"Computational finance is a branch of applied computer science that deals with problems of practical interest in finance."
This subfield focuses on the development of algorithms and data structures for financial applications, such as portfolio optimization or risk management.
Probability theory: A branch of mathematics that deals with the study of random events and their likelihood of occurrence.
Stochastic processes: A mathematical concept used to model random phenomena, such as stock prices or interest rates.
Time series analysis: A statistical technique used to analyze patterns and trends in data over time.
Monte Carlo simulation: A method used to model complex systems by simulating a large number of random inputs and outputs.
Optimization: A mathematical approach used to maximize or minimize an objective function while satisfying certain constraints.
Markov decision processes: A model used to study decision making in systems with sequential interactions.
Bayesian inference: A statistical method for updating beliefs or probabilities in light of new evidence.
Machine learning: A subfield of artificial intelligence that focuses on building algorithms that can learn from data.
Deep learning: A subset of machine learning that involves neural networks with many layers.
Network analysis: A method of analyzing complex systems by examining the relationships between their components.
Data visualization: The graphical representation of data, often used to communicate the patterns or trends in data.
Financial engineering: The application of mathematical and computational tools to solve financial problems.
High-frequency trading: A type of algorithmic trading that uses sophisticated models and algorithms to make trades at high speeds.
Risk management: The process of identifying, assessing, and mitigating risks in financial transactions or investments.
Portfolio optimization: The process of selecting a mix of assets that maximizes expected returns while minimizing risk.
Real options: A method for valuing financial options that takes into account additional sources of uncertainty, such as the timing of future events.
Credit risk modeling: The process of quantifying the risk of default on loans or other credit instruments.
Derivatives pricing: The process of valuing complex financial instruments, such as options, futures, and swaps.
Quantitative trading: The use of mathematical models and algorithms to make trading decisions.
Algorithmic risk assessment: The use of algorithms to evaluate creditworthiness, insurance risk, or other forms of financial risk.
Monte Carlo Simulation: Monte Carlo simulation is used to analyze the risk associated with investment decisions. It uses random sampling techniques for predictive modeling by generating thousands of iterations.
Black Scholes Model: This financial model is used for pricing derivative securities like options. It's based on assumptions made regarding the market conditions and risks associated.
Neural Networks: Neural Networks is a machine learning technique that makes use of artificial intelligence to identify patterns in large datasets. This helps to discover new trading strategies or to automate existing ones.
Decision trees: Decision trees are decision-making models that use the binary approach to decision-making. They are popularly used to forecast stock prices and market trends.
Genetic Algorithms: Genetic algorithms are used to simulate natural selection to arrive at the optimal solution for financial problems.
Time series analysis: Time-series analysis is used to identify patterns in a time-based dataset. This analysis is used to predict future market trends and price movements.
Regression Analysis: Regression analysis is the statistical method used to establish a relationship between two or more variables. This type of analysis is used to predict market trends and forecast future market movements.
Data mining: Data mining is the process of discovering hidden patterns and data insights in large data sets. It's used to carry out predictive modeling, risk analysis, and to create trading algorithms.
Random Forests: This is a type of decision tree-based ensemble learning model that combines a large number of decision trees to produce a more robust model.
Markov Chain: Markov chain models analyze the probabilities of certain events happening based on previous events. They are widely used in finance for asset allocation, forecasting, and risk management.
"Some slightly different definitions are the study of data and algorithms currently used in finance and the mathematics of computer programs that realize financial models or systems."
"Computational finance emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses."
"It is an interdisciplinary field between mathematical finance and numerical methods."
"Two major areas are efficient and accurate computation of fair values of financial securities and the modeling of stochastic time series."
"It deals with problems of practical interest in finance."
"It involves the study of data and algorithms currently used in finance."
"It focuses on techniques that apply directly to economic analyses."
"It is an interdisciplinary field between mathematical finance and numerical methods."
"Computational finance emphasizes practical numerical methods rather than mathematical proofs."
"The study of data and algorithms currently used in finance and the mathematics of computer programs that realize financial models or systems."
"Computational finance emphasizes practical numerical methods rather than mathematical proofs."
"It focuses on techniques that apply directly to economic analyses."
"Problems of practical interest in finance."
"Practical numerical methods rather than mathematical proofs."
"It is an interdisciplinary field between mathematical finance and numerical methods."
"Efficient and accurate computation of fair values of financial securities and the modeling of stochastic time series."
"The mathematics of computer programs that realize financial models or systems."
"It involves the study of data and algorithms currently used in finance."
"The modeling of stochastic time series."