A note on execution costs for stock index futures: Information versus liquidity effects

This paper examines execution costs and the impact of trade size for stock index futures using price-volume transaction data from the London International Financial Futures and Options Exchange. Consistent with Subrahmanyam [Rev. Financ. Stud. 4 (1991) 17] we find that effective half spreads in the stock index futures market are small compared to stock markets, and that trades in stock index futures have only a small permanent price impact. This result is important as it helps to better understand the success of equity index products such as index futures and Exchange Traded Funds. We also find that there is no asymmetry in the post-trade price reaction between purchases and sales for stock index futures across various trade sizes. This result is consistent with the conjecture in Chan and Lakonishok [J. Financ. Econ. 33 (1993) 173] that the asymmetry surrounding block trades in stock markets is due to the high cost of short selling and the general reluctance of traders to short sell on stock markets.     

Are tightened trading rules always bad? Evidence from the Chinese index futures market

This paper investigates the impact of tightened trading rules on the market efficiency and price discovery function of the Chinese stock index futures in 2015. The market efficiency and the price discovery of Chinese stock index futures do not deteriorate after these rule changes. Using variance ratio and spectral shape tests, we find that the Chinese index futures market becomes even more efficient after the tightened rules came into effect. Furthermore, by employing Schwarz and Szakmary [J. Futures Markets, 1994, 14(2), 147–167] and Hasbrouck [J. Finance, 1995, 50(4), 1175–1199] price discovery measures, we find that the price discovery function, to some extent, becomes better. This finding is consistent with Stein [J. Finance, 2009, 64(4), 1517–1548], who documents that regulations on leverage can be helpful in a bad market state, and Zhu [Rev. Financ. Stud., 2014, 27(3), 747–789.], who finds that price discovery can be improved with reduced liquidity. It also suggests that the new rules may effectively regulate the manipulation behaviour of the Chinese stock index futures market during a bad market state, and then positively affect its market efficiency and price discovery function.     

On the conditional relationship between beta and return in international stock returns

This paper examines the conditional relationship between beta and return in international stock returns between January 1970 and July 1998 using the approach of Pettengill et al. [Pettengill, G., Sundaran, S., & Mathur, I. (1995). The conditional relation between beta and return. J Financ Quant Anal, 30, 101–116] (1995).Consistent with previous research, there is a flat unconditional relationship between beta and return. However, when the sample is split into up market and down market months, there is support for the relationship. There is a significant positive relationship between beta and return in up market months and a significant negative relationship between beta and return in down market months. Subsidiary results highlight a January effect in the conditional beta and return relationship.     

Risk-managed industry momentum and momentum crashes

This paper investigates Barroso and Santa-Clara’s [J. Financ. Econ., 2008, 116, 111–120] risk-managed momentum strategy in an industry momentum setting. We investigate several traditional momentum strategies including that recently proposed by Novy-Marx [J. Financ. Econ., 2012, 103, 429–453]. We moreover examine the impact of different variance forecast horizons on average pay-offs and also Daniel and Moskowitz’s [J. Financ. Econ., 2016, 122, 221–247] optionality effects. Our results show in general that neither plain industry momentum strategies nor the risk-managed industry momentum strategies are subject to optionality effects, implying that these strategies have no time-varying beta. Moreover, the benefits of risk management are robust across volatility estimators, momentum strategies and subsamples. Finally, the ‘echo effect’ in industries is not robust in subsamples as the strategy works only during the most recent subsample.     

Equilibrium analysis of volatility clustering

Volatility clustering is a pervasive feature of equity markets. This article studies volatility clustering in an equilibrium setting by generalizing the CRRA and CARA representative agent models of finance. In equilibrium, the market portfolio follows a volatility regime-switching process in which the volatility level is determined by the agent's local risk aversion. Using monthly data, the empirical tests reveal that at least four volatility regimes are necessary to fit the data. While one of the models explains the GARCH effects in the data, an analysis of the Euler equation pricing errors suggests that both models are likely misspecified. Since the models can be used to closely approximate any state-independent utility function, it is doubtful that there exists any representative agent equilibrium (with state-independent utility) that is consistent with the data. An equivalent interpretation is that the market portfolio price process is not a diffusion process of the type studied by Bick [Bick, A., On viable diffusion price processes of the market portfolio, J. Finance 45 (1990) 673–689] and He and Leland [He, H., Leland, H., On equilibrium asset price processes, Rev. Financ. Stud. 6 (1993) 593–617].     

The day of the week effect on stock market volatility and volume: International evidence

This study investigates the day of the week effect on the volatility of major stock market indexes for the period of 1988 through 2002. Using a conditional variance framework, we find that the day of the week effect is present in both return and volatility equations. The highest volatility occurs on Mondays for Germany and Japan, on Fridays for Canada and the United States, and on Thursdays for the United Kingdom. For most of the markets, the days with the highest volatility also coincide with that market's lowest trading volume. Thus, this paper supports the argument made by Foster and Viswanathan [Rev. Financ. Stud. 3 (1990) 593] that high volatility would be accompanied by low trading volume because of the unwillingness of liquidity traders to trade in periods of high stock market volatility.     

Extreme volumes and expected stock returns: Evidence from China's stock market

We examine the relation between extreme trading volumes and expected returns for individual stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange over the July 1994–December 2000 interval. Contrasted with the evidence obtained from the US data [J. Finance 56 (2001) 877], our results show that stocks experiencing extremely high (low) volumes are associated with low (high) subsequent returns. Moreover, this extreme volume–return relation significantly co-varies with security characteristics like past stock performance, firm size, and book-to-market values. In particular, stocks with extreme volumes are related to poorer performance if they are past winners, large firms, and glamour stocks than if they are past losers, small firms, and value stocks, respectively. These results are robust to both daily and weekly samples as well as stock exchange sub-samples. Although the liquidity premium hypothesis of Amihud and Mendelson [J. Financ. Econ. 17 (1986) 223] provides a partial explanation for the extreme volume–return relation, our results fit better the behavioral hypothesis of Baker and Stein [J. Financ. Mark. 7 (2004) 271].     

No-arbitrage bounds for the forward smile given marginals

We explore the robust replication of forward-start straddles given quoted (Call and Put options) market data. One approach to this problem classically follows semi-infinite linear programming arguments, and we propose a discretisation scheme to reduce its dimensionality and hence its complexity. Alternatively, one can consider the dual problem, consisting in finding optimal martingale measures under which the upper and the lower bounds are attained. Semi-analytical solutions to this dual problem were proposed by Hobson and Klimmek [Financ. Stochastics, 2015, 19, 189–214] and by Hobson and Neuberger [Math. Financ., 2012, 22, 31–56]. We recast this dual approach as a finite-dimensional linear program, and reconcile numerically, in the Black–Scholes and in the Heston model, the two approaches.     

Global equity styles and industry effects: the pre-eminence of value relative to size

We extend the model of Heston and Rouwenhorst, (1994) [J. Fianc. Econom. 36, 3–27] to investigate the effects of size, value, industry, and country factors on the volatility of stock returns in international stock markets. In common with previous authors, we find that country factors dominate the other factors in explaining the return variation. The second most important factors are industry factors followed by value and size factors. Furthermore, after removing possible influences from country and industry factors, we find that there still is a global value effect but not a global size effect. Our data set finishes in 1995 — thus, if there are global super-stocks, they do not appear to have been historically important.     

The impact of technology-motivated M&A and joint ventures on the value of IT and non-IT firms: a new examination

We extend Lee and Lim (Rev Quant Financ Account 27:111–123, 2006) who provide empirical evidence on the impact of mergers and acquisitions (M&As) and joint ventures on the value of information technology (IT) and non-IT firms. Using technology-motivated transactions, we examine whether there are differences in market response to the announcement of M&As and joint ventures, and we consider the long-term performance of such firms. We find the market provides no (positive) reaction to joint ventures (M&As) at the announcement. We also present new evidence suggesting the market reacts more favorably to the announcement of technology M&As relative to joint ventures for our full sample, IT sample and non-IT sample. However, our examination of these firms’ long-term performance suggests the initial reaction is not fully supported. The findings suggest improved (declining) operating performance for joint venture (M&A) firms, and evidence to conclude joint venture firms achieve superior long-term performance changes for both accrual- and cash-based measures. To explain these inconsistencies, we employ a set of control variables previously documented as determinants of the innovation ownership decision. For joint venture firms, we find that, while the market fails to consider the importance of the firms’ R&D intensity and growth prospects in its initial reaction, these are ultimately key indicators of their future performance. The evidence also suggests the market overreacts to M&A announcements because it over-weights the impact of R&D intensity on the firms’ future performance in its initial response.     

Normality tests of option-implied risk-neutral densities: evidence from the small Finnish market

This study provides an empirical analysis of option-implied risk-neutral densities. The normality of the risk-neutral density is statistically assessed testing restrictions regarding the skewness and kurtosis of the implied distribution and by applying the traditional test approach involving a comparison of the pricing errors calculated using alternative models. It is found that both the approaches give similar results, whereas the former method has the advantage that the significance of the estimated parameters can be statistically tested. Using data from the small Finnish market, the normality of the distributions is soundly rejected as expected based on the theoretical framework by Damodaran [J. Financ. Quant. Anal. 20 (1985) 423].     

A note on the Wang and Wang measure of the quality of the compass rose

H. Wang and C. Wang [Visibility of the compass rose in financial asset returns: A quantitative study, J. Bank. Financ. 26 (2002), 1099–1111] derive a measure of the visibility of the radial patterns that appear in a plot of current and past returns, which are more commonly known as the compass rose. In theory, this measure should be positively related to the tick/volatility ratio. In practice however, we find that this relationship does not hold for higher tick/volatility ratios that are common to stock market data. Thus, the use of this measure is limited in real world applications. We propose a correction factor that improves the behaviour of the quality measure over higher tick/volatility ratios, however, further research is required to fully identify and correct the problem.     

Unbiased estimation of expected return using CAPM

Estimation of expected return is required for many financial decisions. For example, an estimate for cost of capital is required for capital budgeting and cost of equity estimates are needed for performance evaluation based on measures such as EVA. Estimates for expected return are often based on the Capital Asset Pricing Model (CAPM), which states that expected excess return (expected return minus the risk-free rate) is equal to the asset's sensitivity to the world market portfolio (β) times the risk premium on the “world market portfolio” (the market risk premium). Since the world market portfolio, by definition, contains all assets in the world, it is not observable. As a result, an estimate for expected return is commonly obtained by taking an estimate for β based on some index (as a proxy for the world market portfolio) and an estimate for the market risk premium based on a potentially different index and multiplying them together. In this paper, it is shown that this results in a biased estimate for expected return. This is undesirable since biased estimates lead to misallocation of funds and biased performance measures. It is also shown in this paper that the straightforward procedure suggested by Fama and MacBeth [J. Financ. Econ. 1 (1974) 43] results in an unbiased estimate for expected return. Further from the analysis done, it follows that, for an unbiased estimate, it does not matter what proxy is used, as long as it is used correctly an unbiased estimate for expected return results.     

Jump-robust estimation of volatility with simultaneous presence of microstructure noise and multiple observations

In this paper, we develop the multipower estimators for the integrated volatility in (Barndorff-Nielsen and Shephard in J. Financ. Econom. 2:1–37, 2004); these estimators allow the presence of jumps in the underlying driving process and the simultaneous presence of microstructure noise and multiple records of observations. By multiple records we mean more than one observation recorded on a single time stamp, as often seen in stock markets, in particular, for heavily traded securities, for a data set with even millisecond frequency. We establish the consistency and asymptotic normality of the estimators for both noise-free and noise-present cases. Simulation studies confirm our theoretical results. We apply the estimators to a real high-frequency data set.     

Unraveling a puzzle: the case of value line timeliness rank upgrades

We examine a sample of Value Line’s timeliness rank upgrades that occur immediately following earnings announcements and find that pre-event price momentum has significant incremental explanatory power for post-event drift, after controlling for the level of earnings surprise. Therefore, the stock price drift following Value Line’s timeliness upgrades cannot be viewed as driven only by the post-earnings announcement drift phenomenon. Instead, these findings indicate that, among other factors, Value Line has been exploiting the price momentum effect for decades. Black (Financ. Anal. J. 29:10–14, 1973) clearly stated that it does indeed do this, but his assertion has not yet been verified as an explanation of the puzzling drift that follows Value Line rank upgrades.     

Continuous time and nonparametric modelling of U.S. interest rate models

In this paper we compare the forecasting performance of different models of interest rates using parametric and nonparametric estimation methods. In particular, we use three popular nonparametric methods, namely, artificial neural networks (ANN), k-nearest neighbour (k-NN), and local linear regression (LL). These are compared with forecasts obtained from two-factor continuous time interest rate models, namely, Chan, Karolyi, Longstaff, and Sanders [CKLS, J. Finance 47 (1992) 1209]; Cos, Ingersoll, and Ross [CIR, Econometrica 53 (1985) 385]; Brennan and Schwartz [BR-SC, J. Financ. Quant. Anal. 15 (1980) 907]; and Vasicek [J. Financ. Econ. 5 (1977) 177]. We find that while the parametric continuous time method, specifically Vasicek, produces the most successful forecasts, the nonparametric k-NN performed well.     

Real-time market microstructure analysis: online transaction cost analysis

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The common acronym for investigating the causes of bad and good performance of trading is transaction cost analysis Rosenthal [Performance Metrics for Algorithmic Traders, 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70% in the US, 45% in Europe and 35% in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance (like in Almgren and Chriss [J. Risk, 2000, 3(2), 5–39]), a stochastic control (e.g. Guéant et al. [Math. Financ. Econ., 2013, 7(4), 477–507]), an impulse control (see Bouchard et al. [SIAM J. Financ. Math., 2011, 2(1), 404–438]) or a statistical learning (as used in Laruelle et al. [Math. Financ. Econ., 2013, 7(3), 359–403]) viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order (following for instance Cristianini and Shawe-Taylor [An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 2000]). We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts (i.e. have influence, in the sense of predictive power or information defined in Basseville and Nikiforov [Detection of Abrupt Changes: Theory and Application, 1993], Shannon [Bell Syst. Tech. J., 1948, 27, 379–423] and Alkoot and Kittler [Pattern Recogn. Lett., 1999, 20(11), 1361–1369]) which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method can be taken advantage of to automatically adjust their trading action in the post trade analysis of algorithms.     

The survival probability of the SABR model: asymptotics and application

The stochastic-alpha-beta-rho (SABR) model is widely used by practitioners in interest rate and foreign exchange markets. The probability of hitting zero sheds light on the arbitrage-free small strike implied volatility of the SABR model (see, e.g. De Marco et al. [SIAM J. Financ. Math., 2017, 8(1), 709–737], Gulisashvili [Int. J. Theor. Appl. Financ., 2015, 18, 1550013], Gulisashvili et al. [Mass at zero in the uncorrelated SABR modeland implied volatility asymptotics, 2016b]), and the survival probability is also closely related to binary knock-out options. Besides, the study of the survival probability is mathematically challenging. This paper provides novel asymptotic formulas for the survival probability of the SABR model as well as error estimates. The formulas give the probability that the forward price does not hit a nonnegative lower boundary before a fixed time horizon.     

The impact of rights issues of convertible debt in Australian markets

Australian convertible debt issues are rights issues of non-callable securities and are issued in a market characterised by thin trading, significant institutional investor participation rates and a high number of resource firms. However, this study documents a significant negative announcement effect for rights issues of convertible debt, similar to international evidence. An analysis of the determinants of the announcement effect supports variants of the information asymmetry and agency cost hypotheses. The results do not support the convertible debt models of Kim [Kim, Y., 1990. Informative conversion ratios, a signalling approach. Journal of Financial and Quantitative Analysis 25, 229–243], Brennan and Kraus [Brennan, M., Kraus, A., 1987. Efficient financing under asymmetric information. Journal of Finance 42, 1225–1243], Green [Green, R.C., 1984. Investment incentives, debt and warrants. Journal of Financial Economics 13, 115–136] but some support is found for Stein's [Stein, J., 1992. Convertible bonds as backdoor equity financing. Journal of Financial Economics 32, 3–22], convertible debt model and Mayers [Mayers, D., 1998. Why firms issue convertible bonds: the matching of financial and real investment options. Journal of Financial Economics 47, 83–102], sequential financing model. However, support is found for Brous and Kini [Brous, P.A., Kini, O., 1994. The valuation effects of equity issues and the level of institutional ownership: evidence from analysts’ earnings forecasts. Financial Management 23, 33–46], equity issue based external monitoring model and Eckbo and Masulis [Eckbo, B., Masulis, R., 1992. Adverse selection and the rights offer paradox. Journal of Financial Economics 32, 292–332], rights issue adverse selection model.     

Information Uncertainty and Momentum Phenomenon Amidst Market Swings: Evidence From the Chinese Class A Share Market

This paper empirically investigates how firm-level information uncertainty impacts momentum profits in the Chinese Class A share market. We employ seven different factors to gauge the degree of firm-level information uncertainty—firm size, firm age, analysts’ coverage, return volatility, dispersion in analysts’ earnings forecast, trading volume, and the quality/strength of corporate governance (free float ratio). We find evidence showing that information uncertainty has an amplifying effect over the momentum profits, and the amplifying effect is more pronounced over the time periods following DOWN market state over the sample period from January 1996 to December 2013. The robustness of the empirical evidence is warranted by a risk-adjustment test based on the FF3F model and Wang and Xu’s (Financ Anal J 60(6):65–77, 2004) FF3F model, a sub-period analysis, and a different definition of market states. The empirical findings can provide an important reference point for international and domestic investors when adjusting investment strategies and portfolio positions in relatively volatile financial markets such as the Chinese stock market.