Utilizing Machine Learning to Reassess the Predictability of Bank Stocks

Objectives: Accurate prediction of stock market returns is a very challenging task due to the volatile and non-linear nature of the financial stock markets. In this work, we consider conventional time series analysis techniques with additional information from the Google Trend website to predict stock price returns. We further utilize a machine learning algorithm, namely Random Forest, to predict the next day closing price of four Greek systemic banks. Methods/Analysis: The financial data considered in this work comprise Open, Close prices of stocks and Trading Volume. In the context of our analysis, these data are further used to create new variables that serve as additional inputs to the proposed machine learning based model. Specifically, we consider variables for each of the banks in the dataset, such as 7 DAYS MA,14 DAYS MA, 21 DAYS MA, 7 DAYS STD DEV and Volume. One step ahead out of sample prediction following the rolling window approach has been applied. Performance evaluation of the proposed model has been done using standard strategic indicators: RMSE and MAPE. Findings: Our results depict that the proposed models effectively predict the stock market prices, providing insight about the applicability of the proposed methodology scheme to various stock market price predictions. Novelty /Improvement: The originality of this study is that Machine Learning Methods highlighted by the Random Forest Technique were used to forecast the closing price of each stock in the Banking Sector for the following trading session. Doi: 10.28991/ESJ-2023-07-03-04 Full Text: PD

Ανάλυση δεδομένων μεγάλου όγκου ανθρωπιστικών και οικονομικών επιστημών με τεχνικές μηχανικής μάθησης και χρήση τεχνολογιών υπολογιστικού νέφους

Sentiment Analysis has been extensively investigated in recent years as a method of human emotions’ classification to specific events, products, services etc. It is considered as a very important problem, especially for organizations or companies who want to know the consumers’ view about their products and services. In combination with the evolution of social media, it has been established as an interesting domain of research. Through social media, people tend to express their opinions or feelings, such as happiness or sadness on a daily basis. Thus, the vast amount of available data has made the existing solutions inappropriate and the need for automated analysis methods is imperative. In this thesis, it was examined sentiment polarity analysis on Twitter data in a distributed environment, known as Apache Spark. More specially, in this thesis are propose three classification algorithms for tweet level sentiment analysis in Spark due to its suitability for Big Data processing against its predecessors, MapReduce and Hadoop. Also The research to study the effects of economic policy uncertainty on the return volatility of stock with data from the largest banking institutions in Greece. Volatility is constructed using intraday data, whereas the research period extends over a period of about thirteen years, more specifically from January 5, 2001, to June 30, 2014. This period contains various phases of the market such as stock market crashes along with stock market booms (e.g. the financial crisis of 2007 and 2008 in the United States, and the European sovereign debt crisis). The estimated regressions were used to indicate the direct effects of economic policy uncertainty on the return volatility of the stock in the four large Greek banks. Volatility is constructed based on intraday data, whereas four different estimators of volatility were usedΗ Ανάλυση Συναισθήματος έχει διερευνηθεί εκτενώς τα τελευταία χρόνια ως μέθοδος ταξινόμησης των ανθρώπινων συναισθημάτων σε συγκεκριμένα γεγονότα, προϊόντα, υπηρεσίες κ.λπ. Θεωρείται ως ένα πολύ σημαντικό πρόβλημα, ειδικά για οργανισμούς ή εταιρείες που θέλουν να γνωρίζουν την άποψη των καταναλωτών για τους προϊόντα και υπηρεσίες. Σε συνδυασμό με την εξέλιξη των μέσων κοινωνικής δικτύωσης, έχει καθιερωθεί ως ένας ενδιαφέρον τομέας έρευνας. Μέσω των social media, οι άνθρωποι τείνουν να εκφράζουν τις απόψεις ή τα συναισθήματά τους, όπως ευτυχία ή λύπη σε καθημερινή βάση. Έτσι, ο τεράστιος όγκος των διαθέσιμων δεδομένων έχει καταστήσει τις υπάρχουσες λύσεις ακατάλληλες και η ανάγκη για αυτοματοποιημένες μεθόδους ανάλυσης είναι επιτακτική. Σε αυτή τη διατριβή, εξετάστηκε η ανάλυση πολικότητας συναισθήματος σε δεδομένα από το Twitter σε ένα κατανεμημένο περιβάλλον, γνωστό ως Apache Spark. Πιο συγκεκριμένα, σε αυτή τη διατριβή προτείνονται τρεις αλγόριθμοι ταξινόμησης για ανάλυση συναισθήματος σε επίπεδο tweet στο Spark λόγω της καταλληλότητάς του για επεξεργασία Big Data έναντι των προκατόχων του, MapReduce και Hadoop. Επίσης εξετάστηκαν οι επιπτώσεις της αβεβαιότητας της οικονομικής πολιτικής στη μεταβλητότητα των αποδόσεων των μετοχών με στοιχεία από τα μεγαλύτερα τραπεζικά ιδρύματα στην Ελλάδα. Η μεταβλητότητα κατασκευάζεται με χρήση ημερήσιων δεδομένων, ενώ η ερευνητική περίοδος εκτείνεται σε μια περίοδο περίπου δεκατριών ετών, πιο συγκεκριμένα από τις 5 Ιανουαρίου 2001 έως τις 30 Ιουνίου 2014. Αυτή η περίοδος περιλαμβάνει διάφορες φάσεις της αγοράς, όπως κραχ του χρηματιστηρίου μαζί με μετοχές άνθηση της αγοράς (π.χ. η χρηματοπιστωτική κρίση του 2007 και του 2008 στις Ηνωμένες Πολιτείες και η κρίση του ευρωπαϊκού δημόσιου χρέους). Οι εκτιμώμενες παλινδρομήσεις χρησιμοποιήθηκαν για να υποδείξουν τις άμεσες επιπτώσεις της αβεβαιότητας της οικονομικής πολιτικής στη μεταβλητότητα της απόδοσης της μετοχής στις τέσσερις μεγαλύτερες Ελληνικές τράπεζες. Για τη μελέτη των ημερήσιων δεδομένων της μεταβλητότητας χρησιμοποιήθηκαν τέσσερις διαφορετικοί εκτιμητές

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI

All-organic sulfonium salts acting as efficient solution processed electron injection layer for PLEDs

Herein we introduce the all-organic triphenylsulfonium (TPS) salts cathode interfacial layers (CILs), deposited from their methanolic solution, as a new simple strategy for circumventing the use of unstable low work function metals and obtaining charge balance and high electroluminescence efficiency in polymer light-emitting diodes (PLEDs). In particular, we show that the incorporation of TPS-triflate or TPS-nonaflate at the polymer/Al interface improved substantially the luminous efficiency of the device (from 2.4 to 7.9 cd/A) and reduced the turn-on and operating voltage, whereas an up to 4-fold increase in brightness (∼11 250 cd/m2 for TPS-triflate and ∼14 682 cd/m2 for TPS-nonaflate compared to ∼3221 cd/m2 for the reference device) was observed in poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-(1,4-benzo-2, 1′,3-thiadiazole)] (F8BT)-based PLEDs. This was mainly attributed to the favorable decrease of the electron injection barrier, as derived from the open-circuit voltage (Voc) measurements, which was also assisted by the conduction of electrons through the triphenylsulfonium salt sites. Density functional theory calculations indicated that the total energy of the anionic (reduced) form of the salt, that is, upon placing an electron to its lowest unoccupied molecular orbital, is lower than its neutral state, rendering the TPS-salts stable upon electron transfer in the solid state. Finally, the morphology optimization of the TPS-salt interlayer through controlling the processing parameters was found to be critical for achieving efficient electron injection and transport at the respective interfaces.</p