AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess
Return on Investment
- URL: http://arxiv.org/abs/2206.11072v1
- Date: Wed, 22 Jun 2022 13:37:58 GMT
- Title: AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess
Return on Investment
- Authors: Jimei Shen, Zhehu Yuan, Yifan Jin
- Abstract summary: 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.
- Score: 1.4502611532302039
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: How to quickly and automatically mine effective information and serve
investment decisions has attracted more and more attention from academia and
industry. And new challenges have been raised with the global pandemic. 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 with different Machine Learning models and optimizers. The results
show that our AlphaMLDigger achieves higher accuracy in the test set than
previous works and is robust to the negative impact of COVID-19 to some extent.
Related papers
- Stock Market Price Prediction: A Hybrid LSTM and Sequential
Self-Attention based Approach [3.8154633976469086]
We propose a new model named Long Short-Term Memory (LSTM) with Sequential Self-Attention Mechanism (LSTM-SSAM)
We conduct extensive experiments on the three stock datasets: SBIN,BANK, and BANKBARODA.
The experimental results prove the effectiveness and feasibility of the proposed model compared to existing models.
arXiv Detail & Related papers (2023-08-07T14:21:05Z) - HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and
Regime-Switch VAE [113.47287249524008]
It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting.
We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors.
Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
arXiv Detail & Related papers (2023-06-05T12:58:13Z) - 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) - Quantitative Stock Investment by Routing Uncertainty-Aware Trading
Experts: A Multi-Task Learning Approach [29.706515133374193]
We show that existing deep learning methods are sensitive to random seeds and network routers.
We propose a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms.
AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
arXiv Detail & Related papers (2022-06-07T08:58:00Z) - Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics
in Limit-Order Book Markets [84.90242084523565]
Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics.
By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention.
By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
arXiv Detail & Related papers (2022-03-07T18:59:54Z) - Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets
with Sentiment Measurements [11.97251638872227]
We propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel.
The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.
arXiv Detail & Related papers (2022-01-27T20:32:46Z) - Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective.
We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
arXiv Detail & Related papers (2021-12-08T14:55:21Z) - Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling [11.430440350359993]
We propose LARA, a novel price movement forecasting framework with two main components.
LA-Attention extracts potentially profitable samples through masked attention scheme.
RA-Labeling refines the noisy labels of potentially profitable samples.
LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform.
arXiv Detail & Related papers (2021-07-26T05:52:42Z) - 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) - Capturing dynamics of post-earnings-announcement drift using genetic
algorithm-optimised supervised learning [3.42658286826597]
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.
arXiv Detail & Related papers (2020-09-07T13:27:06Z) - Reinforcement-Learning based Portfolio Management with Augmented Asset
Movement Prediction States [71.54651874063865]
Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks.
In this paper, we propose SARL, a novel State-Augmented RL framework for PM.
Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
arXiv Detail & Related papers (2020-02-09T08:10:03Z)
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.