The development of a predictive system that correctly forecasts trading signals is crucial for algorithmic trading and investment management. Technical analysis has been used by many researchers for financial market prediction. Numerous technical indicators (TIs) are computed by setting a time-frame parameter called the input window length. This paper therefore investigates how the input window length and forecast horizon together affect the predictive performance of the model. Market-specific TIs are extracted through a random forest technique. These TIs are used as inputs for an artificial neural network and a support vector machine to forecast the future direction of trading signals. The data set consists of 22 years of daily prices for the Pakistan Stock Exchange. This research finds the 15 most relevant features for the Pakistan Stock Exchange from a list of 34 TIs. The prediction system performs best when the forecast horizon is more than 15 days, which shows the dependency of the input variable parameter selection and the forecast horizon. This unique pattern is studied using multiple confusion metrics. The findings of this study may improve the prediction accuracy of a trading strategy based on technical analysis.

This study analyses the intraday multifractal behaviour of three Central Eastern European stock markets by deploying five-minute index data ranging from December 2019 to May 2020. With the analysis of multifractality, we can evaluate the degree of efficiency of the stock markets analysed. We divided the whole sample into three different periods of about two months each. Data for the Czech Republic, Hungary and Poland are used and their behaviour is compared with Germany (as a benchmark of the European Union) and Italy and Spain (as the most affected countries by Covid-19 in Europe). For the analysis, we employ multifractal detrended fluctuation analysis after using seasonal-trend decompositions using the loess method. The results confirm that the degree of multifractality varies in the different periods, with increasing multifractality in February-March and a recovery in April-May. Furthermore, the behaviour of these stock markets shifted from persistent to anti-persistent.

During crises, stock market volatility generally rises sharply, and as consequence, spillovers are identified across markets. This study estimates the volatility spillover among twelve European stock markets representing all four regions of Europe. The data consists of 10,990 intraday observations from 2 December 2019 to 29 May 2020. Using the methodology of Diebold and Yilmaz, we use static and rolling windows to characterize five-minute volatility spillovers. Our results show that 77.80% of intraday volatility forecast error variance in twelve European markets comes from spillovers. Furthermore, the highest gross directional volatility spillovers are found in Sweden and the Netherlands, while the minimum spillovers to other stock markets are observed in the stock markets of Poland and Ireland. However, German and Dutch markets transmit the highest net directional volatility spillovers. Splitting the whole sample in pre- and post-pandemic declaration (11 March 2020) we find more stable spillovers in the latter. The findings reveal important information about European stock market interdependence during COVID-19, which will be beneficial to both policy-makers and practitioners.

Forecasting of stock prices has been a challenging area due to its complex and dynamic nature. There are several evidences that traditional econometrics based predictive models encountered significant challenges due to parameter instability. The aim of this study is to apply three classifiers namely, Random Forest (RF), Support Vector Machines (SVM) and Neural Networks (NN) to predict the Pakistani stock market’s direction and to compare the prediction accuracy. Daily closing prices are collected from yahoo server from 2013 to 2018. Famous 30 market indicators are applied to predict the market direction by using Random Forest, Support Vector Machines and Neural Networks. Model accuracy is evaluated using the confusion matrix. The empirical findings reveal that Neural Network performs best with the highest accuracy of 91%. Model specific, top five input indicators are used by applying feature selection in all classifiers. Interestingly, optimization improves the prediction accuracy in case of neural networks (NN) and support vector machine (SVM)) models while Random Forest’s (RF) accuracy did not improve. These findings have great importance for institutional investors and management companies having flexibility to accelerate or postpone their investment decisions.

To improve the profitability and predictability of financial markets we highlight the issue of relevant technical indicators (TI) for different markets. In previous studies, not much attention has been given to feature selection problem by counting on the most popular technical indicators or following the footsteps of related literature. This research work is focusing on the importance of feature selection problem. There is no specific set of TI that is good enough to predict every market price movement. TI which are good predictors of developed markets are not suitable for emerging or frontier markets. The analysis is based on a well diversified sample of nine countries representing developed, emerging and frontier markets as per the categorization of Morgan Stanley Capital International (MSCI). We use random forest (RF) technique for feature selection from a group of 90 technical indicators. The results show that top five technical indicators are different for each market according to Gini index. Even within the three categories of market, these indicators and their ranking varies. Hence, appropriate feature selection according to market eventually improves the accuracy of predictive model and profitability. Purpose: Finding the relevant technical indicators for financial market is crucial for financial market prediction. There is no particular set of technical indicators that fits all financial market since every market has its own dynamics and it is important to first select the technical indicators that reflects the market conditions rather than using the most commonly employed technical indicators. Design/Methodology/Approach: Random forest technique is used to find the relevant technical indicator. And each indicator is ranked according to Gini index. Findings: The analysis suggest that the set of technical indicators is not the same for every financial market. Further with in the categories of developed markets, emerging markets and frontier markets the set of technical indicators changes. Implications/Originality/Value: Hence, it is summarized that to improve the forecasting ability of the predictive model it is important to find the market specific technical indicators that will end up in higher profit for investors.

This paper reconnoitered the holiday effect of East Asian stock markets over each other.SSE-180 index (Shanghai Stock Exchange), NIKKIE 225 (Japan), TWII (Taiwan Weighted Index), HSI (Hang Seng) epitomized East Asian region.The key objective is to investigate the return effect on East Asian stock markets coinciding with the S&P 500 (Standard and poor) holidays further the return effect on East Asian stock markets during the trading session when there is no trading on other East Asian stock markets.By means of TGARCH model using daily return of East Asian Stock Exchanges and S&P 500 from January 1, 2003 to December 31, 2012.Day-of-the-Week effect, as well as regional and international spillovers has been considered, robust results have been found by this study.Outcomes of this paper have implications for international investors.It is of immense importance for an investor to consider the holidays of the interlinked stock markets when investing in a particular market of a region as holidays not only affect the returns of the portfolios but risk as well.