4,089 research outputs found
Enhancing Stock Movement Prediction with Adversarial Training
This paper contributes a new machine learning solution for stock movement
prediction, which aims to predict whether the price of a stock will be up or
down in the near future. The key novelty is that we propose to employ
adversarial training to improve the generalization of a neural network
prediction model. The rationality of adversarial training here is that the
input features to stock prediction are typically based on stock price, which is
essentially a stochastic variable and continuously changed with time by nature.
As such, normal training with static price-based features (e.g. the close
price) can easily overfit the data, being insufficient to obtain reliable
models. To address this problem, we propose to add perturbations to simulate
the stochasticity of price variable, and train the model to work well under
small yet intentional perturbations. Extensive experiments on two real-world
stock data show that our method outperforms the state-of-the-art solution with
3.11% relative improvements on average w.r.t. accuracy, validating the
usefulness of adversarial training for stock prediction task.Comment: IJCAI 201
Robustness, Heterogeneity and Structure Capturing for Graph Representation Learning and its Application
Graph neural networks (GNNs) are potent methods for graph representation learn- ing (GRL), which extract knowledge from complicated (graph) structured data in various real-world scenarios. However, GRL still faces many challenges. Firstly GNN-based node classification may deteriorate substantially by overlooking the pos- sibility of noisy data in graph structures, as models wrongly process the relation among nodes in the input graphs as the ground truth. Secondly, nodes and edges have different types in the real-world and it is essential to capture this heterogeneity in graph representation learning. Next, relations among nodes are not restricted to pairwise relations and it is necessary to capture the complex relations accordingly. Finally, the absence of structural encodings, such as positional information, deterio- rates the performance of GNNs. This thesis proposes novel methods to address the aforementioned problems:
1. Bayesian Graph Attention Network (BGAT): Developed for situations with scarce data, this method addresses the influence of spurious edges. Incor- porating Bayesian principles into the graph attention mechanism enhances robustness, leading to competitive performance against benchmarks (Chapter 3).
2. Neighbour Contrastive Heterogeneous Graph Attention Network (NC-HGAT): By enhancing a cutting-edge self-supervised heterogeneous graph neural net- work model (HGAT) with neighbour contrastive learning, this method ad- dresses heterogeneity and uncertainty simultaneously. Extra attention to edge relations in heterogeneous graphs also aids in subsequent classification tasks (Chapter 4).
3. A novel ensemble learning framework is introduced for predicting stock price movements. It adeptly captures both group-level and pairwise relations, lead- ing to notable advancements over the existing state-of-the-art. The integration of hypergraph and graph models, coupled with the utilisation of auxiliary data via GNNs before recurrent neural network (RNN), provides a deeper under- standing of long-term dependencies between similar entities in multivariate time series analysis (Chapter 5).
4. A novel framework for graph structure learning is introduced, segmenting graphs into distinct patches. By harnessing the capabilities of transformers and integrating other position encoding techniques, this approach robustly capture intricate structural information within a graph. This results in a more comprehensive understanding of its underlying patterns (Chapter 6)
Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction
Sub-new stock price prediction, forecasting the price trends of stocks listed
less than one year, is crucial for effective quantitative trading. While deep
learning methods have demonstrated effectiveness in predicting old stock
prices, they require large training datasets unavailable for sub-new stocks. In
this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning
approach for sub-new stock price prediction. Leveraging prediction tasks
formulated by old stocks, our meta-learning method aims to acquire the fast
generalization ability that can be further adapted to sub-new stock price
prediction tasks, thereby solving the data scarcity of sub-new stocks.
Moreover, we enhance the meta-learning process by incorporating an adaptive
learning strategy sensitive to varying task difficulties. Through wavelet
transform, we extract high-frequency coefficients to manifest stock price
volatility. This allows the meta-learning model to assign gradient weights
based on volatility-quantified task difficulty. Extensive experiments on
datasets collected from three stock markets spanning twenty-two years prove
that our Meta-Stock significantly outperforms previous methods and manifests
strong applicability in real-world stock trading. Besides, we evaluate the
reasonability of the task difficulty quantification and the effectiveness of
the adaptive learning strategy
An empirical study on the various stock market prediction methods
Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
Multi-step stock price prediction over a long-term horizon is crucial for
forecasting its volatility, allowing financial institutions to price and hedge
derivatives, and banks to quantify the risk in their trading books.
Additionally, most financial regulators also require a liquidity horizon of
several days for institutional investors to exit their risky assets, in order
to not materially affect market prices. However, the task of multi-step stock
price prediction is challenging, given the highly stochastic nature of stock
data. Current solutions to tackle this problem are mostly designed for
single-step, classification-based predictions, and are limited to low
representation expressiveness. The problem also gets progressively harder with
the introduction of the target price sequence, which also contains stochastic
noise and reduces generalizability at test-time. To tackle these issues, we
combine a deep hierarchical variational-autoencoder (VAE) and diffusion
probabilistic techniques to do seq2seq stock prediction through a stochastic
generative process. The hierarchical VAE allows us to learn the complex and
low-level latent variables for stock prediction, while the diffusion
probabilistic model trains the predictor to handle stock price stochasticity by
progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va)
model is shown to outperform state-of-the-art solutions in terms of its
prediction accuracy and variance. More importantly, the multi-step outputs can
also allow us to form a stock portfolio over the prediction length. We
demonstrate the effectiveness of our model outputs in the portfolio investment
task through the Sharpe ratio metric and highlight the importance of dealing
with different types of prediction uncertainties.Comment: CIKM 202
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training
In the stock market, a successful investment requires a good balance between
profits and risks. Recently, stock recommendation has been widely studied in
quantitative investment to select stocks with higher return ratios for
investors. Despite the success in making profits, most existing recommendation
approaches are still weak in risk control, which may lead to intolerable paper
losses in practical stock investing. To effectively reduce risks, we draw
inspiration from adversarial perturbations and propose a novel Split
Variational Adversarial Training (SVAT) framework for risk-aware stock
recommendation. Essentially, SVAT encourages the model to be sensitive to
adversarial perturbations of risky stock examples and enhances the model's risk
awareness by learning from perturbations. To generate representative
adversarial examples as risk indicators, we devise a variational perturbation
generator to model diverse risk factors. Particularly, the variational
architecture enables our method to provide a rough risk quantification for
investors, showing an additional advantage of interpretability. Experiments on
three real-world stock market datasets show that SVAT effectively reduces the
volatility of the stock recommendation model and outperforms state-of-the-art
baseline methods by more than 30% in terms of risk-adjusted profits
๊ณผ๊ฑฐ ๊ฐ๊ฒฉ ๋ฐ ํฌ์ํ ํธ์์ ์ด์ฉํ ์ฃผ๊ฐ ๋ณ๋ ์์ธก
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2021.8. ๊ฐ์ .Given historical stock prices and sparse tweets mentioning the stocks to predict, how can we precisely predict stock price movement? Many market analysts strive to use a large amount of information for prediction. However, they confront more noise when utilizing larger data for prediction. Thus, existing methods use only historical prices, or those along with a small amount of refined data such as news articles or tweets mentioning target stocks. However, they have the following limitations: 1) using only historical prices gives low performance since they have insufficient information, 2) news articles lack timeliness compared to social medias for predicting stock price movement, and 3) the previous methods using tweets do not handle stocks without tweets mentioning them.
In this paper, we propose GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets), an accurate stock price movement prediction method that works without tweets mentioning target stocks. GLT pre-trains both of stock and tweet representations in a self-supervised way. Then, GLT generates global and local tweet trends which represent global public opinion and the local trends related to target stocks, respectively. The trend vectors are combined to accurately predict stock price movement. Experimental results show that GLT provides the state-ofthe-art accuracy for stock price movement prediction.๊ณผ๊ฑฐ ์ฃผ๊ฐ์ ์์ธกํ ์ฃผ์์ ์ธ๊ธํ๋ ํฌ์ํ ํธ์์ด ์ฃผ์ด์ก์ ๋ ์ฃผ๊ฐ ๋ณ๋์ ์ด๋ป๊ฒ ์ ํํ๊ฒ ์์ธกํ ์ ์์๊น? ๋ง์ ์์ฅ ๋ถ์๊ฐ๋ค์ ์์ธก์ ์ํด ๋ง์ ์์ ์ ๋ณด๋ฅผ ์ฌ์ฉํ๋ ค๊ณ ๋
ธ๋ ฅํฉ๋๋ค. ํ์ง๋ง ์์ธก์ ์ํด ๋ ๋ง์ ์์ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ ์ ๋ก ๋ ๋ง์ ๋
ธ์ด์ฆ์ ์ง๋ฉดํฉ๋๋ค. ๋ฐ๋ผ์ ๊ธฐ์กด ๋ฐฉ๋ฒ์ ๊ณผ๊ฑฐ ์ฃผ์ ๊ฐ๊ฒฉ๋ง ์ฌ์ฉํ๊ฑฐ๋ ๋ด์ค ๊ธฐ์ฌ ํน์ ๋์ ์ฃผ์์ ์ธ๊ธํ๋ ํธ์๊ณผ ๊ฐ์ ์๋์ ์ ์ ๋ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉ ํฉ๋๋ค. ๊ทธ๋ฌ๋ ๊ธฐ์กด ๋ฐฉ๋ฒ๋ค์ ๋ค์๊ณผ ๊ฐ์ ํ๊ณ๊ฐ ์์ต๋๋ค: 1) ๊ณผ๊ฑฐ ์ฃผ์ ๊ฐ๊ฒฉ๋ง ์ฌ์ฉํ๋ฉด ์ ๋ณด๊ฐ ๋ถ์กฑํ์ฌ ์ฑ๋ฅ์ด ์ ํ๋๊ณ , 2) ๋ด์ค ๊ธฐ์ฌ๋ ์ฃผ๊ฐ ๋ณ๋์ ์์ธกํ๋ ๋ฐ ์์
๋ฏธ๋์ด์ ๋นํด ์ ์์ฑ์ด ๋ถ์กฑํ๋ฉฐ, 3) ํธ์์ ์ฌ์ฉํ๋ ์ด์ ๋ฐฉ๋ฒ๋ค์ ํธ์์ด ์ธ๊ธํ์ง ์์ ์ฃผ์๋ค์ ์ฒ๋ฆฌํ์ง ๋ชปํฉ๋๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ๋ชฉํ ์ฃผ์์ ์ธ๊ธํ๋ ํธ์ ์์ด๋ ์๋ํ๋ ์ ํํ ์ฃผ๊ฐ ๋ณ๋ ์์ธก ๋ฐฉ๋ฒ์ธ GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets)๋ฅผ ์ ์ํฉ๋๋ค. GLT๋ ์๊ฐ ๊ฐ๋
๋ฐฉ์์ ํ์ฉํ์ฌ ์ฃผ์ ๋ฐ ํธ์ ์๋ฒ ๋ฉ์ ์ฌ์ ํ์ตํฉ๋๋ค. ๊ทธ๋ฐ ๋ค์ GLT๋ ๊ฐ๊ฐ ๊ธ๋ก๋ฒ ์ฌ๋ก ๊ณผ ๋ชฉํ ์ฃผ์๊ณผ ๊ด๋ จ๋ ํธ๋ ๋ ๋ฅผ ๋ํ๋ด๋ ๊ธ๋ก๋ฒ ๋ฐ ๋ก์ปฌ ํธ์ ํธ๋ ๋๋ฅผ ์์ฑํฉ๋๋ค. ์ด๋ฌํ ์ถ์ธ ๋ฒกํฐ๋ค์ ์ฃผ๊ฐ ๋ณ๋์ ์ ํํ๊ฒ ์์ธกํ๋๋ฐ ๊ธฐ์ฌํฉ๋๋ค. ์คํ ๊ฒฐ๊ณผ์ ๋ฐ๋ฅด๋ฉด GLT๋ ์ฃผ๊ฐ ๋ณ๋ ์ ์ธก์์ ์ต๊ณ ์์ค์ ์ ํ๋๋ฅผ ์ ๊ณตํฉ๋๋ค.I. Introduction 1
II. Related Work 4
2.1 Stock Price Movement Prediction 4
2.2 Attentive LSTM 4
III. Proposed Method 6
3.1 Overview 6
3.2 Self-supervised Pre-training for Representing Tweets and Stocks 7
3.3 Global Tweet Trend 10
3.4 Local Tweet Trend 11
3.5 Stock Movement Prediction 11
IV. Experiment 13
4.1 Experiment Setting 13
4.2 Classification Performance 15
4.3 Ablation Study 16
4.4 Hyperparameter Robustness 16
V. Conclusion 18
References 19
Abstract in Korean 23์
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