A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The Kelly rule fails to maximize growth in a time-changed return setting.
Long-term lane change prediction model predicts maneuvers with 75% accuracy.
We study the long-term memory in diverse stock market indices and foreign exchange rates using the Detrended Fluctuation Analysis(DFA). For all daily and high-frequency market data studied, no significant long-term memory property is detected in the return series, while a strong long-term memory property is found in th…
The paper proposes Tier Balancing for dynamic fairness in decision-making.
ESNs with transfer learning predict long-term chaotic patterns in spatiotemporal dynamical systems.
This paper discusses the sensitivity of the long-term expected utility of optimal portfolios for an investor with constant relative risk aversion. Under an incomplete market given by a factor model, we consider the utility maximization problem with long-time horizon. The main purpose is to find the long-term sensitivit…
This paper constructs and studies the long-term factorization of affine pricing kernels into discounting at the rate of return on the long bond and the martingale component that accomplishes the change of probability measure to the long forward measure. The principal eigenfunction of the affine pricing kernel germane t…
Study examines how risk tolerance impacts long-term investment returns.
Empirical study on long-term discount rates using historical bond prices.
Combines CNN and Transformer for financial time series forecasting.
Study historical cholera epidemics and simulate long-term mortality impacts.
This paper proposes a framework to predict long-term trends and short-term fluctuations in multivariate time series.
Bayesian optimization for long-term outcomes using fast and slow experiments.
We introduce a deterministic dealer model which implements most of the empirical laws, such as fat tails in the price change distributions, long term memory of volatility and non-Poissonian intervals. We also clarify the causality between microscopic dealers' dynamics and macroscopic market's empirical laws.
We investigate possible origins of trends using a deterministic threshold model, where we refer to long-term variabilities of price changes (price movements) in financial markets as trends. From the investigation we find two phenomena. One is that the trend of monotonic increase and decrease can be generated by dealers…
A novel approach predicts long-term stock price trends using 2D-convolutional encoders and semantic segmentation.
New algorithm tackles non-stationary delayed feedback in recommender systems.
New method robust to random distributional shifts in prediction.
For the prediction with experts' advice setting, we construct forecasting algorithms that suffer loss not much more than any expert in the pool. In contrast to the standard approach, we investigate the case of long-term forecasting of time series and consider two scenarios. In the first one, at each step the learne…
Technological improvement is the most important cause of long-term economic growth. We study the effects of technology improvement in the setting of a production network, in which each producer buys input goods and converts them to other goods, selling the product to households or other producers. We show how this netw…
Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing down all fossil fuel energy generation and replacing them with renewable sources -- careful decisions need to be taken to ensure rapid but …
Modeling daily river flow distribution with seasonal and long-term trends.
In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups. We discuss causal directed a…
Stable Hadamard Memory improves reinforcement learning by efficiently managing memory.
Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of in…
Bayesian method optimizes rescheduling for multipurpose batch processes with incomplete look-ahead information.
Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, one often encounters situations with non-stationary environme…
Using a recently introduced method to quantify the time varying lead-lag dependencies between pairs of economic time series (the thermal optimal path method), we test two fundamental tenets of the theory of fixed income: (i) the stock market variations and the yield changes should be anti-correlated; (ii) the change in…
A new algorithm detects changes in high-dimensional data efficiently under sampling constraints.
Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning models. Most of the conventional deep learning models are not capable of learning new tasks sequentially in one model without forgetting the…
Study uses Bayesian regression to analyze consumer behavior changes in restaurants post-COVID-19.
Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.
The well-known theorem of Dybvig, Ingersoll and Ross shows that the long zero-coupon rate can never fall. This result, which, although undoubtedly correct, has been regarded by many as surprising, stems from the implicit assumption that the long-term discount function has an exponential tail. We revisit the problem in …
This study investigates empirically whether the degree of stock market efficiency is related to the prediction power of future price change using the indices of twenty seven stock markets. Efficiency refers to weak-form efficient market hypothesis (EMH) in terms of the information of past price changes. The prediction …
The study examines how global economic policy uncertainty affects crude oil futures volatility.
Improved stock index analysis using fuzzy parameters and machine learning.
Deep learning predicts employment changes and industry health.
New framework for choosing optimal proxy metrics from past experiments.
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairne…
We introduce here for the first time the long-term swap rate, characterised as the fair rate of an overnight indexed swap with infinitely many exchanges. Furthermore we analyse the relationship between the long-term swap rate, the long-term yield, see Biagini et al. [2018], Biagini and Härtel [2014], and El Karoui et a…
Kernel method estimates long-term effects from short-term data.
Hybrid method reveals true currency correlations.
Researchers have used many different methods to detect the possibility of long-term dependence (long memory) in stock market returns, but evidence is in general mixed. In this paper, three different tests, (namely Rescaled Range (R/S), its modified form, and the semi-parametric method (GPH)), in addition to a new appro…
New method reconstructs past foehn occurrences using unsupervised and supervised learning.
The paper proposes a new method to estimate interest rates consistently under both risk-neutral and real-world measures.
The evolution of the rate of price inflation and unemployment in Japan has been modeled within the Phillips curve framework. As an extension to the Phillips curve, we represent both variables as linear functions of the change rate of labor force. All models were first estimated in 2005 for the period between 1980 and 2…
Nostradamus links climate and stock market performance.
Model combines long-term and short-term memory using conceptors.