It is reported in the present paper that 1‐min returns on TOPIX have exhibited significant autocorrelation at 5‐min intervals since 1997/1998. Special quotes that are issued whenever there is a price jump in excess of a predetermined band seem to be the source of this autocorrelation, because these have been automatically updated at 5‐min intervals since August 1998 and have appeared during the first 30 min from opening. Individual stock returns also exhibit fifth‐order autocorrelation, but this disappears when the data with special quotes are excluded from the sample. Therefore, the autocorrelation is caused by the special quotes: a type of market microstructure noise.

The aim of this paper is to find a way to exploit booms and busts in stock markets to get a return which is higher than the return for a buy-and-hold strategy. The question that gives rise to this goal is whether stock prices follow short run trends during financial booms and busts. The thesis therefore start out by discussing the relevance of historical stock prices. It finds that historical information is fully reflected in prices hence historical prices cannot be used to predict future prices according to proponents of the Efficient Market Hypothesis (Fama 1980) and (Malkiel 1975). Opponents of EMH argue that portfolios of stocks exhibit serial correlation in weekly returns which means that historical stock prices can be used to predict future stock prices (MacKinlay 1999). The strongest evidence of autocorrelation is found for Nordic stock markets. It is therefore chosen to develop a New Model based on a filter strategy on portfolios of Nordic stocks to exploit potential autocorrelation in returns. Financial insights are hence uncovered to give guidance on how to find stocks for a portfolio and how to assign weights. It is decided to use a Naïve portfolio model because it is not prone to estimation errors. Each portfolio should include 18 stocks to secure adequate diversification. The filter strategy is defined and tested in-sample and it is chosen to use an Y-filter of 25% and an X-filter of 16.67%. The empirical analysis tests the New Model on Danish, Norwegian and Swed

Abstract In this paper, we show that the widespread common perception that stock returns must necessarily exhibit negative first-order autocorrelation for the mean-reverting components of stock prices is not quite correct. The necessity of negative autocorrelation in one-period returns is an artifact of assuming an AR(1) process for the transitory components of the underlying stock price and assuming independence between innovations in the transitory process and innovations in the permanent components. The sign of first-order return autocorrelation for mean-reverting property could be positive under a different lag structure of the transitory components of stock prices.

This paper examines the efficiency of Indian stock market by using daily closing price of BSE SENSEX from August 2002 to March 2011.The study has used unit root test, autocorrelation test, runs test, GARCH (symmetric) EGARCH and TARCH (asymmetric) models to determine the random walk behaviour of Indian stock market. Further this study has employed various forecasting approaches to measure the forecasting accuracy of symmetric and asymmetric models. This study suggests that asymmetric models provide better forecasting performance than symmetric models. The result of the study shows that Indian stock market does not exhibit a random walk behaviour and weak form of market efficiency. The study concludes that investor can make abnormal profits by studying and forecasting the prices of assets in this market.

This study demonstrates empirically the impact of stock return autocorrelation on the prices of individual equity option. The option prices are characterized by the level and slope of implied volatility curves, and the stock return autocorrelation is measured by variance ratio and first-order serial return autocorrelation. Using a large sample of U.S. stocks, we show that there is a clear link between stock return autocorrelation and individual equity option prices: a higher stock return autocorrelation leads to a lower level of implied volatility (compared to realized volatility) and a steeper implied volatility curve. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. The relation between stock return autocorrelation and option price structure is more pronounced when market is volatile, especially during financial crisis. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. Thus, stock return autocorrelation can help differentiate the price structure across individual equity options.

Do stock markets exhibit cyclical market efficiency? Emerging markets’ perspective This article assesses cyclical market efficiency under different market conditions. We examine the cyclical return predictability, the time-varying effectiveness of trading strategies and their profitability, along with the relationships between volume and price. We select a diverse set of indices namely Nifty Next 50, BSE Sensex, IBOV (Brazil) and JSE (South Africa) and use the data for the years 2005 to 2022. We use linear and non-linear autocorrelation tests, regression analysis and Granger causality tests to explore the different phases of market efficiency across these indices. Our results offer a multifaceted understanding of market efficiency, highlighting various aspects of the data across different regions and market structures. We cross-validate our findings, adding robustness to our understanding of the manifestation of the adaptive market hypothesis (AMH) in the global stock markets. We use a rolling window framework and structural break tests to ensure that our findings are resilient to potential structural changes.

This study explores medium-term forecasting of investment portfolio profitability by analyzing the stock prices of six Uzbek joint-stock companies using time series models. The research compares classical statistical models such as ARIMA with nonlinear models like GARCH and LSTM to determine their accuracy in volatile market conditions. Over 848 ARIMA model combinations were tested, and the most optimal models were selected based on statistical indicators such as AIC, BIC, and significance of parameters. Findings revealed that combining ARIMA with GARCH models improves forecast precision due to the volatility observed in stock returns. The study also highlights that while residuals exhibit autocorrelation and non-normality, the models remain statistically robust for forecasting daily prices from August 2024 to December 2027. The research supports the need for hybrid approaches to better capture the dynamics of financial markets.

There is overwhelming evidence of the presence of autocorrelation in stock returns in many previous studies. Since stock return correlation is related to predictability of stock prices, it is important to know the extent of autocorrelation and its underlying causes. This article investigates the autocorrelation structure of seven Gulf Cooperation Council (GCC) stock markets. All the markets except for Dubai and Kuwait show significant first-order autocorrelation of returns. Bahrain, Oman and Qatar exhibit strong positive whereas Abu Dhabi exhibits negative autocorrelation of returns. In general, return autocorrelation conditional on a negative return day is higher than that conditional on a positive return day. Autocorrelation between weekdays is usually larger than that between the first and last trading day of the week. Use of dynamic volatility models gives evidence that for almost all the markets negative feedback traders are the dominant players to contribute to the autocorrelation of returns. Thus, traders are very keen to realize their profits too often, resulting in significantly positive return autocorrelation.

This study investigates the application of a truncated spline nonparametric regression model for biresponse analysis of longitudinal data, focusing on modeling monthly stock prices specifically opening and closing prices of three private banks in Indonesia: Bank Mayapada, Bank Mega, and Bank Sinar Mas. The data used in this research are secondary data sourced from the website Id.Investing.com and monthly financial statement publications of three private banks in Indonesia. Longitudinal data, combining cross-sectional and time-series dimensions, are utilized to capture trends and patterns not detectable in traditional cross-sectional analysis. The truncated spline method is selected for its adaptability to nonlinear relationships and abrupt data behavior changes. The model incorporates three predictor variables traded stock volume, total assets, and total liabilities and evaluates their influence on stock prices. Assumptions of longitudinal data are validated using the Ljung-Box autocorrelation test, Bartlett’s sphericity test, and Pearson correlation. Results confirm significant within-subject correlations, independence between subjects, and strong interdependence between response variables. The optimal configuration is determined using Generalized Cross Validation (GCV), with up to three knots considered for segmentation. Weighted Least Squares (WLS) is employed for parameter estimation, accounting for within-subject correlations. Model evaluation based on Mean Absolute Perc