How does one objectively measure the performance of an individual offensive lineman in the NFL? The existing literature proposes various measures that rely on subjective assessments of game film, but has yet to develop an objective methodology to evaluate performance. Using a variety of statistics related to an offensi…
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21 results for “undervaluation”
Is the Indian Stock Market efficient - A comprehensive study of Bombay Stock Exchange Indicesq-fin.ST
How an investor invests in the market is largely influenced by the market efficiency because if a market is efficient, it is extremely difficult to make excessive returns because in an efficient market there will be no undervalued securities i.e. securities whose value is less than its assumed intrinsic value, which of…
Underrepresented scientists produce more novel work but it's undervalued.
problem Underrepresented groups in science face undervaluation of their innovations.
method Text analysis and machine learning of career data of over 1 million US doctoral recipients.
result Underrepresented groups produce more scientific novelty but their contributions are undervalued.
Be it for taking advantage of stock undervaluation or in order to distribute part of their profits to shareholders, firms may buy back their own shares. One of the way they proceed is by including Accelerated Share Repurchases (ASR) as part of their repurchase programs. In this article, we study the pricing and optimal…
New method corrects Markowitz variance for trading volume fluctuations.
problem Incorrect risk estimates from Markowitz variance in trading environments.
method Modeling portfolio variance based on trade volume fluctuations.
result Market-based variance can significantly differ from Markowitz variance.
There is a consensus that human and non-human subjects experience temporal distortions in many stages of their perceptual and decision-making systems. Similarly, intertemporal choice research has shown that decision-makers undervalue future outcomes relative to immediate ones. Here we combine techniques from informatio…
New method corrects correlation bias in feature importance.
problem Correlations between features in high-dimensional data bias statistical and machine learning models.
method Pairwise Permutation Algorithm (PPA)
result PPA corrects correlation bias, identifying biological relevant biomarkers.
Back cover text: Megaprojects and Risk provides the first detailed examination of the phenomenon of megaprojects. It is a fascinating account of how the promoters of multibillion-dollar megaprojects systematically and self-servingly misinform parliaments, the public and the media in order to get projects approved and b…
MaxMax Q-Learning improves coordination in multi-agent reinforcement learning by refining action selection.
problem Relative over-generalization in decentralized multi-agent reinforcement learning.
method MaxMax Q-Learning employs iterative sampling and evaluation of potential next states to refine approximations of ideal state transitions.
result MaxMax Q-Learning frequently outperforms existing baselines, demonstrating enhanced convergence and sample efficiency.
This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were f…
Pricing Chinese convertible bonds using Monte Carlo simulation and dynamic programming.
problem Pricing Chinese convertible bonds accurately.
method Monte Carlo simulation and dynamic programming with regression and backward induction.
result An underpriced strategy significantly outperforms benchmarks.
We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a …
Paper proposes deep learning model for dynamic stock repurchase forecasting.
problem Complex temporal dependencies in corporate financial conditions.
method Hybrid Temporal Convolutional Network (TCN) and Attention-based LSTM.
result Model significantly outperforms static baselines in stock repurchase forecasting.
Continuous-time optimal stopping solved with deep reinforcement learning
problem Optimal stopping problems in continuous time
method CARLOS (Continuous-time Adaptive Reinforcement Learning for Optimal Stopping)
result Higher prices than existing Bermudan solvers, approaching American upper bound
This paper addresses AMMs for expiring assets, ensuring liquidity and risk management.
problem AMMs struggle with assets that expire, leading to liquidity issues and risk exposure.
method Combines AMM and limit-order book features, ensuring liveness and dynamic price adjustment.
result A DEX for expiring assets maintains liquidity and risk management.
Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challen…
Framework optimizes PV-battery investment timing to maximize value.
problem Optimizing investments in residential PV-battery systems under uncertain market conditions.
method Real options valuation framework with multi-stage compound options, incorporating Monte Carlo simulation.
result Optimal timing of PV-battery investment increases overall value.
The aim of this study is to investigate quantitatively whether share prices deviated from company fundamentals in the stock market crash of 2008. For this purpose, we use a large database containing the balance sheets and share prices of 7,796 worldwide companies for the period 2004 through 2013. We develop a panel reg…
The study provides a practical strategy for pricing and hedging equity-release mortgages guarantees.
problem Pricing and hedging the No-Negative-Equity-Guarantee in incomplete markets.
method Discrete-time model, Excess-of-Loss reinsurance, numerical illustrations.
result Superhedge cost decreases with more lives in the portfolio, making it more realistic.
Study examines herding behavior in stocks, US ETFs, and cryptocurrencies.
problem Understanding herding behavior in different types of investment vehicles.
method Cross-sectional Absolute Deviation model, Minimum Spanning Tree, Louvain community detection.
result Herding behavior exists at all times across all types of investment vehicles at a subset level.
Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unitq-bio.QM
Study developed phenotypes for ICU patients' brain dysfunction states.
problem Underdiagnosis of acute brain dysfunction in ICU patients.
method Created algorithms to quantify and cluster brain dysfunction states.
result Developed three phenotypes of ICU patients' brain dysfunction states.