Výsledky vyhledávání - "Karatas, Tugce"

Dissertation/ Thesis

Applications of AI in Non-Stationary Markets

Autor: Karatas, Tugce

Artificial intelligence, AI, has received increasing attention from the finance industry over recent years. There have been many successful applications of AI in finance, including but not limited to derivative pricing, asset management, credit risk,

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Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques

Autor: Karatas, Tugce, Hirsa, Ali

Risk arbitrage or merger arbitrage is a well-known investment strategy that speculates on the success of M&A deals. Prediction of the deal status in advance is of great importance for risk arbitrageurs. If a deal is mistakenly classified as a complet

Externí odkaz: http://arxiv.org/abs/2110.09315

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Supervised Neural Networks for Illiquid Alternative Asset Cash Flow Forecasting

Autor: Karatas, Tugce, Klinkert, Federico, Hirsa, Ali

Institutional investors have been increasing the allocation of the illiquid alternative assets such as private equity funds in their portfolios, yet there exists a very limited literature on cash flow forecasting of illiquid alternative assets. The n

Externí odkaz: http://arxiv.org/abs/2108.02853

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Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques

Autor: Karatas, Tugce, Hirsa, Ali

Market indicators such as CPI and GDP have been widely used over decades to identify the stage of business cycles and also investment attractiveness of sectors given market conditions. In this paper, we propose a two-stage methodology that consists o

Externí odkaz: http://arxiv.org/abs/2108.02838

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Akademický článek

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Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes

Autor: Hirsa, Ali, Karatas, Tugce, Oskoui, Amir

We apply supervised deep neural networks (DNNs) for pricing and calibration of both vanilla and exotic options under both diffusion and pure jump processes with and without stochastic volatility. We train our neural network models under different num

Externí odkaz: http://arxiv.org/abs/1902.05810

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