Stock Market Index Returns Research Articles

Observation of the disconnection between real and financial spheres: the case of the Tunisian stock exchange market during the period 1969-2008

The strong volatility that characterized financial markets all over the word, these last years, leaves to think the existence of a disparity between stock prices and their fundamental values, which gives us the presumption of a disconnection between the real sphere and the financial one (Binswanger ( 1999, 2000, 2004 )). The purpose of this paper, is to focus on this possible disconnection by using the cointegration tests, to detect a possible equilibrium relation between the stock exchange returns and the real economic activity growth (measured by the GDP). The period of study lies between 1969 and 2008, according to an annual frequency of two series: the real yields (Stock Market Index return) and of GDP growth rates (real economic sphere indicator). To settle on the dynamics of short and long term between the stock exchange returns and the GDP growth, we used the Vector Errors Correction Model (VECM). Our results corroborate the existence of the disconnection between the two financial and economic series. Key Words: Stock market return, financial bubbles, real sphere JEL classification: E30, E44, G10, G12

Bootstrapping Fuzzy-GARCH Regressions on the Day of the Week Effect in Stock Returns: Applications in MATLAB

This paper examines the well know day of the week effect on stock returns. Various approaches have been developed and applied in order to examine calendar effects in stock returns and to formulate appropriate financial and risk portfolios. We propose an alternative approach in the estimation of the day of the week effect. More specifically we apply fuzzy regressions with triangular membership function in four major stock market index returns. We expect that if the day of the week is valid, then the Monday returns should be negative or lower than the other days of the week and in addition Friday returns should be the highest. The main findings and results are mixed and based on the fuzzy regression we conclude that there isn’t the day of the week or the Monday effect. Specifically, we find a reverse Monday effect in S&P 500, a negative Friday effect in FTSE-100, a positive Tuesday effect in NIKKEI-225 and no effects in DAX index. The specific approach is appropriate as fuzzy logic regression is appropriate and able to capture the impressions and nonlinearities in finance and human behaviour, which are main characteristics in financial industry. Furthermore fuzzy regression avoids the classification of dummy variables to values of one and zero, as we do in the traditional statistical and econometric methodology

The Availability Heuristic and Investors' Reaction to Company-Specific Events

Contemporary research documents various psychological aspects of economic decision making. The main goal of our study is to analyze the role of the availability heuristic (Tversky and Kahneman [1973, 1974]) in financial markets. The availability heuristic refers to people's tendency to determine the likelihood of an event according to the easiness of recalling similar instances and, thus, to overweight current information as opposed to processing all relevant information. We define and test two aspects of the availability heuristic, which we dub outcome and risk-availability. The former deals with the availability of positive and negative investment outcomes and the latter with the availability of financial risk. We test the availability effect on investors' reactions to analyst recommendation revisions. Employing daily market returns as a proxy for outcome availability, we find that positive stock price reactions to recommendation upgrades are stronger when accompanied by positive stock market index returns, and negative stock price reactions to recommendation downgrades are stronger when accompanied by negative stock market index returns. The magnitude of the outcome availability effect is negatively correlated with firms' market capitalization, and positively correlated with stock beta, as well as with historical return volatility. Regarding risk availability, we find that on days of substantial stock market moves, abnormal stock price reactions to upgrades are weaker, and abnormal stock price reactions to downgrades are stronger. Both availability effects remain significant even after controlling for additional company-specific and event-specific factors, including market capitalization, stock beta, historical volatility of stock returns, cumulative excess stock returns over one month preceding the recommendation revision, rating category before the revision, and number of categories changed in the revision.

Applying the method of simulated moments to estimate a small agent-based asset pricing model

Abstract The paper takes a recent agent-based asset pricing model by Manzan and Westerhoff from the literature and applies the method of simulated moments to estimate its six parameters. In selecting the moments, the focus is on the fat tails and autocorrelation patterns of the daily returns of several stock market indices and foreign exchange rates. It is argued that it may be meaningful to abandon the econometrically optimal weighting matrix in the objective function and instead invoke the moments' t-statistics in an intuitively appealing way. This modification gives rise to estimations whose moment matching, given the model's parsimony, can be largely considered to be satisfactory. Also the parameter estimates across different markets make good economic sense.

Momentum in Stock Market Returns, Risk Premia on Foreign Currencies and International Financial Integration

Momentum in developed countries' stock market index returns can be exploited to form portfolios of excess returns on foreign currencies as relatively high past foreign stock market returns signal a foreign currency appreciation. Two risk factors extracted from the stock index momentum based currency portfolio returns explain more than 80 percent of their cross-sectional variation. In contrast to currency risk factors constructed from forward discount sorted currency portfolios, these risk factors are not related to business cycle or liquidity risk. But high currency risk premia are associated with relatively deep financial integration and a high level of risk sharing.

Extreme News Events, Long-Memory Volatility, and Time Varying Risk Premia in Stock Market Returns

This paper proposes a new class of GARCH-jump in mean models to test the presence of time varying risk premia associated with normal and extreme news events. The model allows for a dynamic jump component with autoregressive jump intensity, long-range dependence in volatility dynamics, and volatility in mean structure separately for normal and extreme news events. The results show significant jump risk premia in five stock market index returns. We also find that ignoring the long-memory feature in volatility dynamics leads to false rejection of time varying risk premia.

Relationships between oil price shocks and stock market: An empirical analysis from China

This paper investigates the interactive relationships between oil price shocks and Chinese stock market using multivariate vector auto-regression. Oil price shocks do not show statistically significant impact on the real stock returns of most Chinese stock market indices, except for manufacturing index and some oil companies. Some “important” oil price shocks depress oil company stock prices. Increase in oil volatility may increase the speculations in mining index and petrochemicals index, which raise their stock returns. Both the world oil price shocks and China oil price shocks can explain much more than interest rates for manufacturing index.

Taxation, regulation and the information efficiency of the Berlin stock exchange, 1892-1913

In this article, we investigate the information efficiency of the Berlin stock exchange using returns of a new daily stock-market index for the years 1892–1913. We focus on the impact of the 1896 stock exchange law and of the increases of the stock-market turnover tax in 1894 and 1900 on information efficiency. We fit an ARMA(0,1)-GARCH(1,1) model to the data and search for structural breaks. This approach yields no convincing evidence that the tax increases had a negative influence on weak information efficiency. In addition, the restriction of derivative trading by the 1896 stock exchange law did not result in measurable changes in the autocorrelation of daily returns.

Testování slabé formy efektivnosti na českém akciovém trhu

Efficient Market Hypothesis has dominated the field of research on capital market theory. It postulates that asset prices are rationally connected to economic realities and always incorporate all the information available to the market. A huge quantity of theoretical works around the world have been devoted to testing this hypothesis. In this paper, the weak form of the Efficient Market Hypothesis is tested on data from the Czech stock market of period 1996-2006. The tested hypothesis is verified by both linear and nonlinear methods. Those linear are: Box-Pierce test, variance ratio test, test of sequences and reversals nad Hurst exponent. The nonlinear ones are: White test, Engle test, Hinich test and BDS test. These tests are carried on stock returns time series of Czech stock market index PX and individual stocks as Telefónica, Komerční banka and ČEZ and series with randomly changed order from original series. The results of the testing indicate that returns, when randomly permutated, are independent, hence they follow a random walk. But it is impossible to maintain it in case of original returns series.It implies that returns of either Czech stock market index or its stocks are not independent and do not follow a random walk.

Bank stock returns and economic growth

Previous research has established (i) that a country’s financial sector influence future economic growth and (ii) that stock market index returns affect future economic growth. We extend and tie together these two strands of the growth literature by analyzing the relationship between banking industry stock returns and future economic growth. Using dynamic panel techniques to analyze panel data from 18 developed and 18 emerging markets, we find a positive and significant relationship between bank stock returns and future GDP growth that is independent of the previously documented relationship between market index returns and economic growth. We also find that much of the informational content of bank stock returns is captured by country-specific and institutional characteristics, such as bank-accounting-disclosure standards, banking crises, enforcement of insider trading law and government ownership of banks.

Assessing symmetry of financial returns series

Testing symmetry of a probability distribution is a common question arising from applications in several fields. Particularly, in the study of observables used in the analysis of stock market index variations, the question of symmetry has not been fully investigated by means of statistical procedures. In this work a distribution-free test statistic T n for testing symmetry, derived by Einmahl and McKeague, based on the empirical likelihood approach, is used to address the study of symmetry of financial returns. The asymptotic points of the test statistic T n are also calculated and a procedure for assessing symmetry for the analysis of the returns of stock market indices is presented.

A Comparison of Different Hybrid ARIMA - Neural Network Models for Stock Index Return Forecasting and Trading Strategy

This study presents the application and development of three different hybrid methodology that combines both the ARIMA and ANN model to take advantage of the unique strengths of both linear and nonlinear modeling to model and predict the stock market index returns. The input to the hybrid ARIMA - neural network models, includes (a) past returns, the forecast and residual (results from ARIMA model), (b) forecast and residual value (results from ARIMA model) - ARIMABP model and (c) the forecast (results from ARIMA model) and the forecast residual (results of ARIMA model) of neural network. On the other hand, the benchmark model includes, ARIMA and neural network models. The performance of the models are evaluated in terms of widely used statistical metrics, correctness of sign and direction change, and various trading performance measures like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc via a trading strategy. The findings of the study reveals that ARIMABP model achieve greater accuracy based on the traditional forecasting accuracy measures, sign and directional change and trading experiments and add value as a forecasting and quantitative trading tool.

Bank Stock Returns and Economic Growth

Previous research has established (i) that a country's financial sector influence future economic growth and (ii) that stock market index returns affect future economic growth. We extend and tie together these two strands of the growth literature by analyzing the relationship between banking industry stock returns and future economic growth. Using dynamic panel techniques to analyze panel data from 18 developed and 18 emerging markets, we find a positive and significant relationship between bank stock returns and future GDP growth that is independent of the previously documented relationship between market index returns and economic growth. We also find that much of the informational content of bank stock returns is captured by country-specific and institutional characteristics, such as bank-accounting-disclosure standards, banking crises, enforcement of insider trading law and government ownership of banks.

Technical analysis and the stochastic properties of the Jordanian stock market index return

PurposeTo investigate the performance of moving average trading rules in an emerging market context, namely that of the Jordanian stock market.Design/methodology/approachThe conditional returns on buy or sell signals from actual data are examined for a range of trading rules. These are compared with conditional returns from simulated series generated by a range of models (random walk with a drift, AR (1), and GARCH‐(M)) and the consistency of the general index series with these processes is examined. Sensitivity analysis of the impact of transaction costs is conducted and standard statistical testing is extended through the use of bootstrap techniques.FindingsThe empirical results show that technical trading rules can help to predict market movements, and that there is some evidence that (short) rules may be profitable after allowing for transactions costs, although there are some caveats on this.Originality/valueNew results for the Jordanian market; use of sensitivity analysis to investigate robustness to variations in transactions costs.

Non-linearity in stock index returns: the volatility and serial correlation relationship

It appears that stock market index returns exhibit (weak) autocorrelation and there is evidence that volatility in equity markets is inversely related to first order autocorrelation. A prominent theory that can address both the aforementioned stylized facts is the feedback trading theory that relates autocorrelation to individual investor trading patterns. We provide a unified framework to simultaneously investigate market wide feedback trading strategies effects and the presence of volatility-correlation relationships. Our empirical application uses market portfolios from six international markets and employs conditional non-linear in mean and in variance models. Parametric specifications for conditional dependence beyond the mean and variance are also modelled following Hansen's (1994) (Hansen, B.E., 1994. Int. Econ. Rev. 35 (a), 705–730) autoregressive conditional density estimation. For the sample period under investigation (post 1990), our results support the presence of feedback traders and an inverse volatility–correlation relationship in three out of six markets while only two of the series under investigation do not provide evidence of autocorrelation. Our empirical work supports volatility induced sign switches in correlation. We document positive and significant risk premium parameters in three markets while we find that volatility at the market level is enhanced by past movements of the daily range (daily high–low price difference) series.

Stock Market Seasonality in an Emerging Market

The objective of this paper is to explore the day-of-the-week effect on the Indian stock market returns in the post-reform era. Till the late seventies, empirical studies provided ample evidence as to the informational efficiency of the capital markets advocating futility of information in consistently generating abnormal returns. However, later studies identified certain anomalies in the efficient market postulate. One major anomaly brought forth was the calendar-related abnormal rates of return. Various studies in this domain empirically demonstrated, through parametric and non-parametric tests on the stock returns data, that turn of the year, month, week, and holidays have consistently generated abnormal equity returns in both the developed and emerging markets unrelated to the attendant risks. Studies on the Indian stock markets' calendar anomalies, especially in the post-reform era, are very few. In an attempt to fill this gap, this study explores the Indian stock market's efficiency in the 'weak form' in the context of calendar anomalies, especially in respect of the weekend effect. Daily returns generated by the SENSEX, NATEX, and BSE200 during January 1st 1996 to August 10th 2002 comprising a total of 1,667 observations for each of the indices are considered for testing the seasonality. While most of the studies have considered the returns of one of the major indices based on the closing values, this study examines the multiple indices for possible seasonality. An analysis of returns' pattern of multiple indices is helpful in identifying the presence or otherwise of the stock market seasonality associated with various portfolios and for testing the efficacy of investment game based on the observed patterns of the returns. This study employed the daily mean index value for generating the daily returns to relax the implied assumption of the earlier studies — by considering the closing values of the indices — that trading is done at the closing values. A non-parametric test — Kruskall-Wallis test using 'H' statistic — is employed for testing the seasonality in the Indian stock market returns. The null hypothesis tested is that there are no differences in the mean daily returns across the weekdays. The major findings of the study are as follows: The Indian stock markets do manifest seasonality in their returns' pattern. The Monday-Tuesday, Monday-Friday, and Wednesday-Friday sets have positive deviations for all the indices. The Monday-Friday set for all the indices has the highest positive deviation thereby indicating the presence of opportunity to make consistent abnormal returns through a trading strategy of buying on Mondays and selling on Fridays. The above-mentioned active strategy is found to be beneficial in case of SENSEX The above-mentioned active strategy is found to be beneficial in case of SENSEX alone during the study period while for the others — NATEX and BSE200 — a passive ‘buy and hold’ strategy is more effective. The study concludes that the observed patterns are useful in timing the deals thereby exploring the opportunity of exploiting the observed regularities in the Indian stock market returns.

Non‐linear Predictability of Value and Growth Stocks and Economic Activity

Recent empirical evidence suggests that stock market index returns are predictable from a variety of financial and macroeconomic variables. We extend this research by examining value and growth portfolios constructed by book‐to‐market ratio, and consider whether such predictability is evident here. Further, we assess whether such predictability is better characterised by a non‐linear form and whether such non‐linear predictability can be exploited to provide superior forecasts to those obtained from a linear model. General non‐linearities are examined using non‐parametric techniques, which suggest possible threshold behaviour. This leads to estimation of a smooth‐transition threshold model, with the results indicating an improved in‐sample performance and marginally superior out‐of‐sample forecast results.

Co-integração e causalidade entre variáveis macroeconômicas, “risco Brasil” e retornos no mercado de ações brasileiro

This paper analyzes the relationship of a few macroeconomic variables with Brazils risk premium (spread of Brazilian C-bonds in relation to U.S. Treasury bonds), and with the returns of the Brazilian stock market index, Ibovespa, during the period of January 1995 to December 2001. Co-integration, error correction model, and Granger causality tests were used in the paper. The co-integration tests and the error correction model suggest that the Ibovespa returns are co-integrated with macroeconomic variables such as real exchange rate and real GDP, and also with Brazils country risk premium. Additionally, unidirectional

Recurrence Quantification Analysis of Wavelet Pre-Filtered Index Returns

We investigate the issue of deterministic vs. stochastic dynamics in financial time series. We demonstrate a way to to reveal nonstochastic dynamical structures in daily stock market index returns, combining Recurrence Quantification Analysis (RQA) and wavelet filtering. Assuming a dynamical system generating the returns sequences, we reproduce its dynamics from the data with minimum assumptions. We reconstruct the phase-space dynamics by time-delay embedding of the wavelet denoised returns, in order to apply the RQA. The results indicate that through wavelet pre-filtering we can obtain very clean sequences and reveal nonstochastic dynamics. Our results also suggest the existence of chaos. We provide both quantitative and qualitative evidence supporting our findings.

Measures of globalization based on cross-correlations of world financial indices

The cross-correlation matrix of daily returns of stock market indices in a diverse set of 37 countries worldwide was analyzed. Comparison of the spectrum of this matrix with predictions of random matrix theory provides an empirical evidence of strong interactions between individual economies, as manifested by three largest eigenvalues and the corresponding set of stable, non-random eigenvectors. The observed correlation structure is robust with respect to changes in the time horizon of returns ranging from 1 to 10 trading days, and to replacing individual returns with just their signs. This last observation confirms that it is correlations between signs and not absolute values of fluctuations, which are mostly responsible for the observed effect. Negative changes in the index are somewhat more correlated than the positive ones. Also, in our data set the reaction of Asian stock indices to changes in European and American ones persists for about 3 days.