Research

Abstract

The article sets out to provide an overview of the adoption of algorithms for the management of financial investments by companies. Through an analysis of the current scenario, it examines the trend under way and the technological channels. The study shows how the rise of algorithmic trading appears unstoppable, promising to redefine the balance between banks and firms.

Abstract

This study explores an innovative approach to portfolio optimization, bridging traditional Modern Portfolio Theory (MPT) with advanced Machine Learning techniques. We start by recognizing the significance of Markowitz's model in MPT and quickly proceed to focus on the Hierarchical Risk Parity (HRP) method. HRP overcomes some of the limitations of Markowitz's model, particularly in managing complex asset correlations, by offering a more refined risk management strategy that ensures balanced risk distribution across the portfolio. The paper then introduces an innovative Machine Learning approach that employs the Logic Learning Machine (LLM) method to enhance the explainability of the Hierarchical Risk Parity strategy. Such integration is considered the core research part of the study, given that its application makes the output of the model more accessible and transparent. A case study based on the Turkish stock market has been provided as an example. The combination of traditional financial theories with modern Machine Learning tools marks a significant advancement in investment management and portfolio optimization, emphasizing the importance of clarity and ease of understanding in complex financial portfolio models.

Abstract

The Nelson-Siegel, Svensson, and De Rezende-Ferreira models are the most common approaches currently used for modeling the term structures of the risk-free interest rates. However, when markets become turbulent, they may not provide reliable results. Given the importance of the term structure in determining the time value of money and thus in pricing all securities, this study aims to improve the statistical performance of the above mentioned models using more advanced approaches, starting from evolutionary algorithms, such as genetic algorithms and particle swarm optimization, to a more comprehensive hybrid approach, combining the above mentioned artificial intelligence-based methodologies with the traditional Levenberg-Marquardt method. When also this latter approach is unsatisfactory, we rely on machine learning techniques, specifically using Gaussian Process Regression. This study considers the currencies of eight countries belonging to the Pacific area, in addition to the US dollar, as a reference currency. Results show that the use of artificial intelligence-based approaches improve the performance of traditional parametric models currently used in the field.

Abstract

In this study, we have designed an advanced tool for comprehensive analysis of the financial market with pronounced emphasis on option pricing and risk quantification of 11 different assets in a portfolio. The central part of the paper focused on volatility and drift estimation through a suite of methodologies followed by various option pricing models operationalized through Monte Carlo simulation techniques. Following this, we compute the Value at Risk (VaR) metrics which is an essential element in the domain of financial risk management and finally compute the quantile functions to assess the potential financial losses in cases where the markets go through a downturn.

Abstract

This study aims to forecast future demand for a manufacturing company in the automotive sector by leveraging a comprehensive sales dataset spanning from 2012 to 2024. The research begins by analyzing the historical sales patterns of all products, classifying their time series behavior to group them into distinct families based on their demand characteristics. This classification enables the selection of the most suitable forecasting model for each product category, ensuring tailored and accurate predictions. The methodology explores multiple forecasting approaches, including SARIMA, recurrent neural networks (RNNs), and specialized intermittent demand models like TSB, acknowledging that no single model universally outperforms others. Instead, the optimal choice depends on the unique patterns and features of each product's historical demand data.

Abstract

Dollar-Cost Averaging (DCA) is one of the most widespread investment strategies among retail investors, yet its standard implementation is inherently rigid: a fixed amount is allocated to a fixed instrument on a fixed date each month, regardless of market conditions. This study proposes a Smart DCA framework that employs machine learning to dynamically optimize the three decisions typically left unchanged by conventional practice: the optimal entry day, the amount to allocate, and the selection of the equity ETF. To assess the potential of ETFs while circumventing the limitation of their short track record, we train supervised models on a broad panel of global stocks and technical indicators, subsequently transferring the learned signal to the ETF universe (cross-asset transfer). The empirical analysis shows that optimizing timing and sizing yields no benefit, systematically succumbing to the inertia of equity drift. The only genuine value added by Artificial Intelligence lies in asset selection. The results highlight how success critically depends on the alignment between the cost function and the downstream application: models trained for pure ranking objectives (NDCG), tuned to intermediate horizons (6-9 months) to balance responsiveness and stability, prove to be the only ones capable of generating an absolute outperformance (beating the passive S&P 500 benchmark) and structurally reducing the portfolio's intrinsic riskiness.