This paper compares token and equity financing for startups.
problem Understanding differences in return rates between token and equity financing.
method Developed a three-period model to analyze liquidity and return differences.
result Entrepreneurs can achieve higher payoffs by issuing tokens, especially for risk-averse investors with liquidity needs.
In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as anomalies the theory of rational finance cannot explain: Predictability of asset returns, The Equity Premium, (The Volatility Puzzle. We offer resolutions of those objections within the rational finance…
Model shows how discount rates affect intergenerational equity in climate mitigation.
problem Intergenerational equity in climate mitigation decisions.
method Extended DICE model with stochastic discount rates and financing extensions.
result Discount-rate uncertainty amplifies intergenerational inequality in climate mitigation.
Equity risk premium is a central component of every risk and return model in finance and a key input to estimate costs of equity and capital in both corporate finance and valuation. An article by Damodaran examines three broad approaches for estimating the equity risk premium. The first is survey based, it consists in …
QRAFTI uses multi-agent framework to improve equity factor research.
problem Replicating and developing new equity factors in large financial datasets.
method Integrates a research toolkit with MCP servers for data access and custom coding operations.
result Improves performance and explainability in multi-step empirical tasks.
Proponents of behavioral finance have identified several "puzzles" in the market that are inconsistent with rational finance theory. One such puzzle is the "excess volatility puzzle". Changes in equity prices are too large given changes in the fundamentals that are expected to change equity prices. In this paper, we of…
Paper presents a faster method for computing cost of equity and performing comparable company analysis.
problem Tedium and subjectivity in traditional cost of equity and comparable company analysis methods.
method Uses spectral and agglomerative clustering to compute cost of equity and perform comparable company analysis.
result Reduces time required for comps by orders of magnitude and improves consistency and reliability.
Generative models simulate S&P 500 returns for financial analysis.
problem Modeling the joint distribution of S&P 500 equities.
method Conditional importance weighted autoencoders and conditional normalizing flows.
result Generative models accurately capture the complex joint distribution of S&P 500 returns.
Model analyzes debt recycling strategies under various fiscal regimes and jurisdictions.
problem Understanding debt recycling dynamics and their impact on repayment times and equity growth.
method Developed a calibrated model incorporating mortgage interest rates, borrowing costs, and tax shields.
result Introducing positive interest rates without tax shields contracts success regions and lengthens repayment times, but tax shields partially reverse these effects.
We illustrate a problem in the self-financing condition used in the papers "Funding beyond discounting: collateral agreements and derivatives pricing" (Risk Magazine, February 2010) and "Partial Differential Equation Representations of Derivatives with Counterparty Risk and Funding Costs" (The Journal of Credit Risk, 2…
This study assesses how share capital affects financial growth of non-financial firms listed at NSE.
problem Non-financial firms listed at NSE struggle with financial growth due to declining performance and lack of investor interest.
method Descriptive and panel data analysis of 45 non-financial firms over 10 years.
result Share capital positively and significantly influences financial growth, explaining 32.73% and 11.62% of variations in earnings per share and market capitalization growth, respectively.
For nearly every major stock market there exist equity and implied volatility indices. These play important roles within finance: be it as a benchmark, a measure of general uncertainty or a way of investing or hedging. It is well known in the academic literature, that correlations and higher moments between different i…
Study uses deep learning to predict stock trends with superior performance.
problem Predicting short-term equity trends with high accuracy.
method Dual-task multilayer perceptron (MLP) integrating technical signals and deep learning.
result Deep learning model outperforms linear baselines in multi-factor stock selection.
Extends insurance-finance arbitrage concept to include model uncertainty.
problem Evaluating hybrid insurance products in uncertain financial markets.
method Introduces robust asymptotic insurance-finance arbitrage and QP-evaluations. result No robust asymptotic insurance-finance arbitrage exists under certain conditions.
ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.
problem Impact of Bitcoin ETF approval on Bitcoin's relationships with traditional assets.
method Rolling correlation analysis, Chow tests, and DCC-GARCH models.
result Bitcoin's correlation with equities increased significantly post-ETF approval, while its relationship with gold stabilized and remained negatively correlated with fiat currencies.
Detects lead-lag clusters in US equity market time series.
problem Identifying lead-lag relationships in multivariate time series.
method Directed network clustering of lead-lag relationships.
result Validated on US equity market data, detects statistically significant lead-lag clusters.
Study compares Fourier estimators to mitigate asynchrony effects in finance.
problem Impact of asynchrony on instantaneous financial estimates.
method Comparison of Malliavin-Mancino and Cuchiero-Teichmann estimators.
result Malliavin-Mancino estimator produces more stable estimates under asynchrony.
Transformer models outperform LSTM in financial forecasting with MADL loss.
problem Optimizing loss functions for Transformer models in financial forecasting.
method Empirical experiments with MADL loss function on equity and cryptocurrency assets.
result Transformer models significantly outperform LSTM models in financial forecasting.
Enhances time-series regression trees with latent factors for robust financial analysis.
problem Handling predictors with measurement error, trends, seasonality, and missing data.
method Integrates latent stationary factors extracted via state-space methods into time-series regression trees.
result Factor-augmented trees provide a reliable approach for macro-finance problems, exemplified by the lead-lag effect between equity volatility and the business cycle.
New method identifies whether equity return predictability is due to magnitude shrinkage or directional reversal.
problem Determining the nature of equity return predictability (directional reversal vs magnitude shrinkage).
method Developed the Fourier-Residue Identity (FRI) to decompose return autocorrelation into sign and magnitude channels.
result The lag-1 autocorrelation in SPY is driven entirely by magnitude shrinkage, not directional reversal.
The paper calculates extreme measures in continuous time conic finance.
problem Determining valuation bounds for financial claims.
method Using dynamic spectral risk measures and estimating extreme measures from market data.
result Explicit formulas for extreme measures' Radon-Nykodim derivatives and estimation methods.
The Hype Index measures media attention to equities using NLP.
problem Quantifying media attention to equities for volatility analysis.
method Constructs News Count-Based and Capitalization Adjusted Hype Indices using NLP.
result The Hype Index family provides valuable tools for stock volatility analysis.
Paper proposes an alternative method to price American options using HJM approach.
problem Price American options efficiently and accurately.
method Utilizes HJM technique to model term structure of volatility for equity markets.
result Proposes a new value function, stopping criteria, and stopping time for American options.
Machine learning models outperform traditional CAPM in forecasting financial asset prices.
problem Predicting and forecasting financial asset prices and returns.
method Comparison of modern Machine Learning algorithms with the Capital Asset Pricing Model (CAPM) on U.S. equities data.
result Implemented Machine Learning models significantly outperform the CAPM on out-of-sample test data.
Chronos models improve financial forecasting by integrating multivariate data.
problem Improving financial forecasting accuracy using multivariate data.
method Evaluation of Chronos-2 on multivariate and univariate financial forecasting models.
result Multivariate forecasts consistently outperform univariate forecasts, especially for interest rates.
Co-branding improves stock performance for firms.
problem Little research on co-branding's impact on firm stock value.
method Developed a conceptual framework and tested hypotheses.
result Co-branding events lead to positive abnormal returns.
This paper explores how insurance contracts can be traded in financial markets.
problem The exclusion of arbitrage in insurance contracts due to their non-tradability.
method Defining strategies on insurance portfolios and combining them with financial trading strategies.
result The existence of an insurance-finance-consistent probability, leading to the expected discounted cash-flows.
Study uses LLMs to simplify financial regulation interpretation.
problem Complex financial regulations are hard to interpret and implement.
method Developed prompts to guide LLMs in extracting key information from regulations.
result GPT-4 outperforms other LLMs in processing and executing regulatory requirements.
Novel weak MLMC scheme for Lévy-driven SDEs, applied to financial derivatives pricing.
problem Approximating solutions to Lévy-driven SDEs for financial derivatives pricing.
method Weak multilevel Monte-Carlo scheme with state space discretization of Lévy processes.
result Efficient approximation of financial derivatives pricing models.
We consider systems of diffusion processes ("particles") interacting through their ranks (also referred to as "rank-based models" in the mathematical finance literature). We show that, as the number of particles becomes large, the process of fluctuations of the empirical cumulative distribution functions converges to t…
A new model for heterogeneous populations optimizes consumption and investment over short horizons.
problem Optimizing consumption and investment in economies with a heterogeneous population over short time periods.
method Continuous-time general equilibrium framework with Brownian flow on a type space, solving vanishing-horizon problems under relative-income criteria.
result Existence and characterization of short-horizon Duesenberry equilibrium, with sharp asset-pricing implications.
We discuss a possible solution to an unintended consequence of having grades, certificates, rankings and other diversions in the act of transferring knowledge; and zoom in specifically to the topic of having grades, on a curve. We conduct a thought experiment, taking a chapter (and some more?) from the financial market…
Fat tails in financial time series and increase of stocks cross-correlations in high volatility periods are puzzling facts that ask for new paradigms. Both points are of key importance in fundamental research as well as in Risk Management (where extreme losses play a key role). In this paper we present a new model for …
The global financial crisis, beginning in 2008, took an historic toll on national economies around the world. Following equity market crashes, unemployment rates rose significantly in many countries: Italy was among those. What will be the impact of such large shocks on Italian healthcare finances? An empirical model f…
The paper analyzes how stock market dimensionality changes impact portfolio performance.
problem Impact of dimensional changes on portfolio performance in a changing market.
method Development of self-financing stock portfolios in a stochastic portfolio theory framework with dimensional jumps.
result Quantification of how listing or delisting events and market shocks affect portfolio return.
PEARL uses AI to replicate private equity performance with liquid assets.
problem Lack of access to private equity due to high costs and complexity.
method Combines AI with liquid assets, incorporating asymmetry for better performance.
result Model outperforms liquid proxies and aligns with private equity benchmarks.
Examines US equity risk premiums amid COVID-19.
problem Analyzing equity risk premiums during the pandemic.
method Not specified in the abstract.
result Not specified in the abstract.
Trading-R1 uses LLMs for financial trading, improving risk-adjusted returns.
problem Lack of interpretability and trust in AI for finance.
method Supervised fine-tuning and reinforcement learning with a curriculum.
result Improved risk-adjusted returns and lower drawdowns compared to other models.
Marketron model extended to option markets, solving incomplete market challenges.
problem Tackling the challenge of incomplete markets in option pricing.
method Utility-based pricing approach, dual solution of optimal investment problem, Hamilton-Jacobi-Bellman (HJB) equation, novel calibration method.
result The Marketron model calibrated to option markets can reproduce statistical properties of underlying asset's log-returns.
The paper analyzes bank decisions in a three-step model, focusing on equity and debt raising.
problem Bank decision-making in a three-time-step model with equity and debt raising.
method Theoretical analysis of raising new equity and debt, considering capital requirements and equity holders constraints.
result Raising equity and debt can increase or decrease return on equity, depending on specific cases.
AI investors signal higher debt in ESG firms, boosting portfolio management.
problem Determining the value of ESG investing amid AI investment trends.
method Cross-sectional regressions of ESG scores and debt ratios of S&P 500 firms.
result ESG scores signal higher debt in firms, supporting ESG investing.
Deep RL optimizes US stock allocations with better performance.
problem Optimizing asset allocation in US equities markets.
method Reinforcement learning applied to asset allocation problems.
result Deep RL models outperform traditional methods in asset allocation.
In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profita…
Study finds no significant impact of US sovereign credit rating downgrade on equity market.
problem Impact of US sovereign credit rating downgrade on US equity market.
method Event study methodology using three companies and S&P500 index.
result No significant effects of US sovereign credit rating downgrade on US equity market.
We consider a financial market in which two securities are traded: a stock and an index. Their prices are assumed to satisfy the Black-Scholes model. Besides assuming that the index is a tradable security, we also assume that it is efficient, in the following sense: we do not expect a prespecified self-financing tradin…
Study on diversifying equity portfolios during financial crises and stability.
problem Determining the effectiveness of diversification strategies during financial crises and stability.
method Analysis of 20 years of US stock price data, including GFC and COVID-19 crashes, using eigenvalues, graph-theoretic diagnostics, and hierarchical clustering.
result During financial crises, diversification via sector-based portfolios is ineffective, while during stability, 30-40 stocks provide sufficient diversification.
The rough Bergomi model, introduced by Bayer, Friz and Gatheral [Quant. Finance 16(6), 887-904, 2016], is one of the recent rough volatility models that are consistent with the stylised fact of implied volatility surfaces being essentially time-invariant, and are able to capture the term structure of skew observed in e…
Interprets deep learning models for rough volatility pricing.
problem Lack of interpretability in deep learning models for financial models.
method Detailed analysis of neural network learned inverse map between rough volatility model parameters and implied volatilities.
result Provides insights into neural network outputs for rough volatility models.