Financial Big Data: Correlations and Response Functions

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Explores the use of machine learning in algorithmic trading to optimize price signals and minimize slippage in futures markets.

Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Covers piecewise linear payoffs, types of contracts, binomial model, and trading strategies.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Explores high frequency trading, ethical dilemmas in finance, and the importance of structural reforms in the industry.

Covers data representations and processing techniques essential for effective machine learning algorithms.

Explores model complexity, overfitting, and the role of cross-validation and regularization in machine learning.

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Explores network functions in s-domain circuit analysis, covering stability, poles, responses, and design.