulas. The top pairs are transferred to an out-of-sample trading period, and traded with individualized exit thresholds. In particular, we differentiate between pairs exhibiting mean-reversion and momentum effects and apply idiosyncratic take-profit and stop-loss rules. For the top 5 mean-reversion pairs, we find out-of-sample returns of 7.98% per year; the top 5 momentum pairs yield 7.22% per year. Standard deviations are low, leading to annualized Sharpe ratios of 1.52 (top 5 mean-reversion) and 1.33 (top 5 momentum), respectively.     

Performing an event study: An exercise for finance students

This exercise helps instructors teach students how to perform a simple event study. The study tests to see if stocks earn abnormal returns when added to the S&P 500. Students select a random sample of stocks that were added to the index between January 2000 and July 2015. The accompanying spreadsheet calculates cumulative abnormal returns and cumulative abnormal trading volume and plots them in separate graphs. Students are asked to analyze the data and draw conclusions. Through this exercise, students learn how to conduct an event study and determine if a statistically significant event has occurred.     

R&D, patents and stock return volatility

Recent finance literature highlights the role of technological change in increasing firm specific (idiosyncratic) and aggregate stock return volatility, yet innovation data is not used in these analyses, leaving the direct relationship between innovation and stock return volatility untested. The paper investigates the relationship between volatility and innovation using firm level patent data. The analysis builds on the empirical work by Mazzucato (Rev Econ Dyn 5:318–345, 2002; J Evol Econ 13(5):491–512, 2003) where it is found that stock return volatility is highest during periods in the industry life-cycle when innovation is the most ‘radical’. In this paper we ask whether firms which invest more in innovation (more R&D and more patents) and/or which have more important innovations (patents with more citations) experience more volatility in their returns. Given that returns should in theory be higher, on average, for higher risk stocks, we also look at the effect of innovation on the level of returns. To take into account the competition between firms within industries, firm returns and volatility are measured relative to the industry average. We focus the analysis on firms in the pharmaceutical industry between 1974 and 1999. Results suggest that there is a positive and significant relationship between volatility, R&D intensity and the various patent related measures—especially when the innovation measures are filtered to distinguish the very innovative firms from the less innovate ones.     

Exogeneity of Money and its Policy Implications for Price Control: Evidence from South Asia

Abstract. We investigate the policy role that could be assigned to money stock in controlling the price level in four South Asian countries, namely, India, Nepal, Pakistan and Sri Lanka. The problem of policy assignment associated with the Granger non‐causality tests is pointed out. Various forms of exogeneity are tested. Money stocks (M₁ and M₂), consumer price index (CPI) and real GDP are cointegrated and causally related but we find overwhelming evidence of endogeneity of money. The endogeneity of money does not support the authorities’ policy stance of controlling price level through the control of money stock.