Capturing dynamics of post-earnings-announcement drift using genetic
algorithm-optimised supervised learning
- URL: http://arxiv.org/abs/2009.03094v1
- Date: Mon, 7 Sep 2020 13:27:06 GMT
- Title: Capturing dynamics of post-earnings-announcement drift using genetic
algorithm-optimised supervised learning
- Authors: Zhengxin Joseph Ye and Bjorn W. Schuller
- Abstract summary: Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies.
We use a machine learning based approach instead, and aim to capture the PEAD dynamics using data from a large group of stocks.
- Score: 3.42658286826597
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While Post-Earnings-Announcement Drift (PEAD) is one of the most studied
stock market anomalies, the current literature is often limited in explaining
this phenomenon by a small number of factors using simpler regression methods.
In this paper, we use a machine learning based approach instead, and aim to
capture the PEAD dynamics using data from a large group of stocks and a wide
range of both fundamental and technical factors. Our model is built around the
Extreme Gradient Boosting (XGBoost) and uses a long list of engineered input
features based on quarterly financial announcement data from 1,106 companies in
the Russell 1000 index between 1997 and 2018. We perform numerous experiments
on PEAD predictions and analysis and have the following contributions to the
literature. First, we show how Post-Earnings-Announcement Drift can be analysed
using machine learning methods and demonstrate such methods' prowess in
producing credible forecasting on the drift direction. It is the first time
PEAD dynamics are studied using XGBoost. We show that the drift direction is in
fact driven by different factors for stocks from different industrial sectors
and in different quarters and XGBoost is effective in understanding the
changing drivers. Second, we show that an XGBoost well optimised by a Genetic
Algorithm can help allocate out-of-sample stocks to form portfolios with higher
positive returns to long and portfolios with lower negative returns to short, a
finding that could be adopted in the process of developing market neutral
strategies. Third, we show how theoretical event-driven stock strategies have
to grapple with ever changing market prices in reality, reducing their
effectiveness. We present a tactic to remedy the difficulty of buying into a
moving market when dealing with PEAD signals.
Related papers
- Beyond Trend Following: Deep Learning for Market Trend Prediction [49.89480853499917]
We advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends.
These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.
arXiv Detail & Related papers (2024-06-10T11:42:30Z) - Combining Deep Learning on Order Books with Reinforcement Learning for
Profitable Trading [0.0]
This work focuses on forecasting returns across multiple horizons using order flow and training three temporal-difference imbalance learning models for five financial instruments.
The results prove potential but require further minimal modifications for consistently profitable trading to fully handle retail trading costs, slippage, and spread fluctuation.
arXiv Detail & Related papers (2023-10-24T15:58:58Z) - Diffusion Variational Autoencoder for Tackling Stochasticity in
Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility.
Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions.
We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction.
Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z) - Can ChatGPT Forecast Stock Price Movements? Return Predictability and
Large Language Models [57.70351255180495]
We use ChatGPT to assess whether each headline is good, bad, or neutral for firms' stock prices.
We find that ChatGPT outperforms traditional sentiment analysis methods.
Long-short strategies based on ChatGPT-4 deliver the highest Sharpe ratio.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - TM-vector: A Novel Forecasting Approach for Market stock movement with a
Rich Representation of Twitter and Market data [1.5749416770494706]
We will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour.
In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information.
Various factors have been used for the effectiveness of the proposed forecasting approach.
arXiv Detail & Related papers (2023-03-13T18:55:41Z) - Novel Modelling Strategies for High-frequency Stock Trading Data [4.639889477442706]
We propose three novel modelling strategies for processing raw data.
We show how our strategies often lead to statistically significant improvement in predictions.
The three strategies improve the F1 scores of the SVM models by 0.056, 0.087, and 0.016, respectively.
arXiv Detail & Related papers (2022-11-30T22:50:11Z) - Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights.
Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
arXiv Detail & Related papers (2022-10-22T14:47:11Z) - AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess
Return on Investment [1.4502611532302039]
This paper proposes a two-phase AlphaMLDigger that effectively finds excessive returns in the highly fluctuated market.
In phase 1, a deep sequential NLP model is proposed to transfer blogs on Sina Microblog to market sentiment.
In phase 2, the predicted market sentiment is combined with social network indicator features and stock market history features to predict the stock movements.
arXiv Detail & Related papers (2022-06-22T13:37:58Z) - REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods.
To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.
To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
arXiv Detail & Related papers (2021-02-15T07:22:09Z) - A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements.
The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility.
We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
arXiv Detail & Related papers (2020-12-10T01:15:48Z) - Deep learning for Stock Market Prediction [0.0]
This paper concentrates on the future prediction of stock market groups.
Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen.
The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance.
arXiv Detail & Related papers (2020-03-31T22:50:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.