Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market.

This study compares the effectiveness of fine-tuning Transformer models, specifically BERT, RoBERTa, DeBERTa, and GPT-2, against using prompt engineering in LLMs like ChatGPT and GPT-4 for multi-class classification of hotel reviews. As the hospitality industry increasingly relies on online customer feedback to improve services and strategize marketing, accurately analyzing this feedback is crucial. Our research employs a multi-task learning framework to simultaneously conduct sentiment analysis and categorize reviews into aspects such as service quality, ambiance, and food. We assess the capabilities of fine-tuned Transformer models and LLMs with prompt engineering in processing and understanding the complex user-generated content prevalent in the hospitality industry. The results show that fine-tuned models, particularly RoBERTa, are more adept at classification tasks due to their deep contextual processing abilities and faster execution times. In contrast, while ChatGPT and GPT-4 excel in sentiment analysis by better capturing the nuances of human emotions, they require more computational power and longer processing times. Our findings support the hypothesis that fine-tuning models can achieve better results and faster execution than using prompt engineering in LLMs for multi-class classification in hospitality reviews. This study suggests that selecting the appropriate NLP model depends on the task’s specific needs, balancing computational efficiency and the depth of sentiment analysis required for actionable insights in hospitality management.

Automatic Identification System (AIS) represents an important improvement in the fields of maritime security and vessel tracking. It is used by the signatory countries to the SOLAS Convention and by private and public providers. Its main...

Digitalna ekonomija i evolucija turističkog sektora u artificial intelligence (AI)i environmental, social and governance konceptu (ESG) predstavljaju ključne čimbenike koji oblikuju njegovu sadašnjost i budućnost, s osobitim naglaskom na online rezervacijske platforme. Ovaj rad istražuje kako napredak u digitalnim tehnologijama – umjetna inteligencija te integracija ESG principa u ponudu turističkih proizvoda – mogu utjecati na cjelokupni turistički sektor. Rad pruža deskriptivnu analizu primjera mogućeg korištenja AI tehnologija za unaprjeđenje održivosti, personalizaciju korisničkog iskustva(CX – customer experience) i optimizaciju operativnih procesa, kao i osvrt na ulogu ESG čimbenika u oblikovanju održivijih i odgovornijih turističkih praksi te deskriptivnu analizu trendova iz relevantnih svjetskih istraživanja vezanih za stavove o korištenju AI-ja u kreiranju, ponudi i promociji proizvoda.U kontekstu umjetne inteligencije, tehnologije poput strojnog učenja i analize velikih podataka omogućuju online rezervacijskim platformama da razvijaju sofisticirane algoritme za personalizirane pretrage i preporuke. To ne samo da poboljšava korisničko iskustvo nego i pridonosi većoj učinkovitosti u upravljanju resursima i operacijama.S druge strane, ESG principi usmjeravaju industriju prema održivijim praksama, potičući organizacije da minimiziraju svoj ekološki otisak, promiču društvenu odgovornost i osiguravaju visoke standarde upravljanja. Integracija AI tehnologija može dodatno potaknuti napredak u tim područjima,omogućavajući precizniju analizu utjecaja na okoliš,poboljšanje angažmana vezanog za društvenuo dgovornost i unaprjeđenje upravljačkih praksi.Osim toga, AI nudi nove mogućnosti rješavanja specifičnih ESG izazova unutar turističkog sektora.Primjerice, algoritmi umjetne inteligencije mogu pomoći u identifikaciji i promociji održivih turističkih destinacija te u razvoju personaliziranih iskustava koja podržavaju lokalne zajednice, a koja nemaju negativan utjecaj na okoliš ili je taj utjecaj minimalan. U kontekstu društvene odgovornosti,AI može olakšati kreiranje inkluzivnih turističkih proizvoda koji odgovaraju potrebama različitih skupina putnika, odnosno turista, uključujući osobes posebnim potrebama.Integracija ESG čimbenika i primjena AI tehnologija u turizmu ne samo da pridonosi ekološkoj i društvenoj održivosti nego i omogućava pružateljima turističkih usluga da se istaknu na tom vrlo konkurentnom tržištu, povećavajući privlačnost svoje ponude među sve svjesnijim potrošačima. No takav pristupdonosi i određene izazove, uključujući potrebu razvoja novih vještina, osiguravanja privatnostii sigurnosti podataka te postupanja s obzirom na etičke dvojbe.Zaključno, kombinacija dostupnih AI tehnologija i ESG principa ima potencijal radikalno transformirati turistički sektor, omogućavajući razvoj personaliziranih, učinkovitih i održivih turističkih iskustava. Ovi čimbenici ne samo da predstavljaju ključni element napretka u digitalnoj ekonomiji nego i pružaju platformu za inovacije i postavljanje novih standarda u turističkom sektoru, istodobno promičući održivost i društvenu odgovornost.

Stock trading faces significant challenges due to market volatility and the complexity of integrating diverse data sources, such as financial texts and numerical market data. This paper proposes an innovative automated trading system that integrates advanced natural language processing (NLP) and deep reinforcement learning (DRL) to address these challenges. The system combines two novel components: PrimoGPT, a Transformer-based NLP model fine-tuned on financial texts using instruction-based datasets to generate actionable features like sentiment and trend direction, and PrimoRL, a DRL model that expands its state space with these NLP-derived features for enhanced decision-making precision compared to traditional DRL models like FinRL. An experimental evaluation over seven months of leading technology stocks reveals cumulative returns of up to 58.47% for individual stocks and 27.14% for a diversified portfolio, with a Sharpe ratio of 1.70, outperforming traditional and advanced benchmarks. This work advances AI-driven quantitative finance by offering a scalable framework that bridges qualitative analysis and strategic action, thereby fostering smarter and more equitable participation in financial markets.