Explore the vast range of titles available.

Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.

In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.

This paper examines whether investor sentiment about the stock market affects prices of the S&P 500 options. The findings reveal that the index option volatility smile is steeper (flatter) and the risk-neutral skewness of monthly index return is more (less) negative when market sentiment becomes more bearish (bullish). These significant relations are robust and become stronger when there are more impediments to arbitrage in index options. They cannot be explained by rational perfect-market-based option pricing models. Changes in investor sentiment help explain time variation in the slope of index option smile and risk-neutral skewness beyond factors suggested by the current models.

PurposeThe aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period.Design/methodology/approachThe wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market.FindingsResults of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine.Originality/valueThis study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.

The aim of this paper is to examine the linkages between stock market index, Dubai Fateh oil spot price, interest rate and FDI using monthly data on Abu Dhabi stock index for the period 2006- 2019. Vector Autoregressive Model have been employed to analyse the relationship between the variables. Using monthly data from 2006 to 2019, the results of Vector Error Correction Model (VECM) estimates suggest that there is long-run integration between oil price and monthly stock index series in which monthly oil prices have a positive impact on stock index. The Granger Causality indicates significant bidirectional causality running from ADX index, oil price and EIBOR. Meanwhile there is unidirectional causality from stock market index to FDI. Furthermore, Impulse Response Function was employed to examine market response to oil price shocks and our study reveals that UAE stock market is efficient as it responds immediately to the oil shock. These findings are relevant for investors for portfolio management and for the policymakers such that more aggressive economic diversification policies are initiated to wane the significant persistent oil-stock integration.

The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market.

Purpose:  The article aims to study the impact of public governance on the relationship between consumer confidence and the stock market index.   Theoretical framework: The concept of consumer confidence index: According to consumer confidence, and stock market index is a way to measure expected changes in income. Katona also argues that consumer trust includes emotional and intellectual factors.   Design/methodology/approach: Data is collected from 2012 to 2021 from 10 middle-income countries. The public governance variable is measured by 6 component variables, including (1) Voice and accountability, (2) Political stability, (3) Government efficiency, (4) Regulatory quality, (5) the rule of law, and (6) Control of corruption. Consumer confidence is measured by the consumer confidence index (CCI) and the stock market index (SMI). The authors use P. VAR model to solve the set goal.   Findings: The research results show that public governance positively affects the relationship between consumer confidence and the stock market price index in high-middle-income countries. In contrast, public administration does not influence the relationship between consumer confidence and the stock market index in low-income countries.   Research, Practical & Social implications: Based on the research results, the authors propose policy implications for middle-income countries for investors' confidence and investment activities on the stock market, contributing to boosting capital in the future more efficient circular economy.   Originality/value: Government must increase its accountability to the people and investors for all activities and decisions of the Government. Creating a stable political environment, prioritizing dispute settlement by peaceful negotiations.

Stock market indices play a central role in portfolio and risk management and performance evaluation, as well as academic research. This paper presents a fully updated and extended stock market index for the Finnish stock market using new and updated historical databases that cover the period from the establishment of the Helsinki Stock Exchange in October 1912 to the end of 1981. In addition to the all-share market index, four industry indices are presented for the first time. The observed geometric mean market return is 1.034 percent per month (13.14% p.a.). Of the industry indices, the banking sector performed the worst as it was found to have clearly lagged behind in the market, whereas the paper and forest and the metal and manufacturing industries performed the best during the sample period. The results also highlight the importance of taking into account corporate capital actions—which are, historically, often the hardest information to obtain—as they can have a material effect on the index performance.

While many studies have looked into the determinants of yields on externally issued sovereign bonds of emerging economies, analysis of domestically issued bonds has hitherto been limited, despite their growing relevance. This paper finds that the extent to which fiscal variables affect domestic bond yields in emerging economies depends on the level of global risk aversion. During tranquil times in global markets, fiscal variables do not seem to be a significant determinant of domestic bond yields in emerging economies. However, when market participants are on edge, they pay greater attention to country-specific fiscal fundamentals, revealing greater alertness about default risk.

Purpose - This study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method. Design/methodology/approach - In this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data. Findings - It is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices. Originality/value -The important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out comovements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.