Investor skill levels affect optimal portfolio size, study shows.
problem Optimal portfolio size for different skill levels of investors.
method Mathematical methods to study annual and continuous portfolio diversification, regression analysis.
result Strong investors should hold concentrated portfolios, poor investors should hold diversified portfolios.
Study reveals investor behavior in NFT bubbles.
problem Understanding retail investor behavior in asset bubbles.
method Systematic study of NFTs using public blockchain data.
result Sophisticated investors outperform others in NFT bubbles.
Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills. As a step toward creating intelligent agents with this capability for fully cooperative multi-agent settings, we propose a two-level hierarchica…
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are parameterized in a way that makes them easily reusable. This diversity and p…
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Leaving the skills fixed can le…
SUPE combines unlabeled data with RL to efficiently explore tasks.
problem Efficient exploration in reinforcement learning with sparse rewards.
method Extract low-level skills using VAE, pseudo-label unlabeled data, and use as off-policy data for online RL.
result SUPE outperforms prior methods across 42 long-horizon tasks.
A planning approach learns skills from interactions, balancing exploration and exploitation.
problem Learning robust high-level skills in noisy environments with unknown pre-conditions.
method Formulates skills as high-level policies, learns plans via bandit problems, balances exploration and exploitation.
result A planner capable of learning robust high-level skills in high-dimensional state spaces.
Paper learns skill distributions from game outcomes, proving minimax optimality.
problem Learning skill distributions from noisy pairwise game outcomes.
method Proposes a simple algorithm using kernel density estimation and minimax techniques.
result Near-optimal minimax mean squared error scaling for skill density estimation.
Risk and uncertainty will always be a matter of experience, luck, skills, and modelling. Leverage is another concept, which is critical for the investor decisions and results. Adaptive skills and quantitative probabilistic methods need to be used in successful management of risk, uncertainty and leverage. The author ex…
Bayesian nonparametrics enables dynamic skill discovery from expert data.
problem Fixed K for offline skill discovery in reinforcement learning.
method Variational inference, continuous relaxations, Bayesian nonparametrics.
result Nonparametric model with dynamically-changing number of options.
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i.e., ankle, hip, foot tilting, and stepping strategies. The policy is trained in a physics simulator with realistic setting of robot model and low-level impeda…
Covid lockdown increased interest in Italian stock market, leading to new investors.
problem Impact of Covid lockdown on Italian stock market investors.
method Analysis of trading activity and investor demographics before and during lockdown.
result New investors during lockdown were more skilled traders than pre-lockdown investors.
Study finds stocks with common firm fears earn lower returns.
problem Identifying and quantifying firm-level investor fears.
method Analysis of equity options to identify common firm-level fears and their impact on stock returns.
result Stocks with exposure to common bad fears earn lower returns and require higher compensation.
The paper explains how language models acquire complex skills through scaling laws and statistical analysis.
problem Understanding how language models acquire new skills as their size and training data increase.
method Statistical framework and mathematical analysis of scaling laws.
result Language models can learn complex skills efficiently due to a strong inductive bias.
The paper examines skill estimation and variance under model misspecification in IRT.
problem Underestimation and overestimation of skills when non-compensatory model is misspecified as compensatory.
method Theoretical approach to analyze underestimation and overestimation of skills and variance.
result Overestimation of skills occurs around the origin and asymptotic variance differs under model misspecification.
Researchers study how skills are learned in neural networks using physics principles.
problem Understanding how skills are sequentially learned in neural networks.
method Abstract and simplify the problem using physics principles, proposing three models: Geometry, Resource, and Domino.
result Models reveal insights into neural scaling laws, learning dynamics, and the benefits of modularity.
Study on stock portfolio concentration among Finnish households and investors.
problem Understanding the concentration of stock portfolios owned by Finnish households and investors.
method Analysis of stock portfolios using Herfindahl-Hirschman index over 20 years.
result High portfolio concentration observed in Finnish retail investors, similar to institutional investors.
The paper presents a method for personalized exercise recommendations that improves learner skill gain.
problem Adapting to individual needs in large, diverse groups of learners in digital environments.
method Contextual Thompson Sampling to select exercises that advance learner skill.
result The method recommends exercises associated with greater skill improvement and adapts to learner differences.
A new method for solving complex sequential decision-making problems by decomposing them into multiple levels.
problem Sequential decision-making with natural multi-level structure.
method Multi-level meta-reinforcement learning with skill-based curriculum.
result Efficiently reduces stochasticity and policy search space, leading to fewer iterations and computations.
Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence o…
When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker's skill. Traditional economics papers address screening models where employers access worker skill via a…
E-learning systems are capable of providing more adaptive and efficient learning experiences for students than the traditional classroom setting. A key component of such systems is the learning strategy, the algorithm that designs the learning paths for students based on information such as the students' current progre…
Three methods detect informed trading on prediction markets, each focusing on different aspects.
problem Detecting informed trading in decentralized prediction markets.
method Composite screen, event-level sign-randomization test, and Information Leakage Score (ILS) framework.
result Different methods detect informed trading on prediction markets, each focusing on different aspects.
Study compares employers with and without anticipating strategic labor force responses.
problem Understanding and optimizing strategic interactions in labor markets.
method Formulation of causal strategic classification, theory, and experiments.
result Performatively optimal hiring policies improve employer and labor outcomes, but can also harm labor force utility.
The paper learns robot skills from demonstrations without supervision.
problem Discovering robotic options from unlabelled demonstrations.
method Temporal variational inference for latent variable learning.
result The framework can learn options across multiple datasets.
New method learns robot skills from data, matching or outperforming existing methods.
problem Learning robot skills from fixed datasets.
method Offline Reinforcement Learning via Supervised Learning using implicit models.
result Implicit models can match or outperform explicit models in acquiring robotic skills.
New method automates asymmetric choice for better skill transfer in reinforcement learning.
problem Improving sample efficiency and transferability of reinforcement learning agents.
method Attentive Priors for Expressive and Transferable Skills (APES) using hierarchical KL-regularization.
result APES automates asymmetric choice, leading to better skill transfer across sequential tasks.
Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment framewor…
System detects relevant financial news and predictions from unstructured text.
problem Manual extraction of relevant financial information from news is cumbersome and error-prone.
method Topic modeling with LDA, co-reference resolution, multi-paragraph segmentation, and temporal analysis.
result ROUGE-L values for relevant text and predictions/forecasts were 0.662 and 0.982, respectively.
Bayesian model identifies skill difficulties and student subgroups in engineering education.
problem Identifying and supporting diverse student needs in entry-level university engineering modules.
method Hierarchical Bayesian modeling of student response data.
result Clear patterns of skill mastery and distinct student subgroups identified.
The paper analyzes how ESG investors can prioritize green stocks without sacrificing overall wealth.
problem Balancing sustainability goals with financial returns.
method Multivariate utility analysis with a one-factor CAPM structure.
result Investors can allocate more to high-rated ESG stocks without reducing overall wealth.
MOVDA improves skill ratings by considering margin of victory deviations.
problem Traditional rating systems discard valuable performance data.
method Margin of Victory Differential Analysis (MOVDA) learns a non-linear function to predict expected MOV and uses the difference between true and expected MOV for rating updates.
result MOVDA significantly outperforms standard ELO and Bayesian baselines in NBA basketball data.
The study compares M6 competitors' performance to industry benchmarks and discusses incentives for investment managers.
problem Investors seek to understand the performance and skill of M6 competitors beyond the competition's metrics.
method Comparative analysis using financial metrics, factor models, and new strategies.
result Most competitors do not generate significant out-performance compared to industry benchmarks, but some show skill in recent performance.
ReSkill reconciles RL skill creation with policy optimization.
problem RL policies lack reusable strategies across tasks.
method Integrates skill creation into RL loop with three mechanisms.
result Consistently outperforms existing methods, especially on unseen tasks.
Solving complex, temporally-extended tasks is a long-standing problem in reinforcement learning (RL). We hypothesize that one critical element of solving such problems is the notion of compositionality. With the ability to learn concepts and sub-skills that can be composed to solve longer tasks, i.e. hierarchical RL, w…
Investors with anxiety about drawdowns may use stop-loss and trailing stops as optimal selling strategies.
problem Investors' anxiety about drawdowns affects optimal selling strategies.
method Mathematical analysis of optimal stopping with random discounting.
result Stop-loss and trailing stops can be optimal selling strategies under anxiety about drawdowns.
Sharpe ratio (sometimes also referred to as information ratio) is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the (excess) net return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and …
Investment disputes increase stock volatility, especially for companies with negative outcomes.
problem Investment disputes affect stock market volatility and investor uncertainty.
method Analysis of abnormal share fluctuations and various explanatory variables.
result Investment disputes lead to increased stock volatility, particularly for companies with negative outcomes.
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
A new portfolio model considers investor aversion to loss and risk.
problem Constructing a robust portfolio under uncertain asset returns and investor aversion.
method Distributional robust optimization (DRP) with a Wasserstein ball centered on empirical distribution, mixed-integer quadratic programming, and hybrid algorithm.
result Empirical testing shows superior performance in asset allocation compared to common strategies.
VarFA efficiently estimates student skill levels with uncertainty for adaptive testing.
problem Efficiently estimating student skill levels with uncertainty for adaptive testing.
method VarFA uses variational inference to extend factor analysis models for educational data.
result VarFA efficiently handles large datasets and produces uncertainty estimates.
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algo…
Background: Virtual reality simulators and machine learning have the potential to augment understanding, assessment and training of psychomotor performance in neurosurgery residents. Objective: This study outlines the first application of machine learning to distinguish "skilled" and "novice" psychomotor performance du…
Study shows how algorithmic prediction affects US housing market, reducing racial wealth disparities.
problem Impact of algorithmic prediction on housing market and racial wealth disparities.
method Natural experiment using digitization of housing records to study entry, allocation, and prices.
result Digitization leads to increased sale prices for minority-owned homes, reducing racial wealth disparities.
A model corrects Lithuanian grammatical errors.
problem Lack of language skills and typing errors in Lithuanian.
method Transformer architectures for subword and byte-level approaches.
result F0.5=0.92 for Lithuanian grammatical error correction. Social media hype can misprice IPO stocks, leading to short-term gains but long-term losses.
problem Mispricing of IPO stocks due to social media hype.
method Analysis of investor emotions on StockTwits and Twitter.
result High pre-IPO enthusiasm leads to higher short-term returns but lower long-term returns.
Social media reduces individual investors' disposition effect through negative information.
problem The disposition effect in individual investors selling profitable assets too early and holding onto losing assets for too long.
method Analysis of post data and trading data from Xueqiu.com.
result Social media information significantly reduces the disposition effect.
All people have to make risky decisions in everyday life. And we do not know how true they are. But is it possible to mathematically assess the correctness of our choice? This article discusses the model of decision making under risk on the example of project management. This is a game with two players, one of which is…