ML-Assisted Optimization of Securities Lending
Authors: 
Abhinav Prasad, Prakash Arunachalam, Ali Motamedi, Ranjeeta Bhattacharya, Beibei Liu, Hays Mccormick, Shengzhe Xu, Nikhil Muralidhar, Naren RamakrishnanAuthors Info & Claims
Pages 628 - 636
Abstract
This paper presents an integrated methodology to forecast the direction and magnitude of movements of lending rates in security markets. We develop a sequence-to-sequence (seq2seq) modeling framework that integrates feature engineering, motif mining, and temporal prediction in a unified manner to perform forecasting at scale in real-time or near real-time. We have deployed this approach in a large custodial setting demonstrating scalability to a large number of equities as well as newly introduced IPO-based securities in highly volatile environments.
References
[1]
[1] 2023. https://www.morningstar.com/etfs/understanding-securities-lending-etfs
[2]
Carlo Altavilla, Miguel Boucinha, and Paul Bouscasse. 2022. Supply or demand: What drives fluctuations in the bank loan market? (2022).
[3]
Wei Bao, Jun Yue, and Yulei Rao. 2017. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12, 7 (2017), e0180944.
[4]
Joao A Bastos. 2010. Forecasting bank loans loss-given-default. Journal of Banking & Finance 34, 10 (2010), 2510–2517.
[5]
SM Husnain BOKHARI and Mete Feridun. 2006. Forecasting inflation through econometric models: An empirical study on Pakistani data. Doğuş Üniversitesi Dergisi 7, 1 (2006), 39–47.
[6]
Edwin Burmeister, Kent D Wall, and James D Hamilton. 1986. Estimation of unobserved expected monthly inflation using Kalman filtering. Journal of Business & Economic Statistics 4, 2 (1986), 147–160.
[7]
Bowen Cai. 2021. Deep learning-based economic forecasting for the new energy vehicle industry. Journal of Mathematics 2021 (2021), 1–10.
[8]
Junyi Chai, Hao Zeng, Anming Li, and Eric WT Ngai. 2021. Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications 6 (2021), 100134.
[9]
Hongjie Chen, Ryan A Rossi, Kanak Mahadik, Sungchul Kim, and Hoda Eldardiry. 2023. Graph Deep Factors for Probabilistic Time-series Forecasting. ACM Transactions on Knowledge Discovery from Data 17, 2 (2023), 1–30.
[10]
Jorio Cocola and Paul Hand. 2020. Global Convergence of Sobolev Training for Overparameterized Neural Networks. In Machine Learning, Optimization, and Data Science: 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I 6. Springer, 574–586.
[11]
A Davidović, EH Huntington, and MR Frater. 2009. Discretization in time gives rise to noise-induced improvement of the signal-to-noise ratio in static nonlinearities. Physical Review E 80, 1 (2009), 011119.
[12]
Jan G De Gooijer and Rob J Hyndman. 2006. 25 years of time series forecasting. International journal of forecasting 22, 3 (2006), 443–473.
[13]
Li Deng and Yang Liu. 2018. Deep learning in natural language processing. Springer.
[14]
Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2015. Deep learning for event-driven stock prediction. In Twenty-fourth international joint conference on artificial intelligence.
[15]
Thomas Fischer and Christopher Krauss. 2018. Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270, 2 (2018), 654–669.
[16]
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss, and John Patrick Cunningham. 2020. Uses and abuses of the cross-entropy loss: Case studies in modern deep learning. (2020).
[17]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
[18]
Zexin Hu, Yiqi Zhao, and Matloob Khushi. 2021. A survey of forex and stock price prediction using deep learning. Applied System Innovation 4, 1 (2021), 9.
[19]
Licheng Jiao and Jin Zhao. 2019. A survey on the new generation of deep learning in image processing. Ieee Access 7 (2019), 172231–172263.
[20]
Jungsuk Kim, Abhishek Kumar, Sushanta Mallick, and Donghyun Park. 2021. Financial uncertainty and interest rate movements: is Asian bond market volatility different?Annals of Operations Research (2021), 1–29.
[21]
Akshit Kurani, Pavan Doshi, Aarya Vakharia, and Manan Shah. 2023. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science 10, 1 (2023), 183–208.
[22]
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278–2324.
[23]
Nikhil Muralidhar, Sathappan Muthiah, Kiyoshi Nakayama, Ratnesh Sharma, and Naren Ramakrishnan. 2019. Multivariate long-term state forecasting in cyber-physical systems: A sequence to sequence approach. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 543–552.
[24]
Kevin P Murphy. 2022. Probabilistic machine learning: an introduction. MIT press.
[25]
Mehtabhorn Obthong, Nongnuch Tantisantiwong, Watthanasak Jeamwatthanachai, and Gary Wills. 2020. A survey on machine learning for stock price prediction: Algorithms and techniques. (2020).
[26]
Kyong Jo Oh and Ingoo Han. 2000. Using change-point detection to support artificial neural networks for interest rates forecasting. Expert systems with applications 19, 2 (2000), 105–115.
[27]
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In International conference on machine learning. Pmlr, 1310–1318.
[28]
Gurnain Kaur Pasricha. 2006. Kalman filter and its economic applications. (2006).
[29]
Pranav Patel, Eamonn Keogh, Jessica Lin, and Stefano Lonardi. 2002. Mining motifs in massive time series databases. In 2002 IEEE International Conference on Data Mining, 2002. Proceedings. IEEE, 370–377.
[30]
Debprakash Patnaik, Manish Marwah, Ratnesh K Sharma, and Naren Ramakrishnan. 2011. Temporal data mining approaches for sustainable chiller management in data centers. ACM Transactions on Intelligent Systems and Technology (TIST) 2, 4 (2011), 1–29.
[31]
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J Bessa, Jakub Bijak, John E Boylan, 2022. Forecasting: theory and practice. International Journal of Forecasting 38, 3 (2022), 705–871.
[32]
Anna Rakitianskaia and Andries Engelbrecht. 2015. Measuring saturation in neural networks. In 2015 IEEE symposium series on computational intelligence. IEEE, 1423–1430.
[33]
Srinath Ravikumar and Prasad Saraf. 2020. Prediction of stock prices using machine learning (regression, classification) Algorithms. In 2020 International Conference for Emerging Technology (INCET). IEEE, 1–5.
[34]
Claudio Filipi Gonçalves Dos Santos and João Paulo Papa. 2022. Avoiding overfitting: A survey on regularization methods for convolutional neural networks. ACM Computing Surveys (CSUR) 54, 10s (2022), 1–25.
[35]
Omer Berat Sezer, Mehmet Ugur Gudelek, and Ahmet Murat Ozbayoglu. 2020. Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied soft computing 90 (2020), 106181.
[36]
Fei Tan, Xiurui Hou, Jie Zhang, Zhi Wei, and Zhenyu Yan. 2018. A deep learning approach to competing risks representation in peer-to-peer lending. IEEE transactions on neural networks and learning systems 30, 5 (2018), 1565–1574.
[37]
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis, 2018. Deep learning for computer vision: A brief review. Computational intelligence and neuroscience 2018 (2018).
[38]
Robert L Winkler. 1973. Bayesian models for forecasting future security prices. Journal of Financial and Quantitative Analysis 8, 3 (1973), 387–405.
[39]
Shengzhe Xu, Manish Marwah, Martin Arlitt, and Naren Ramakrishnan. 2021. Stan: Synthetic network traffic generation with generative neural models. In Deployable Machine Learning for Security Defense: Second International Workshop, MLHat 2021, Virtual Event, August 15, 2021, Proceedings 2. Springer, 3–29.
[40]
Yong Yu, Xiaosheng Si, Changhua Hu, and Jianxun Zhang. 2019. A review of recurrent neural networks: LSTM cells and network architectures. Neural computation 31, 7 (2019), 1235–1270.
[41]
Yang Zhao, Jianping Li, and Lean Yu. 2017. A deep learning ensemble approach for crude oil price forecasting. Energy Economics 66 (2017), 9–16.
Index Terms
- ML-Assisted Optimization of Securities Lending
Recommendations
Dynamic Pricing in Securities Lending Market: Application in Revenue Optimization for an Agent Lender Portfolio
ICAIF '24: Proceedings of the 5th ACM International Conference on AI in FinanceSecurities lending is an important part of the financial market structure, where agent lenders help long term institutional investors to lend out their securities to short sellers in exchange for a lending fee. Agent lenders within the market seek to ...
An Integrated Model for Hybrid Securities
We develop a model for pricing securities whose value may depend simultaneously on equity, interest-rate, and default risks. The framework may also be used to extract probabilities of default (PD) functions from market data. Our approach is entirely ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2023
697 pages
ISBN:9798400702402
DOI:10.1145/3604237
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 November 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICAIF '23
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 466Total Downloads
- Downloads (Last 12 months)455
- Downloads (Last 6 weeks)43
Reflects downloads up to 14 Dec 2024
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in