The information content of the term structure of risk-neutral skewness

We seek to reconcile the debate about the price effect of risk-neutral skewness (RNS) on stocks. We document positive predictability from short-term skewness, consistent with informed-trading demand, and negative predictability from long-term skewness, consistent with skewness preference. A term spread on RNS captures different information from long- and short-term contracts, resulting in stronger predictability. The quintile portfolio with the lowest spread outperforms that with highest spread by 14.64% annually. The term structure of RNS predicts earnings surprises and price crashes. We extract the slope factor from RNS term structure, estimate its risk premium, and explore its relation with several macroeconomic variables.     

Higher moments and beta asymmetry: evidence from Australia

We examine whether systematic higher moments capture beta asymmetry in an asset pricing model whereby the conditional beta of a risky asset increases (decreases) during a bear (bull) market state. We first provide a simple conceptual outline from the microeconomic literature to show that beta asymmetry is driven by time‐varying higher‐order risk preferences (prudence and temperance) across different market states. We then empirically relate these higher‐order risk preferences to systematic skewness and systematic kurtosis. We find that beta asymmetry in Australian stock returns cannot be explained by Carhart (1997) 4‐factor model but is subsumed by systematic higher moments.     

Detection of arbitrage in a market with multi-asset derivatives and known risk-neutral marginals

In this paper we study the existence of arbitrage opportunities in a multi-asset market when risk-neutral marginal distributions of asset prices are known. We first propose an intuitive characterization of the absence of arbitrage opportunities in terms of copula functions. We then address the problem of detecting the presence of arbitrage by formalizing its resolution in two distinct ways that are both suitable for the use of optimization algorithms. The first method is valid in the general multivariate case and is based on Bernstein copulas that are dense in the set of all copula functions. The second one is easier to work with but is only valid in the bivariate case. It relies on results about improved Fréchet–Hoeffding bounds in presence of additional information. For both methods, details of implementation steps and empirical applications are provided.     

Pricing of time-varying liquidity risk in Finnish stock market: new evidence

Using two recently developed illiquidity measures, we estimate a conditional version of liquidity-adjusted capital asset pricing model (LCAPM), which allows for a time-varying decomposition of the total illiquidity premium into a level component and three risk components. The total estimated annualized illiquidity premium for the Finnish equities during 1997–2015 is 1.13–1.90% depending on the illiquidity measure. Of the three systematic liquidity risk components, risk arising from hedging of wealth shocks is the most important followed by commonality in liquidity risk, whereas flight to liquidity risk is not significantly priced in the Finnish stock market. Our results show that the liquidity risk is time varying, therefore the models estimating the risk-return relationship should address the issue of conditionality.     

A mixture of Gaussians approach to mathematical portfolio oversight: the EF3M algorithm

An analogue can be made between: (a) the slow pace at which species adapt to an environment, which often results in the emergence of a new distinct species out of a once homogeneous genetic pool and (b) the slow changes that take place over time within a fund, mutating its investment style. A fund’s track record provides a sort of genetic marker, which we can use to identify mutations. This has motivated our use of a biometric procedure to detect the emergence of a new investment style within a fund’s track record. In doing so, we answer the question: What is the probability that a particular PM’s performance is departing from the reference distribution used to allocate her capital? The EF3M algorithm, inspired by evolutionary biology, may help detect early stages of an evolutionary divergence in an investment style and trigger a decision to review a fund’s capital allocation.     

How candlestick features affect the performance of volatility forecasts: evidence from the stock market

In this study, we used asymmetric GJR-X models to investigate how the return and volatility estimates in the stock market on any given day are affected by the features of the preceding day's candlestick. Empirical results show that, first, for symmetric volatility specification, the upper and lower shadows of yesterday can, respectively, lower and raise the return today, whereas both upper and lower shadows of yesterday can increase today's volatility. Notably, the upper and lower shadows elicited asymmetric responses in the sizes of the volatility and return increments. Conversely, for asymmetric volatility specification, leverage effect may affect the asymmetric response and prevent the upper shadow from influencing the return and volatility. Second, for symmetric volatility specification, the black and white real bodies of yesterday can, respectively, augment and abate today's return and volatility, indicating that the black real body produces a distinct type of leverage effect to influence volatility. Importantly, for asymmetric specification, the effects of the black and white real bodies appear the same as for the symmetric specification, but are less significant. Lastly, the real bodies (or, respectively, asymmetric volatility specification) influenced the accuracy of volatility forecasts more strongly than the upper and lower shadows (or, respectively, symmetric volatility specification).     

The empirical relationship between risk and return: evidence from the UK stock market

Previous studies reach no consensus on the relationship between risk and return using data from one market. We argue that the world market factor should not be ignored in assessing the risk-return relationship in a partially integrated market. Applying a bivariate generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) model to the weekly stock index returns from the UK and the world market, we document a significant positive relationship between stock returns and the variance of returns in the UK stock market after controlling for the covariance of the UK and the world market return. In contrast, conventional univariate GARCH-M models typically fail to detect this relationship. Nonnested hypothesis tests supplemented with other commonly used model selection criteria unambiguously demonstrate that our bivariate GARCH-M model is more likely to be the true model for UK stock market returns than univariate GARCH-M models. Our results have implications for empirical assessments of the risk-return relationship, expected return estimation, and international diversification.     

Bond market access and acquisitions: empirical evidence from the European market

We investigate whether access to bond markets affects acquisition activity of the European firms between 1999 and 2014. Our study provides insight into the effect that the growing European bond market has on corporate investment activity. We find that access to the bond markets, measured by the existence of a credit rating, has a significant effect on the tendency of firms to make acquisitions. The effect is strongest in Continental Europe and during times of high acquisition activity. We further find that consistent with prior U.S. evidence, bond market access has an inverse effect on abnormal returns generated by the acquisitions. That finding suggests that firms with superior access to financing pursue targets of lesser quality.     

A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests

This paper examines the return predictability of the US stock market using portfolios sorted by size, book-to-market ratio and industry. We use novel panel variance ratio tests, based on the wild bootstrap proposed in this paper, which exhibit desirable size and power properties in small samples. We have found evidence that stock returns have been highly predictable from 1964 to 1996, except for a period leading to the 1987 crash and its aftermath. After 1997, stock returns have been unpredictable overall. At a disaggregated level, we find evidence that large-cap portfolios have been priced more efficiently than small- or medium-cap portfolios; and that the stock returns from high-tech industries are far less predictable than those from non-high-tech industries.     

Deposit insurance and the stock market: evidence from Denmark

Previous studies of the relationship between deposit insurance and bank market values have usually been limited to consideration of minor changes in bank regulations, but the 1987 initiation of deposit insurance in Denmark permits examination of a potentially major policy shift. It is found that the market values of large Danish banks exhibited a modest positive reaction to the announcement of insurance, but that small risky banks responded negatively. These results partially contrast with those previously found for the USA, an outcome that seems likely to reflect the interaction of deposit insurance with the particular characteristics of the pre-existing Danish regulatory system.     

Individual investors repurchasing behaviour: evidence from the Portuguese stock market

We study the repurchasing behaviour of individual investors and identify-related stock- and investor-specific attributes that affect the preference to repurchase stocks previously owned. Using a unique database of Portuguese individual investors, we find that investors prefer to repurchase stocks that were associated with a gain during their previous roundtrip (i.e. prior winners) and have suffered price declines subsequent to their last sale. Consistent with the extant literature based on the US market, our results suggest that different market characteristics do not seem to affect investors’ preference regarding stock repurchases. Moreover, we find that this preference increases with the magnitude of the prior gain or the decline in price following the last sale. We also demonstrate that larger and more visible domestic stocks are more likely to be repurchased and that less active, under-diversified and home-biased investors are more likely to engage in such behaviour. Finally, we find that repurchased stocks yield poor post-performance – approximately 267 basis points less than newly purchased stocks. Our main conclusion is that repurchases are essentially emotionally driven and penalize investor’s performance.     

Performance of technical trading rules: evidence from the crude oil market

ABSTRACT

This study investigates the debatable success of technical trading rules, through the years, on the trending energy market of crude oil. In particular, the large universe of 7846 trading rules proposed by Sullivan, Timmermann, and White (1999. “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” The Journal of Finance 54 (5): 1647–1691. doi:10.1111/0022-1082.00163), divided into five families (filter rules, moving averages, support and resistance rules, channel breakouts, and on-balance volume averages), is applied to the daily prices of West Texas Intermediate (WTI) light, sweet crude oil futures as well as the United States Oil (USO) fund, from 2006 onwards. We employ the k-familywise error rate (k-FWER) and false discovery rate (FDR) techniques proposed by Romano, J. P., and M. Wolf. (2007. “Control of Generalized Error Rates in Multiple Testing.” The Annals of Statistics 35 (4): 1378–1408. doi:10.1214/009053606000001622) and Bajgrowicz, P., and O. Scaillet. (2012. “Technical Trading Revisited: False Discoveries, Persistence Tests, and Transaction Costs.” Journal of Financial Economics 106 (3): 473–491. doi:10.1016/j.jfineco.2012.06.001) respectively, accounting for data snooping in order to identify significantly profitable trading strategies. Our findings explain that there is no persistent nature in rules performance, contrary to the in-sample outstanding results, although tiny profits can be achieved in some periods. Overall, our results seem to be in favor of interim market inefficiencies.     

Anticipating critical transitions of the housing market: new evidence from China

We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's metropolises. Building on the Log-Periodic Power Law Singularity (LPPLS) model of self-reinforcing feedback loops, we use the quantile regression calibration approach recently introduced by two of us to build confidence intervals and explore possible distinct scenarios. We propose to consolidate the quantile regressions into the arithmetic average of the quantile-based LPPLS Confidence indicator, which accounts for the robustness of the calibration with respect to bootstrapped residuals. We make three main contributions to the literature of real estate bubbles. First, we verify the validity of the arithmetic average of the quantile-based LPPLS Confidence indicator by studying the critical times of historical housing price bubbles in the U.S., Hong Kong, U.K. and Canada. Second, the LPPLS detection methods are applied to provide early warning signals of the housing markets in some metropolises in China. Third, we determine the possible turning points of the markets in Beijing, Shanghai, Shenzhen, Guangzhou, Tianjin and Chengdu and anticipate critical transitions of China's housing markets via our multi-scales and multi-quantiles analyses. Finally, given these projections performed in February 2017, the price trajectories from March 2017 to January 2018 that became available from the time of submission to the time of revision of the present article offer quite unique genuine out-of-sample tests of the performances of our indicators.     

American futures options arbitrage: evidence from the Nikkei 225 options market

Previous studies have examined the profitability of European index options arbitrage. This paper adds to the literature by investigating the arbitrage profitability of American index options—the Nikkei 225 index futures options traded on the Singapore Stock Exchange (SGX). Using the real-time bid–ask prices, we find evidence of profitable arbitrage opportunities, while the frequency of observations violating no-arbitrage bounds and the magnitude of arbitrage profits decrease with the level of transaction costs. Our results have implications for the analysis of American options market efficiency. Failure to use bid–ask prices may lead to biased conclusions.     

Corporate risk management and firm value: evidence from the UK market

The paper evaluates the effect of corporate risk management activities on firm value, using a sample of large UK non-financial firms. Following recent changes in financial reporting standards, we are able to collect detailed information on risk management activities from audited financial reports. This enables us to gain a better understanding of risk management practices and to investigate value implications of different types of hedging. Overall 86.88% of the firms in the sample use derivatives to manage at least one type of price risk. The hedging premium is statistically and economically significant for foreign currency derivative users, while we provide weak evidence that interest rate hedging increases firm value. The extent of hedging and the hedging horizon have an impact on the hedging premium, whereas operational risk management activities do not significantly influence the market value of the firm.     

Point and density prediction of intra-day volume using Bayesian linear ACV models: evidence from the Polish stock market

Trading volume is one of the key measures of trading activity intensity and plays a crucial role in the financial market microstructure literature. In this paper, we examine the out-of-sample point and density forecasting performance of Bayesian Autoregressive Conditional Volume (ACV) models for intra-day volume data. Based on 5-min traded volume data for stocks quoted on the Warsaw Stock Exchange, a leading stock market in Central and Eastern Europe, we find that, in terms of point forecasts, the considered linear ACV models significantly outperform benchmarks such as the naïve and Rolling Means methods but not necessarily Autoregressive Moving Average (ARMA) models. Moreover, the point forecasts obtained within the exponential error ACV model are significantly superior to those calculated in other competing structures for which Burr or generalized gamma distributions are specified. The main finding from the analysis of density forecasts is that, in many cases, the linear ACV models with the Burr and generalized gamma distributions provide significantly better density forecasts than the linear ACV model with exponential innovations and the ARMA models in terms of the log-predictive score, calibration and sharpness.     

The determinants of short selling: evidence from the Hong Kong equity market

While most financial regulators agree that short sellers have an important role to play in ensuring an efficiently functioning market, it is interesting to note that many did not hesitate to ban short selling during the recent financial crisis. This apparent contradiction most likely stems from a lack of understanding about what motivates short trading. In this paper, we focus on the determinants of short selling during ‘normal’ trading in the Hong Kong stock market. We find that dividend payments, company fundamentals, risk, option trading, the interest rate spread and past returns and short selling are all significant determinants of short selling.     

The ex-dividend day stock price anomaly: evidence from the Greek stock market

This paper examines the ex-dividend stock price and trading volume behavior in the Greek stock market for the period 2000–2004. We use both standard event-study methodology and cross-sectional regression analysis in assessing the ex-dividend stock price anomaly. We find that stock prices drop less than the dividend amount. By examining abnormal returns as well as abnormal trading volume around the ex-dividend day, we find strong evidence of short-term trading, which is consistent with the presence of dividend-capturing activities around the ex-dividend day. The results from the cross-sectional regression analysis confirm that the short-term trading hypothesis explains the ex-dividend day stock price anomaly in Greece.