Turnover-adjusted IR is always lower than classic IR, suggesting managers can improve performance by limiting turnover.
problem The classic relationship between IR and its determinants does not account for turnover costs.
method Mathematical derivations and simulations considering volatility of information coefficient and portfolio turnover.
result Turnover-adjusted IR is lower and managers can improve performance by limiting turnover.
On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundam…
A strategy to beat benchmarks by investing in heavily shorted but fundamentally sound securities.
problem Overcoming behavioral biases in investing, particularly the 'rebound effect'.
method Quantitative metrics, historical data, and securities lending modeling.
result The Bounce Basket strategy can outperform market returns during market downturns.
Hierarchical AI multi-agent framework optimizes equity portfolios in China's A-share market.
problem Optimizing equity portfolios in China's A-share market using AI and multi-agent systems.
method A hierarchical multi-agent design integrating macro, firm-level, and reinforcement learning approaches.
result Consistently outperforms benchmarks and state-of-the-art systems on risk-adjusted returns and drawdown control.
Enhances financial analysis with multi-agent collaboration.
problem Limited use of AI-agent collaboration in financial research.
method Proposes a multi-agent system for financial investment research.
result Multi-agent system outperforms single-agent models.
Investment strategy for NYSE stocks minimizes market correlation.
problem Minimizing market correlation for steady returns.
method Combining momentum, fundamentals, and analyst recommendations; feature selection; backtesting various portfolio construction methods.
result Risk parity outperformed other methods, offering higher Sharpe ratio and lower beta.
"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…
Study shows cooperation can reduce investment risk and price gaps.
problem Investment risk and price gaps in cooperative markets.
method Introduced Collective Arbitrage and Collective Super-replication, established asset pricing theorems.
result Reduction of price intervals through collective super-replication.
Deep learning models outperform classical methods in forecasting company fundamentals.
problem Forecasting company fundamentals for investment and econometrics.
method Compared 24 deterministic and probabilistic models on real company data.
result Deep learning models provide superior forecasting performance, especially in uncertainty estimation.
Evology models US equity mutual funds interactions for investment strategies.
problem Understanding complex interactions in financial markets.
method Agent-based model (ABM) of US stock market participants and their strategies.
result Trading strategies interact with other market participants and conditions.
Improved stock selection through predictive fundamentals and uncertainty estimates.
problem Selecting stocks based on future financial data to outperform traditional factor models.
method Train deep nets to forecast future fundamentals, incorporate uncertainty estimates, and adjust portfolios to manage risk.
result Simulated annualized return of 17.7% and Sharpe ratio of 0.84 for uncertainty-aware model, significantly higher than 14.0% and 0.52 for standard factor models.
Analyst reports contain valuable information for investment decisions.
problem Investment value in analyst reports is not fully understood or utilized.
method Embedded analyst reports with LLMs and ML forecasts of future returns.
result Portfolios formed on analyst report narratives outperform numerical forecasts and established factors.
The study reveals distinct patterns in retail investors' holding periods affecting stock returns.
problem Understanding the impact of retail investors' investment horizons on stock returns.
method Using self-reported holding periods from StockTwits, the study categorizes retail investors into long-horizon and short-horizon groups and analyzes their return patterns.
result Long-horizon retail investors exhibit underreaction to earnings announcements, while short-horizon investors show overreaction.
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor shou…
This note develops an arbitrage theory for a discrete-time market model without the assumption of the existence of a numéraire asset. Fundamental theorems of asset pricing are stated and proven in this context. The distinction between the notions of investment-consumption arbitrage and pure-investment arbitrage provide…
This paper introduces a new market-based carbon risk measure for portfolio optimization.
problem The challenge of measuring and managing carbon risk in investment portfolios.
method Develops a market-based carbon risk measure and applies it to minimum variance portfolio construction.
result Market-based carbon risk measures can complement fundamental-based approaches in portfolio optimization.
Enhances thematic investing with stock embeddings from textual data.
problem Challenges in constructing thematic portfolios due to overlapping sector boundaries and evolving market dynamics.
method Introduces THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning, aligning themes and stocks using their hierarchical relationship and incorporating stock returns.
result Theme-aligned portfolios demonstrate compelling performance, significantly outperforming large language models in thematic asset retrieval.
Designed to compete with fiat currencies, bitcoin proposes it is a crypto-currency alternative. Bitcoin makes a number of false claims, including: solving the double-spending problem is a good thing; bitcoin can be a reserve currency for banking; hoarding equals saving, and that we should believe bitcoin can expand by …
Study uses machine learning to predict stock trends based on fundamental data.
problem Predicting stock trends using fundamental analysis.
method Used LSTM, 1D CNN, and LR models on financial data.
result Logistic Regression models outperformed other models.
AI model automates financial investment research tasks.
problem Manual labor-intensive tasks in financial analysis.
method Fine-tuning language models on diverse financial data.
result Improved model performance in financial tasks.
Two critical questions about intergenerational outcomes are: one, whether significant barriers or traps exist between different social or economic strata; and two, the extent to which intergenerational outcomes do (or can be used to) affect individual investment and consumption decisions. We develop a model to explicit…
We introduce and study a non-equilibrium continuous-time dynamical model of the price of a single asset traded by a population of heterogeneous interacting agents in the presence of uncertainty and regulatory constraints. The model takes into account (i) the price formation delay between decision and investment by the …
LLMs improve financial analysis by processing large data sets.
problem Traditional financial analysis methods struggle with large data volumes.
method Integrating LLMs for enhanced data processing and analysis.
result LLMs offer new capabilities for real-time financial decision-making.
A new perspective on portfolio selection using realized returns.
problem Choosing between two investments with the same expected return.
method Modeling realized returns as random variables and applying the CAPM formula.
result The CAPM formula applies to realized returns, not just their expectations.
A new framework AlphaMix combines multiple trading experts to improve stock investment decisions.
problem Inconsistent financial predictions and lack of model uncertainty in investment decisions.
method Reformulate quantitative investment as a multi-task learning problem, and propose AlphaMix framework.
result AlphaMix significantly outperforms state-of-the-art baselines in financial criteria.
Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.
problem Inaccurate financial analysis by LLMs leading to investment and regulatory issues.
method Proposes FinAR-Bench, a benchmark dataset with three steps: key info extraction, financial indicator calculation, and logical reasoning.
result LLMs perform better in key info extraction and indicator calculation but struggle with logical reasoning.
Paper uses inverse optimization to measure risk preference from investment portfolios.
problem Measuring subjective risk preference in investment portfolios.
method Inverse optimization on mean-variance framework.
result Quantified risk preference parameters validated with existing measures.
This research develops a new framework to measure AI investment returns considering both gains and risks.
problem Traditional ROI calculations fail to account for AI's dual impact on risk reduction and new exposures.
method Integrates ISO 42001 and regulatory exposure into a comprehensive financial framework using risk quantification methods.
result Accurate AI investment evaluation requires modeling both productivity gains and risk exposures.
Study financial contracts pricing in markets with nonproportional costs and constraints.
problem Financial contract pricing in markets with nonproportional transaction costs and portfolio constraints.
method Direct and dual characterization of market-consistent prices with acceptable risk thresholds.
result Extension of the Fundamental Theorem of Asset Pricing to include good deals and scalable good deals.
Study finds similar companies in Dhaka Stock Exchange using technical data.
problem Analyzing all companies in Dhaka Stock Exchange is impractical.
method Used technical data to identify companies moving together.
result Technical data can reveal company relationships without fundamental data.
This paper explores crypto, blockchain, and Metaverse risks and opportunities.
problem Understanding crypto crashes and blockchain technologies.
method Interdisciplinary approach combining fintech, machine learning, and risk assessment.
result Blockchain technologies will continue to dominate, but discerning genuine projects is crucial.
Investment strategy using fractional Kelly portfolios for better growth expectations.
problem Understanding optimal growth strategies for investors with varying risk appetites.
method Developed a mathematical framework for fractional-Kelly portfolios, analyzing Sharpe ratios and log-returns.
result Fractional Kelly portfolios provide a simple distributional relationship between Sharpe ratio, fractional coefficient, and log-returns.
This study examines representation bias in open-source Qwen models for investment decisions.
problem Representation bias in financial applications of large language models.
method Balanced round-robin prompting over 150 U.S. equities, constrained decoding, token-logit aggregation.
result Firm size and valuation increase model confidence, while risk factors decrease it.
Paper finds a new principle for optimizing consumption and wealth using Tsallis entropy.
problem Optimal consumption-investment problem with recursive utility.
method Established connection to quadratic BSDE, derived stochastic maximum principle.
result Proved existence of optimal strategy and analyzed coupled system.
Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a hi…
Generative model learns investment strategies without explicit utility specification.
problem Challenges in modeling complex, multi-objective fund optimization.
method Generative adversarial network (GAN) framework that learns latent strategy representations.
result Framework captures diverse investment styles and realizations of optimization parameters.
MarketSenseAI uses AI to select stocks with 10-30% excess alpha.
problem Selecting profitable stocks in financial markets.
method Integrates GPT-4 for analyzing diverse data and decision-making.
result Demonstrated exceptional performance with up to 72% cumulative return.
A general framework is suggested to describe human decision making in a certain class of experiments performed in a trading laboratory. We are in particular interested in discerning between two different moods, or states of the investors, corresponding to investors using fundamental investment strategies, technical ana…
Model shows how relaxed leverage can lead to asset price bubbles.
problem Understanding how financial leverage affects asset prices and growth.
method Developed a macro-finance model with feedback loops between investment and land prices.
result Relaxed leverage can cause unbalanced growth and asset price bubbles.
The distribution of wealth among the members of a society is herein assumed to result from two fundamental mechanisms, trade and investment. An empirical distribution of wealth shows an abrupt change between the low-medium range, that may be fitted by a non-monotonic function with an exponential-like tail such as a Gam…
The paper defines the time function of stock prices using a mathematical model.
problem Understanding the movement and predictability of stock prices over time.
method Empirical evidence and mathematical modeling of white noise.
result Derives auto-correlation function, displacement formula, and power spectral density of stock price movement.
Diffusion in a linear potential in the presence of position-dependent killing is used to mimic a default process. Different assumptions regarding transport coefficients, initial conditions, and elasticity of the killing measure lead to diverse models of bankruptcy. One "stylized fact" is fundamental for our considerati…
Different investment strategies are adopted in short-term and long-term depending on the time scales, even though time scales are adhoc in nature. Empirical mode decomposition based Hurst exponent analysis and variance technique have been applied to identify the time scales for short-term and long-term investment from …
The paper analyzes arbitrage theory in a fluctuating market of stochastic dimension.
problem Arbitrage opportunities in a market with time-varying asset numbers.
method Develops the fundamental theorem of asset pricing and optional decomposition theorem in a stochastic dimension market.
result Equivalence of conditions for no arbitrage and viability in a stochastic dimension market.
Young investors, especially students, dominate Indonesian stock exchanges.
problem Investment behavior of young and rookie investors in the stock market.
method Qualitative approach with descriptive analysis and interviews.
result Perception of behavioral control influences investment decisions.
HireVAE adapts to market regimes for online stock prediction.
problem Building an online and adaptive factor model for stock prediction.
method HireVAE uses a hierarchical latent space to estimate latent factors from historical market information.
result HireVAE outperforms previous methods in active returns across benchmarks.
Investigates price dynamics of two assets with and without bubbles, deriving conditions for equilibrium prices.
problem Understanding price dynamics and bubbles in multi-asset markets.
method Derives sufficient and necessary conditions for average equilibrium price dynamics in a two-asset model.
result Assets with positive average dividends display hump-shaped bubbles, while those with constant fundamental values show misvaluation effects.
The paper finds a pervasive and severe bias in accounting semi-identity models.
problem Bias in investment-cash flow sensitivity models.
method Augmented specification with a bias-capturing variable tested across multiple databases.
result The Accounting Semi-Identity (ASI) distortion is universal and severe, affecting 100% of databases and explaining more than 83% of total explained variance.