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1. Large Language Models for Financial Aid in Financial Time-series Forecasting

   https://doi.org/10.1109/BigData62323.2024.10824953  

   Islam, Md Khairul; Karmacharya, Ayush; Sue, Timothy; Fox, Judy (December 2024, IEEE Computer Society Digital Library)

   na (Ed.)

   Considering the difficulty of financial time series forecasting in financial aid, much of the current research focuses on leveraging big data analytics in financial services. One modern approach is to utilize ”predictive analysis”, analogous to forecasting financial trends. However, many of these time series data in Financial Aid (FA) pose unique challenges due to limited historical datasets and high dimensional financial information, which hinder the development of effective predictive models that balance accuracy with efficient runtime and memory usage. Pre-trained foundation models are employed to address these challenging tasks. We use state-of-the-art time series models including pre-trained LLMs (GPT-2 as the backbone), transformers, and linear models to demonstrate their ability to outperform traditional approaches, even with minimal (”few-shot”) or no fine-tuning (”zero-shot”). Our benchmark study, which includes financial aid with seven other time series tasks, shows the potential of using LLMs for scarce financial datasets. 

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2. Certifiably Pseudorandom Financial Derivatives

   https://doi.org/10.1137/17M1161828  

   Zuckerman, David (January 2019, SIAM Journal on Computing)
3. Clustering heterogeneous financial networks

   https://doi.org/10.1111/mafi.12407  

   Amini, Hamed; Chen, Yudong; Minca, Andreea; Qian, Xin (June 2023, Mathematical Finance)

   Abstract We develop a convex‐optimization clustering algorithm for heterogeneous financial networks, in the presence of arbitrary or even adversarial outliers. In the stochastic block model with heterogeneity parameters, we penalize nodes whose degree exhibit unusual behavior beyond inlier heterogeneity. We prove that under mild conditions, this method achieves exact recovery of the underlying clusters. In absence of any assumption on outliers, they are shown not to hinder the clustering of the inliers. We test the performance of the algorithm on semi‐synthetic heterogenous networks reconstructed to match aggregate data on the Korean financial sector. Our method allows for recovery of sub‐sectors with significantly lower error rates compared to existing algorithms. For overlapping portfolio networks, we uncover a clustering structure supporting diversification effects in investment management. 

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4. Speculative urbanism and the urban-financial conjuncture: Interrogating the afterlives of the financial crisis

   https://doi.org/10.1177/0308518X211016003  

   Goldman, Michael (April 2021, Environment and Planning A: Economy and Space)

   This article proposes an analytical–methodological approach to understand this historical conjuncture of speculative urbanism in which global finance capital plays an increasingly important role in urban transformation. Whereas the scholarship on urban financialization makes sharp distinctions between what occurs in the global North and the South, portraying the process in the South as one of subordination or peripheralization and in the North as mature and stable (although volatile), this article seeks to demonstrate that the North–South divide is less effective as an explanatory power. Instead, it presents an analytical approach that is attuned to the relentless dynamism and inter-scalar hyper-mobility of finance capital working across the postcolonial map—in other words, a relational–conjunctural approach. The article suggests the method of “following the financial strategy” by analyzing urban forms and projects as processes constituted by the nexus of practices in finance and city planning. It looks closely at finance’s use of inter-scalar financial tools (such as arbitrage, interest rate swaps, collateralized debt obligations, and currency hedges) across borders, sectors, infrastructures, and conditions, as mediated by national and international state actors. The value of this analytical–methodological approach will be illustrated through notable financial transactions occurring in and across cities to emphasize their speculative and financial characteristics—specifically highlighting investments traversing cities of Spain, the USA, and India. The focus here is on financial strategies emerging from the detritus of the 2008 global financial crisis and shaped by the expanding discursive-material formation of speculative urbanism. 

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5. Breaking Down Financial Barriers to Fieldwork

   https://doi.org/10.5670/oceanog.2024.104  

   Kamer, Krista; Jassowski, Kimberly; Martel, Georgia; Whitcraft, Christine (January 2023, Oceanography)

   Participation in field experiences has been shown to increase students’ confidence, scientific identity, retention, and academic performance (Beltran et al., 2020; Zavaleta et al., 2020). This is particularly true for students from historically excluded groups in ecology and evolutionary biology (EEB). For the purposes of this paper and the novel funding program described herein, field experiences are learning and research opportunities in natural settings that provide students with hands-on, discipline-specific practice and experience (e.g., Morales et al. 2020). 

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