Advances in sensor, computing, and communication technologies are enabling big data analytics by providing time series data. However, conventional models struggle to identify sequence features and forecast accuracy. This paper investigates time series features and shows that some machine learning algorithms can outperform deep learning models. In particular, the problem analyzed concerned predicting the number of vehicles passing through an Italian tollbooth in 2021. The dataset, composed of 8766 rows and 6 columns relating to additional tollbooths, proved to have high stationarity and was treated through machine learning methods such as support vector machine, random forest, and eXtreme gradient boosting (XGBoost), as well as deep learning through recurrent neural networks with long short-term memory (RNN-LSTM) cells. From the comparison of these models, the prediction through the XGBoost algorithm outperforms competing algorithms, particularly in terms of MAE and MSE. The result highlights how a shallower algorithm than a neural network is, in this case, able to obtain a better adaptation to the time series instead of a much deeper model that tends to develop a smoother prediction.

In financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) with their polarity (positive, negative, or neutral) and derive a sentiment score. In this paper, we use this sentiment (polarity) score to improve the forecasting of stocks and use it as a new “view” in the Black and Litterman model. This score is related to various events (both positive and negative) that have affected some stocks. The sentences used to determine the scores are taken from articles published in Financial Times (an international financial newspaper). To improve the forecast using this average sentiment score, we use a Monte Carlo method to generate a series of possible paths for several trading hours after the article was published to discretize (or approximate) the Wiener measure, which is applied to the paths and returning an exact price as results. Finally, we use the price determined in this way to calculate a yield to be used as views in a new type of “dynamic” portfolio optimization, based on hourly prices. We compare the results by applying the views obtained, disregarding the sentiment and leaving the initial portfolio unchanged.

Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specificallyNondominated Sorting Genetic Algorithm-II (NSGA-II), which tries tomaximizethe yield andminimizethe risk, simultaneously. The system, namedEvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators.EvoFoliois aninteractivegenetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users’ preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show howEvoFoliocan significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor’s preferences has proved to be very effective in the portfolio’s composition and made it more attractive for end-users. We argue thatEvoFoliocan be effectively used by investors as a support tool for portfolio formation.

Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency market has witnessed the bursting of different bubbles in the last decade, which in turn have had spillovers on all the markets and real economies of countries. These kinds of markets and their unique characteristics are of great interest to researchers. Generally, investors and financial operators study market trends to understand when bubbles might occur using technical analysis tools. Such tools, which have been historically used, resulted in being precious allies at the basis of more advanced systems. In this regard, different autonomous, adaptive and automated trading agents have been introduced in the literature to study several kinds of markets. Among these, we can distinguish between agents withZero/Minimal Intelligence (ZI/MI)andComputational Intelligence (CI)-based agents. The first ones typically trade on the market without resorting to complex learning strategies; the second ones usually use (deep) reinforcement learning mechanisms. However, these trading agents have never been tested on the cryptocurrencies market and related financial bubbles, which are still mostly overlooked in the literature. It is unclear how these agents can make profits/losses before, during, and after a bubble to adjust their strategy and avoid critical situations. This paper compares a broad set of trading agents (betweenZI/MIandCIones) and evaluates them with well-known financial indicators (e.g., volatility, returnsSharpe ratio, drawdown,SortinoandOmega ratio). Among the experiment’s outcomes,ZI/MIagents were more explainable thanCIones. Based on the results obtained above, we introduceGGSMZ, a trading agent relying on a neuro-fuzzy mechanism. The neuro-fuzzy system is able to learn from the trades performed by the agents adopted in the previous stage.GGSMZ’s performances overcome those of other tested agents. We argue thatGGSMZcould be used by investors as a decision support tool.

Among the 17 Sustainable Development Goals (SDGs) that make up the 2030 Agenda and refer to different areas of social, economic and environmental development, goal 11.2 concerns access to safe, cheap, accessible and sustainable transport systems, increasing road safety particularly through the enhancement of public transport. Universities can also contribute to increasing the use of more sustainable means of transport through policies and strategies to encourage students and staff in choosing sustainable transport modes. Numerous universities around the world and in Italy have adopted initiatives to reduce the environmental impact related to the mobility of the entire academic community. In Italy, the Italian Network of Sustainable Universities has set up, within its organization, a working group that has drawn up numerous studies on the sustainable mobility of Italian universities. The University of Foggia also conducted a study on mobility to detect and evaluate the mobility routines of community members (students, academic and administrative staff). In this paper, the first results in terms of descriptive analysis are shown. We submitted a survey consisting of 17 questions, and we obtained 3495 answers. After cleaning the data set, we were able to extract various contingency tables, through which we can statistically describe the main means of transport used by members of the University of Foggia community and, thanks to detailed data about the different means of transport, we can estimate their emissions. According to the results shown in the paper, further considerations could be made concerning the environmental implications of the choices of transportation modes. This could address policies about mobility at universities and provide useful information for applying actions to enhance these sustainable choices.

Nowadays, the information obtainable from the markets are potentially limitless. Economic theory has always supported the possible advantage obtainable from having more information than competitors, however quantifying the advantage that these can give has always been a problem. In particular, in this paper we study the amount of information obtainable from the markets taking into account only the time series of the prices, through the use of a specific Generative Adversarial Network. We consider two types of financial instruments traded on the market, stocks and cryptocurrencies: the first are traded in a market subject to opening and closing hours, whereas cryptocurrencies are traded in a 24/7 market. Our goal is to use this GAN to be able to “convert” the amount of information that the different instruments can have in discriminative and predictive power, useful to improve forecast. Finally, we demonstrate that by using the initial dataset with the 5 most important feature useds by traders, the prices of cryptocurrencies present higher discriminatory and predictive power than stocks, while by adding a feature the situation can be completely reversed.