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Modelling and trading the English stock market with new forecasting techniques

Karathanasopoulos, Andreas - In: Economics and Business Letters : EBL 5 (2016) 2, pp. 50-57

The motivation of this paper is to introduce a short term adaptive model (Partial Swarm Optimizer combined with linear and nonlinear models when applied to the task of forecasting and trading the daily closing returns of the FTSE100 exchange traded funds (ETFs). This is done by benchmarking its...

Persistent link: https://www.econbiz.de/10011573208

Forecasting Bond Risk Premia using Stationary Yield Factors

Hoogteijling, Tobias; Martens, Martin; van der Wel, Michel - 2021

The standard way to summarize the yield curve is to use the first three principal components of the yield curve, resulting in level, slope and curvature factors. Yields, however, are non-stationary. We analyze the first three principal components of yield changes, which correspond to changes in...

Persistent link: https://www.econbiz.de/10013233328

What Matters When? Time-Varying Sparsity in Expected Returns

Bianchi, Daniele - 2020

We provide a measure of sparsity for expected returns within the context of classical factor models. Our measure is inversely related to the percentage of active predictors. Empirically, sparsity varies over time and displays an apparent countercyclical behavior. Proxies for financial conditions...

Persistent link: https://www.econbiz.de/10012848158

Do the FAMA and FRENCH five-factor model forecast well using ANN?

Jan, Muhammad Naveed; Ayub, Usman - In: Journal of business economics and management 20 (2019) 1, pp. 168-191

Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily,...

Persistent link: https://www.econbiz.de/10012175006

The Recursive Partitioning Algorithm (RPA) : A Nonparametric Classification System

Perera, Luiz C. J. - 2012

develop several original studies. Based on the work begun by Friedman (1977), he developed a very accurate classification … present the work of Breiman known as the Recursive Partitioning Algorithm. The RPA will be introduced as a nonparametric …

Persistent link: https://www.econbiz.de/10013100691

Time Series Forecasting With a Learning Algorithm : An Approximate Dynamic Programming Approach

Collado, Ricardo - 2019

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

Persistent link: https://www.econbiz.de/10012894079

Identifying information types in probability of informed trading (PIN) models : An improved algorithm

Ersan, Oguz; Ghachem, Montasser - 2022

in a dataset is applied through a layer detection algorithm suggested in Ersan (2016). The algorithm is based on … trading intensity is assumed to be identical in the buy and sell sides, the algorithm performs extremely well. When uninformed … of buys and sells in the data. We improve the algorithm of Ersan (2016) in two ways. We provide accurate estimates of …

Persistent link: https://www.econbiz.de/10013406178

Multilayer Probability of Informed Trading

Ersan, Oguz - 2017

algorithm which determines the number of information layers with the accuracy rates between 95% and 97% in simulated datasets …

Persistent link: https://www.econbiz.de/10012967326

Deep Unsupervised Anomaly Detection in High-Frequency Markets

Poutré, Cédric; Chételat, Didier; Morales, Manuel - 2023

Inspired by recent advances in the deep learning literature, this article introduces a novel hybrid anomaly detection framework specifically designed for limit order book (LOB) data. A modified Transformer autoencoder architecture is proposed to learn rich temporal LOB subsequence...

Persistent link: https://www.econbiz.de/10014353405

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

Persistent link: https://www.econbiz.de/10013475217