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Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.

The authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.

The results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.

This study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.

The purpose of this study is to determine whether the fine wine market is efficient between homogeneous lots and heterogeneous lots.

Auction price data for homogeneous (or solid) lots of fine wines was analyzed to create price prediction models. Those models were used to predict the expected auction price for the bottles within heterogeneous lots. Lastly, models were created to explain and predict the differences between expected and realized prices for heterogenous wine lots.

The results show that large inefficiencies exist. The more complex and expensive the heterogeneous lot, the greater the discount relative to what would have been realized if the bottles had been sold individually. This discount can exceed 50% of the expected auction price.

Heterogeneous lots may arise as a practical requirement from the auction house. Restaurant buyers probably have little interest in such lots because of the inclusion of wines the restaurant will be unable to sell. Collectors may be uniquely positioned to benefit from this price discount.

These results are unique in the literature, because the price dynamics of heterogeneous (or mixed) lots of fine wines have not previously been studied.

The recommendation of the analyst report is not only limited to a small number of ratings, but also biased toward a buy opinion with the absence of sell opinion. As an alternative to this, this paper aims to extract analysts' textual opinions embedded in the report body through text analysis and examine the profitability of investment strategies. Analyst opinion about a firm is measured by calculating the frequency of positive and negative words in the report text through the Korean sentiment lexicon for finance (KOSELF). To verify the usefulness of textual opinions, the author constructs a calendar-time based portfolios by the analysts' textual opinion variable of each stock. When opinion level is used, investment strategy has no significant hedged portfolio return. However, hedged portfolio constructed by opinion change shows significant return of 0.117% per day (2.57% per month). In addition, the hedged return increases to 0.163% per day (3.59% per month) when the opening price is used instead of closing price. This study show that the analysts’ opinion extracted from text analysis contains more detailed spectrum than recommendation and investment strategies using them give significant returns.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The economic and administrative conditions of countries normatively have an effect on the economy and level of market development. Moreover, it is of great importance for a healthy economy whether the public institutions and organizations are transparent and functioning in accordance with their purpose. The aim of this study is to show whether there is a relationship between transparency and market efficiency.

Correlation analysis has been conducted between prediction accuracy rates, which are obtained by seven different machine learning algorithms and Corruption Perception Index (CPI) levels.

It has been statistically shown that the indices of countries with low corruption levels are harder to predict, which, in turn, can be interpreted as having higher weak-form market efficiency. According to that, an intermediate negative correlation has been found between CPI scores and predictability levels of stock indices. Considering the findings, it can be interpreted that the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency.

The study can be extended with cutting-edge machine learning and deep learning techniques in future studies. There are very few studies which try to explain factors related to market efficiency. Thus, the authors claim that there is still room for further research in order to determine the factors related to market efficiency, implying that current literature is still far from explaining the causation behind the inefficiencies.

According to findings, the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency. Based on these findings, in practice, it can be said that more successful predictions can be made using machine learning algorithms in countries with relatively lower CPI scores.

In literature, the factors related to market efficiency are still far from explaining the causation behind the inefficiencies. Thus, it has been investigated whether transparent and well-functioning public institutions and organizations have any relation with market efficiency.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

This paper aims to explore the extreme effect of crude oil price fluctuations and its volatility on the economic growth of Middle East and North Africa (MENA) countries. It also investigates the asymmetric and dynamic relationship between oil price and economic growth. Further, a separate analysis for each MENA oil-export and oil-import countries is conducted. Furthermore, it studies to what extent the quality of institutions will change the effect of oil price fluctuations on economic growth.

As the effect of oil price fluctuations is not the same over different business cycles or oil price levels, the paper uses a panel quantile regression approach with other linear models such as fixed effects, random effects and panel generalized method of moments. The panel quantile methodology is an extension of traditional linear models and it has the advantage of exploring the relationship over the different quantiles of the whole distribution.

The paper can summarize results as following: changes in oil price and its volatility have an opposite effect for each oil-export and oil-import countries; for the former, changes in oil prices have a positive impact but the volatility a negative effect. While for the latter, changes in oil prices have a negative effect but volatility a positive effect. Further, the impact of oil price changes and their uncertainty are different across different quantiles. Furthermore, there is evidence about the asymmetric effect of the oil price changes on economic growth. Finally, accounting for institutional quality led to a reduction in the impact of oil price changes on economic growth.

The study concludes more detailed results on the impact of oil prices on gross domestic product growth. Thus, it can be used as a decision-support tool for policymakers.

This present study uses political, economic, social, technological, legal and environmental (PESTLE) analysis and the strategic management theory to examine how external factors, namely the coronavirus (COVID-19) pandemic, the industrial revolution (IR) 4.0 technologies, the fuel price crisis and Sultanate of Oman Logistics Strategy (SOLS) 2040, affect the performance of container terminals in Oman.

A hybrid decision-making method that combined the analytical hierarchy process technique and Bayesian network model was used to achieve the objective of the present study.

The COVID-19 pandemic (54.60%) most significantly affected the performance of container terminals in Oman, followed by IR 4.0 technologies (19.66%), SOLS (17.00%) and fuel price crisis (8.74%). Container terminals in Oman were also found to perform “moderately,” considering the uncertainty of external factors.

This study enriches existing literature by using PESTLE analysis to assess the impact of the external business environment on firm performance in the context of the maritime industry as well as highlights how challenging external environmental factors affect the performance of container terminals in Oman.

This study contributes to developing novel study models and determining the performance level of container terminals in Oman considering integrated uncertainties and external factors such as the COVID-19 pandemic, IR 4.0 technologies, the SOLS 2040 and the fuel price crisis.