Financial Technology

The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging ArXiv ID: 2409.19854 “View on arXiv” Authors: Unknown Abstract This paper proposes a novel method for constructing instruction-tuned large language models (LLMs) for finance without instruction data. Traditionally, developing such domain-specific LLMs has been resource-intensive, requiring a large dataset and significant computational power for continual pretraining and instruction tuning. Our study proposes a simpler approach that combines domain-specific continual pretraining with model merging. Given that general-purpose pretrained LLMs and their instruction-tuned LLMs are often publicly available, they can be leveraged to obtain the necessary instruction task vector. By merging this with a domain-specific pretrained vector, we can effectively create instruction-tuned LLMs for finance without additional instruction data. Our process involves two steps: first, we perform continual pretraining on financial data; second, we merge the instruction-tuned vector with the domain-specific pretrained vector. Our experiments demonstrate the successful construction of instruction-tuned LLMs for finance. One major advantage of our method is that the instruction-tuned and domain-specific pretrained vectors are nearly independent. This independence makes our approach highly effective. The Japanese financial instruction-tuned LLMs we developed in this study are available at https://huggingface.co/pfnet/nekomata-14b-pfn-qfin-inst-merge. ...

The Effect of Digital Marketing, DigitalFinanceand Digital Payment onFinancePerformance of Indonesian SMEs ArXiv ID: ssrn-3965339 “View on arXiv” Authors: Unknown Abstract The purpose of this study is to analyze the effect of digital finance, digital marketing and digital payment variables on finance performance. This study uses q Keywords: Digital Finance, Digital Marketing, Digital Payment, Finance Performance, Financial Technology, Financial Technology Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper applies basic econometric models (OLS, PLS-SEM) with minimal mathematical derivations, and while it uses real survey data and statistical metrics, it lacks code, backtests, or detailed implementation steps for trading strategies. flowchart TD A["Research Goal: Effect of Digital Marketing, Finance, & Payment on Indonesian SMEs Finance Performance"] --> B["Methodology: Quantitative Analysis using Survey Data from Indonesian SMEs"] B --> C["Data Inputs: SME Survey Responses<br>on Digital Adoption & Financial Metrics"] C --> D["Computational Process:<br>Regression Analysis & Hypothesis Testing"] D --> E["Key Finding: Digital Finance, Marketing, & Payment<br>Significantly Improve Finance Performance"] E --> F["Outcome: Recommendation for SMEs to<br>Adopt Financial Technology for Growth"]

A Survey of Fintech Research and Policy Discussion ArXiv ID: ssrn-3622468 “View on arXiv” Authors: Unknown Abstract The intersection of finance and technology, known as fintech, has resulted in the dramatic growth of innovations and has changed the entire financial landscape. Keywords: Fintech, Financial Technology, Digital Innovation, Financial Landscape, Technology in Finance, Financial Technology Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a survey and policy discussion, which focuses on broad themes and high-level analysis rather than specific mathematical derivations or empirical backtesting data. flowchart TD A["Research Goal: Understand fintech's impact on the financial landscape"] --> B["Methodology: Literature Review & Data Synthesis"] B --> C["Data/Inputs: Academic Papers, Policy Reports, Industry Trends"] C --> D["Computational Process: Thematic Analysis & Trend Mapping"] D --> E["Key Findings: Innovation Acceleration, Regulatory Challenges, & Market Transformation"]

Fintech in India – Opportunities and Challenges ArXiv ID: ssrn-3354094 “View on arXiv” Authors: Unknown Abstract Fintech is financial technology; Fintech provides alternative solutions for banking services and non-banking finance services. Fintech is an emerging concept in Keywords: fintech, digital banking, financial technology, alternative finance, technology finance Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 2.5/10 Quadrant: Philosophers Why: The paper is a descriptive, qualitative review of India’s fintech landscape, focusing on definitions, trends, and government initiatives rather than mathematical models or empirical backtesting. flowchart TD A["Research Goal:<br>Fintech in India - Opportunities & Challenges"] --> B["Methodology: Mixed Methods Approach"] B --> C["Data Inputs: Academic Papers, RBI Reports, Market Data"] C --> D["Computational Process:<br>Analysis & Thematic Synthesis"] D --> E["Key Outcome 1: Opportunities<br>Alternative Finance & Digital Banking"] D --> F["Key Outcome 2: Challenges<br>Regulation & Tech Adoption"]

Advances in Financial Machine Learning (Chapter 1) ArXiv ID: ssrn-3104847 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: machine learning, deep learning, algorithmic trading, predictive modeling, Financial Technology Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The excerpt focuses on practical implementation and real-world data challenges in finance with an empirical approach, but does not present dense mathematical derivations or advanced formulas. flowchart TD A["Research Goal:<br>Application of ML in Finance"] --> B["Key Methodology:<br>Algorithmic Trading &<br>Predictive Modeling"] B --> C["Computational Process:<br>Deep Learning &<br>ML Algorithms"] C --> D["Data Input:<br>Financial Market Data"] D --> C C --> E["Key Findings:<br>ML replacing expert human tasks<br>in FinTech & Finance"]