Annals of Finance最新文献

It is argued that socially optimal real rates of interest cannot be positive in a stationary state. Most economies approximating a stationary state have real rates of interest depending on the interplay between utility and production functions and the structure of the exact social objective function being maximized. The objective studied here maximizes a probability distorted expectation of the sum of undiscounted utilities. The utility functions studied display constant and declining relative risk aversion coefficients. The production functions are Cobb–Douglas, asymptotically linear versions of the same and those with a declining elasticity of the marginal productivity of capital. It is observed that declining relative risk aversion utilities coupled with asymptotically linear Cobb–Douglas type production functions can deliver real rates observed in the US economy over the period January 2010, to December 2023. An analysis of income inequality considerations shows that for the economies studied there is a positive relationship between the real return on capital and the share of capital income in total income. These results support the thesis advanced by as reported by Piketty (Capital in the Twenty First Century. Harvard Business School, Cambridge, 2014)

We derive a Clark-Ocone-Haussmann (COH) type formula under a change of measure for ( L^1 )-canonical additive processes, providing a tool for representing financial derivatives under a risk-neutral probability measure. COH formulas are fundamental in stochastic analysis, providing explicit martingale representations of random variables in terms of their Malliavin derivatives. In mathematical finance, the COH formula under a change of measure is crucial for representing financial derivatives under a risk-neutral probability measure. To prove our main results, we use the Malliavin-Skorohod calculus in ( L^0 ) and ( L^1 ) for additive processes, as developed by Di Nunno and Vives (2017). An application of our results is solving the local risk minimization (LRM) problem in financial markets driven by pure jump additive processes. LRM, a prominent hedging approach in incomplete markets, seeks strategies that minimize the conditional variance of the hedging error. By applying our COH formula, we obtain explicit expressions for locally risk-minimizing hedging strategies in terms of Malliavin derivatives under the market model underlying the additive process. These formulas provide practical tools for managing risks in financial market price fluctuations with (L^1)-additive processes.

In absence of a closed form expression such as in the Heston model, the option pricing is computationally intensive when calibrating a model to market quotes. this article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs). A PINN integrates principles from physics into its learning process to enhance its efficiency in solving complex problems. In this article, the driving principle is the Feynman-Kac (FK) equation, which is a partial differential equation (PDE) governing the derivative price in the Heston model. We focus on the valuation of European options and show that PINNs constitute an efficient alternative for pricing options with various specifications and parameters without the need for retraining.

A simple model is developed to analyze the effects of social media use by a bank’s uninsured depositors. While social media increases the likelihood of bank runs, it can be ex-ante beneficial to a bank by raising its shareholders’ equity. Social media enhances monitoring of a bank’s financial condition, thereby giving uninsured depositors a valuable option to withdraw early and avoid potential losses in states when a bank is likely to be insolvent. Recognizing this option, uninsured depositors require a lower promised interest rate that reduces the bank’s cost of funding at the expense of a greater liability for the bank’s deposit insurer.

We show that banks can provide loans at low costs to high-risk borrowers in the form of a group lending contract in which all members are jointly liable for their loans. By providing such contracts borrowers self-insure against some of the default risk the bank faces. We determine the optimal group size in a competitive banking system and find that it is reasonably small and borrowers internalize an increasing fraction of the risk the higher their risks are.

Large banks in the United States waive their monthly account fee if depositors maintain above a certain minimum balance in their account. This article analyzes the conditions under which banks benefit from applying this pricing strategy. I find that the minimum balance strategy is profitable when banks possess only moderate market power. In contrast, under strong market power, this strategy is less profitable than charging monthly fees to all depositors regardless of their deposit amount. Common ownership of banks reduces the gains from the minimum balance pricing strategy. Interest rate competition together with fee competition eliminate these gains.

This study explores the risk of the traditional momentum strategy in terms of its realized variance using various data frequencies. It is shown that momentum risk is infinite regardless of the data frequency, implying that (a) t-statistics for this strategy do not exist, (b) correlation-based metrics such as Sharpe ratios do not exist either, and (c) the momentum premium is not observable in reality. It is further shown that the time-honored lognormal distribution is unable to accurately model extreme events observed at various variance data frequencies. Finally, it is shown that the well-known effect of time aggregation does not work for this investment vehicle. Hence, the study is forced to conclude that momentum stories have no valid foundation for their claims.

This paper presents a Markov-modulated stochastic volatility model that captures the dependency of market regimes on investor sentiment. The main contribution lies in developing a modified version of the classical Heston model by allowing for a sentiment-driven bias in the volatility of the asset. Specifically, a two-factor Markov-modulated stochastic volatility model is proposed, integrating a diffusion coefficient in the risky asset dynamics and a correlation parameter influenced by both the volatility process and a continuous-time Markov chain accounting for the sentiment-bias. Diverging from conventional approaches in option pricing models, this framework operates under the real-world probability measure, necessitating considerations about the existence of an equivalent martingale pricing measure. The purpose of this paper is to derive a closed formula for the pricing of European-style derivatives and to fit the model on market data through a suitable calibration procedure. A comparison with the Heston benchmark model is provided for a sample of Apple, Amazon, and Bank of America stock options.

Objective

To study the interactions among trading strategies and their profitability from an ecological perspective.

Methods

A market ecosystem model is established, and simulations are conducted to examine the interactions and profitability of trading strategies in different market ecologies.

Results

Strategies compete with themselves, and different time-window trend strategies exhibit competition and predator–prey relationships. Value and trend strategies demonstrate both symbiosis and predator–prey relationships. The profitability of a strategy depends on the balance of supporting and inhibiting effects, with greater supporting effects leading to higher maximum profit and market capacity, while greater inhibiting effects result in losses. The model suggests that fundamental analysis has a larger market capacity than technical analysis.

In this study, I examine whether public attention to a natural disaster affects the likelihood of firms using external financing (only debt, only equity, or a mix of debt and equity) following the disaster. The helpful (or harmful) view of public attention predicts that increasing public attention to the disaster can help companies to use (or hinder them from using) external financing following the disaster. For this investigation, I construct two indices: (1) an index of public attention to natural disasters and (2) an index of natural disaster intensity. Consistent with the prediction of the helpful view, the likelihood of using a combination of debt and equity financing in the aftermath of a severe disaster is higher when people pay more attention to it. By contrast, when people pay less attention to severe disasters, firms are more likely to use only debt or equity financing, which confirms the harmful view. I provide evidence that the moderating effect of public attention on the relationship between the likelihood of using external financing and natural disaster intensity varies across types of natural disasters and types of online information sources (websites, online news, images, and YouTube videos). Furthermore, smaller firms that often face difficulties obtaining external financing are more likely to benefit from increased public attention to disasters when they want to use only debt financing and less likely to benefit when using equity financing. Finally, I provide evidence that the substitution of internal funding sources for external sources in the aftermath of a disaster depends on the level of public attention to the disaster.