Mean–variance convergence around the world

In this paper, we show (i) that the risk-return characteristics of our sample of 17 developed stock markets of the world have converged significantly toward each other during our study period 1974–2007, and (ii) that this international convergence in risk-return characteristics is driven mainly by the declining ‘country effect’, rather than the rising ‘industry effect’, suggesting that the convergence is associated with international market integration. Specifically, we first compute the risk-return distance among international stock markets based on the Euclidean distance and find that the distance thus computed has been decreasing significantly over time, implying a mean–variance convergence. In particular, the average risk-return distance has decreased by about 50% over our sample period. We also document that the risk-return characteristics of our sample of 14 emerging markets have been converging rapidly toward those of developed markets in recent years. This development notwithstanding, emerging markets still remain as a distinct asset class. Lastly, we show that the convergence in risk-return characteristics has exerted a negative impact on the efficiency of international investment during our sample period.     

Mean–variance and mean–semivariance portfolio selection: a multivariate nonparametric approach

While univariate nonparametric estimation methods have been developed for estimating returns in mean-downside risk portfolio optimization, the problem of handling possible cross-correlations in a vector of asset returns has not been addressed in portfolio selection. We present a novel multivariate nonparametric portfolio optimization procedure using kernel-based estimators of the conditional mean and the conditional median. The method accounts for the covariance structure information from the full set of returns. We also provide two computational algorithms to implement the estimators. Via the analysis of 24 French stock market returns, we evaluate the in-sample and out-of-sample performance of both portfolio selection algorithms against optimal portfolios selected by classical and univariate nonparametric methods for three highly different time periods and different levels of expected return. By allowing for cross-correlations among returns, our results suggest that the proposed multivariate nonparametric method is a useful extension of standard univariate nonparametric portfolio selection approaches.     

Does information drive trading in option strategies?

We study trading in option strategies in the FTSE-100 index market. Trades in option strategies represent around 37% of the total number of trades and over 75% of the total trading volume in our sample. We find some evidence that order flow in volatility–sensitive option strategies contains information about future realized volatility. We do not find evidence that order flow in directionally–sensitive option strategies contains information about future returns. Overall, our evidence suggests that option strategies are used both by traders who possess non-public information about future volatility and by uninformed speculators who appear to follow unprofitable trend chasing strategies.     

Do momentum-based trading strategies work in emerging currency markets?

We appear to be the first to present correctly calculated results for the profitability of emerging currency momentum strategies using a long time series and a good cross-sectional sample. Using a 1985–2009 sample period and six emerging currencies, we find that long-short momentum strategies gained about 1–3% per annum after actual transaction costs. These profits declined through time (both economically and statistically), however, with most of our strategies losing money after transaction costs during the last five years of our sample. These results are similar to, though slightly more volatile in the cross section, than those published for major currencies.     

Are candlestick technical trading strategies profitable in the Japanese equity market?

We show that candlestick charting, the oldest known form of technical analysis, is not profitable in the Japanese equity market over the 1975–2004 period. Candlestick technical analysis, which was developed in Japan in the 1600s, is deeply intertwined with Japanese culture and is very popular in Japan. However, there is no evidence candlestick technical trading strategies add value in either the entire 30 year period, in three 10 year sub-periods or in bull or bear markets.

Investor sentiment and the mean–variance relation

This study shows the influence of investor sentiment on the market's mean–variance tradeoff. We find that the stock market's expected excess return is positively related to the market's conditional variance in low-sentiment periods but unrelated to variance in high-sentiment periods. These findings are consistent with sentiment traders who, during the high-sentiment periods, undermine an otherwise positive mean–variance tradeoff. We also find that the negative correlation between returns and contemporaneous volatility innovations is much stronger in the low-sentiment periods. The latter result is consistent with the stronger positive ex ante relation during such periods.     

Dispersion of analysts’ forecasts and the profitability of trading strategies around the preannouncement

Contrary to prior research, the preannouncement of earnings before taxes, either good or bad, partially reduces the forecast error in the French Stock Market. Moreover, this study shows that the abnormal return at the formal announcement is negatively related to the dispersion of analysts’ forecasts after the preannouncement. It is positively related to the actual earnings surprise, especially for bad news. After controlling for the precision of the preannouncement and the actual earnings surprise, investors should buy stocks with negative (positive) abnormal return at the preannouncement of bad (good) news.     

Are investors consistent in their trading strategies? An examination of individual investor-level data

While it has been demonstrated that momentum or contrarian trading strategies can be profitable in a range of institutional settings, less evidence is available concerning the actual trading strategies investors adopt. Standard definitions of momentum or contrarian trading strategies imply that a given investor applies the same strategy to both their buy and sell trades, which need not be the case. Using investor-level, transaction-based data from China, where tax effects are neutral, we examine investors' buy-sell decisions separately to investigate how past returns impact differentially on the trading strategies investors adopt when buying and selling stock. After controlling for a wide range of stock characteristics, extreme price changes and portfolio value, a clear asymmetry in trading is observed; with investors displaying momentum behavior when buying stocks, but contrarian behavior when selling stocks. This asymmetry in behavior is not driven purely by reactions to stock characteristics or extreme stocks. We discuss behavioral and cultural explanations for our findings.     

Pairs trading with a mean-reverting jump–diffusion model on high-frequency data

This paper develops a pairs trading framework based on a mean-reverting jump–diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially, we conduct a three-step calibration procedure to the spreads of all pair combinations in a formation period. Top pairs are selected based on their spreads’ mean-reversion speed and jump behaviour. Afterwards, we trade the top pairs in an out-of-sample trading period with individualized entry and exit thresholds. In the back-testing study, the strategy produces statistically and economically significant returns of 60.61% p.a. and an annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs trading strategy against variants based on traditional distance and time-series approaches and find its performance to be superior relating to risk–return characteristics. The mean-reversion speed is a main driver of successful and fast termination of the pairs trading strategy.     

Market liquidity and institutional trading during the 2007–8 financial crisis

This paper shows that institutional sell-side herding increased bid–ask spreads and liquidity risk during the 2007–8 financial crisis. Such an impact on liquidity is most pronounced in firms with large numbers of institutions that sold the same stocks, that is, have correlated trades. For the same reason, we find institutional investors with a dedicated, buy-and-hold, investment style to be the least likely to herd; their trading activity did not affect stock market liquidity during the crisis. Our results are robust to alternative explanations, different test specifications and consistent with recent theories highlighting the negative impact of institutional trading activity on market liquidity during a crisis.     

Non-implementability of Arrow–Debreu equilibria by continuous trading under volatility uncertainty

In diffusion models, a few suitably chosen financial securities allow to complete the market. As a consequence, the efficient allocations of static Arrow–Debreu equilibria can be attained in Radner equilibria by dynamic trading. We show that this celebrated result generically fails if there is Knightian uncertainty about volatility. A Radner equilibrium with the same efficient allocation as in an Arrow–Debreu equilibrium exists if and only if the discounted net trades of the equilibrium allocation display no ambiguity in the mean. This property is violated generically in endowments, and thus Arrow–Debreu equilibrium allocations are generically unattainable by dynamically trading a few long-lived assets.     

Searching for a listed infrastructure asset class using mean–variance spanning

This study examines the portfolio-diversification benefits of listed infrastructure stocks. We employ three different definitions of listed infrastructure and tests of mean–variance spanning. The evidence shows that viewing infrastructure as an asset class is misguided. We employ different schemes of infrastructure asset selection (both traditional asset classes and factor exposures) and discover that they do not provide portfolio-diversification benefits to existing asset allocation choices. We also find that defining and selecting infrastructure investments by business model as opposed to industrial sectors can reveal a very different investment profile, albeit one that improves the mean–variance efficient frontier since the global financial crisis. This study provides new insights into defining and benchmarking infrastructure equity investments in general, as well as into the extent to which public markets can be used to proxy the risk-adjusted performance of privately held infrastructure investments.     

Do voluntary corporate restrictions on insider trading eliminate informed insider trading?

We investigate whether voluntary corporate restrictions on insider trading effectively prevent insiders from exploiting their private information. Our results show that insiders of firms with seeming restrictions on insider trading continue to take advantage of positive private information while being more cautious when exploiting negative private information. The results suggest that insiders continue to exploit their informational advantages in a way that minimizes their legal risk. We also find that the degree of information asymmetry is significantly lower in firms with restriction policies and that corporate governance significantly affects firms' decisions to adopt these policies.     

Equity index variance: Evidence from flexible parametric jump–diffusion models

This paper analyzes a wide range of flexible drift and diffusion specifications of stochastic-volatility jump–diffusion models for daily S&P 500 index returns. We find that model performance is driven almost exclusively by the specification of the diffusion component whereas the drift specifications is of second-order importance. Further, the variance dynamics of non-affine models resemble popular non-parametric high-frequency estimates of variance, and their outperformance is mainly accumulated during turbulent market regimes. Finally, we show that jump diffusion models yield more reliable estimates for the expected return of variance swap contracts.     

Institutional trading and stock resiliency: Evidence from the 2007–2009 financial crisis

We examine the impact of institutional trading on stock resiliency during the financial crisis of 2007–2009. We show that buy-side institutions have different exposure to liquidity factors based on their trading style. Liquidity supplying institutions absorb the long-term order imbalances in the market and are critical to recovery patterns after a liquidity shock. We show that these liquidity suppliers withdraw from risky securities during the crisis and their participation does not recover for an extended period of time. The illiquidity of specific stocks is significantly affected by institutional trading patterns; participation by liquidity supplying institutions can ameliorate illiquidity, while participation by liquidity demanding institutions can exacerbate illiquidity. Our results provide guidance on why some stocks take longer to recover in a crisis.     

Do IPO index portfolios improve the investment opportunities for mean–variance investors?

We set out in this study to examine whether investors can improve their investment opportunity sets through the addition of an IPO index portfolio into various sets of benchmark portfolios. Using the IPOX indices from the years 1980–2006, we find that adding an IPO index portfolio does lead to a statistically significant enlargement of the investment opportunity set for investors. Our empirical findings are robust, demonstrating that there is scope for the further development of financial products relating to IPO stocks, since investors can gain diversification benefits through investing in such IPO-related products.     

Structural changes,bid–ask spread composition and tick size in inter-bank futures trading

This paper studies a period containing three major structural changes, which constitute a natural experiment in the NYSE.Euronext-LIFFE European short-term interest rate (STIR) futures market. These changes comprise (1) a 50% reduction in minimum tick size for the most heavily traded contract, (2) European Monetary Union and (3) the transition from open outcry to electronic trading. We analyse a number of microstructure features of the four largest European interest rate futures contracts throughout this period. In particular, we focus on bid–ask spread composition using a recent model which is appropriate for this market structure. Our analysis identifies the tick size as the largest bid–ask spread component in almost every instance, which suggests that participants in this STIR future market might benefit from a reduction in minimum tick sizes.     

How effective are trading pauses?

Exploiting Nasdaq order book data and difference-in-differences methodology, we identify the distinct effects of trading pause mechanisms introduced on US stock exchanges after May 2010. We show that the mere existence of such a regulation makes market participants behave differently in anticipation of a pause. Pauses enhance price discovery during the break but have adverse effects on price stability and liquidity after the pause. We find that pauses ultimately do not “cool off” markets but cause extra volatility. This implies a regulatory trade-off between the protective role of trading pauses and their adverse effects on market quality.     

Mean square error for the Leland–Lott hedging strategy: convex pay-offs

Leland’s approach to the hedging of derivatives under proportional transaction costs is based on an approximate replication of the European-type contingent claim V _T using the classical Black–Scholes formula with a suitably enlarged volatility. The formal mathematical framework is a scheme of series, i.e., a sequence of models with transaction cost coefficients k _n =k ₀ n ^−α , where α∈[0,1/2] and n is the number of portfolio revision dates. The enlarged volatility [^(s)]_n\widehat{\sigma}_{n} in general depends on n except for the case which was investigated in detail by Lott, to whom belongs the first rigorous result on convergence of the approximating portfolio value Vⁿ_TV^{n}_{T} to the pay-off V _T . In this paper, we consider only the Lott case α=1/2. We prove first, for an arbitrary pay-off V _T =G(S _T ) where G is a convex piecewise smooth function, that the mean square approximation error converges to zero with rate n ^−1/2 in L ² and find the first order term of the asymptotics. We are working in a setting with non-uniform revision intervals and establish the asymptotic expansion when the revision dates are t_iⁿ=g(i/n)t_{i}^{n}=g(i/n), where the strictly increasing scale function g:[0,1]→[0,1] and its inverse f are continuous with their first and second derivatives on the whole interval, or g(t)=1−(1−t) ^β , β≥1. We show that the sequence n^1/2(V_Tⁿ-V_T)n^{1/2}(V_{T}^{n}-V_{T}) converges in law to a random variable which is the terminal value of a component of a two-dimensional Markov diffusion process and calculate the limit. Our central result is a functional limit theorem for the discrepancy process.