# q-fin.PM Published articles for q-fin.PM. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns DevFeed: [Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns]() Original publisher: [Read original article]() Author: Weijia Han, Lisha Qu Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Finance](), [Neural Network](), [Cryptocurrency]() Tags: [autoencoders](), [coding](), [cryptocurrency](), [cs-lg](), [factor](), [features](), [financial](), [portfolios](), [q-fin-pm](), [q-fin-st](), [return](), [risk]() ### AI overview The study evaluates sparse autoencoders (SAEs) as a way to represent cross-sectional cryptocurrency returns with a small number of active features, using equities as a secondary test and time-ordered comparisons with common benchmarks. SAE portfolios show consistently lower volatility, which the authors associate with lower correlations among selected assets. They do not outperform a simple contrarian strategy on risk-adjusted return, and the learned factor identities vary across training runs. The reported evidence supports a diversification effect, not claims of alpha or stable factors. ### Source excerpt arXiv:2610.39205v1 Announce Type: new Abstract: Factor models are central to asset pricing and portfolio construction, but neural factors are often difficult to interpret, and their effect on portfolio risk is unclear. A sparse autoencoder (SAE) may address both concerns by describing daily returns across many assets with only a few active features. We ask whether this sparse representation produces stable financial factors and better-diversified portfolios. We study cryptocurrency returns, use equities as a secondary test, and compare the SAE with common benchmarks in a time-ordered evaluation. We find consistently lower volatility in SAE portfolios and link this result to lower correlations among the selected assets. However, the SAE does not outperform a simple contrarian strategy on risk-adjusted return, and its factor identities are not stable across training runs. Our contribution is to show that sparse coding can support portfolio diversification, while clearly separating this risk effect from claims about alpha or stable factor identities. ## Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading DevFeed: [Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading]() Original publisher: [Read original article]() Author: Hrishikesh Singh, Aarti Sharma, Millie Pant Published: 2026-10-26T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Reinforcement learning](), [Artificial Intelligence]() Tags: [3](), [abstract](), [actor](), [advanced](), [agents](), [algorithmic-trading](), [approach](), [based](), [context](), [cs-ai](), [deep](), [deep-reinforcement](), [deep-reinforcement-learning](), [financial](), [learning](), [q-fin-cp](), [q-fin-pm](), [reinforcement-learning](), [rich](), [stock](), [stock-trading](), [strategy](), [trading]() ### AI overview The abstract reports that sentiment deep-infusion improved profit maximization and risk-averse trading for agents combining policy optimization with Q-learning (DDPG, TD3, SAC). It contrasts these with policy-optimization-only agents (A2C, PPO), but provides no comparable improvement claim for that group. The paper proposes partial and deep sentiment integration to help trading agents account for context beyond price indicators. This overview is based on the abstract excerpt. ### Source excerpt arXiv:2610.32237v1 Announce Type: new Abstract: In modern financial markets, more than 3/4th of the large volume trade is executed by algorithmic trading based on advanced trading strategies to attain higher precision and speed than traditional human traders. The conventional strategies focused on price-based indicators are inadequate in capturing the market dynamism arising from real-world events such as social sentiments, political instability, or sudden economic shifts. With the rise of Deep Reinforcement Learning (DRL) and data availability, there is a requirement for more efficient algorithms that can incorporate these external factors in trading strategies. DRL has shown potential in simulating real-world scenarios and in simplifying complex decision-making since DRL agents can automate the trading process i.e. executing trades in a manner comparable to human traders. They have shown reliable performance since they can update their policies based on risk and quantitative factors. In this paper, we propose a DRL approach based on partial and deep-infusion of sentimental analysis. It features distinct policy designs of actor-critic-based agents: only policy optimization (A2C & PPO) and policy optimization along with Q-learning principles (DDPG, TD3 & SAC). Our findings reveal, that the agents based on policy optimization combined with Q-learning, significantly and consistently demonstrate improved performance upon sentiment deep-infusion in profit maximization as well as risk-averse trading. ## When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models DevFeed: [When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models]() Original publisher: [Read original article]() Author: Bolin Song (Cornell University), Surui Xiang (United Nations) Published: 2026-10-23T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [Reinforcement learning](), [Large Language Model](), [Predictive AI]() Tags: [cs-lg](), [large-language-models](), [news](), [portfolio](), [q-fin-pm](), [reinforcement-learning]() ### AI overview The study compares retaining news age as timing information with filtering old news before portfolio selection. A fixed LLM extracts signals from public SEC disclosures, while reinforcement learning (RL) policies and LLMs select portfolios. In historical tests from 2023-2025 after trading costs, RL policies responded to timing information in a controlled test, but adding it showed no consistent return benefit. Filtering old news for the direct portfolio-selecting LLM achieved the highest reported average net return: 18.43% annually when delivery was delayed by four weeks, exceeding every news-using RL strategy. ### Source excerpt arXiv:2610.30564v1 Announce Type: new Abstract: Financial news can influence investment decisions long after publication. Timely news may reach an investor before prices fully adjust, whereas older news may already be reflected in prices. We examine two ways to handle aging signals: retain them and give a portfolio policy timing information, or filter them out before portfolio selection. A fixed large language model (LLM) extracts signals from public SEC disclosures. We compare these treatments in reinforcement learning (RL) policies and LLMs that choose portfolios directly. We evaluate returns after trading costs in historical tests over 2023-2025. RL choices respond to timing information in a controlled test, but historical returns show no consistent benefit from adding it. For the LLM, filtering old news produced the highest average net return, reaching 18.43% per year when news delivery was delayed by four weeks. This exceeded the returns of all RL strategies that used news. ## Governed Self-Evolution for Autonomous Investing: A Multi-Agent LLM System with an Invariant Core DevFeed: [Governed Self-Evolution for Autonomous Investing: A Multi-Agent LLM System with an Invariant Core]() Original publisher: [Read original article]() Author: Yebo Feng, Jingyang Dai, Liangrui Pan, Nelson Lau, Jiahua Xu, Yang Liu Published: 2026-10-23T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Multi Agent Systems](), [AI Context Management](), [LLM security](), [Large Language Model]() Tags: [2015](), [2026](), [abstract](), [agent](), [agents](), [analysts](), [arithmetic](), [arxiv](), [autonomous](), [autonomous-investing](), [backtest](), [code](), [context](), [core](), [cs-ai](), [cs-cl](), [cs-ma](), [evolution](), [invariant](), [investing](), [llm](), [multi-agent](), [q-fin-pm](), [system]() ### AI overview Steward constrains self-evolution in autonomous investing by keeping order arithmetic, risk rules and discipline-agent prompts outside the reach of self-edits. Nine role-specialised agents combine reports from news, SEC filings, macroeconomic series and market data; shared memory supplies decision context, while nightly code-based grading and quarterly prompt updates pass through a reversible gate and replay check. In the reported five-month live deployment, it contained a 24.6% sector drawdown to a 0.3% loss. Replay comparisons and a 2015-2026 backtest also report favorable results, but the supplied abstract does not give enough detail to assess methods or broader performance. ### Source excerpt arXiv:2610.30557v1 Announce Type: new Abstract: LLM agents that act on live Web data must combine heterogeneous evidence, remember what they have done and improve from outcomes without weakening their own safeguards. We study this problem in autonomous investing, where every action is irreversible and based on untrusted Web content. We present Steward, a multi-agent LLM system that draws the boundary of self-evolution in its type system. First, nine role-specialised agents decide jointly: four analysts read news feeds, SEC filings, macroeconomic series and market data and emit typed reports, from which a Portfolio Manager sets target positions that a protected Risk Manager audits. Second, a seven-layer shared memory, assembled by code from the system's own record, gives each decision its position context, the market narrative, calibration and the agent's past decisions. Third, the system improves itself by review: code grades every trade and forecast nightly and writes the grades into memory, and each quarter a reflector proposes prompt learnings that a deterministic gate applies reversibly and replay of recorded inputs verifies. All three operate around an invariant core of order arithmetic, risk rules and discipline-agent prompts that no self-edit can reach. In a five-month live deployment (102 trading days, 1,632 LLM calls), the system contained a 24.6% sector drawdown to a 0.3% loss; in replay of its recorded inputs, a single model given the same raw data traded more and lost 1.35 points against the market over five days where the joint design showed no significant loss, and no injected instruction was followed; and a 2015-2026 backtest shows the core alone lowering the maximum drawdown under all 50 random entry policies. The reflector traced the dominant failure to its root cause, and the gate applied a learning that the agent adopted on trigger evenings and on no control evening. Code: https://github.com/yebof/quant-agent