Nurmaganbet Smatov

Investment risks in IT project development are heightened by uncertainty, incomplete information, and fluctuating projected cash flows. These challenges are exacerbated by the lack of robust statistical data, leaving stakeholders with limited tools for making informed decisions. This research addresses these issues by proposing a novel methodology for optimizing risk management in investment processes using advanced deep learning techniques. The study aims to develop and validate an algorithm that quantifies and mitigates investment risks through the integration of machine learning models and convolutional neural networks. A key component of this work is the Risk, Investment, and Compliance (RIC) method, which combines multiple financial indicators into a composite scoring system. The methodology was validated using five years of historical financial datasets from reputable sources, and applied to ten companies across diverse industries to analyse financial performance, market behaviour, and consumer sentiment. Key datasets include Kaggle’s Twitter Dataset, encompassing 1.5 million tweets to assess market sentiment, McKinsey’s dataset of 500 million consumer interactions, and daily updates from Yahoo Finance. The findings demonstrate that the RIC methodology effectively distinguishes between high-risk and secure investments. Companies scoring above 60% were identified as strong investment opportunities, while those below 30% were flagged as high-risk ventures. These results provides a robust framework for managing risks in IT investment projects, enabling more reliable decision-making under uncertainty and offering broad applications across industries.

This paper presents a novel approach to sentiment analysis specifically customized for predicting stock market movements, bypassing the need for external dictionaries that are often unavailable for many languages. Our methodology directly analyzes textual data, with a particular focus on context-specific sentiment words within neural network models. This specificity ensures that our sentiment analysis is both relevant and accurate in identifying trends in the stock market. We employ sophisticated mathematical modeling techniques to enhance both the precision and interpretability of our models. Through meticulous data handling and advanced machine learning methods, we leverage large datasets from Twitter and financial markets to examine the impact of social media sentiment on financial trends. We achieved an accuracy exceeding 75%, highlighting the effectiveness of our modeling approach, which we further refined into a convolutional neural network model. This achievement contributes valuable insights into sentiment analysis within the financial domain, thereby improving the overall clarity of forecasting in this field.

This paper presents a novel approach to sentiment analysis specifically customized for predicting stock market movements, bypassing the need for external dictionaries which are often unavailable for many languages. Our methodology directly analyzes textual data, with a particular focus on context-specific sentiment words within neural network models. This specificity ensures our sentiment analysis is both relevant and accurate in identifying trends in the stock market. We employ sophisticated mathematical modeling techniques to enhance both the precision and interpretability of our models. Through meticulous data handling and advanced machine learning methods, we leverage large datasets from Twitter and financial markets to examine the impact of social media sentiment on financial trends. We achieved an accuracy exceeding 75%, highlighting the effectiveness of our modeling approach, which we further refined into a convolutional neural network model. This achievement contributes valuable insights into sentiment analysis within the financial domain, thereby improving the overall clarity of forecasting in this field.