Search: volatility - ai.jp.net

business #llm 📝 BlogAnalyzed: Jan 6, 2026 07:15

LLM Agents for Optimized Investment Portfolio Management

Published:Jan 6, 2026 01:55

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1 min read

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Qiita AI

Analysis

The article likely explores the application of LLM agents in automating and enhancing investment portfolio optimization. It's crucial to assess the robustness of these agents against market volatility and the explainability of their decision-making processes. The focus on Cardinality Constraints suggests a practical approach to portfolio construction.

Key Takeaways

•Focuses on investment portfolio optimization.
•Utilizes LLM agents for decision-making.
•Addresses Cardinality Constraints in portfolio construction.

Reference

“Cardinality Constrain...”

Permalink Qiita AI

Paper #Finance 🔬 ResearchAnalyzed: Jan 3, 2026 18:33

Broken Symmetry in Stock Returns: A Modified Distribution

Published:Dec 29, 2025 17:52

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1 min read

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ArXiv

Analysis

This paper addresses the asymmetry observed in stock returns (negative skew and positive mean) by proposing a modified Jones-Faddy skew t-distribution. The core argument is that the asymmetry arises from the differing stochastic volatility governing gains and losses. The paper's significance lies in its attempt to model this asymmetry with a single, organic distribution, potentially improving the accuracy of financial models and risk assessments. The application to S&P500 returns and tail analysis suggests practical relevance.

Key Takeaways

•Addresses the asymmetry (negative skew, positive mean) in stock returns.
•Proposes a modified Jones-Faddy skew t-distribution to model this asymmetry.
•Attributes the asymmetry to differing stochastic volatility for gains and losses.
•Applies the model to S&P500 returns and analyzes tails.

Reference

“The paper argues that the distribution of stock returns can be effectively split in two -- for gains and losses -- assuming difference in parameters of their respective stochastic volatilities.”

Permalink ArXiv

Research Paper #Blockchain, Auctions, Volatility 🔬 ResearchAnalyzed: Jan 3, 2026 18:54

Volatility Impact on Transaction Ordering

Published:Dec 29, 2025 11:24

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1 min read

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ArXiv

Analysis

This paper investigates the impact of volatility on the valuation of priority access in a specific auction mechanism (Arbitrum's ELA). It hypothesizes and provides evidence that risk-averse bidders discount the value of priority due to the difficulty of forecasting short-term volatility. This is relevant to understanding the dynamics of transaction ordering and the impact of risk in blockchain environments.

Key Takeaways

•The study focuses on Arbitrum's Express Lane Auction (ELA).
•It examines how volatility affects the valuation of priority access.
•Risk-averse bidders discount priority access due to volatility forecasting challenges.
•The findings are supported by empirical evidence from ELA bid records and ETH price data.

Reference

“The paper finds that the value of priority access is discounted relative to risk-neutral valuation due to the difficulty of forecasting short-horizon volatility and bidders' risk aversion.”

Permalink ArXiv

Research Paper #Financial Forecasting, Time Series Analysis, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 16:33

Bitcoin Price Forecasting with Global Liquidity using TimeXer

Published:Dec 26, 2025 15:36

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of Bitcoin price volatility by incorporating global liquidity as an exogenous variable in a TimeXer model. The integration of macroeconomic factors, specifically aggregated M2 liquidity, is a novel approach that significantly improves long-horizon forecasting accuracy compared to traditional models and univariate TimeXer. The 89% improvement in MSE at a 70-day horizon is a strong indicator of the model's effectiveness.

Key Takeaways

•Bitcoin price forecasting benefits from incorporating global liquidity data.
•The TimeXer architecture, when conditioned on global liquidity, outperforms other models.
•Long-horizon forecasts are significantly improved by considering macroeconomic factors.

Reference

“At a 70-day forecast horizon, the proposed TimeXer-Exog model achieves a mean squared error (MSE) 1.08e8, outperforming the univariate TimeXer baseline by over 89 percent.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:36

MASFIN: AI for Financial Forecasting

Published:Dec 26, 2025 06:01

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1 min read

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ArXiv

Analysis

This paper introduces MASFIN, a multi-agent AI system leveraging LLMs (GPT-4.1-nano) for financial forecasting. It addresses limitations of traditional methods and other AI approaches by integrating structured and unstructured data, incorporating bias mitigation, and focusing on reproducibility and cost-efficiency. The system generates weekly portfolios and demonstrates promising performance, outperforming major market benchmarks in a short-term evaluation. The modular multi-agent design is a key contribution, offering a transparent and reproducible approach to quantitative finance.

Key Takeaways

•MASFIN is a multi-agent AI system for financial forecasting.
•It uses LLMs (GPT-4.1-nano) and integrates structured and unstructured data.
•The system incorporates bias mitigation and focuses on reproducibility and cost-efficiency.
•MASFIN generated a 7.33% cumulative return in an 8-week evaluation, outperforming major benchmarks in most weeks.
•The modular multi-agent design is a key contribution for transparent and reproducible quantitative finance.

Reference

“MASFIN delivered a 7.33% cumulative return, outperforming the S&P 500, NASDAQ-100, and Dow Jones benchmarks in six of eight weeks, albeit with higher volatility.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 17:44

AI Automated Trading Agent Evolves to Possess "Market Sentiment" (Implementation of Adaptive Trading Span)

Published:Dec 26, 2025 05:01

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1 min read

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Zenn Gemini

Analysis

This article discusses the development of an AI-powered automated trading system that can adapt its trading strategy based on market volatility. The key innovation is the implementation of an "Adaptive Trading Horizon" feature, which allows the system to switch between different trading spans, such as scalping, depending on the perceived volatility. This represents a step forward from simple BUY/SELL/HOLD decisions, enabling the AI to react more dynamically to changing market conditions. The use of Google Gemini 2.5 Flash as the decision-making engine is also noteworthy, suggesting a focus on speed and responsiveness. The article highlights the potential for AI to not only automate trading but also to learn and adapt to market dynamics, mimicking human traders' ability to adjust their strategies based on "market sentiment."

Key Takeaways

•AI trading systems are evolving to incorporate market sentiment analysis.
•Adaptive trading spans allow for more dynamic responses to market volatility.
•Google Gemini 2.5 Flash is being used for rapid decision-making in trading systems.

Reference

“"Implemented function: Adaptive Trading Horizon"”

Permalink Zenn Gemini

Paper #Finance, Deep Learning, Generative Models 🔬 ResearchAnalyzed: Jan 4, 2026 00:04

Deep Generative Models for Synthetic Financial Data

Published:Dec 25, 2025 22:28

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1 min read

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ArXiv

Analysis

This paper explores the application of deep generative models (TimeGAN and VAEs) to create synthetic financial data for portfolio construction and risk modeling. It addresses the limitations of real financial data (privacy, accessibility, reproducibility) by offering a synthetic alternative. The study's significance lies in demonstrating the potential of these models to generate realistic financial return series, validated through statistical similarity, temporal structure tests, and downstream financial tasks like portfolio optimization. The findings suggest that synthetic data can be a viable substitute for real data in financial analysis, particularly when models capture temporal dynamics, offering a privacy-preserving and cost-effective tool for research and development.

Key Takeaways

•Deep generative models (TimeGAN and VAEs) can generate realistic synthetic financial data.
•Synthetic data can be used as a substitute for real financial data in portfolio analysis and risk simulation.
•TimeGAN performs well in capturing distributional shapes, volatility, and autocorrelation.
•Synthetic data offers privacy-preserving, cost-effective, and reproducible tools for financial experimentation.

Reference

“TimeGAN produces synthetic data with distributional shapes, volatility patterns, and autocorrelation behaviour that are close to those observed in real returns.”

Permalink ArXiv

Research #Modeling 🔬 ResearchAnalyzed: Jan 10, 2026 08:02

Analyzing State Transitions During COVID-19 Turbulence

Published:Dec 23, 2025 16:13

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1 min read

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ArXiv

Analysis

This ArXiv article likely explores how various factors, possibly including AI models or simulations, have shifted states during the COVID-19 pandemic. The analysis might offer insights into how different systems or populations adapted to the unprecedented circumstances.

Key Takeaways

•The research likely focuses on the application of computational methods.
•The analysis probably includes the observation of changing factors during the COVID-19 pandemic.
•The work may reveal new understanding of how systems adjust to the volatility.

Reference

“The article's key fact would depend on the specific content of the ArXiv paper, which is not provided. Without access to the paper, it is impossible to determine a specific fact.”

Permalink ArXiv

Research #Options Pricing 🔬 ResearchAnalyzed: Jan 10, 2026 08:12

Analyzing On-Chain Options Pricing for Wrapped Bitcoin and Ethereum

Published:Dec 23, 2025 09:29

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1 min read

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ArXiv

Analysis

This article likely delves into the financial modeling and valuation of options contracts for wrapped Bitcoin (WBTC) and wrapped Ethereum (WETH) on blockchain platforms. The study probably explores the specific challenges and considerations involved in pricing these on-chain derivatives compared to traditional financial markets.

Key Takeaways

•Focuses on on-chain options pricing, a niche area within cryptocurrency derivatives.
•Examines wrapped Bitcoin and Ethereum, important tokens in the DeFi ecosystem.
•Potentially explores volatility modeling and risk management within this context.

Reference

“The article's context provides information on the pricing of options, specifically for wrapped Bitcoin and Ethereum on-chain.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:20

srvar-toolkit: A Python Implementation of Shadow-Rate Vector Autoregressions with Stochastic Volatility

Published:Dec 22, 2025 17:15

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1 min read

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ArXiv

Analysis

This article announces the release of a Python toolkit for implementing Shadow-Rate Vector Autoregressions with Stochastic Volatility. The focus is on providing a practical tool for researchers and practitioners in finance and econometrics to model and analyze financial time series data, particularly those involving shadow interest rates and volatility. The toolkit's availability on ArXiv suggests it's a pre-print or working paper, indicating ongoing research and development.

Key Takeaways

•A Python toolkit is available for Shadow-Rate Vector Autoregressions with Stochastic Volatility.
•The toolkit is aimed at researchers and practitioners in finance and econometrics.
•The project is likely in active development, as indicated by its ArXiv publication.

Reference

“”

Permalink ArXiv

Research #DeFi 🔬 ResearchAnalyzed: Jan 10, 2026 08:40

Stabilizing DeFi: A Framework for Institutional Crypto Adoption

Published:Dec 22, 2025 10:35

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1 min read

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ArXiv

Analysis

This research paper proposes a hybrid framework to address the volatility issues prevalent in Decentralized Finance (DeFi) by leveraging institutional backing. The paper's contribution lies in its potential to bridge the gap between traditional finance and the crypto space.

Key Takeaways

•Proposes a hybrid framework for DeFi stabilization.
•Focuses on incorporating institutional backing to mitigate volatility.
•Potentially facilitates greater adoption of crypto assets.

Reference

“The paper originates from ArXiv, suggesting peer-review may be pending or bypassed.”

Permalink ArXiv

Research #Verification 🔬 ResearchAnalyzed: Jan 10, 2026 08:53

Analyzing Voter Verification in Volatile Environments: An AI-Driven Human-Information Interaction Study

Published:Dec 21, 2025 20:52

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1 min read

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ArXiv

Analysis

This ArXiv article examines the cognitive load and information processing challenges faced by individuals involved in voter verification, particularly in environments marked by high volatility. The study's focus on human-information interaction in this context is crucial for understanding and mitigating potential biases and misinformation.

Key Takeaways

•The research investigates human-computer interaction in a high-stakes setting.
•It likely analyzes the impact of misinformation and information overload.
•The study has implications for improving verification processes and mitigating cognitive strain.

Reference

“The article likely explores the challenges of information overload and the potential for burnout among those verifying voter information.”

Permalink ArXiv

Research #Volatility 🔬 ResearchAnalyzed: Jan 10, 2026 11:34

LSTM-Based Hybrid Approach to Forecasting S&P 500 Volatility

Published:Dec 13, 2025 09:21

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1 min read

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ArXiv

Analysis

This research explores a hybrid approach leveraging LSTM networks for forecasting the volatility of the S&P 500 index. The focus on a specific financial instrument and the use of a hybrid model suggests a practical application of AI in finance.

Key Takeaways

•Applies LSTM networks to model and forecast financial market volatility.
•Employs a hybrid modeling approach.
•Focuses on the S&P 500 index, indicating a market-specific application.

Reference

“The paper uses LSTM Networks for Volatility Forecasting.”

Permalink ArXiv

Finance #Machine Learning in Finance 👥 CommunityAnalyzed: Jan 3, 2026 15:46

Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

Published:Mar 14, 2025 11:10

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1 min read

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Hacker News

Analysis

The article likely discusses the application of machine learning techniques in financial markets, specifically focusing on how Jane Street, a quantitative trading firm, uses these methods to identify profitable trading opportunities. It may cover topics like data analysis, model building, and risk management within the context of market volatility and complexity.

Key Takeaways

•Machine learning is used to analyze market data and identify trading signals.
•Jane Street, a quantitative trading firm, is likely the source of the article.
•The article probably discusses the challenges of finding profitable opportunities in noisy market data.

Reference

“”

Permalink Hacker News

Business #Artificial Intelligence, Leadership, Corporate Governance 👥 CommunityAnalyzed: Jan 3, 2026 16:22

OpenAI investors try to get Sam Altman back as CEO after sudden firing

Published:Nov 19, 2023 00:36

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1 min read

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Hacker News

Analysis

The article highlights a significant internal conflict within OpenAI, a leading AI company. The investors' attempt to reinstate Sam Altman suggests a disagreement with the decision to fire him, potentially indicating concerns about the company's future direction or strategic alignment. This news is important because it reflects the high stakes and rapid evolution of the AI industry, where leadership changes can have substantial impacts.

Key Takeaways

•OpenAI investors are actively trying to reverse the CEO firing.
•This indicates internal conflict and potential disagreement over the company's direction.
•The situation highlights the volatility and high-stakes nature of the AI industry.

Reference

“N/A - The provided text is a headline and summary, not a direct quote.”

Permalink Hacker News

Podcast Analysis #Bitcoin Philosophy 📝 BlogAnalyzed: Dec 29, 2025 17:27

Robert Breedlove: Philosophy of Bitcoin from First Principles

Published:Apr 17, 2021 22:42

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1 min read

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Lex Fridman Podcast

Analysis

This article summarizes a podcast episode featuring Robert Breedlove discussing the philosophy of Bitcoin. The episode, hosted by Lex Fridman, covers various topics related to Bitcoin, including sovereignty, property, inflation, volatility, and its relationship to concepts like anarchism and capitalism. The outline provided offers a structured overview of the conversation, highlighting key timestamps for different discussion points. The article also includes links to the guest's and host's social media and podcast platforms, as well as sponsor information. The focus is on exploring the philosophical underpinnings of Bitcoin and its implications.

Key Takeaways

•The podcast episode delves into the philosophical aspects of Bitcoin.
•Key topics include sovereignty, property, and the nature of inflation.
•The discussion touches upon Bitcoin's relationship to broader economic and philosophical concepts.

Reference

“The episode explores the philosophical underpinnings of Bitcoin.”

Permalink Lex Fridman Podcast

LLM Agents for Optimized Investment Portfolio Management

Analysis

Key Takeaways

Broken Symmetry in Stock Returns: A Modified Distribution

Analysis

Key Takeaways

Volatility Impact on Transaction Ordering

Analysis

Key Takeaways

Bitcoin Price Forecasting with Global Liquidity using TimeXer

Analysis

Key Takeaways

MASFIN: AI for Financial Forecasting

Analysis

Key Takeaways

AI Automated Trading Agent Evolves to Possess "Market Sentiment" (Implementation of Adaptive Trading Span)

Analysis

Key Takeaways

Deep Generative Models for Synthetic Financial Data

Analysis

Key Takeaways

Analyzing State Transitions During COVID-19 Turbulence

Analysis

Key Takeaways

Analyzing On-Chain Options Pricing for Wrapped Bitcoin and Ethereum

Analysis

Key Takeaways

srvar-toolkit: A Python Implementation of Shadow-Rate Vector Autoregressions with Stochastic Volatility

Analysis

Key Takeaways

Stabilizing DeFi: A Framework for Institutional Crypto Adoption

Analysis

Key Takeaways

Analyzing Voter Verification in Volatile Environments: An AI-Driven Human-Information Interaction Study

Analysis

Key Takeaways

LSTM-Based Hybrid Approach to Forecasting S&P 500 Volatility

Analysis

Key Takeaways

Finding Signal in the Noise: Machine Learning and the Markets (Jane Street)

Analysis

Key Takeaways

OpenAI investors try to get Sam Altman back as CEO after sudden firing

Analysis

Key Takeaways

Robert Breedlove: Philosophy of Bitcoin from First Principles

Analysis

Key Takeaways

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