Performance persistence in hedge funds: Australian evidence

Using the most comprehensive database on Australian hedge funds, we test the performance persistence for the period July 2000 to June 2005. We employ both parametric and nonparametric approaches to identify persistence. We report evidence of short-term persistence and no evidence of long-term winning persistence. Tests of multiperiod performance reveal weak evidence of losing persistence. We also do not find any evidence of persistence in both stock picking and market timing. We report evidence of mean reversion for both stock picking and market timing at the medium horizon.     

Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures

We investigate the median and tail dependence between cryptocurrency and stock market returns of BRICS and Developed countries using a newly developed nonparametric cumulative measure of dependence over the period January 4, 2016 – December 31, 2019 as well as before and after the introduction of Bitcoin futures on December 17, 2017. The new measure is model-free and permits measuring tail risk. The results highlight the leading role of S&P500, Nasdaq and DAX 30 in predicting BRICS and developed countries’ stock market returns. Among BRICS countries, BVSP shows a starring role in predicting stock market returns. BSE 30 is the most predictor of cryptocurrencies, which have a little predictability on stock market returns. Ethereum has the leading role in predicting cryptocurrencies and stock market returns followed by Bitcoin. Tail dependence shows substantial role of S&P500, Nasdaq and BVSP in predicting stock market returns. Subsample analysis show the role of Bitcoin futures in reshaping the mean and tail dependence between cryptocurrency and stock market returns. Our results have important policy implications for portfolio managers, hedge funds and investors.     

A Complete Nonparametric Event Study Approach

Event studies have been used to examine the direction, magnitude, and speed of security price reactions to various phenomenon. Concerns over the lack of normality in stock return distributions motivated the introduction of nonparametric test statistics in the event study literature. A parametric procedure (OLS), however, has been extensively employed in the estimation of parameters for the market model. This paper, in contrast, applies Theil's nonparametric regression in the estimation of abnormal returns; an approach which is distribution free and provides a complete nonparametric approach for the detection of abnormal performance. Simulation results indicate Theil's estimation procedure offers a slight improvement in power in the detection of abnormal performance over the traditionally employed methodology. The results suggest employing Theil's nonparametric estimation procedure combined with the rank statistic. This complete nonparametric combination offers similar power with fewer underlying assumptions.     

Predicting the bear stock market: Macroeconomic variables as leading indicators

This paper investigates whether macroeconomic variables can predict recessions in the stock market, i.e., bear markets. Series such as interest rate spreads, inflation rates, money stocks, aggregate output, unemployment rates, federal funds rates, federal government debt, and nominal exchange rates are evaluated. After using parametric and nonparametric approaches to identify recession periods in the stock market, we consider both in-sample and out-of-sample tests of the variables’ predictive ability. Empirical evidence from monthly data on the Standard & Poor’s S&P 500 price index suggests that among the macroeconomic variables we have evaluated, yield curve spreads and inflation rates are the most useful predictors of recessions in the US stock market, according to both in-sample and out-of-sample forecasting performance. Moreover, comparing the bear market prediction to the stock return predictability has shown that it is easier to predict bear markets using macroeconomic variables.     

Turn-of-the-month and intramonth effects in government bond markets: Is there a role for macroeconomic news?

This paper focuses on the turn-of-the-month (TOM) and intramonth anomalies in government bond returns. In particular, we examine whether the TOM and intramonth effects exist in government bond markets, and moreover, whether these anomalies are related to the release of macroeconomic news as suggested in recent stock market studies. Using data on the 2-year and 10-year US Treasury Notes and German government bonds, we document a modest TOM effect in government bond returns. This effect does not disappear after controlling for the release of macroeconomic announcements, thereby suggesting that the origin of the TOM effect is not necessarily the same across asset classes.     

A nonparametric approach to measuring the sensitivity of an asset’s return to the market

In the market model the return on an asset is modeled as a linear function of the return on a market index with slope parameter beta. The coefficient beta is often used as a measure of the sensitivity of the asset’s return to the market and to measure the component of the variance of the return that is explained by the market. However, both of these interpretations require the additional assumption that the error term in the market model has mean 0 conditional on the return on the market index, an assumption that is often difficult to verify in practice. In this paper, a nonparametric version of the market model is proposed that does not require such an assumption. This nonparametric model replaces the beta coefficient of the market model with a “beta curve” describing the relationship between the asset’s return and that of the market locally near a given value of the market return. The proposed model is applied to stock returns, as well as to returns on mutual funds. Corresponding tests of the market model are given and it is shown that the nonparametric model often provides an improvement over the standard parametric market model.     

Does Cross-Listing Benefit the Shareholders? Evidence from Companies in the GCC Countries?

The goal of this study is to estimate the impact of cross-listing on stock returns, on liquidity, and on risk. A sample of 24 companies from the Gulf Cooperation Council countries which cross-listed their stocks in a foreign market over the period 2000–2010 were chosen for study. An event study estimating abnormal returns related to the cross-listing event as well as parametric and nonparametric tests find that there is (1) a significant abnormal return of about 6 % that lasts until 6 days after the cross-listing day and starts fading away thereafter (2) a significant increase in liquidity during the event period for most firms and (3) on average a decrease in risk. Our results also suggest that cross-listing had a small impact on market risk measured by the average beta but led to a decrease in the total risk measured by standard deviation of returns and a decrease in the potential loss measured by the average value at risk at the 5 % confidence. Additionally, an analysis based on the foreign market of secondary listing suggests that the benefit of cross-listing varies with the market of secondary listing. The positive abnormal return is more obvious for companies that cross-listed in Kuwait, Bahrain, and London. The most obvious increase in liquidity is for firms that cross-listed in London or in Bahrain and the biggest decrease in risk is for companies that cross-listed in London. We conclude overall that cross-listing in London benefits the shareholders the most as it leads to positive significant abnormal returns, an increase in liquidity, and a decrease in risk.     

Multi-country event-study methods

We provide the first simulation evidence of event-study test performance in multi-country non-US samples. The nonparametric rank and generalized sign tests are more powerful than two common parametric tests, especially in multi-day windows. The two nonparametric tests are mostly well specified, but neither is perfectly specified in all situations. The parametric standardized cross-sectional test can provide a useful robustness check but is less powerful than the nonparametric tests and rejects too often in single-market samples and when firm-specific events affect the market index. Local-currency market-model abnormal returns using national market indexes are sufficient.     

Linkages among European and world stock markets

Causal relations and dynamic interactions among equity returns in ten countries for the period 1983–1994 are analysed. An innovation accounting approach based on a multivariate vector autoregressive (VAR) model is used to estimate the proportion of each market return's forecast error attributable to innovations in foreign market returns. Three major results appear. The variance decompositions indicate a strong degree of economic interaction among stock markets. The US stock market has a considerable influence on stock market performance in almost every country, while there is no substantial inter-continental influence from the European stock markets on the world's two largest equity markets in New York and Tokyo. Finally, the pattern of the impulse-response functions illustrates a rapid international transmission of stock market events, supporting the hypothesis of international stock market efficiency.     

The Market Timing Ability of UK Mutual Funds

Abstract:  We apply a recent nonparametric methodology to test the market timing skills of UK equity and balanced mutual funds. The methodology has a number of advantages over the widely used regression based tests of Treynor-Mazuy (1966) and Henriksson-Merton (1981) . We find a relatively small number of funds (around 1%) demonstrate positive market timing ability at a 5% significance level while around 19% of funds exhibit negative timing and on average funds miss-time the market. However, controlling for publicly available information we find very little evidence of market timing ability based on private timing signals. In terms of investment styles, there are a small number of successful positive market timers amongst Equity Income and 'All Company' funds but not among either Small Stock funds or Balanced funds, although a few small stock funds are found to time a small stock index rather than a broad market index.     

Smooth nonparametric Bernstein vine copulas

We consider the problem of accurately modelling the distribution of the market risk of a multivariate financial portfolio. We employ a multivariate GARCH model in which the dependence structure between the assets is modelled via a vine copula. We address the problem of how the parametric pair-copulas in a vine copula should be chosen by proposing to use nonparametric Bernstein copulas as bivariate pair-copulas. An extensive simulation study illustrates that our smooth nonparametric vine copula model is able to match the results of a competing parametric vine model calibrated via Akaike’s Information Criterion while at the same time significantly reducing model risk. Our empirical analysis of financial market data demonstrates that our proposed model yields Value-at-Risk forecasts that are significantly more accurate than those of a benchmark parametric model.     

The turn of the month effect in India: A case of large institutional trading pattern as a source of higher liquidity

We examine (via parametric and non-parametric tests) the turn of the month effect in the returns of various, size-conditioned Indian stock indices, across time, in up and down markets and independent of other seasonal anomalies. We find little support for the payday and the US macroeconomic news announcements hypotheses. Instead, we show that institutional traders (foreign and domestic) significantly increase their trading volumes (on the buying side) at month end, potentially pushing prices up. There is no evidence of a similar behavior on the retail side. We suggest this to be a major cause of the observable TOM effect in India.     

Does country risks predict stock returns and volatility? Evidence from a nonparametric approach

We use the k-th order nonparametric causality test at monthly frequency over the period of 1984:1–2015:12 to analyze whether aggregate country risk, and its components (economic, financial and political) can predict movements in stock returns and volatility of eighty-three developed and developing economies. The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the weak evidence of causality obtained under the standard Granger tests cannot be relied upon. When we apply the nonparametric test, we find that, while there is no evidence of predictability of squared stock returns barring one case, at times, there are nearly 50 percent of the countries where the aggregate risks and its components tend to predict stock returns and realized volatility.     

Nonparametric rank tests for event studies

Because stock prices are not normally distributed, the power of nonparametric rank tests dominate parametric tests in event study analyses of abnormal returns on a single day. However, problems arise in the application of nonparametric tests to multiple day analyses of cumulative abnormal returns (CARs) that have caused researchers to normally rely upon parametric tests. In an effort to overcome this shortfall, this paper proposes a generalized rank (GRANK) testing procedure that can be used on both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived, and their empirical properties are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to popular parametric tests.     

A New Variance Ratio Test of Random Walk in Emerging Markets: A Revisit

Using a nonparametric variance ratio (VR) test, we revisit the empirical validity of the random walk hypothesis in eight emerging markets in the Middle East and North Africa (MENA). After correcting for measurement biases caused by thin and infrequent trading prevalent in nascent and small stock markets, we cannot reject the random walk hypothesis for the MENA markets. We conclude that a nonparametric VR test is appropriate for emerging stock markets, and argue that our findings can reconcile previously contradictory results regarding the efficiency of MENA markets.     

A note on the turn of the month and year effects in international stock returns

This examination of the turn of the month (TOM) and turn of the year (TOY) effects in 50 international stock indices, for the period 1994–2006, characterises the degree that the effects are influenced by: (i) the gross domestic product of the economy, (ii) the sign of the return on the prior day (called the prior day effect), (iii) a temporal indicator and (iv) the Monday effect. These effects are assessed by the use of an estimated generalised least squares (EGLS) panel regression model incorporating panel-corrected standard errors. Three important results relating to the TOM and TOY effects are observed. When the prior day effect on control days is used as the reference and controls are made for market development and year, we find that: (i) there is a relatively enhanced return on all TOM days, (ii) there is a relatively enhanced return on good TOY days and (iii) returns of bad TOY days are not remarkable.     

Seasonality in stock returns and volatility: The Ramadan effect

Calendar anomalies in stock returns are well documented. Less obvious is the existence of seasonality in return volatility associated with moving calendar events such as the Muslim holy month of Ramadan. Using a GARCH specification and data for the Saudi Arabian stock market – now the largest stock market in the Muslim world – this paper documents a systematic pattern of decline in volatility during Ramadan, implying a predictable variation in the market price of risk. An examination of trading data shows that this anomaly appears to be consistent with a decline in trading activity during Ramadan. Evidence of systematic decline in volatility during Ramadan has significant implications for pricing of securities especially option-like products and asset allocation decisions by investors in the Islamic countries.     

On the Existence of Visual Technical Patterns in the UK Stock Market

The aim in this paper is to replicate and extend the analysis of visual technical patterns by Lo et al. (2000) using data on the UK market. A non‐parametric smoother is used to model a nonlinear trend in stock price series. Technical patterns, such as the 'head‐and‐shoulders' pattern, that are characterised by a sequence of turning points are identified in the smoothed data. Statistical tests are used to determine whether returns conditioned on the technical patterns are different from random returns and, in an extension to the analysis of Lo et al. (2000), whether they can outperform a market benchmark return. For the stocks in the FTSE 100 and FTSE 250 indices over the period 1986 to 2001, we find that technical patterns occur with different frequencies to each other and in different relativities to the frequencies found in the US market. Our extended statistical testing indicates that UK stock returns are less influenced by technical patterns than was the case for US stock returns.     

Stock Market Development and Financing Choices of Firms

In many developing countries with emerging stock markets, banksare fearful of stock market development because they think thatstock markets will reduce the volume of their business. Thisarticle empirically analyzes the effects of stock market developmenton firms' financing choices using data from thirty developingand industrial countries from 1980 to 1991. The results implythat initial improvements in the functioning of a developingstock market produce a higher debt-equity ratio for firms andthus more business for banks. In stock markets that are alreadydeveloped, further development leads to a substitution of equityfor debt financing. By contrast, in developing stock markets,large firms become more levered as the stock market develops,whereas small firms do not appear to be significantly affectedby stock market development.     

Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest

In this study we examine the dynamic structural relationship between oil price shocks and stock market returns or volatility for a sample of both net oil–exporting and net oil–importing countries between 1995:09 and 2013:07. We accomplish that, by extending the Diebold and Yilmaz (2014) dynamic connectedness measure using structural forecast error variance decomposition. The results for both stock market returns and volatility suggest that connectedness varies across different time periods, and that this time–varying character is aligned with certain developments that take place in the global economy. In particular, aggregate demand shocks appear to act as the main transmitters of shocks to stock markets during periods characterised by economic–driven events, while supply–side and oil–specific demand shocks during periods of geopolitical unrest. Furthermore, differences regarding the directions and the strength of connectedness can be reported both between and within the net oil–importing and net oil–exporting countries. These results are of particular importance to investors and portfolio managers, given the recent financialisation of the oil market.