Data Driven Methods in Finance and Industries

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Recent emergence of high frequency market data thanks to automated and algorithmic trading has necessitated new methodologies to analyze and interpret such data. Classical stochastic models often struggle to incorporate all new necessary features of such data while also maintaining mathematical tractability. For instance, traditional stochastic models of financial markets often become too complicated to contain and explain market frictions, price impacts, information effects, microstructure noises, bid and ask spreads, etc., notions which can no longer be ignored in a high frequency market. Factoring these previously ignored features inside a good model is difficult, and the most likely candidate models pose challenges in analysis due to the inherent complexity of financial markets.

In this workshop, we explore innovative data-driven methods to address practical problems faced by traders in today's financial markets. The workshop consists of four parts:

1. Introduction to Data-Driven Methods in Finance: We will cover various data-driven techniques used in finance, including GANs, reinforcement learning, RNN, LSTM, DNN, and transformers, and examine their advantages and disadvantages.
2. Financial Time Series: In this section, we will delve into modern financial market issues, with a focus on time series analysis and its unique characteristics.
3. Limit Order Book Dynamics: We will continue our examination of modern financial markets by exploring the dynamics of limit order books and the challenges they pose.
4. Beyond the Financial Industry: Lastly, we will expand our discussion of data-driven methods to other industrial problems, such as smart factories and yards, and show how these techniques can be applied in these fields.

This workshop provides a valuable guide for anyone interested in cutting-edge data science approaches to financial markets and other manufacturing industries.