Earn the most prestigious title in your career and develop leadership skills suited to today’s global business challenges. This online DBA program empowers you to innovate and lead at the highest levels.
Doctorate
Mar 31, 2025
36 Months
This course was designed to empower experienced professionals with advanced knowledge and research skills to enable them to drive innovation. Upon completion, learners will be awarded an DBA degree from Euro Asian, Geneva.
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Doctor Degree in Business Administration
1. Microeconomic Theory
In a PhD in Finance and Investment program, consumer and producer theory provides foundational insights into market behavior. Key learnings include utility maximization, budget constraints, production functions, cost minimization, and profit maximization. These concepts help analyze investor behavior, firm decisions, resource allocation, and market efficiency—critical for advanced financial modeling, investment analysis, and policy evaluation.
In a PhD in Finance and Investment, studying market equilibrium involves understanding how supply and demand interact to determine asset prices, interest rates, and capital allocation. Key learning areas include general equilibrium theory, market efficiency, price discovery, and the effects of policy or external shocks on equilibrium. These insights guide advanced financial modeling and investment strategies.
In a PhD in Finance and Investment, game theory offers insights into strategic decision-making among investors, firms, and regulators. Key learnings include equilibrium concepts, signaling, auction theory, and competitive dynamics. These tools help analyze market behavior, corporate strategies, and contract design, enhancing understanding of financial interactions under uncertainty and strategic interdependence.
From the topic *General Equilibrium and Welfare Theorems*, a PhD in Finance and Investment student should grasp how markets achieve efficiency, the conditions under which equilibrium is Pareto optimal, and implications for resource allocation. Understanding these theorems helps evaluate financial market efficiency, policy interventions, and the broader impact of investment decisions on economic welfare.
2. Macroeconomic Theory
In a PhD in Finance and Investment, studying economic growth models provides insights into long-term capital accumulation, productivity, and innovation. It helps analyze how macroeconomic policies, financial markets, and investments influence growth. Understanding models like Solow, endogenous growth, and real business cycles aids in evaluating economic stability, forecasting, and formulating growth-oriented financial strategies and policies.
In a PhD in Finance and Investment, *Introduction to Business Cycles* provides foundational insights into economic fluctuations, their causes, and impacts on markets. Key learnings include identifying phases of cycles, analyzing macroeconomic indicators, understanding monetary and fiscal policy responses, and evaluating implications for financial markets, investment strategies, and risk management in dynamic economic environments.
From the topic *Monetary and Fiscal Policy*, a PhD in Finance and Investment student should understand the macroeconomic tools used to influence economic growth, inflation, interest rates, and employment. Key learnings include policy transmission mechanisms, central banking strategies, fiscal sustainability, policy coordination, and their effects on investment decisions, asset pricing, capital markets, and economic stability.
In the context of a PhD in Finance and Investment, Open Economy Macroeconomics provides insights into exchange rate dynamics, capital flows, international monetary systems, and policy coordination. It helps understand how global economic interdependence affects investment decisions, risk management, and financial market behavior across borders, supporting advanced research in international finance and macro-financial linkages.
3. Quantitative Methods for Finance
For a PhD in Finance and Investment, learning Linear Algebra and Calculus is crucial for modeling financial systems, optimizing portfolios, and conducting quantitative analysis. Key topics include matrix operations, eigenvalues, differentiation, integration, multivariable calculus, and optimization techniques. These tools underpin econometric models, risk assessment, asset pricing, and advanced financial theories used in research.
From the topic *Introduction to Optimization Techniques*, PhD students in Finance and Investment can learn how to model financial problems, optimize portfolio allocation, minimize risk, and maximize returns. It provides foundational tools for quantitative analysis, decision-making under constraints, and empirical research, aiding in asset pricing, investment strategies, and risk management applications.
In a PhD in Finance and Investment, learning Probability Theory and Statistics is essential for modeling uncertainty, risk assessment, and empirical analysis. Key takeaways include understanding distributions, hypothesis testing, regression, Bayesian inference, and stochastic processes. These tools enable rigorous quantitative research, data-driven decision-making, and advanced modeling of financial markets and investment strategies.
Time-Series Analysis & Forecasting equips PhD students in Finance and Investment with tools to model and predict financial data trends over time. Key learning areas include stationarity, ARIMA models, volatility modeling (e.g., GARCH), seasonality, and forecasting accuracy. These skills are essential for empirical research, risk assessment, asset pricing, and investment strategy development.
4. Research Methods in Finance
From the topic *Introduction to Research Design*, PhD students in Finance and Investment learn how to structure research systematically, define clear research questions, choose appropriate methodologies, and ensure data validity and reliability. It emphasizes aligning design with financial theories, ethical considerations, and the practical relevance of investment research, forming a foundation for impactful scholarly work.
From the topic *Literature Review Techniques* in the context of a PhD in Finance and Investment, students learn how to critically evaluate existing research, identify gaps, synthesize findings, and structure reviews effectively. It enhances skills in academic writing, understanding theoretical frameworks, and developing a solid foundation for framing research questions and justifying the significance of proposed studies.
From the topic *Empirical Research Approaches*, PhD students in Finance and Investment learn how to apply quantitative methods, analyze real-world data, and test financial theories. They gain skills in econometrics, data interpretation, and research design to investigate investment behavior, market efficiency, risk, and financial decision-making, enhancing their ability to produce impactful, evidence-based research.
From the topic *Hypothesis Development* in a PhD in Finance and Investment program, students learn to formulate testable research questions, build theoretical frameworks, and derive hypotheses based on literature and financial theories. It emphasizes aligning hypotheses with empirical methods, ensuring clarity, falsifiability, and relevance to investment phenomena, market behavior, or financial decision-making processes.
5. Corporate Finance
Capital Structure Theory explores how firms optimize debt and equity financing to maximize value. In a PhD in Finance and Investment, it’s essential to understand foundational models (e.g., Modigliani-Miller), agency costs, signaling, trade-off vs. pecking order theories, and empirical testing. This knowledge supports advanced research in corporate finance, firm valuation, and investment decision-making.
From the topic "Dividend Policy & Agency Theory," PhD students in Finance and Investment should explore how dividend decisions align with shareholder interests, mitigate agency conflicts between managers and owners, signal firm value, and impact capital structure. Understanding theoretical models and empirical evidence helps analyze corporate governance mechanisms and their influence on firm valuation and investor behavior.
From a PhD in Finance and Investment perspective, studying Mergers and Acquisitions involves understanding valuation techniques, deal structuring, regulatory frameworks, and strategic motivations. It also includes analyzing market efficiency, shareholder value creation, corporate governance, and post-merger integration. Advanced econometric modeling and empirical analysis are essential to evaluate M&A impacts on firm performance, industry dynamics, and investment decisions.
In a PhD in Finance and Investment program, studying Corporate Governance involves understanding the mechanisms that align management actions with shareholder interests. Key areas include board structure, executive compensation, ownership concentration, regulatory frameworks, and their impact on firm performance, risk, and valuation. It also explores governance's role in investor protection, financial markets, and corporate accountability.
6. Investment Analysis and Portfolio Management
In a PhD in Finance and Investment, studying asset pricing models like CAPM and APT involves understanding risk-return relationships, market efficiency, and investor behavior. It also includes empirical testing, model assumptions, factor identification, and critiques of model limitations. These models form the foundation for advanced research in portfolio theory, valuation, and financial market dynamics.
From the Efficient Market Hypothesis (EMH), PhD students in Finance and Investment learn about market efficiency levels (weak, semi-strong, strong), implications for asset pricing, investor behavior, and the limits of arbitrage. It fosters critical analysis of anomalies, behavioral finance, and empirical testing methods, shaping advanced understanding of financial markets and investment strategies.
The Risk-Return Trade-off explores how higher potential returns are associated with higher risks. In a PhD in Finance and Investment, this topic deepens understanding of portfolio theory, asset pricing, investor behavior, and market efficiency. It lays the groundwork for advanced quantitative modeling and strategic decision-making in uncertain financial environments.
Behavioral Finance explores how psychological biases and emotions influence investor behavior and market outcomes. In a PhD in Finance and Investment program, key learning areas include cognitive biases, heuristics, market anomalies, investor sentiment, and their implications for asset pricing and portfolio management. It challenges traditional finance assumptions, offering deeper insights for research and practical investment strategies.
7. Financial Econometrics
In a PhD in Finance and Investment program, regression analysis is essential for understanding relationships between financial variables, testing hypotheses, and modeling asset pricing, risk, and returns. Students learn to interpret coefficients, assess model validity, handle multicollinearity, and apply techniques like OLS, time-series regression, and panel data analysis for empirical research and financial decision-making.
From the ARCH/GARCH models topic, PhD students in Finance and Investment should learn how to model and forecast financial market volatility. Key takeaways include understanding time-varying variance, volatility clustering, risk management, asset pricing implications, and applications in financial econometrics. Mastery aids in analyzing market behavior, improving investment strategies, and enhancing empirical financial research.
In a PhD in Finance and Investment program, learning Panel Data Analysis involves understanding how to analyze multi-dimensional data involving time series and cross-sectional elements. Key areas include fixed and random effects models, endogeneity, dynamic panels, and model selection. This analysis is crucial for empirical research on firm performance, investment behavior, and financial policy evaluation over time.
From the topic *Cointegration and Error Correction*, PhD students in Finance and Investment learn to analyze long-run equilibrium relationships between non-stationary financial time series. They gain skills to model short-run dynamics using Error Correction Models (ECMs), enhancing understanding of market efficiency, asset pricing, and financial integration, which are crucial for empirical research and policy analysis.
8. Asset Pricing Theory
From the Intertemporal CAPM (ICAPM), PhD students in Finance and Investment should learn how investors make decisions under dynamic conditions, incorporating changing investment opportunities over time. Key concepts include multi-period optimization, state variables, consumption-portfolio choices, and risk premia adjustments. Understanding ICAPM helps in modeling asset pricing, market behavior, and long-term investment strategies under uncertainty.
In a PhD in Finance and Investment program, studying Stochastic Discount Factors (SDFs) involves understanding asset pricing, risk-adjusted returns, and the intertemporal marginal rate of substitution. Key learning includes deriving SDFs from economic models, applying them to empirical asset pricing, and analyzing their role in explaining risk premiums, market inefficiencies, and consumption-based finance theories.
Consumption-Based Asset Pricing (CBAP) provides foundational insights into how individuals allocate consumption over time and how this affects asset prices. In a PhD in Finance and Investment, it helps understand intertemporal choices, risk aversion, and stochastic discount factors. It also forms the basis for testing asset pricing models and exploring market anomalies and investor behavior.
In a PhD in Finance and Investment, *Derivative Pricing* provides a foundation for understanding risk management, asset pricing, and financial modeling. Key areas include stochastic calculus, arbitrage theory, option pricing models (like Black-Scholes and binomial models), and hedging strategies. This knowledge is essential for empirical research, quantitative finance, and advancing theoretical models in financial markets.
Conduct original research to address a real-world business problem. Learn to formulate research questions, apply theoretical frameworks, and contribute to academic and professional knowledge. Every Learner will go through these following six simple steps to complete their Thesis with the help of a Professional Expert.
What Our Learners Have To Say About Us
Pursuing my Doctorate in Business Administration was more than just an academic pursuit—it was a transformational journey. The research support and global exposure helped me establish myself as a thought leader in strategic management.
Analyze how Amazon or Netflix navigated shifting market conditions through strategic foresight, innovative thinking, and effective change management. Examine key decisions, adaptations to technology and consumer behavior, and leadership in driving transformation. Highlight lessons in resilience, long-term vision, and innovation that enabled sustained competitive advantage.
Analyze Satya Nadella’s transformational leadership at Microsoft, focusing on how his leadership style influenced employee motivation and drove cultural change. Examine key initiatives, communication strategies, and leadership behaviors that reshaped the company’s vision, collaboration, and innovation. Evaluate outcomes through performance improvements, employee engagement, and organizational culture transformation.
Analyze how Apple maintained supply chain resilience during COVID-19, focusing on logistics optimization, risk management strategies, and supplier relationship management. Examine disruptions faced, Apple’s response, and lessons learned. Highlight how Apple adapted operations, diversified suppliers, and leveraged technology to ensure continuity and meet global demand during the pandemic.
In this case study, analyze Tesla’s approach to raising capital and taking financial risks. Evaluate its valuation methods, capital structure decisions, and strategic financial choices. Assess how these influenced growth, investor confidence, and market positioning, while considering implications for long-term sustainability and competitive advantage in the electric vehicle industry.
In this case study, analyze how Airbnb achieved rapid growth through disruptive innovation. Focus on its unique business model, how it scaled operations globally, and secured funding to fuel expansion. Examine key strategies, challenges faced, and the impact of innovation on the hospitality industry’s traditional dynamics.
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Analyze how Coca-Cola tailors its branding and marketing strategies to different regions using consumer psychology insights and data-driven approaches. Examine specific regional campaigns, cultural adaptations, and how consumer behavior influences branding decisions. Highlight the effectiveness of personalized marketing and the role of data in shaping Coca-Cola’s global yet local brand presence.
Analyze the Volkswagen emissions scandal by examining the ethical lapses, failures in compliance, and the role of the board. Evaluate how decisions were made, who was responsible, and how stronger governance could have prevented it. Recommend strategies to enhance ethical decision-making, regulatory compliance, and board accountability in corporate settings.
Frequently Asked Questions
This is a doctoral-level program for professionals who want to lead through research and
innovation. It blends academic depth with real-world impact, helping you turn workplace
challenges into meaningful, research-driven solutions.
Yes, absolutely. It's built with your schedule in mind. You can pursue this PhD alongside your
job, with flexible study hours and a structure that respects your work-life balance.
This is a blended program, primarily conducted online. You'll learn through a mix of live virtual
sessions, recorded lectures, guided mentorship, and independent research. No campus visits
required—unless you choose to attend optional events.
You’ll learn from globally recognized faculty—experienced researchers, tenured professors, and
industry experts. They’ll not only teach you but guide your research journey with real insight and
personalized attention.
Instead of a traditional thesis, you’ll work on a Practicum Research Project. It’s based on a real
issue from your work or industry. With your advisor’s help, you’ll research it rigorously and may
even publish it, depending on your goals.
Not at all. This PhD is designed for professionals, not career academics. You’ll be supported
through every research step—from forming questions to analyzing data—with practical
guidance tailored to your experience level.
Most learners complete the program in about 2.5 to 3 years, depending on how much time you
dedicate. The flexible design means you can move at your own pace, balancing study with your
personal and professional life.
Yes. The degree is awarded by Euro Asian University in Estonia, a recognized institution within
the European Higher Education Area. It holds academic value across Europe, the U.S., and
beyond.
Publishing is not required but highly encouraged. If your work has practical or academic value,
your advisor can guide you in submitting it to journals or presenting it at conferences.
The cohort includes senior executives, consultants, educators, entrepreneurs, and mid-career
professionals. Everyone brings unique experiences, making for rich peer discussions and
networking opportunities.
Whether you want to teach, lead strategic transformation, consult, or start your own research
firm, this PhD helps position you as a subject matter expert and decision-maker in your domain.
The application is simple. Share your academic and professional background, express your
research interests, and have a short conversation with our admissions team. From there, we’ll
guide you through every step
Our advisors are available around the clock to answer questions and support your educational journey. Connect with us today to explore how upGrad can help you meet your career goals.
