A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper uses time series transformers to predict investment success.
MOPO-LSI offers a user guide for sustainable investments.
This study examines representation bias in open-source Qwen models for investment decisions.
AI platforms disrupt investment by personalizing deal sourcing and insights.
"What are the origins of risks?" and "How material are they?" -- these are the two most fundamental questions of any risk analysis. Quantitative Structuring -- a technology for building financial products -- provides economically meaningful answers for both of these questions. It does so by considering risk as an inves…
FinDKG uses LLMs to detect financial trends from news articles.
Investment strategy optimized for ambiguity and interest rate risk.
Investigates optimal consumption and investment using alternative data sources.
WSB community outperforms investment banks in stock picks.
This paper investigates how two important sources of risk -- market tail risk and extreme market volatility risk -- are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral beta representation of risk based on the decomposition of cova…
We derive the optimal investment decision in a project where both demand and investment costs are stochastic processes, eventually subject to shocks. We extend the approach used in Dixit and Pindyck (1994), chapter 6.5, to deal with two sources of uncertainty, but assuming that the underlying processes are no longer ge…
The study shows interest rates impact investment and funding negatively but positively on dividend decisions.
Investment strategy in ambiguous financial markets with learning
Shai-am simplifies ML for finance, solving code structure and scalability issues.
Game theory models storage investment to balance market competition and profits.
Paper uses LLMs to analyze annual reports for stock investment, improving efficiency.
New metrics quantify implementation risk in portfolio backtesting, revealing systematic differences in engine implementations.
In this paper we characterise the propensity of big capital investments to systematically deliver poor outcomes as "fragility," a notion suggested by Nassim Taleb. A thing or system that is easily harmed by randomness is fragile. We argue that, contrary to their appearance, big capital investments break easily - i.e. d…
SVAT reduces investment risks by making stock models sensitive to adversarial perturbations.
We show that the mutual fund theorems of Merton (1971) extend to the problem of optimal investment to minimize the probability of lifetime ruin. We obtain two such theorems by considering a financial market both with and without a riskless asset for random consumption. The striking result is that we obtain two-fund the…
Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and government (ESG) problems. They create their investment exclusion lists by gathering information from variou…
Graph database outperforms in filtering ESG stocks efficiently.
MarketSenseAI uses AI to select stocks with 10-30% excess alpha.
FiNCAT tool automatically identifies financial numerals in documents.
Study uses LLMs to improve Black-Litterman portfolio optimization.
LLMs compress financial texts, but distort decision-making.
PriceSeer benchmarks LLMs in real-time stock prediction.
Trading-R1 uses LLMs for financial trading, improving risk-adjusted returns.
This paper acts as a collection of various trading strategies and useful pieces of market information that might help to implement such strategies. This list is meant to be comprehensive (though by no means exhaustive) and hence we only provide pointers and give further sources to explore each strategy further. To set …
We consider a finite horizon optimal stopping problem related to trade-off strategies between expected profit and cost cash-flows of an investment under uncertainty. The optimal problem is first formulated in terms of a system of Snell envelopes for the profit and cost yields which act as obstacles to each other. We th…
Social media reduces individual investors' disposition effect through negative information.
Systemic risk in banking systems remains a crucial issue that it has not been completely understood. In our toy model, banks are exposed to two sources of risks, namely, market risk from their investments in assets external to the banking system and credit risk from their lending in the interbank market. By and large, …
We study a continuous-time asset-allocation problem for an insurance firm that backs up liabilities from multiple non-life business lines with underwriting profits and investment income. The insurance risks are captured via a multidimensional jump-diffusion process with a multivariate compound Poisson process with depe…
This work simplifies data valuation for LLMs using Shapley value computation.
This paper investigates two mechanisms of financial contagion that are, firstly, the correlated exposure of banks to the same source of risk, and secondly the direct exposure of banks in the interbank market. It will consider a random network of banks which are connected through the inter-bank market and will discuss t…
Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to underst…
Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing down all fossil fuel energy generation and replacing them with renewable sources -- careful decisions need to be taken to ensure rapid but …
The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion. Our proposed model first utilizes tensor to integrat…
New model recommends stocks considering individual preferences and diversification.
A framework tackles model uncertainty in ALM, providing robust investment strategies.
Retirees who exhaust their savings while still alive are said to experience financial ruin. These savings are typically grown during the accumulation phase then spent during the retirement decumulation phase. Extensive research into invest-and-harvest decumulation strategies has been conducted, but recommendations diff…
FinMem enhances LLM trading agents with layered memory and character design.
Study proposes a multi-agent system using LLMs for REIT trading, outperforming benchmarks.
Analyzes 6M Python notebooks and 2M enterprise DS pipelines to guide investments in data science.
Benchmark evaluates LLM trading agents by masking identifiers to prevent memory leaks.
We give a complete algorithm and source code for constructing general multifactor risk models (for equities) via any combination of style factors, principal components (betas) and/or industry factors. For short horizons we employ the Russian-doll risk model construction to obtain a nonsingular factor covariance matrix.…
In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is i…
Private equity deals predict public market returns with up to 70% accuracy.