Stock exchange mergers and weak form of market efficiency: The case of Euronext Lisbon

This exploratory paper is among the first to examine the impact of stock exchange mergers on informational market efficiency. We focus on the merger of Bolsa de Valores de Lisboa e Porto (Portuguese Stock Exchange) with Euronext in 2002 (that created Euronext Lisbon). To investigate this question we perform numerous statistical tests: serial correlation test (ACF test), runs test, unit root test (Kwiatkowski, Philips, Schmidt, & Shin, 1992), multiple variance ratio test (Chow & Denning, 1993) and ranks and signs test (Wright, 2000).The results indicate that the Portuguese Equity Market is inefficient in weak form during pre-merger period implying that investors possessed an opportunity to earn abnormal returns though small in magnitude. The results, sensitive to the methodology used, indicate a mixed evidence of improvement in market efficiency during the post-merger period. Although the findings are mixed, yet most tests show a tendency of improved efficiency.     

On the Use of the Moving Average Trading Rule to Test for Weak Form Efficiency in Capital Markets

The examination for the possible existence of predictive power in the moving average trading rule has been used extensively to test the hypothesis of weak form market efficiency in capital markets. This work focuses mainly on the study of the variation of the moving average (MA) trading rule performance as a function of the length of the longer MA. Empirical analysis of daily data from NYSE and the Athens Stock Exchange reveal high variability of the performance of the MA trading rule as a function of the MA length and on some occasions the series of successive trading rule total returns is non‐stationary. These findings have direct implications in weak form market efficiency testing. Indeed, given this high variability of the performance of the MA trading rule, by just finding out that trading rules with some specific combinations of MA lengths can or cannot beat the market, as is the case in most of the published work thus far, is not enough evidence for or against the existence of weak form market efficiency. Results also show that on average in about three out of four cases trading rule signals are false, a fact that leaves a lot of space for improved trading rule performance if trading rule signals are combined with other information (e.g. filters, or volume of trade). Finally, some evidence of enhanced trading rule performance for the shorter MA lengths was found. This enhanced performance is partly attributed to the higher probability that a trading rule signal is not a whipsaw, as well as to the larger number of days out‐of‐the‐market which are associated with shorter MA lengths.     

Bootstrap Methods in Econometrics*

There are many bootstrap methods that can be used for econometric analysis. In certain circumstances, such as regression models with independent and identically distributed error terms, appropriately chosen bootstrap methods generally work very well. However, there are many other cases, such as regression models with dependent errors, in which bootstrap methods do not always work well. This paper discusses a large number of bootstrap methods that can be useful in econometrics. Applications to hypothesis testing are emphasized, and simulation results are presented for a few illustrative cases.     

Stock exchange mergers and return co-movement: A flexible dynamic component correlations model

The creation of a common cross-border stock trading platform is found, by use of a Flexible Dynamic Component Correlations (FDCC) model, to have increased long-run trends in conditional correlations between foreign and domestic stock market returns.     

The impact of information signals on market prices when agents have non-linear trading rules

Several methods have been developed for filtering seasonal influences and extreme returns in financial and economic time series. The theoretical support for these approaches is rather questionable since it focuses on the effects of shocks on prices and not on their sources. Removing such effects modifies the true generating system of market dynamics because of the non-proportional character of non-linearity. Thus, taking into account that the underlying process of economic time series is highly non-linear we cannot be certain a priori what the impact of new information will be on the dynamic structure of a system. The main contribution of this paper is to demonstrate using the methodology of simulations the eventual distortions in time series data arising from the arrival of news when agents follow non-linear trading strategies. We argue that if news can really modify the dynamical behaviour of a system, then the methodology of filtering exogenous distortions needs to be re-examined.     

Interest Rates, Stock Returns and Credit Spreads: Evidence from German Eurobonds

We investigate daily variations in credit spreads on investment‐grade Deutschemark‐denominated Eurobonds during the challenging 1994–1998 period. Empirical results from a Longstaff and Schwartz (1995) two‐factor regression, extended for correlated spread changes and heteroskedasticity, indicate strong persistence in spread changes. Consistent with theory and previous findings, changes in spreads are significantly negatively related to the term‐structure level while, contrary to theory, the proxy for asset value does not yield a significant negative contribution. We even find a significant positive relation for Eurobonds with long maturity. Tentative interpretations are portfolio‐rebalancing activities or differing risk factor sensitivities on short‐ vs. long‐maturity bonds.     

Unilateral CVA for CDS in a contagion model with stochastic pre-intensity and interest

Price of a financial derivative with unilateral counterparty credit risk equals to the price of an otherwise risk-free derivative minus a credit value adjustment (CVA) component, which can be seen as a call option on investor's NPV with strike 0. Thus modeling volatility of NPV is the foundation for CVA valuation. This paper assumes that default times of counterparty and reference firm follow a special contagion model with stochastic pre-intensities that allows for explicit formulas for default probabilities. Stochastic interest rate is also incorporated to account for positive correlation between pre-intensity and interest. Survival measure approach is employed to calculate NPV of a risk-free CDS, and semi-analytical solution for CVA is obtained through affine specifications. Numerical analysis shows that contagion has more significant impact on CVA than diffusion of pre-intensities, and the positive correlation between interest and reference firm's pre-intensity has monotonic decreasing impact on CVA.     

Why don’t you trade only four days a year? An empirical study into the abnormal returns of quarters first trading day

In this research I examined a calendar anomaly that occurs at the beginning of each quarter. Through an examination of 34 years of daily and annual returns for the S&P500 and 13 years of returns for popular ETFs, I have demonstrated the existence of the First Day of Quarter (FDQ) effect. By trading only four days a year from the beginning of 2000 until the end of 2013, an investor could have gained 113.1% of the S&P500 returns for that period, while being exposed to stock risk for only 56 days. Moreover, for 11 of those 14 years of trading, the FDQ was responsible for more than 10% of the annual returns. Only for two years since 2000 (2001, 2005) has the FDQ yielded a negative return. The biggest beneficiary of the FDQ is the financial sector, which for the last 13 years of investing has been non-fertile, showing −6.12% total return. Investing only at the beginning of each quarter for a total of 52 days would have yielded a return of 40.17%. The next beneficiary of the FDQ is the technological sector. The 82.5% of total return gained in this sector over the last 13 years could have been gained in only 52 days of trading.     

Measuring and Optimizing Portfolio Credit Risk: A Copula‐based Approach*

In this work, we present a methodology for measuring and optimizing the credit risk of a loan portfolio taking into account the non‐normality of the credit loss distribution. In particular, we aim at modelling accurately joint default events for credit assets. In order to achieve this goal, we build the loss distribution of the loan portfolio by Monte Carlo simulation. The times until default of each obligor in portfolio are simulated following a copula‐based approach. In particular, we study four different types of dependence structure for the credit assets in portfolio: the Gaussian copula, the Student's t‐copula, the grouped t‐copula and the Clayton n‐copula (or Cook–Johnson copula). Our aim is to assess the impact of each type of copula on the value of different portfolio risk measures, such as expected loss, maximum loss, credit value at risk and expected shortfall. In addition, we want to verify whether and how the optimal portfolio composition may change utilizing various types of copula for describing the default dependence structure. In order to optimize portfolio credit risk, we minimize the conditional value at risk, a risk measure both relevant and tractable, by solving a simple linear programming problem subject to the traditional constraints of balance, portfolio expected return and trading. The outcomes, in terms of optimal portfolio compositions, obtained assuming different default dependence structures are compared with each other. The solution of the risk minimization problem may suggest us how to restructure the inefficient loan portfolios in order to obtain their best risk/return profile. In the absence of a developed secondary market for loans, we may follow the investment strategies indicated by the solution vector by utilizing credit default swaps.     

Bank of Canada communication, media coverage, and financial market reactions

We examine how Bank of Canada communications and media reporting on them impacts Canadian bond and stock market returns. Official communications exert a relatively larger influence on the bond market, whereas media coverage is more relevant for the stock market.     

HOW FAST DO TOKYO AND NEW YORK STOCK EXCHANGES RESPOND TO EACH OTHER? AN ANALYSIS WITH HIGH‐FREQUENCY DATA

This paper uses one‐min returns on the TOPIX and S&P500 to examine the efficiency of the Tokyo and New York Stock Exchanges. Our major finding is that Tokyo completes reactions to New York within six min, but New York reacts within fourteen min. Dividing the sample period into three subperiods, we found that the response time has shortened and the magnitude of reaction has become larger over the period in both markets. The magnitude of response in New York to a fall in Tokyo is roughly double that of a rise.     

Institutional development and stock price synchronicity: Evidence from China

Better developed legal and political institutions result in greater availability of reliable firm-specific information. When stock prices reflect more firm-specific information there will be less stock price synchronicity. This paper traces the experience of China, an economy undergoing dramatic institutional change in the last 20 years with rich variation in experiences across provinces. We show that stock price synchronicity is lower when there is institutional development in terms of property rights protection and rule of law. Furthermore, we investigate the influence of political pluralism on synchronicity. A more pluralistic regime reduces uncertainty and opaqueness regarding government interventions and therefore increases the value of firm-specific information that reduces synchronicity.     

Moment conditions for Almost Stochastic Dominance

This study establishes necessary conditions for Almost Stochastic Dominance criteria of various orders. These conditions take the form of restrictions on algebraic combinations of moments of the probability distributions in question. The relevant set of conditions depends on the relevant order of ASD but not on the critical value for the admissible violation area. These conditions can help to reduce the information requirement and computational burden in practical applications. A numerical example and an empirical application to historical stock market data illustrate the moment conditions. The first four moment conditions in particular seem appealing for many applications.     

Are Co-integrated Stock Prices Consistent with the Efficient Market Hypothesis?

This paper responds to the unsatisfactory argument that there is no correspondence between co-integration and the efficient market hypothesis. A law of one co-integrating vector of prices is proposed for the exchange rate and domestic and overseas stock prices. Markets must therefore be efficient in long-run equilibrium because no arbitrage opportunities exist. However, arbitrage activity via the disequilibrium error correction allows above-average (risk-adjusted) returns to be earned in the short run. The elimination of these arbitrage opportunities means that stock market inefficiency in the short run ensures stock market efficiency in the long run.     

R&D, risks and overreaction in a market with the absence of the book-to-market effect

Prior studies document that the book-to-market (BM) effect is absent in the Taiwan stock market. Using Taiwanese data covering from 1991 to 2006, we show that, after controlling for the size effect and the Fama and French's (1993) risk factors, the BM effect only exists for those firms with low R&D intensity essentially because these stocks suffer less from investors’ underreaction to R&D investment. The BM effect arises primarily from fundamental reversals acting as a proxy for investors’ overreaction.     

Price manipulation in an experimental asset market

We analyze in the laboratory whether an uninformed trader is able to manipulate the price of a financial asset by comparing the results of two experimental treatments. In the benchmark treatment, 12 subjects trade a common value asset that takes either a high or a low value. Only three subjects know the actual value of the asset while the market is open for trading. The manipulation treatment is identical to the benchmark treatment apart from the fact that we introduce a computer program as an additional uninformed trader. This robot buys a fixed number of shares in the beginning of a trading period and sells them again afterwards. Our main result shows that the last contract price is significantly higher in the manipulation treatment if the asset takes a low value and that private information is very well disseminated by both markets if the value of the asset is high. Finally, even though this simple manipulation program loses money on average, it is profitable in some instances.     

Trade openness and the informational efficiency of emerging stock markets

This paper examines the empirical link between trade openness and the informational efficiency of stock markets in 23 developing countries. Our fixed effects panel regression results document a significant negative relation between trade openness and stock return autocorrelations only when the de facto measure is used. On this basis, we argue that a greater level of de facto trade openness is associated with a higher degree of informational efficiency in these emerging stock markets because the former signals higher future firm profitability, and investors tend to react faster to information when there is less uncertainty about a firm's future earnings or cash flows. Further analyses find no significant association between the extent of financial openness and the degree of informational efficiency.     

Structural change and international stock market interdependence: Evidence from Asian emerging markets

This study investigates the evolving pattern of the interdependence among selected Asian emerging markets and three major stock markets (Japan, UK and US). Using rolling cointegration methods and the recently developed algorithms of inductive causation, we found that time-varying cointegration relationships exist among these stock markets. The results indicate that the wave of financial liberalization policies in the early 1990s led to a significant increase in market linkages which was later weakened during the 1997 Asian financial crisis. Furthermore, the data indicate that Japan and the US have the greatest influence on the emerging markets while the influence of Singapore and Thailand has increased since the Asian financial crisis.     

Detecting Some Dynamic Properties of the Euro/Dollar Exchange Rate

Financial prices and returns have been the subject of empirical and theoretical analysis for many years, and their dynamic properties and other characteristics are still of interest. Many different tools have been applied to describe financial markets. The presented paper is addressed to examine the euro/dollar exchange rate and the related financial returns in the context of detecting exact and stochastic unit roots, and in the consequence, modelling them using time varying parameters model. The estimated STUR models are compared with standard ARMA-GARCH representations. We also examine causal relationships in the Granger sense. Upon the results of causality testing, some ADL-GARCH models are built, which are further used to examine their forecasting performance.     

The Efficiency of the Realized Range Measure of Daily Volatility: Evidence from Greece

In this study we estimate and compare the realized range volatility, a novel efficient volatility estimator computed by summing high–low ranges for intra‐day intervals, to the recently popularized realized variance estimator obtained by summing squared intra‐day returns. Our results, derived from a Greek equity high‐frequency data set, show that realized range‐based measures improve upon the corresponding realized variance‐based ones in most cases, especially for the most actively traded stocks. The usefulness of high‐frequency data in measuring and forecasting financial volatility is apparent throughout the paper.