Ali Habibnia

Virginia Tech | VT · Department of Economics

This study presents a Reinforcement Learning (RL)-based portfolio management model tailored for high-risk environments, addressing the limitations of traditional RL models and exploiting market opportunities through two-sided transactions and lending. Our approach integrates a new environmental formulation with a Profit and Loss (PnL)-based reward...

This paper proposes a geometric-based technique for compressing convolutional neural networks to accelerate computations and improve generalization by eliminating non-informative components. The technique utilizes a geometric index called separation index to evaluate the functionality of network elements such as layers and filters. By applying this...

This chapter explores the dynamic FinTech landscape in the Middle East with a focus on the UAE and Turkey by considering the impact of cutting-edge technologies, particularly artificial intelligence (AI). Amidst rapid economic changes and a burgeoning youth population, FinTech has emerged as a pivotal force driving innovation. The UAE and Turkey, p...

We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series. This framework employs an informat...

This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors (“big data” environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a neural net with skip-layer connections. We expressly include bo...

This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors (big data environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of deep neural nets with skip-layer connections. We expressly include...

This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a deep neural net with skip-layer connections. We expressly inclu...

Attached MATLAB code is developed to test whether the underlying structure within the recorded data is linear or nonlinear. The nonlinearity measure introduced in Kruger et al (2005) performs a multivariate analysis assessing the underlying relationship within a given variable set by dividing the data series into smaller regions, calculating the su...

* Preliminaries: Ways to get help, File extensions, Common data types, Data import/export, Basic commands, Create basic variables, Basic math functions, Trigonometric functions, Linear algebra, Accessing/assignment elements, Character and string, Regular expression, "IS*" functions, Convert functions, Programming, Errors, Parallel computing (CPU &...

Forecasting in Big Data Environments High-dimensional Nonlinear Time Series Analysis Nonlinear Forecasting Using a Large Number of Predictors Forecasting in Big Data Environments with a Shrinkage Estimation of Skip-layer Neural Networks Past, Present and Future of Testing for Nonlinearity in Time Series Motivation and Inspirations, Nonlinear Factor...

Financial instability and its destructive effects on the economy can lead to financial crises due to its contagion or spillover effects to other parts of the economy. Having an accurate measure of systemic risk gives central banks and policy makers the ability to take proper policies in order to stabilize financial markets. Much work is currently b...

Machine Learning & Deep Learning Applied to Trading

In this thesis, I study high-dimensional nonlinear time series analysis, and its applications in financial forecasting and identifying risk in highly interconnected financial networks. The first chapter is devoted to the testing for nonlinearity in financial time series. I present a tentative classification of the various linearity tests that have...

This study proposes a nonlinear generalization of factor models based on artificial neural networks for forecasting financial time series with many predictors. http://eprints.lse.ac.uk/62916/

Foreign Exchange Rate Risk Measurement and Managemen - Ali Habibnia - LSE Risk & Stochastic Group

In recent years, support vector regressions (SVRs), a novel artificial neural network (ANN) technique, has been successfully used as a nonparametric tool for regression estimation and forecasting time series data. In this thesis, we deal with the application of SVRs in financial markets volatility forecasting.An accurate forecast of volatility is e...

Indubitably investing in the gold market has been one of the most captivating investments in the world over the centuries. The gold standard has been played a key role in the international monetary system here before. It is utilized to mint coins and gold bullion as the money reserves. Because of the volatile and uncertain nature of the paper curre...