Media Moments and Corporate Connections: A Deep Learning Approach to Stock Movement Classification

The financial industry poses great challenges with risk modeling and profit generation. These entities are intricately tied to the sophisticated prediction of stock movements. A stock forecaster must untangle the randomness and ever-changing behaviors of the stock market. Stock movements are influenced by a myriad of factors, including company history, performance, and economic-industry connections. However, there are other factors that aren't traditionally included, such as social media and correlations between stocks. Social platforms such as Reddit, Facebook, and X (Twitter) create opportunities for niche communities to share their sentiment on financial assets. By aggregating these opinions from social media in various mediums such as posts, interviews, and news updates, we propose a more holistic approach to include these "media moments" within stock market movement prediction. We introduce a method that combines financial data, social media, and correlated stock relationships via a graph neural network in a hierarchical temporal fashion. Through numerous trials on current S&P 500 index data, with results showing an improvement in cumulative returns by 28%, we provide empirical evidence of our tool's applicability for use in investment decisions.Comment: 10 page

ALGA: Automatic Logic Gate Annotator for Building Financial News Events Detectors

We present a new automatic data labelling framework called ALGA - Automatic Logic Gate Annotator. The framework helps to create large amounts of annotated data for training domain-specific financial news events detection classifiers quicker. ALGA framework implements a rules-based approach to annotate a training dataset. This method has following advantages: 1) unlike traditional data labelling methods, it helps to filter relevant news articles from noise; 2) allows easier transferability to other domains and better interpretability of models trained on automatically labelled data. To create this framework, we focus on the U.S.-based companies that operate in the Apparel and Footwear industry. We show that event detection classifiers trained on the data generated by our framework can achieve state-of-the-art performance in the domain-specific financial events detection task. Besides, we create a domain-specific events synonyms dictionary

과거 가격 및 희소한 트윗을 이용한 주가 변동 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. 강유.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.과거 주가와 예측할 주식을 언급하는 희소한 트윗이 주어졌을 때 주가 변동을 어떻게 정확하게 예측할 수 있을까? 많은 시장 분석가들은 예측을 위해 많은 양의 정 보를 사용하려고 노력합니다. 하지만 예측을 위해 더 많은 양의 데이터를 사용할수 록 더 많은 노이즈에 직면합니다. 따라서 기존 방법은 과거 주식 가격만 사용하거나 뉴스 기사 혹은 대상 주식을 언급하는 트윗과 같은 소량의 정제된 데이터를 사용 합니다. 그러나 기존 방법들은 다음과 같은 한계가 있습니다: 1) 과거 주식 가격만 사용하면 정보가 부족하여 성능이 저하되고, 2) 뉴스 기사는 주가 변동을 예측하는 데 소셜 미디어에 비해 적시성이 부족하며, 3) 트윗을 사용하는 이전 방법들은 트윗이 언급하지 않은 주식들을 처리하지 못합니다. 본 논문에서는 목표 주식을 언급하는 트윗 없이도 작동하는 정확한 주가 변동 예측 방법인 GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets)를 제안합니다. GLT는 자가 감독 방식을 활용하여 주식 및 트윗 임베딩을 사전 학습합니다. 그런 다음 GLT는 각각 글로벌 여론과 목표 주식과 관련된 트렌드 를 나타내는 글로벌 및 로컬 트윗 트렌드를 생성합니다. 이러한 추세 벡터들은 주가 변동을 정확하게 예측하는데 기여합니다. 실험 결과에 따르면 GLT는 주가 변동 예 측에서 최고 수준의 정확도를 제공합니다.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 23석

A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising