Kernel method estimates long-term effects from short-term data.
problem Estimating long-term effects from short-term data in continuous actions.
method Kernel ridge regression to embed and extrapolate long-term effects.
result Uniform consistency and nonasymptotic error bounds for the estimator.
Study finds short-term wage increases due to COVID-19, contrary to expectations.
problem Impact of COVID-19 on wages over time.
method Empirical analysis controlling for GDP as a demand proxy.
result Short-term positive wage effect, contrary to expectations.
We find a sharp local maximum in cross-correlation of EUR/USD and BTC/USD pairs, indicating short-term momentum trading.
problem The Epps effect is observed in various markets but deviates in foreign exchange and cryptocurrency markets.
method We document and analyze the cross-correlation function of EUR/USD and BTC/USD pairs to identify the Epps effect deviation.
result The sharp local maximum in cross-correlation function reveals the activity of short-term momentum traders.
Model combines long-term and short-term memory using conceptors.
problem Transfer between long-term and short-term memory.
method Recurrent neural network with gated reservoir for short-term memory and conceptors for long-term memory.
result Standard operations on conceptors allow combining long-term memories and describing their effect on short-term memory.
New framework estimates long-term outcomes from short-term data.
problem Estimating long-term outcomes from short-term data.
method Reward function decomposition-based framework (LOPE).
result LOPE outperforms existing methods, especially when surrogacy is violated.
This paper balances short-term and long-term rewards in policy learning.
problem Balancing short-term and long-term rewards in policy learning.
method Formalizes a new framework to balance rewards, identifies rewards under mild assumptions, deduces efficiency bounds, and develops a policy learning approach.
result The proposed method improves the estimator of long-term reward and reduces regret.
The paper tackles long-term treatment effects with persistent confounders using sequential short-term outcomes.
problem Estimating long-term treatment effects with persistent unmeasured confounders.
method Exploiting the sequential structure of short-term outcomes, the paper develops three novel identification strategies and corresponding estimators.
result The proposed methods outperform existing approaches in handling persistent confounders.
Behavioral theories posit that investor sentiment exhibits predictive power for stock returns, whereas there is little study have investigated the relationship between the time horizon of the predictive effect of investor sentiment and the firm characteristics. To this end, by using a Granger causality analysis in the …
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term predictions.
Digital transformation boosts corporate financial asset allocation, especially short-term.
problem Understanding how digital transformation affects corporate financial decisions.
method Fixed-effects models and staggered DID design using A-share listed companies data.
result Digital transformation significantly promotes corporate financial asset allocation, more pronounced in short-term.
Estimates long-term effects of new treatments using historical and short-term data.
problem Estimating long-term effects of novel treatments with limited historical data.
method Surrogate indices, dynamic treatment effect estimation, and double machine learning combined in a unified pipeline.
result Consistent and asymptotically normal estimates of long-term effects under Markovian assumption.
The paper presents the comparative study of the nature of stock markets in short-term and long-term time scales with and without structural break in the stock data. Structural break point has been identified by applying Zivot and Andrews structural trend break model to break the original time series (TSO) into time ser…
Combining experimental and observational data for long-term causal effects.
problem Estimating causal effects of treatment on long-term outcomes using mixed data types.
method Three approaches for fusing experimental and observational data: equal confounding, shared confounder, and proxy variables.
result Developed estimators for each approach and analyzed their robustness.
In this work a relation between a measure of short-term arbitrage in the market and the excess growth of portfolios as a notion of long-term arbitrage is established. The former originates from "Geometric Arbitrage Theory" and the latter from "Stochastic Portfolio Theory". Both aim to describe non-equilibrium effects i…
The study improves load forecasting for electricity consumers using advanced machine learning models.
problem Improving short-term load forecasting for effective scheduling and decision-making.
method Proposes and evaluates statistical nonlinear models, including LSTM and GRU, for 15-min frequency electricity load forecasting.
result Advanced models outperform other models in out-of-sample forecasting accuracy, as shown by the Diebold-Mariano test.
This work learns effective dynamics from short-term data of stochastic systems.
problem Learning effective dynamics from short-term data of stochastic systems.
method Proposes a novel algorithm using a neural network (Auto-SDE) to learn invariant slow manifold from data.
result Validated through numerical experiments to be accurate, stable, and effective.
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.
Estimates long-term effects from short-term experiments and observational data with unobserved confounders.
problem Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding.
method Combining regression residuals with short-term experimental outcomes to create an instrumental variable for estimating long-term causal effects.
result The estimator is unbiased and its variance is analytically studied.
TimeBridge addresses non-stationarity in long-term time series forecasting.
problem Non-stationarity in multivariate time series leads to spurious regressions and obscures long-term relationships.
method TimeBridge segments series into patches, applying Integrated Attention for short-term non-stationarity and Cointegrated Attention for long-term cointegration.
result TimeBridge achieves state-of-the-art performance in both short-term and long-term forecasting.
With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural networks which are often considered as a black box. In this paper it is analyzed how recurrent neural network (RNNs) cope with temporal dependen…
We propose a stylized model of production and exchange in which long-term investors set their production decision over a horizon τ , the "time to produce", and are liquidity constrained, while financial investors trade over a much shorter horizon δ (<< τ ) and are therefore more duly informed on the exogenous shocks af…
Proposes LSR-IGRU for improved stock trend prediction.
problem Challenges in stock price prediction due to complex relationships and nonlinear dynamics.
method Long short-term relationships matrix and improved GRU input for better temporal and relationship integration.
result Significantly improved accuracy in predicting stock trend changes.
We discuss a concept denoted as Conformal Prediction (CP) in this paper. While initially stemming from the world of machine learning, it was never applied or analyzed in the context of short-term electricity price forecasting. Therefore, we elaborate the aspects that render Conformal Prediction worthwhile to know and e…
Stacked LSTM networks improve traffic volume forecasting.
problem Accurate traffic volume prediction for better planning.
method Applying stacked Long Short-Term Memory (LSTM) networks for time series forecasting.
result Stacked LSTM networks enhance the accuracy of traffic volume predictions.
We apply the Zipf power law to financial time series of WIG20 index daily changes (open-close). Thanks to the mapping of time series signal into the sequence of 2k+1 'spin-like' states, where k=0, 1/2, 1, 3/2, ..., we are able to describe any time series increments, with almost arbitrary accuracy, as the one of such 's…
Yield curve modeling is an essential problem in finance. In this work, we explore the use of Bayesian statistical methods in conjunction with Nelson-Siegel model. We present the hierarchical Bayesian model for the parameters of the Nelson-Siegel yield function. We implement the MAP estimates via BFGS algorithm in rstan…
A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.
problem Lack of effective short-term load forecasting methods.
method Hybrid strategy integrating four different inference models: tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB).
result Demonstrates effectiveness of hybrid strategy on real datasets.
Simplified LSTM models improve sentiment analysis on Twitter debate data.
problem Performing sentiment analysis on long sequence data from Twitter debates.
method Developed six parameter-reduced LSTM models (slim LSTM) for faster training and reduced computational cost.
result Slim LSTM models outperform standard LSTM model in sentiment analysis of GOP Debate Twitter dataset.
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
problem Accurate short-term load forecasting for optimizing electrical sources and protecting energy.
method Uses SARIMA-GARCH model with T-student Distribution to forecast electric load.
result The proposed model outperforms the ARIMA model with Normal Distribution.
NeuroMemFPP uses LSTM to estimate FPP parameters with high accuracy.
problem Estimating parameters of fractional Poisson process with memory and long-range dependence.
method Recurrent Neural Network (RNN), specifically Long Short-Term Memory (LSTM), for parameter estimation.
result The LSTM-based approach reduces MSE by about 55.3% compared to traditional MOM method.
We propose a multifractal model for short-term interest rates. The model is a version of the Markov-Switching Multifractal (MSM), which incorporates the well-known level effect observed in interest rates. Unlike previously suggested models, the level-MSM model captures the power-law scaling of the structure functions a…
The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An…
Investigates geometric mean reversion process using Lie symmetry method.
problem Describes dynamics of short-term interest rates.
method Lie symmetry method and optimal system of invariant solutions.
result Constructs an optimal system of invariant solutions.
Improves A/B testing for long-term outcomes in dynamic systems.
problem Estimating long-term effects from short-term A/B testing data.
method Develops optimal inference techniques and localized information sharing methods.
result New estimator reduces variance linearly with test arms and matches lower bounds.
Predicts S&P 500 trends using machine learning models.
problem Market trend prediction for S&P 500 index.
method Feature engineering, machine learning models (Logistic Regression, Decision Trees, Random Forests, Neural Networks, KNN, XGBoost), data preprocessing, hyperparameter tuning, SMOTE.
result KNN for short-term predictions, XGBoost for long-term forecasts.
Study examines short-term stress of COVID-19 on major global stock indices.
problem Short-term impact of COVID-19 on global stock markets.
method Secondary data from 41 stock exchanges in 32 countries, focusing on first reported cases.
result Volatility in stock markets increases with the rise of COVID-19 cases, and there is a significant negative correlation.
This paper examines the volatility and covariance dynamics of cash and futures contracts that underlie the Optimal Hedge Ratio (OHR) across different hedging time horizons. We examine whether hedge ratios calculated over a short term hedging horizon can be scaled and successfully applied to longer term horizons. We als…
We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated…
Enhances social spam detection using multi-level dependency of relational sequences.
problem Social spam detection in multi-relation social networks.
method Developed the Multi-level Dependency Model (MDM) to exploit long-term and short-term dependencies in user relational sequences.
result MDM improves social spam detection accuracy on a real-world multi-relational social network.
Novel method for estimating SIRD model parameters and forecasting COVID-19 deaths in Poland.
problem Estimating epidemiological parameters for SARS-CoV-2 spread in Poland.
method Modified SIRD model, GPU architecture for computation.
result Effectiveness of short-term forecasts (up to 2 weeks) and stability of the method.
Study short-term wind power and speed predictions using machine learning.
problem Accurate short-term wind power and speed predictions for energy systems.
method Combining numerical weather prediction models with local observations, using machine learning for variable selection and forecasting.
result Improved wind power and speed predictions for 4-hour ahead using machine learning.
Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as black boxes, yielding no insight of the underlying learned patterns. In this paper we conside…
Model forecasts motor vehicle collision rates with high accuracy.
problem Forecasting motor vehicle collision rates with high accuracy.
method Adopted Heston Stochastic Volatility model and extended it to account for seasonality and accelerated safety periods.
result Short-term forecasts show high accuracy (over 95%) and outperform existing models.
QLSTM outperforms LSTM in predicting KSE 100 index movements.
problem Predicting stock market movement in uncertain economic conditions.
method Used LSTM and QLSTM models on monthly data of economic indicators.
result QLSTM provided more accurate predictions of KSE 100 index values.
Intense volatility in financial markets affect humans worldwide. Therefore, relatively accurate prediction of volatility is critical. We suggest that massive data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market move…
Study shows risk-averse investors have consistent ranking of risky assets.
problem Ranking of risky assets in short-term investments.
method Analyzes various decision problems regarding risky assets with continuous returns.
result Risk-averse decision makers have the same ranking over risky assets.
This paper uses Gaussian processes to forecast short-term stock price volatility.
problem Inaccurate short-term volatility forecasts for high-frequency trades.
method Combines numerical and probabilistic models, specifically Gaussian Processes (GPs), to correct and forecast stock price data.
result Effective short-term volatility forecasts for high-frequency trades using Gaussian Processes.
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.