Daily Financial Data Research Articles

Are regime changes always bad economics? Evidence from daily financial data

Abstract Political instability is commonly thought to discourage investment and reduce economic growth. We challenge this consensus by showing that instability does not systematically depress investment. Using an event study approach, we examine daily returns of national financial indices in every country that experienced an irregular regime change subject to data availability. Returns following resignations are large and positive (+4 percent), while those following assassinations are negative and smaller in magnitude (−2 percent). The impact of coups tends to be negative (−2 percent), but we show that a pro-business coup results in large positive returns (+10 percent). We also find evidence that authoritarian or anti-business regime changes are more likely to lead to capital flight than democratic or pro-business changes. The immediate impact of political instability on investment is therefore dependent on the type of regime change and its expected impact on future growth.

Internal and External Determinants of Bank Syariah Indonesia Capital Gain during the Pandemic

This paper seeks to confirm and explain the relationship between internal factors of stock-lagged return and trading volume and external factors of composite price on returns of Bank Syariah Indonesia or BRIS. We collect the daily financial data of BRIS stock, Indonesia composite index, gold price, exchange rate, and Bank of Indonesia's deposit rate from the Refinitiv database. We obtained 1,230 time-series data to be analyzed further by implementing time-series analyses. Our research shows that the autoregressive integrated moving average (1,1,0) is the best model for univariate and multivariate analysis. Our study finds that lagged return has a negative effect on the bank’s daily return while trading volume has a positive impact on daily return. Our research reveals that composite price has the same effect as trading volume on daily return. Investors, especially short-term traders, may use our findings to formulate a different strategy for dealing with the volatility of Bank Syariah Indonesia stock. Our research contributes to the investment field by developing a new measurement for predicting the bank’s return when conducting short-term trading.

Forecasting Turkish real GDP growth in a data-rich environment

This study generates nowcasts and forecasts for the growth rate of the gross domestic product in Turkey using 204 daily financial series with mixed data sampling (MIDAS) framework. The daily financial series include commodity prices, equity indices, exchange rates, and global and domestic corporate risk series. Forecasting exercises are also carried out with the daily factors extracted from separate financial data classes and from the whole dataset. The findings of the study suggest that MIDAS regression models and forecast combinations provide advantage in exploiting information from daily financial data compared to the models using simple aggregation schemes. In addition, incorporating daily financial data into the analysis improves the forecasts substantially. These results indicate that both the information content of the financial data and the flexible data-driven weighting scheme of MIDAS regressions play an essential role in forecasting the future state of the Turkish economy.

The effect of non-trading days on volatility forecasts in equity markets

Weekends and holidays lead to gaps in daily financial data. Standard models ignore these irregularities. Because this issue is particularly important for persistent time series, we focus on volatility modelling, specifically modelling of realized volatility. We suggest a simple way of adjusting volatility models, which we illustrate on an AR(1) model and the HAR model of Corsi (2009). We investigate daily series of realized volatilities for 21 equity indices around the world, covering more than 15 years, and we find that our extension improves the volatility models—both in sample and out of sample. For HAR models and for consecutive trading days, the mean squared error decreased by 2.34% in average and for the QLIKE loss function by 1.41%.

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.

Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico

This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. The model uses both quarterly and daily sampling frequencies while remaining parsimonious. In particular, the mixed data sampling (MIDAS) regression model is employed to deal with the multi-frequency problem. To preserve parsimony, factor analysis and forecast combination techniques are used to summarize the information contained in a data set containing 392 daily financial series. Our findings suggest that the MIDAS model incorporating daily financial data leads to improvements in quarterly forecasts of GDP growth over traditional models that either rely only on quarterly macroeconomic data or average daily frequency data. The evidence suggests that this methodology improves the forecasts for the Mexican GDP notwithstanding its higher volatility relative to that of developed countries. Furthermore, we explore the ability of the MIDAS model to provide forecast updates for GDP growth (nowcasting).
 JEL Classifications: C22, C53, E37

Are Daily Financial Data Useful for Forecasting GDP?: Evidence from Mexico

This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. The model uses both quarterly and daily sampling frequencies while remaining parsimonious. In particular, the mixed data sampling (MIDAS) regression model is employed to deal with the multi-frequency problem. To preserve parsimony, factor analysis and forecast combination techniques are used to summarize the information contained in a data set containing 392 daily financial series. Our findings suggest that the MIDAS model incorporating daily financial data leads to improvements in quarterly forecasts of GDP growth over traditional models that either rely only on quarterly macroeconomic data or average daily frequency data. The evidence suggests that this methodology improves the forecasts for the Mexican GDP notwithstanding its higher volatility relative to that of developed countries. Furthermore, we explore the ability of the MIDAS model to provide forecast updates for GDP growth (nowcasting).

Does the financial crisis influence the month and the trading month effects?

Purpose – This paper aims to examine the month and the trading month effects under changing financial trends. The Greek stock market was chosen to implement the authors' assumptions because during the period 2002-2012, there were clear and long-term periods of financial growth and recession. Thus, the authors examine whether the financial trends influence not only the Greek stock market’s returns, but also its anomalies. Design/methodology/approach – Daily financial data from the Athens Exchange General Index for the period 2002-2012 are used. The sample is separated into two sub-periods: the financial growth sub-period (2002-2007), and the financial recession sub-period (2008-2012). Several linear and non-linear models were applied to find which is the most appropriate, and the results suggested that the T-GARCH model better fits the sample. Findings – The empirical results show that changing economic and financial conditions influence the calendar effects. The trading month effect, especially, completely changes in each fortnight following the financial trend. Regarding the January effect, which is the most popular month effect, the results confirm its existence during the growth period, but during the recession period, we find that it fades. Therefore, by examining the aforementioned calendar effects in different periods, different conclusions may be reached, perhaps because the financial trends’ influence is ignored. Research limitations/implications – The empirical results confirm the authors' assumption that a possible explanation for the controversial empirical findings regarding the calendar anomalies may be the different financial trends. However, these are some primary results that are confirmed only for the Greek case. Further empirical research for deeper stock markets and/or a group of countries may be useful to reach conclusions regarding the financial trends’ influence on the calendar anomalies patterns. Practical implications – The findings are helpful to anyone who invests and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, proving useful for investors who take these anomalies into account when they plan their investment strategy. Originality/value – This paper contributes to the literature by presenting an alternative methodological approach regarding the calendar anomalies study and a new explanation for the calendar effects existence/fade through time by examining the calendar anomalies patterns under a changing economic environment and financial trends.

Does the Financial Crisis Influence the Month and the Trading Month Effects? Evidence from the Athens Stock Exchange

The aim of this study is to examine the month and the trading month effect under changing financial trends. We choose the Greek stock market to implement our assumption because there are clear and long term periods of financial growth and recession. Daily financial data from Athens Exchange General Index for the period 2002-2012 are considered. The paper employs several linear and non-linear models, although the TGARCH asymmetry model best fits in this sample and for this reason we mainly present the TGARCH results. Empirical results show that changing economic and financial conditions influence the calendar effects. Especially, the trading month effect totally changes in each fortnight according to the financial trend. On the other hand, in Greece the January effect exists during the growth periods, although it does not exist when the financial trend changes. The findings are helpful to anybody who invest and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, so it may be useful to investors who take into account these anomalies when they draw their investment strategy.

Should Macroeconomic Forecasters Use Daily Financial Data and How?

Should Macroeconomic Forecasters Use Daily Financial Data and How?

Should Macroeconomic Forecasters Use Daily Financial Data and How?

We introduce easy-to-implement, regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of mixed data sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about 1000 daily financial assets. Our analysis is designed to elucidate the value of daily financial information and provide real-time forecast updates of the current (nowcasting) and future quarters of real GDP growth.

Should Macroeconomic Forecasters Use Daily Financial Data and How?

We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis, the models we propose suffer relatively less losses. Moreover, these predictive gains are primarily driven by the asset classes of government securities, equities, and especially corporate risk.

ANALYSIS OF EFFECT OF DETRENDING OF TIME-SCALE STRUCTURE OF FINANCIAL DATA USING DISCRETE WAVELET TRANSFORM

One of the key features of wavelet analysis is its ability to decompose non-stationary signals according to time and scale. In this work, we use discrete wavelets to analyze the influence of detrending techniques on the time-scale information structure of daily financial data. We examine the use of log returns, a linear trend and the Hodrick–Prescott (HP) filter. Quantitative measurements of information distortion are given using the mean-squared error (MSE) and correlation of the wavelet coefficients between the detrended and original data. We find that log returns and linear detrending are most distortional. We also conclude that the HP-filter is most effective, depending on appropriate selection of the filter parameter, λ, which is [Formula: see text] for the given data set.