Study optimal portfolio strategies with periodic evaluation under short-selling prohibition.
problem Optimal portfolio strategies with periodic evaluation under short-selling prohibition.
method Reformulate the original problem into an auxiliary one-period optimization problem and introduce dual control problem.
result Derive and verify the value function and optimal constrained portfolio for the original problem.
Model predicts spending behavior of average consumer over short period.
problem Understanding consumer spending dynamics during economic crises.
method Simple hydrodynamical model to describe spending behavior over brief period.
result Model predicts spending behavior of average consumer over short period.
Simplified proof of K3 surface period map surjectivity.
problem Surjectivity of period map on K3 surfaces.
method Utilizes hyperkähler geometry and collapsing techniques.
result Simple proof of Todorov's result on K3 surfaces.
Meta-learning improves event prediction from short sequences.
problem Predicting events from short sequences is challenging.
method Meta-learning approach using recurrent neural networks and monotonic neural networks.
result Meta-learning enhances long-term prediction performance.
We propose a new framework for measuring connectedness among financial variables that arises due to heterogeneous frequency responses to shocks. To estimate connectedness in short-, medium-, and long-term financial cycles, we introduce a framework based on the spectral representation of variance decompositions. In an e…
In this paper, we use Floer theory to study the Hofer length functional for paths of Hamiltonian diffeomorphisms which are sufficiently short. In particular, the length minimizing properties of a short Hamiltonian path are related to the properties and number of its periodic orbits.
This paper uses DRL for long-short portfolio optimization, improving risk-adjusted returns.
problem Traditional portfolio optimization limits diversification by excluding short-selling.
method Developed a DRL framework with a short-selling mechanism for continuous trading.
result DRL model with short-selling achieves superior risk-adjusted returns.
Geometric flow on curves in S^3 generates YO equations solutions.
problem Modeling short wave-long wave interaction.
method Simple geometric flow on curves in S3. result Constructs transverse curves for YO equations periodic solutions.
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.
Analyzed Bitcoin market index volatility changes over two distinct periods using anomalous diffusion and multifractal analysis.
problem Characterizing volatility changes in Bitcoin market index over two distinct periods.
method Analyzed high-frequency Bitcoin data from 2019 to 2022, using anomalous diffusion and multifractal analysis.
result Volatility changes from subdiffusion to weak superdiffusion over time, with multifractal and self-similar properties.
Two new triply periodic minimal surfaces of genus 4 discovered.
problem Finding new triply periodic minimal surfaces of genus 4.
method Combination of asymptotic analysis and geometric methods to solve the period problem.
result Two new 1-parameter families of embedded triply periodic minimal surfaces of genus 4.
Improved financial performance through better regime prediction.
problem Predicting financial market regimes for profitable trading.
method A novel method combining contrarian trading and frequent short positions.
result Significant performance improvements over four years across three asset classes.
Kulkarni showed that, if g is greater than 3, a periodic map on an oriented surface S_g of genus g with order more than or equal to 4g is uniquely determined by its order, up to conjugation and power. In this paper, we show that, if g is greater than 30, the same phenomenon happens for periodic maps on the surfaces wit…
New proof classifies orbit closures in Hodge bundle.
problem Classifying mGL+(2,R)-orbit closures in Hodge bundle. method Using deformations of flat pairs of pants.
result Short proof of absolute period foliation classification.
In financial time series there are periods in which the value increases or decreases monotonically. We call those periods elemental trends and study the probability distribution of their duration for the indices DJIA, NASDAQ and IPC. It is found that the trend duration distribution often differs from the one expected u…
Predicts short-term futures contract direction using neural networks and order flow data.
problem Challenges in predicting short-term directional movement of futures contracts.
method Engineering features from technical analysis, order flow, and order-book data; training a Tabnet neural network.
result Achieved an accuracy of 0.601 in predicting directional change on the Silver Futures Contract.
We give a dynamical characterisation of odd-dimensional balls within the class of all contact manifolds whose boundary is a standard even-dimensional sphere. The characterisation is in terms of the non-existence of short periodic Reeb orbits.
Empirical study shows carriers ignore past shippers' behavior, focusing only on current actions.
problem Opportunistic behavior by shippers and carriers in dynamic freight markets.
method Empirical analysis of carrier reciprocity in US truckload transportation sector.
result Carriers do not remember shippers' past behaviors but respond to current actions.
Chinese stock market shows time series momentum and contrarian effects over different periods.
problem Analyzing momentum and contrarian effects in Chinese stock market performance.
method Examined time series momentum and contrarian strategies applied to major indices in China.
result Time series momentum effect in short run, contrarian effect in long run, performance dependent on look-back and holding periods.
Study detects endogenous bubbles in meme stocks using CI.
problem Detecting endogenous bubbles in meme stocks.
method Used Log-Periodic Power Law (LPPL) Confidence Indicator (CI).
result CI detected numerous bubbles in meme stocks but struggled with predicting exogenous rallies.
Deep learning models improve stock portfolio performance.
problem Improving stock portfolio allocation strategies.
method Used MLP, CNN, LSTM, and Transformer models to predict stock returns.
result Deep learning models enhance long-short stock portfolio performance.
Enhances financial time series forecasting with a multi-period learning framework.
problem Accurate financial time series forecasting requires considering both short-term and long-term trends.
method Proposes a Multi-period Learning Framework (MLF) with three modules: Inter-period Redundancy Filtering, Learnable Weighted-average Integration, and Multi-period self-Adaptive Patching.
result Improves financial time series forecasting accuracy and efficiency.
A short survey on the type numbers of closed geodesics, on applications of the Morse theory to proving the existence of closed geodesics and on the recent progress in applying variational methods to the periodic problem for Finsler and magnetic geodesics
The Teichmüller metric is locally Hölder to period coordinates.
problem Comparing metrics on moduli spaces of Riemann surfaces.
method Showed locally bi-Lipschitz relationship between Teichmüller metric and period coordinates.
result Projection map is locally Hölder with exponent dependent on g and n. This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contraria…
LSTM-MDNs improve risk forecasting during turbulent periods.
problem Forecasting Value-at-Risk (VaR) during volatile market conditions.
method Implemented Long Short-Term Memory mixture density networks (LSTM-MDNs) for VaR forecasting and compared them with established models.
result LSTM-MDNs outperformed benchmark models in turbulent periods but not in calm periods.
Optimizes trading strategies over multiple periods using convex optimization.
problem Evaluating and optimizing trading strategies over multiple periods.
method Single-period optimization using convex problems, extended to multi-period planning.
result A framework for multi-period trading that can exploit predictions of future quantities.
Deep learning predicts cryptocurrency price movements from trade data.
problem Predicting short-term price changes in cryptocurrencies.
method Long Short-term Memory Network (LSTM) trained on trade-by-trade data.
result Optimal LSTM model achieves over 60% accuracy on out-of-sample test periods.
In this short note we prove that the number of deformation types of compact hyperkaehler manifolds with prescribed second cohomology and second Chern class is finite. The proof uses the finiteness result of Kollar and Matsusaka, a formula by Hitchin and Sawon and the surjectivity of the period map.
Study assesses short-term debt's impact on non-financial firms' financial growth.
problem Declining financial performance and reluctance to lend to non-financial firms listed at Nairobi Securities Exchange.
method Explanatory research design, descriptive statistics, and panel data analysis.
result Short-term debt positively and significantly influences financial growth.
PQ-learning improves Q-learning by periodically updating target estimates.
problem Improving sample complexity in Q-learning for finding optimal policies.
method Maintains two Q-value estimates, one online and one target, updated periodically.
result PQ-learning achieves better sample complexity for finding epsilon-optimal policies.
Paper develops adaptive models for robust energy forecasting with missing data.
problem Operational models assume complete data; missing data can degrade forecast accuracy.
method Adaptive robust optimization and adversarial machine learning for missing data.
result Proposed models perform well even with short-term missing data and significantly outperform imputation with longer-term missing data.
The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of 9 improved covariance estimation procedures by using daily returns of 90 highly capitalized US stocks for th…
What predicts the evolution over time of subjective well-being? We correlate the trends of subjective well-being with the trends of social capital and/or GDP. We find that in the long and medium run social capital largely predicts the trends of subjective wellbeing in our sample of countries. In the short-term this rel…
Proposes combining SARIMA and STL for real-time anomaly detection.
problem Need for accurate and fast anomaly detection systems for massive data.
method Combines SARIMA and STL models for anomaly detection.
result Demonstrates high accuracy in detecting anomalies in noisy, non-periodic data.
Study shows house buyers in Christchurch value earthquake risk differently based on time since 2011 quake.
problem Understanding how house buyers' perception of earthquake risk changes over time.
method Used a hedonic price model to analyze house prices in Christchurch over three periods.
result Buyers value earthquake risk differently based on the time since the 2011 Christchurch earthquake.
We show short time existence and uniqueness of $\C^{1,1}$ solutions to the mean curvature flow with obstacles, when the obstacles are of class $\C^{1,1}$. If the initial interface is a periodic graph we show long time existence of the evolution and convergence to a minimal constrained hypersurface.
Recently, there have been several progresses for the conjugacy search problem (CSP) in Garside groups, especially in braid groups. All known algorithms for solving this problem use a sort of exhaustive search in a particular finite set such as the super summit set and the ultra summit set. Their complexities are propor…
Modern excavations yielded a distribution of the house areas in the ancient Egyptian city Akhetaten, which was populated for a short period during the 14th century BC. Assuming that the house area is a measure of the wealth of its inhabitants allows us to make a comparison of the wealth distributions in ancient and mod…
Margin trading and short selling boost green tech innovation in China.
problem Encouraging green technology innovation in Chinese companies.
method Quasi-experimental research using panel data of Chinese listed companies, double difference model.
result Margin trading and short selling increase green tech innovation significantly.
Optimizes market-neutral portfolios using fractal models.
problem Improving stability and performance of market-neutral portfolios.
method Fractal walk model of returns, covariance matrix optimization, Hurst stability analysis.
result Portfolio system outperforms benchmark with higher risk-adjusted returns.
Study the energy distribution of harmonic 1-forms on Riemann surfaces with a short geodesic.
problem Energy distribution of harmonic 1-forms on Riemann surfaces with a short geodesic.
method Analyzes the Jacobian torus of Riemann surfaces with a short geodesic, considering both separating and nonseparating cases.
result Estimates the energy distribution in terms of geometric data of the surface.
Proposes using Dynamic Mode Decomposition with delays for short-term human motion anticipation.
problem Lack of interpretability and explainability in neural network-based motion anticipation methods.
method Dynamic Mode Decomposition with delays for motion representation and prediction.
result Anticipation errors comparable or better than recurrent neural networks for very short times.
Study finds financial YouTube channel 3PROTV predicts stock market performance and sentiment changes.
problem Determining the informational value of financial YouTube channels.
method Analyzing 3PROTV's content and its impact on stock market performance and sentiment.
result 3PROTV's content, particularly negative sentiment, predicts stock market performance and sentiment changes.
Gradient descent mostly converges to a small subspace of eigenvectors.
problem Understanding the dynamics of gradient descent in deep learning.
method Analyzing the convergence of gradients in various deep learning scenarios.
result Gradient descent converges to a small subspace spanned by a few top eigenvectors of the Hessian.
Study infers volatility indicators from Bitcoin blockchain data.
problem Predicting extreme price volatility in Bitcoin.
method Non-negative decomposition of Bitcoin transaction graphs.
result EWI provides more predictive information than other methods.
We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investmen…
Study analyzes Bitcoin price dynamics from 2012 to 2018, identifying major price peaks.
problem Understanding Bitcoin price fluctuations and predicting market crashes.
method Automatic peak detection, Lagrange Regularisation Method, LPPLS model for predicting crashes.
result Identification of 3 major and 10 smaller price peaks over the analyzed period.