CF969-7-SP-CO Big Data for Computational Finance

CF969-7-SP-CO Assignment Help

Big Data for Computational Finance Assignment help

Please refer to the Student’s handbook on the School’s Policy on Plagiarism and Late Submission.
All deliverables below must be uploaded on FASER by the deadline as independent items, i.e., not bundled together in a zip file.

Part I (55%): Report on Machine Learning in Finance
You are asked to write a report on one (1) recent research paper in applications of machine learning for computational finance from the following list:
 “Learning to simulate realistic limit order book markets from data as a World Agent” by A. Coletta, A. Moulin, S. Vyetrenko, and T. Balch.
Available at: https://arxiv.org/abs/2210.09897
 “FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance” by X.-Y.Liu et al.
Available at: https://arxiv.org/abs/2111.09395
 “Deep Learning Statistical Arbitrage” by J. Guijjaro-Ordonez, M. Pelger, and G. Zanotti.
Available at: https://arxiv.org/abs/2106.04028
 “Trading via Selective Classification” by N. Chalkidis and R. Savani.
Available at: https://arxiv.org/pdf/2110.14914.pdf
 “Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture” by K. Wood, S. Giegerich, S. Roberts, and S. Zohren.
Available at: https://arxiv.org/abs/2112.08534
 “Deep learning with long short-term memory networks for financial market predictions” by T. Fischer and C. Krauss.
Available at: https://www.econstor.eu/bitstream/10419/157808/1/886576210.pdf
 “A deep learning framework for financial time series using stacked autoencoders and long-short term memory” by W. Bao, J. Yue , and Y. Rao.
Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180944
 “Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks” by O. Barkan et al.
Available at: https://scholar.harvard.edu/files/jbenchimol/files/forecasting-cpi-inflation-components-hrnn.pdf
 “Event prediction within directional change framework using a CNN-LSTM model” by A. Rostamian and J. O’Hara.
Available at: https://repository.essex.ac.uk/33313/1/Published_Version.pdf
 “Ascertaining price formation in cryptocurrency markets with Deep Learning” by F. Fang, W. Chung, C. Ventre, M. Basios, Leslie Kanthan, L. Lid, and F. Wu.
Available at https://arxiv.org/abs/2003.00803
 “FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining” by Z. Liu, D. Huang, K. Huang, Z. Li, and J. Zhao. Available at: https://www.ijcai.org/proceedings/2020/622
 “The Efficient Hedging Frontier with Deep Neural Networks” by Z. Gong, C. Ventre, and J. O’Hara.