Optimal portfolio choice in the bond market

We consider the Merton problem of optimal portfolio choice when the traded instruments are the set of zero-coupon bonds. Working within a Markovian Heath–Jarrow–Morton model of the interest rate term structure driven by an infinite-dimensional Wiener process, we give sufficient conditions for the existence and uniqueness of an optimal trading strategy. When there is uniqueness, we provide a characterization of the optimal portfolio as a sum of mutual funds. Furthermore, we show that a Gauss–Markov random field model proposed by Kennedy [Math. Financ. 4, 247–258(1994)] can be treated in this framework, and explicitly calculate the optimal portfolio. We show that the optimal portfolio in this case can be identified with the discontinuities of a certain function of the market parameters.     

The modified mixture of distributions model: a revisit

Andersen (J Financ 51, 169–204 (1996)) introduced a modification of the mixture of distributions model based on microstructure arguments. Based on a small sample of five stocks, he infers that this modified mixture of distributions (MMD) model adequately captures the joint behavior of trading volume and volatility. We re-examine this claim using a larger sample of twenty-two stocks and two sample periods. Our tests show that 59% of the sample rejects the MMD model in the period 1973–1991, the same period studied by Andersen. Results for the second period (1993–1999) are more supportive of the MMD, especially for number of trades, although nearly one-third of the sample still rejects the MMD. We conclude that further tests are needed before the general validity of the MMD can be established. JEL Classification Numbers C12, C52     

Event study with imperfect competition and private information: earnings announcements revisited

Although prior research documents that prices respond to earnings announcements, only a little of the price variation is explained by these announcements. To further investigate the properties of the information environment around these announcements we use NYSE TAQ data and compute the maximum likelihood estimates (MLEs) of the primitive parameters of a Kyle (Econometrica 53(6):1315–1336, 1985) type model within and around earnings announcement windows. These include the precision of fundamentals given only public information, the precision of private signals, and the variance of uninformed liquidity trading (noise). We find that liquidity noise is higher while the precision of beliefs given only public information is lower within an earnings announcement window. The precision of private information is higher in an event window, consistent with greater information acquisition to try and interpret a public announcement. We also document that Kyle’s λ is higher in an event window, showing an overall increase in information asymmetry. Our overall findings suggest that the earnings announcement window is distinguished from the preceding and subsequent windows not by being a period with more public information but as a period with different public information.     

A super-replication theorem in Kabanov’s model of transaction costs

We prove a general version of the super-replication theorem, which applies to Kabanov’s model of foreign exchange markets under proportional transaction costs. The market is described by a matrix-valued càdlàg bid-ask process evolving in continuous time. We propose a new definition of admissible portfolio processes as predictable (not necessarily right- or left- continuous) processes of finite variation related to the bid-ask process by economically meaningful relations. Under the assumption of existence of a strictly consistent price system (SCPS), we prove a closedness property for the set of attainable vector-valued contingent claims. We then obtain the super-replication theorem as a consequence of that property, thus generalizing to possibly discontinuous bid-ask processes analogous results obtained by Kabanov (Financ. Stoch. 3, 237–248, 1999), Kabanov and Last (Math. Financ. 12, 63–70, 2002) and Kabanov and Stricker (Advances in Finance and Stochastics: Essays in Honour of Dieter Sondermann, pp 125–136, 2002). Rásonyi’s counter-example (Lecture Notes in Mathematics 1832, 394–398, 2003) served as an important motivation for our approach.     

CLIENTELE TRADING IN RESPONSE TO PUBLISHED INFORMATION: EVIDENCE FROM THE DARTBOARD COLUMN

We present an analysis of clientele trading effects in response to the widespread dissemination of public information. Employing analyst recommendations published in the Wall Street Journal's Dartboard column as the informational stimulus and the NYSE's trades and quotes (TAQ) transactions database as the data resource, we document that investors in different trading clienteles exhibit dramatically different trading responses to identical innovations in the informational environment.     

Underwriting profit margin of P/L insurance in the fuzzy-ICAPM

The ICAPM is used to study the underwriting profit margin of the P/L insurance company, including the insurances of automobile damage, automobile liability and fire, in which the parameters are the symmetric or non-symmetric triangular fuzzy numbers. From the ten-year data of a company in Taiwan we determine the lower and upper limits associated with the various α-level of the fuzzy numbers. Our results show that the best-fitting parameters of the model from our data are the asymmetric triangular fuzzy numbers. The skew factors in each insurance are determined, which could be used to perform the forecasting of the underwriting profit margin. Our results show that the systematic risk in the fuzzy environment (with best-fitting value of skew factor) becomes larger than that in the crisp environment. However, the insurance underwriting leverage and insurance financial leverage in the fuzzy environment are smaller than those in the crisp environment. JEL Classification G22 · G32 The author is grateful to the two anonymous reviewers for The Geneva Risk and Insurance Review for their comments on an earlier version of this paper.     

Retail investor information demand – speculating and investing in structured products

We study the impact of retail investor information demand on trading in bank-issued investment and leverage structured products, which are specifically designed for retail investors. Stock-specific information demand positively predicts speculative trading activity. Furthermore, we find a positive relationship between market-wide information demand and order aggressiveness and order uncertainty for speculating and investing activity. Whereas information supply is associated with speculative long positions, information demand does not induce investors to be predominantly long or short. Finally, we do not find retail investor information demand to contribute to an upward price pressure on security prices. In contrast, information supply exerts negative price pressure. Overall, retail investor trading in individual stocks is much more strongly influenced by market-wide information demand instead of firm-specific information demand. This implies a low informational efficiency of retail investor speculation and investing activity.     

Herding,momentum and investor over-reaction

In this paper we study the impact of noise or quality of prices on returns. The noise arises from herding by market participants beyond what is justified by information. We construct a firm-quarter-specific measure of speculative intensity (SPEC) based on autocorrelation in daily trading volume adjusted for the amount of information available, and find that speculative intensity has a significant positive impact on returns. Both cross-sectional and time series variation in SPEC are consistent with conventional wisdom, and with implications of theories of herding as in DeLong et al. (1990, J Political Econ 98(4):703–738). We find that high-SPEC firms drive the returns to momentum trading strategies and that investor over-reaction is significant only in the case of high-SPEC firms.

Short-term Dynamics in the Cyprus Stock Exchange

Abstract

This paper investigates the short-term dynamics of stock returns in an emerging stock market namely, the Cyprus Stock Exchange (CYSE). Stock returns are modelled as conditionally heteroscedastic processes with time-dependent serial correlation. The conditional variance follows an EGARCH process, while for the conditional mean three nonlinear specifications are tested, namely: (a) the LeBaron exponential autoregressive model; (b) the Sentana and Wadhwani positive feedback trading model; and finally (c) a model that nests both (a) and (b). There is an inverse relationship between volatility and autocorrelation consistent with the findings from several other stock markets, including the US. This pattern could be the manifestation of a certain form of noise trading namely positive feedback trading or, momentum trading strategies. There is little evidence that market declines are followed with higher volatility than market advances, the so-called ‘leverage effect’, that has been observed in almost all developed stock markets. In out of sample forecasts, the nonlinear specifications provide better results in terms of forecasting both first and second moments of the distribution of returns.     

Dynamic Asset Allocation and the Informational Efficiency of Markets

Markets have an allocational role; even in the absence of news about payoffs, prices change to facilitate trade and allocate resources to their best use. Allocational price changes create noise in the signal extraction process, and markets where such trading is important are markets in which we may expect to find a failure of informational efficiency. An important source of allocational trading is the use of dynamic trading strategies caused by the incomplete equitization of risks. Incomplete equitization causes trade. Trade implies the inefficiency of passive strategies, thus requiring investors to determine whether price changes are informational or allocational.     

Are margin traders informed?

The literature offers contradictory views on the informativeness of margin trading using various measures of information content. Utilising data from a Chinese margin‐trading pilot programme initiated in 2010, this paper investigates whether margin traders are informed by directly examining the return predictability of margin‐trading activity. We find that margin‐trading activities cannot positively predict future stock returns. Moreover, we explore some non‐informational trading strategies used by margin traders, e.g., positive‐feedback strategies and moving‐average trading rules. These results suggest that margin traders are noise traders rather than informed traders, and margin trading conveys no new firm‐specific information.     

Trading Volume and Transaction Costs in Specialist Markets

Prior work with competitive rational expectations equilibrium models indicates that there should be a positive relation between trading volume and differences in beliefs or information among traders. We show that this result is sensitive to whether and how transaction costs are modeled. In a specialist market with endogenous transaction costs we show that trading volume can be negatively related to the degree of informational asymmetry in the market. Our analysis highlights the dependence of volume on market structure, and our results suggest that the “volume effects” of corporate or macroeconomic events reflect a decrease, rather than an increase, in heterogeneity of beliefs or asymmetry of information.     

Speed of convergence to market efficiency: The role of ECNs

Chordia, Roll and Subrahmanyam (2005, CRS) estimate the speed of convergence to market efficiency based on short-horizon return predictability of the 150 largest NYSE firms. We extend CRS to a broad panel of NYSE stocks and are the first to examine the relation between electronic communication networks (ECNs) and the corresponding informational efficiency of prices. Overall, we confirm CRS's result that price adjustments to new information occur on average within 5–15 min for large NYSE stocks. We further show that it takes about 20 min longer for smaller firms to incorporate information into prices. Most importantly, we demonstrate that the speed of convergence to market efficiency is significantly related to the type of trading platform where orders are executed, even after controlling for relative order flows, trading costs, volatility, informational effects, trading conditions, market quality, institutional trading activity, and other firm-specific characteristics. Our findings provide direct answers and insights to issues raised in a recent SEC concept release document.     

Insider Trading around Dividend Announcements: Theory and Evidence

The informational role of strategic insider trading around corporate dividend announcements is studied based on the efficient equilibrium in a signalling model with endogenous insider trading. Insider trading immediately prior to the announcement of dividend initiations has significant explanatory power. For firms with insider selling prior to the dividend initiation announcement, the excess returns are negative and significantly lower than for the remaining firms (with no insider trading or just insider buying) as implied by our model. Another implication is that dividend increases may elicit a positive or negative stock price response depending on the firm's investment opportunities.     

Price-volume Correlation in the Housing Market: Causality and Co-movements

Housing market cycles are featured by a positive correlation of prices and trading volume, which is conventionally attributed to a causal relationship between prices and volume. This paper analyzes the housing markets in 114 metropolitan statistical areas in the United States from 1990 to 2002, treats both prices and volume as endogenous variables, and studies whether and how exogenous shocks cause co-movements of prices and volume. At quarterly frequency, we find that, first, both home prices and trading volume are affected by conditions in labor markets, the mortgage market, and the stock market, and the effects differ between markets with low and high supply elasticity. Second, home prices Granger cause trading volume, but the effects are asymmetric—decreases in prices reduce trading volume, and increases in prices have no effect. Third, trading volume also Granger causes home prices, but only in markets with inelastic supply. Finally, we find a statistically significant positive price–volume correlation; which, however, is mainly explained by co-movements of prices and volume caused by exogenous shocks, instead of the Granger causality between prices and volume.     

Informed short sales and option introductions

This study examines how the introduction of options affects the level of informed short selling. In particular, we test whether option introductions increases or decreases the level of informed short selling. Our tests are motivated by a theoretical debate in the literature. The first stream of literature argues that introducing options into markets may increase speculative trading which can result in less informed trading when informed traders perceive speculative trades as noise. The second stream argues that introducing options into markets improves the informational environment of the market because option prices provide an additional information mechanism for informed traders. We approximate informed short selling by examining (i) non-exempt short sales, (ii) contrarian short-selling activity, and (iii) the return predictability contained in shorting activity. Results show that non-exempt shorting activity increases after options become available. Further, we show that both the level of contrarian short selling and the return predictability contained in short selling increase after options are listed. Our results suggest that informed short selling increases after options are introduced.     

Informational Frictions and Commodity Markets

This paper develops a model with a tractable log‐linear equilibrium to analyze the effects of informational frictions in commodity markets. By aggregating dispersed information about the strength of the global economy among goods producers whose production has complementarity, commodity prices serve as price signals to guide producers' production decisions and commodity demand. Our model highlights important feedback effects of informational noise originating from supply shocks and futures market trading on commodity demand and spot prices. Our analysis illustrates the weakness common in empirical studies on commodity markets of assuming that different types of shocks are publicly observable to market participants.     

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.