Management

The rapid development of computer technologies has led to a significant increase in algorithmic trading in financial markets, which, in turn, has resulted in a surge in trading volume and diversity, generating large amounts of data. This data has enabled the use of artificial intelligence systems in analyzing and predicting parameters. The analysis of market efficiency, statistical arbitrage operations, the creation of buy-sell strategies, price prediction, and the measurement of market and credit risk are among the areas where these systems are applied.

Risk management has become a key activity for all companies. Market fluctuations, volatility in interest rates, credit defaults, and internal fraud cases in recent times have resulted in significant financial losses. These developments, which have also damaged company reputations, have increased the importance of monitoring and managing all the risk factors companies are exposed to. In this context, financial risk measurement and the interactions between market risks, as well as studies on risk management activities of companies in Turkey, are being conducted.

This research focuses on revenue management and price optimization strategies to maximize profitability and efficiency. By examining dynamic pricing models, demand forecasting, and consumer behavior analysis, the most effective pricing strategies are identified. The study also explores the use of advanced analytics, machine learning, and artificial intelligence tools to enhance decision-making processes, improve resource allocation, and adapt to market fluctuations. Particular emphasis is placed on the development of pricing frameworks that integrate real-time data, align with business objectives, and maintain a competitive advantage.

The desires and needs of individuals and companies are diversifying more and more each day. To meet this growing demand, entrepreneurs are coming to the forefront. The entrepreneurial process begins with a business idea motivated by the desire to seize an emerging opportunity or address an existing need, and there are various resources required throughout this process. The most important of these requirements are network and capital, and to meet these needs, there are many corporate and individual supporters. Especially in today's world, where technology is advancing and globalization is widespread, entrepreneurial activities receive support in many ways; however, with the rise of competition, the failure rates in entrepreneurship are also increasing.

This research develops decision-making frameworks grounded in statistical and probabilistic principles by employing machine learning techniques and combining them with the methods I developed for probability distribution parameter estimation, financial volatility and success prediction, operational analytics, and consumer choice modeling. It utilizes statistical modeling and parameter estimation to develop models of uncertainty that influence decision-making for individuals and firms and to create basis for artificial intelligence applications that model and predict these decisions.

SMEs, which make up approximately 99.89% of businesses in Turkey, face challenges in areas such as access to technology, innovation, investment and financing sources, and strategic planning. For SMEs, whose share in the financial system remains very low, efforts are being made in areas such as accessible financing sources, financial failure prediction methods, institutionalization, and efficiency management.

This research examines the integration of Business Intelligence (BI) and data analytics to strengthen informed decision-making processes in organizational contexts. It addresses fundamental BI principles, data management strategies, and the use of effective database queries within the field of social sciences. Additionally, the research explores data science concepts such as machine learning and artificial intelligence, as well as their impacts on business operations and data-driven marketing strategies. A particular emphasis is placed on how these advanced analytics can be applied within Enterprise Resource Planning (ERP) systems to ensure seamless integration of insights across business processes. The ultimate goal is to evaluate how data-driven approaches and analytical tools can be utilized to interpret complex datasets, inform strategic decision-making processes, and optimize business outcomes across various industries.

Sustainable finance is an approach in which financial decisions are made by considering environmental, social, and governance (ESG) factors. This understanding aims to create long-term positive impacts on society and the environment, alongside economic gains. Sustainable finance encourages investors to consider not only short-term profits but also aspects such as environmental responsibility, social justice, and ethical governance. In this way, financial markets contribute to a more sustainable future and become a powerful tool for achieving sustainable growth objectives. Sustainable finance analysis examines companies’ and investors’ financial decisions within this framework. This analysis helps in developing a sustainable investment strategy by evaluating not only ethical and environmental concerns but also risk management and opportunities.

Investment analysis is an evaluation process aimed at identifying the best investment opportunities in financial markets and minimizing risks. This process involves examining a company's financial condition, industry trends, economic indicators, and other relevant factors to predict future value gains. With this analysis, investors can make informed decisions between stocks, bonds, commodities, and other financial instruments, thereby optimizing their portfolios. Investment analysis is carried out through methods like fundamental analysis and technical analysis, both of which are used to better understand market opportunities and risks.