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We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model.
Generating step-by-step "chain-of-thought" rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering.
hybrid LSTM models, significantly outperform the traditional GARCH models
Liquid alternative strategies, specifically trend-following and long/short quality stocks, could be viewed as the new bonds.
We introduce a conditional machine learning approach to forecast the stock index return. Our approach is designed to work well for short-horizon forecasts to ad
Momentum trading strategies are thoroughly described in the academic literature and used in many trading strategies by hedge funds, asset managers, and propriet
This paper presents a novel approach to identifying potential bubbles in the US stock market by employing alternative time series methods based on long memory,
Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.
a high carry predicts future crypto price crashes. They further imply that there is “excess volatility” of crypto futures relative to spot prices, i.e. our estimates imply that changes in futures prices are about ten times more volatile than changes in spot prices
the crypto futures basis tends to be elevated when smaller entities seek leveraged upside exposure.
This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.
We document return predictability from deep-learning models that cannot be explained by common risk factors or limits to arbitrage.
a strong positive effect of debt refinancing risk, as measured by refinancing intensity, on excess bond returns in the subsequent year, supporting the rollover risk channel
signals as linear combinations of exogenous variables
statistical arbitrage portfolios with graph clustering algorithms