arXiv research

Explains the difference between EMA and moving EMA, focusing on market trend indicators.

We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood …

New moving average adapts weight dynamically based on polynomial and wavefunction.

This study uses moving average cluster entropy to analyze financial market dynamics.

In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient $ρ_{DMCA}(λ)$ with a moving average window length $λ$. We analytically show that the coefficient…

The Hurst exponent $H$ of long range correlated series can be estimated by means of the Detrending Moving Average (DMA) method. A computational tool defined within the algorithm is the generalized variance $σ_{DMA}^2={1}/{(N-n)}\sum_i [y(i)-\widetilde{y}_n(i)]^2\:$, with $\widetilde{y}_n(i)= {1}/{n}\sum_{k}y(i-k)$ the…

The possibility that price dynamics is affected by its distance from a moving average has been recently introduced as new statistical tool. The purpose is to identify the tendency of the price dynamics to be attractive or repulsive with respect to its own moving average. We consider a number of tests for various models…

We propose a method for pricing American options whose pay-off depends on the moving average of the underlying asset price. The method uses a finite dimensional approximation of the infinite-dimensional dynamics of the moving average process based on a truncated Laguerre series expansion. The resulting problem is a fin…

The paper analyzes MACD using operator theory.

Neural moving average model speeds up state space model inference for time series data.

Paper predicts cryptocurrency bull and bear phases using Bitcoin's moving averages.

PACE optimizes training for averaged language models, improving performance.

Optimal weight windows are found by projecting the origin onto a convex polytope.

Efficient method for pricing Bermudan moving average options using GPR-GHQ.

Paper proposes an online adaptation algorithm for improving model performance.

We introduce an autoregressive-type model with self-modulation effects for a foreign exchange rate by separating the foreign exchange rate into a moving average rate and an uncorrelated noise. From this model we indicate that traders are mainly using strategies with weighted feedbacks of the past rates in the exchange …

New method predicts spatio-temporal data with short and long-range dependence.

Machine learning models outperform traditional technical analysis in Bitcoin trading.

A new method for exponentially weighted moving models using approximations.

Adaptive t-distribution estimates nonstationary time series using moving moments.

This paper analyzes the skewness of momentum trading strategies.

In this paper we consider backward stochastic differential equations with time-delayed generators of a moving average type. The classical framework with linear generators depending on $(Y(t),Z(t))$ is extended and we investigate linear generators depending on $(\frac{1}{t}\int_0^tY(s)ds, \frac{1}{t}\int_0^tZ(s)ds)$. We…

Paper analyzes how EMA improves SGD in linear regression.

BEMA reduces bias in EMA, leading to faster convergence and better performance.

Improved averaging method for noisy observations converges strongly.

Enhances trading signals using image analysis and weighted moving averages.

We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide the -- to our knowledge …

On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to …

Improving optimization for iterate-averaged language models

Deep CNN model predicts stock prices with high accuracy.

GPA improves LLM training speed by 8.71% for Llama-160M models.

We introduce a stochastic price model where, together with a random component, a moving average of logarithmic prices contributes to the price formation. Our model is tested against financial datasets, showing an extremely good agreement with them. It suggests how to construct trading strategies which imply a capital g…

Adaptive estimation for nonstationary time series reduces computational cost.

We study capital process behavior in the fair-coin game and biased-coin games in the framework of the game-theoretic probability of Shafer and Vovk (2001). We show that if Skeptic uses a Bayesian strategy with a beta prior, the capital process is lucidly expressed in terms of the past average of Reality's moves. From t…

Transformations of macroeconomic data affect machine learning forecasts, especially with regularization and nonlinearity.

New method estimates robust multi-period portfolios using entropy.

We derive an explicit formula for likelihood function for Gaussian VARMA model conditioned on initial observables where the moving-average (MA) coefficients are scalar. For fixed MA coefficients the likelihood function is optimized in the autoregressive variables $Φ$'s by a closed form formula generalizing regression c…

The detrending moving average (DMA) algorithm is a widely used technique to quantify the long-term correlations of non-stationary time series and the long-range correlations of fractal surfaces, which contains a parameter $θ$ determining the position of the detrending window. We develop multifractal detrending moving a…

STORM-PG uses momentum for faster policy gradient updates.

We introduce a covariance matrix estimator that both takes into account the heteroskedasticity of financial returns (by using an exponentially weighted moving average) and reduces the effective dimensionality of the estimation (and hence measurement noise) via techniques borrowed from random matrix theory. We calculate…

Long short-term memory network outperforms seasonal model in JSE Top 40 forecasting.

We propose an explicit recursive method to approximate a power-law with a finite sum of weighted exponentials. Applications to moving averages with long memory are discussed in relationship with stochastic volatility models.

How effective are the most common trading models? The answer may help investors realize upsides to using each model, act as a segue for investors into more complex financial analysis and machine learning, and to increase financial literacy amongst students. Creating original versions of popular models, like linear regr…

Extends double linear policy with time-varying weights and proves robust positive expectation.

Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted M…

A new method normalizes flow mixtures for better inference across different data types.

Study classifies stock price data into stationary and non-stationary periods for mechanical trading.

The minute-by-minute move of the Hang Seng Index (HSI) data over a four-year period is analysed and shown to possess similar statistical features as those of other markets. Based on a mathematical theorem [S. B. Pope and E. S. C. Ching, Phys. Fluids A {\bf 5}, 1529 (1993)], we derive an analytic form for the probabilit…