Ruppa K. Thulasiram

This study assesses the Large Language Models (LLMs) in creating investment portfolios. We implement a few-shot learning technique, followed by Retrieval Augmented Generation (RAG) enhanced with comprehensive up-to-date financial data, using Meta's latest LLM, Llama 3.1-8b. In the first phase, We assess the models' efficacy using key financial indicators, including total returns, annualized volatility, riskadjusted performance (Sharpe ratio), potential loss estimates (value-at-risk), and their pre-training knowledge with the S&P 500 Index performance baseline. In the second phase, we enhance the LLM's knowledge base by RAG and the latest historical and statistical metrics (such as earnings per share (EPS), dividends per share (DPS), profit margin, and many more) for each asset from different classes. The study constrains model inputs to specific sets of financial assets, such as equities, exchangetraded funds (ETFs), commodities, cryptocurrencies, and bonds. To evaluate model performance and adaptability, we analyzed across two distinct time frames: (1) within the models' training data cutoff, and (2) from the cutoff date to the present. This approach enables the assessment of model generalization to past and present market conditions. The research quantifies LLMs’ capabilities in financial asset allocation, comparing baseline performance against RAG-augmented strategies. Our results demonstrate that RAG-enhanced LLM significantly outperforms vanilla LLM in portfolio construction across various asset classes. We contemplate that these results could influence AI-driven financial decision-making processes such as automated trading, real-time sentiment analysis, and investment management.

In this paper, we study the parallelization of hybrid evolutionary heuristic for solving multi-objective optimization problems on heterogeneous CPU-GPU multi-core architectures. Two independent heuristics, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) are evolved concurrently, periodically exchanging information to explore and exploit the objective space. NSGA-II is parallelized on GPU accelerator and MOEA/D on CPU multithreaded multicore architecture following an island model. The parallel algorithm's performance is evaluated on bi-objective travelling salesperson benchmark data set.

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, a novel hybridized architecture, AE-GRU, integrating the encoder-decoder principle with GRU is designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

Financial market is in general volatile with so many uncertainties and volatility is one of the main measures of uncertainty in the market among other measures. Hence, forecasting volatility is a critical component in risk management, optimizing portfolios, and in algorithmic trading among other financial problems. There have been few machine learning and artificial intelligence techniques used in the literature for the forecasting problem. Transformer Network (TN) architecture is one of newest such techniques proposed. In this work, we utilized this architecture with multi-head attention mechanism for volatility forecasting. To enhance the performance of the TN, we incorporated different variations of the feed forward layer. The performance of three distinct TN models was evaluated by implementing three different deep learning layers (CNN, LSTM, and a hybrid layer (CNN-LSTM)) in the encoder block of TN as the feed forward layer. The results clearly demonstrate that the TN model with the hybrid layer (CNN-LSTM) outperformed the other models, including a recently proposed data-driven approach.

The articles in this special section focus on blockchain deployment in future networks and vertical industries. Blockchain has been successfully applied into a number of sectors, including finance, energy, logistics and supply chains, due to its ability of creating a tamper-proof digital ledger of transactions and the resulting novel trust mechanisms. Future networks will be heterogeneous, connecting together satellite, 5G, the Internet, marine networks, and so on; security and trust management is a major issue in such kind of integrated converged networks. Blockchain technology is a promising solution to achieve the desired level of security and trust in future networks, e.g., reliable network data collection and network trust evaluation. However, the application of blockchain technology in 5G, vertical industries and future heterogeneous networks is still in its infancy. Many challenging issues need to be resolved before its application can be deployed in real-world networking environments.