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Aidana Bekboeva's Projects

american-option-binomial-trees icon american-option-binomial-trees

This project provides an implementation of the American Option pricing model using Binomial Trees. American Options offer the unique feature of being exercisable at any time prior to expiration, adding complexity to the pricing process.

data-analysis-visualization-real-estate icon data-analysis-visualization-real-estate

This project provides hands-on experience in data handling, cleaning, exploration, and visualization using R. By delving into both real estate and ad click datasets, participants gain insights into user behavior, website usability, and advertising trends.

etf-portfolio-optimization-and-sml-analysis icon etf-portfolio-optimization-and-sml-analysis

This project showcases a comprehensive application of financial analytics concepts, portfolio optimization techniques, and statistical software in the R programming language and Microsoft Excel

financial-data-science-predictive-analysis icon financial-data-science-predictive-analysis

This project involves exploratory data analysis and predictive modeling using various statistical and machine learning techniques. In the financial domain, we analyze the Weekly dataset, containing weekly returns spanning two decades. We aim to identify patterns and trends in the data, perform logistic regression, and compare different classificati

forecasting-using-fama-french-model-factors icon forecasting-using-fama-french-model-factors

This project involves forecasting the price direction of public US companies' market index (VTI) using the Fama-French Five-Factor Model. The dataset includes VTI's daily returns and various factors. The project involves data preprocessing, exploratory data analysis, and building forecasting models.

loan-default-prediction-using-machine-learning icon loan-default-prediction-using-machine-learning

This project employed multiple machine learning models including Logistic Regression, Decision Trees, Bagging, Random Forest, and Ada Boost, to predict loan sizes for small businesses. The dataset utilized is sourced from the U.S. SBA, an organization that supports and encourages small enterprises in the U.S. credit market

tick-level-trade-analysis-and-trade-direction-classification icon tick-level-trade-analysis-and-trade-direction-classification

In this completed project, tick-level trading data was analyzed using R programming using the dataset, "sampleTQdata.RData". This project offers a practical understanding of working with tick-level trading data and applying diverse methods to ascertain trade direction, encompassing tick tests and the Lee-Ready rule

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