Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks

• URL: http://arxiv.org/abs/2206.12528v1
• Date: Sat, 25 Jun 2022 01:54:53 GMT
• Title: Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks
• Authors: Fengyu Han and Yue Wang
• Abstract summary: This work study the predicting the tendency of the stock price on the second day right after the disclosure of the companies' annual reports. We use a variety of different models, including decision tree, logistic regression, random forest, neural network, prototypical networks. We conclude that according to the financial indicators based on the just-released annual report of the company, the predictability of the stock price movement is weak.
• Score: 4.5885930040346565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the current stock market, computer science and technology are more and more widely used to analyse stocks. Not same as most related machine learning stock price prediction work, this work study the predicting the tendency of the stock price on the second day right after the disclosure of the companies' annual reports. We use a variety of different models, including decision tree, logistic regression, random forest, neural network, prototypical networks. We use two sets of financial indicators (key and expanded) to conduct experiments, these financial indicators are obtained from the EastMoney website disclosed by companies, and finally we find that these models are not well behaved to predict the tendency. In addition, we also filter stocks with ROE greater than 0.15 and net cash ratio greater than 0.9. We conclude that according to the financial indicators based on the just-released annual report of the company, the predictability of the stock price movement on the second day after disclosure is weak, with maximum accuracy about 59.6% and maximum precision about 0.56 on our test set by the random forest classifier, and the stock filtering does not improve the performance. And random forests perform best in general among all these models which conforms to some work's findings.

Related papers

• 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)
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets [45.758436505779386]
  We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
  arXiv  Detail & Related papers  (2023-06-01T01:36:51Z)
• 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)
• Benchmarking Machine Learning Models to Predict Corporate Bankruptcy [1.933681537640272]
  Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models. We find that gradient boosted trees outperform other models in one-year-ahead forecasts.
  arXiv  Detail & Related papers  (2022-12-22T22:01:25Z)
• Predicting Day-Ahead Stock Returns using Search Engine Query Volumes: An Application of Gradient Boosted Decision Trees to the S&P 100 [0.0]
  This paper aims to answer the question whether this information can be facilitated to predict future returns of stocks on financial capital markets. It implements gradient boosted decision trees to learn relationships between abnormal returns of stocks within the S&P 100 index and lagged predictors derived from historical financial data. It gives guidance on how to use and transform data on internet usage behavior for financial and economic modeling and forecasting.
  arXiv  Detail & Related papers  (2022-05-31T14:58:46Z)
• Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model [0.0]
  This paper presents an optimized predictive model built on long-and-short-term memory (LSTM) architecture for automatically extracting past stock prices from the web over a specified time interval. The model is deployed for making buy and sell transactions based on its predicted results for 70 important stocks from seven different sectors listed in the National Stock Exchange (NSE) of India. The results indicate that the model is highly accurate in predicting future stock prices.
  arXiv  Detail & Related papers  (2021-11-09T07:50:48Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• Design and Analysis of Robust Deep Learning Models for Stock Price Prediction [0.0]
  Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India.
  arXiv  Detail & Related papers  (2021-06-17T17:15:02Z)
• 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)
• Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models [0.0]
  We present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. We build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices.
  arXiv  Detail & Related papers  (2020-11-07T16:07:10Z)

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.