Research Focus
Our research has led to groundbreaking insights in the field of quantitative finance.
Financial Data Science
Machine Learning
Delving into the intricate world of financial markets, Financial Data Science aims to extract valuable insights from vast datasets, identifying patterns and predicting market behaviors. By harnessing the power of data-driven decisions, this domain is revolutionizing the finance industry, from algorithmic trading to customer service.
Feature Engineering
Statistics
At the heart of machine learning and predictive analytics, Feature Engineering involves the transformation and construction of input variables to enhance model performance. By creatively engineering these features, we can enable algorithms to decipher complex patterns, often transforming raw data into a goldmine of insights.
Financial Modeling
Machine Learning
The cornerstone of investment decisions and corporate financial planning, Financial Modeling is the art of building abstract representations of an organization's real-world financials. These models can be used to forecast future earnings, analyze potential investments, and assess risks, thereby guiding optimal business strategies.
Model Validation
Machine Learning
An essential phase in the trading algorithm development process, Strategy Validation and Back-testing ensures the robustness and viability of a strategy by testing it on historical data. This helps quantify its potential risks and returns, providing a safeguard against potential real-world pitfalls.
Backtesting
Machine Learning
An essential phase in the trading algorithm development process, Strategy Validation and Back-testing ensures the robustness and viability of a strategy by testing it on historical data. This helps quantify its potential risks and returns, providing a safeguard against potential real-world pitfalls.
Deep Learning
Machine Learning
Delving into neural networks inspired by the human brain, Deep Learning is revolutionizing a plethora of industries by automating the extraction of intricate patterns from large datasets. From image recognition to natural language processing, its applications are vast and transformative.
Partial Differential Equations
Financial Mathematics
An indispensable tool in mathematics and physics, PDEs describe phenomena that vary with multiple variables. In the realm of finance, they play a pivotal role in modeling the evolution of various financial instruments, assisting in pricing options and understanding market dynamics.
Financial Derivatives and Hedging Strategies
Risk Management
Offering both opportunities and protection, Financial Derivatives are contracts whose values derive from underlying assets. Combined with strategic Hedging, these instruments allow market participants to manage and mitigate potential risks, ensuring a more stable financial landscape.
Portfolio Optimization
Portfolio Management
A sophisticated blend of art and science, Portfolio Optimization is the process of selecting and combining investments to achieve desired returns while managing risks. By optimizing asset allocation and diversification, it seeks to harness market opportunities and deliver consistent performance.
Risk Management
Risk Management
A paramount concern in the financial domain, Risk Management identifies, evaluates, and prioritizes uncertainties in investment decisions. With the right tools and strategies, it ensures the stability of portfolios and protects against adverse market conditions.
High-Performance Computing (HPC)
HPC
Pushing the boundaries of computational capabilities, HPC involves the use of supercomputers and parallel processing techniques. In finance, it's the driving force behind real-time data analytics, complex simulations, and the rapid execution of algorithmic trades.
Quantum Computing
QC
Tapping into the principles of quantum mechanics, Quantum Computing holds the promise of solving problems deemed insurmountable by classical computers. Its potential in finance lies in areas like portfolio optimization, risk analysis, and cryptographic security.
Natural Language Processing
NLP
Bridging the gap between machines and human language, NLP employs algorithms to read, decipher, and respond to human text. Its applications in finance span from sentiment analysis of financial news to chatbots serving clients.
Large Language Models
NLP
At the forefront of artificial intelligence, Large Language Models are trained on vast amounts of text, enabling them to generate coherent and contextually relevant content. These models have myriad applications, from content generation to answering complex queries, showcasing the future of human-machine collaboration.
Papers
At RiskLab AI, our team produces top-tier research papers on various financial topics. Our expertise covers Financial Data Science, Feature Engineering, Financial Modelling, Strategy Validation and Backtesting, Deep Learning, Financial Derivatives and Hedging, Natural Language Processing, and Large Language Models. We also make our research accessible by providing links to full papers, ensuring transparency in our quantitative research. You can also find any associated code on our GitHub repositories.
Stability Weighted Ensemble Feature Importance
Financial Machine Learning
Abstract: "Stability Weighted Ensemble Feature Importance" likely refers to a method that uses ensemble models to compute feature importance scores and then weighs these scores based on their consistency across different data subsets or models. This aims to provide more robust and reliable feature importance values.
S. Alireza Mousavizade
Active Causal Discovery in Cryptocurrency Markets: the Impact of Energy Prices
Financial Causal Inference
Abstract: "Active Causal Discovery in Cryptocurrency Markets: the Impact of Energy Prices" likely delves into identifying the causative influence of energy prices on cryptocurrency market behaviors. Using active causal discovery, the study would actively probe and test hypotheses, seeking robust evidence of energy costs driving crypto market dynamics.
S. Alireza Mousavizade
Optimizing Portfolio Diversification through Causal Inference The Impact of Digital Innovation on Traditional Asset Classes
Financial Causal Inference
Abstract: "Optimizing Portfolio Diversification through Causal Inference: The Impact of Digital Innovation on Traditional Asset Classes" examines how digital innovations affect traditional assets. Using causal inference, the study identifies the direct effects and optimizes portfolio diversification, ensuring better risk management in the face of technological advances.
S. Alireza Mousavizade
Stability Weighted Ensemble Feature Importance
Financial Machine Learning
Abstract: "Stability Weighted Ensemble Feature Importance" likely refers to a method that uses ensemble models to compute feature importance scores and then weighs these scores based on their consistency across different data subsets or models. This aims to provide more robust and reliable feature importance values.
S. Alireza Mousavizade
Active Causal Discovery in Cryptocurrency Markets: the Impact of Energy Prices
Financial Causal Inference
Abstract: "Active Causal Discovery in Cryptocurrency Markets: the Impact of Energy Prices" likely delves into identifying the causative influence of energy prices on cryptocurrency market behaviors. Using active causal discovery, the study would actively probe and test hypotheses, seeking robust evidence of energy costs driving crypto market dynamics.
S. Alireza Mousavizade
Optimizing Portfolio Diversification through Causal Inference The Impact of Digital Innovation on Traditional Asset Classes
Financial Causal Inference
Abstract: "Optimizing Portfolio Diversification through Causal Inference: The Impact of Digital Innovation on Traditional Asset Classes" examines how digital innovations affect traditional assets. Using causal inference, the study identifies the direct effects and optimizes portfolio diversification, ensuring better risk management in the face of technological advances.
S. Alireza Mousavizade
Projects
At RiskLab AI, our research spans several cutting-edge areas of financial science, with an emphasis on High-Performance, Cutting-Edge Financial Intelligence. Our core research areas include:
Subjects
Feature Engineering
At the core of every predictive model lies the art of feature engineering. This process involves selecting, transforming, or creating variables that improve the performance of machine learning algorithms. A well-crafted feature can significantly boost a model's accuracy and provide deeper insights into complex datasets.
S. Alireza Mousavizade
2022 Sep 8
Cross-Validation
At the core of every predictive model lies the art of feature engineering. This process involves selecting, transforming, or creating variables that improve the performance of machine learning algorithms. A well-crafted feature can significantly boost a model's accuracy and provide deeper insights into complex datasets.
S. Alireza Mousavizade
2021 Dec 12
Dangers of Backtesting
At the core of every predictive model lies the art of feature engineering. This process involves selecting, transforming, or creating variables that improve the performance of machine learning algorithms. A well-crafted feature can significantly boost a model's accuracy and provide deeper insights into complex datasets.
S. Alireza Mousavizade
2022 Sep 16
Denoising
At the core of every predictive model lies the art of feature engineering. This process involves selecting, transforming, or creating variables that improve the performance of machine learning algorithms. A well-crafted feature can significantly boost a model's accuracy and provide deeper insights into complex datasets.
S. Alireza Mousavizade
2022 Jun 8
Ensemble Learning
At the core of every predictive model lies the art of feature engineering. This process involves selecting, transforming, or creating variables that improve the performance of machine learning algorithms. A well-crafted feature can significantly boost a model's accuracy and provide deeper insights into complex datasets.
S. Alireza Mousavizade
2021 Jan 2
Entropy Features
At the core of every predictive model lies the art of feature engineering. This process involves selecting, transforming, or creating variables that improve the performance of machine learning algorithms. A well-crafted feature can significantly boost a model's accuracy and provide deeper insights into complex datasets.
S. Alireza Mousavizade
2022 Jun 8
Financial Bars
At the core of every predictive model lies the art of feature engineering. This process involves selecting, transforming, or creating variables that improve the performance of machine learning algorithms. A well-crafted feature can significantly boost a model's accuracy and provide deeper insights into complex datasets.