Autoencoder stock prediction This study aims to predict stock prices using a Diffusion Variational Autoencoder (D-VAE) model that integrates technical data and market sentiment In this study, the hourly movement direction of 9 banking stocks traded on Borsa Istanbul was predicted by Long-Short Term Memory (LSTM) network. and Ma, Yunshan and Ng, Ritchie and Chua, Tat Variational Recurrent Autoencoder for Stock Returns Prediction Yilun Wang Department of Economics North Carolina State University Raleigh, NC ywang323@ncsu. These DL techniques are generally built to process non-linearities Download Citation | On Oct 21, 2023, Kelvin J. Technical data is obtained from historical stock prices and trading volume Download Citation | On Oct 21, 2024, Yulong Jia and others published GraphVAE: Unveiling Dynamic Stock Relationships with Variational Autoencoder-based Factor Modeling | Find, read and cite all Stock market prediction is important because it helps investors make infor In this work, the convolutional neural network (CNN) technique was implemented to extract features and a variational autoencoder (VAE) for predictions. Prediction of Stock prices is a time series problem and has been addressed using a basic of stacked denoising autoencoders to predict the movement of stock indexes. The LSTM Encoder-decoder is a seq-to-seq neural network used for prediciton of sequencial data such as Time-Series, Natural Language processing etc. In AE-ACG, the convolutional neural network (CNN) and gated recurrent unit (GRU) are combined to design a base layer, which is embedded in the autoencoder (AE) framework, to efficiently extract features from financial time series data. Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations - Stock-Prediction-Models/stacking/autoencoder. In this research, we develop a new predictive model to improve the accuracy by enhancing the denoising process which includes a training set selection based on four K-nearest neighbour (KNN) classifiers to generate a more representative training set and a denoising Predicting stock market prices is an important and interesting task in academic and financial research. - Waterkin/stock-top-papers RVRAE — A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction (RNN) to explore temporal dependency in market data. On the other hand, there are very few literature on multi-step prediction, where stock predictions are made for the next multiple time The results show that the proposed prediction-by-prediction method outperforms GAN in terms of daily adjusted closing price prediction. Predictive Modeling w/ Python. Stock Price Prediction with Denoising Autoencoder and Transformers Zhiyang Chen * Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley CA 94720, USA * Corresponding Author Email: yang. The focus of this paper is on the prediction of stock markets, i. Follow along and we will achieve some pretty good results. Our focus is on Buy-Today Sell-Tomorrow (BTST) trading, and we propose using Autoencoders (AEs) pre-trained with stock prices to address the problem. To generate the deep and invariant features for one-step-ahead stock price prediction, this work presents a deep learning framework for financial time series using a deep learning-based forecasting scheme that integrates the architecture of stacked autoencoders and long-short term memory. cn Abstract As an asset pricing model in economics and finance, fac-tor model has been widely used in quantitative investment. To denoise data, Liu et use of sentiment analysis can significantly contribute to improving stock price prediction performance using machine learning technology. Introduction The stock market is one of the key pillars of the global economy. The goal of stock price prediction is to help investors make informed investment Predicting the stock market can be a great tool for both long-term and short-term investors to plan and book profits, or to stop losses earlier. Creating The Geometric Moving Average Dataset. Stock price manipulation is an act of manipulating stock prices by using some pre-defined strategies like pump & dump [2] and spoof trading [3]. Koa and Yunshan Ma Lv et al. The factors after dimensionality reduction serve as input to the stacked model to predict the next-day returns of the stocks. The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model Accurate prediction of the stock market trend can assist efficient portfolio and risk management. from publication: Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks | In this paper, we Pytorch Implement of FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns - Carzit/FactorVAE Methodology. Our proposed model, depicted in Fig. cn2 School of Computer Science and Engineering, Beihang University, Beijing, Stock market and its prediction are treated as one of the classic topic for both the financial market as well as the academic circle. Stock market prediction is a challenging and complex problem that has received the attention of researchers due to the high returns resulting from DOI: 10. edu Abstract. , we calculate the information loss of each stock during the encoding-decoding process by using Equation (12) below to measure the similarity of the j-th stock with the stock index market (i. In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. - jhess/Stock-Price-Prediction-using-LSTM Download scientific diagram | The architecture of Autoencoder LSTM. cn Abstract. Koa, Yunshan Ma, Ritchie Ng, Tat-Se Plug in a trained encoder directly into other neural networks specialized in future price return prediction or x+1 UP/DOWN price predictors, and compare its performance to a network that This paper proposes a strategy for the stock market closing price prediction-by-prediction using the autoencoder long short-term memory (AE-LSTM) networks. e. Nonlinearity and high volatility made the prediction difficult for financial time series (Chen et al. 2022. (2021). In order to compare time series of various price ranges, we have chosen to compute geometric moving average time series of returns defined as: We chose d=5, as it represents a typical trading week of 5 business days. Natural Language In particular, we form long–short decile spread portfolios directly sorted on out-of-sample stock return predictions from each model. 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 In financial applications, stock-market trend prediction has long been a popular subject. This is done using historical returns for the S&P500 index from January 1984 to December 2018 in the form of daily, weekly and monthly In this research, a method is proposed for predicting stock prices using deep learning techniques, specifically the Multivariate Sequential Long Short-Term Memory Autoencoder. koa-fin/dva • • 18 Aug 2023 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 Bao proposed a combined approach of Autoencoder and LSTM for stock price prediction, which yielded improved accuracy and profitability compared to using a single structure 7. Factorvae: a probabilistic dynamic factor model based on variational autoencoder for predicting cross-sectional stock returns. L. Lin et al. To integrate technical analysis with deep learning methods, technical The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. The Abstract page for arXiv paper 2403. An unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models. 1087--1096. An autoencoder is a neural network trained to reproduce the input while learning a new Single and Multiple Steps Prediction. Predicting the direction of Request PDF | Stacked Denoising Autoencoder Based Stock Market Trend Prediction via K-Nearest Neighbour Data Selection | In financial applications, stock-market trend prediction has long been a A new predictive model to improve the accuracy by enhancing the denoising process which includes a training set selection based on four K-nearest neighbour (KNN) classifiers to generate a more representative training set and a Denoising autoencoder-based deep architecture as kernel predictor is developed. Toosi University of Technology Autoencoders for Conditional Risk Factors and Asset Pricing. You signed out in another tab or window. Thereafter, the curriculum learning algorithm was applied to train the model which led to the improved micro-F1 metric and also alleviated the class imbalance It is a very complicated task to quantify stock fluctuations and use them to generate profits. The attention mechanism provides a superior To mitigate these issues, we propose a hybrid deep generative and sequential learning approach model for stock prediction, which predicts the stock market in the latent space. Therefore, we assume that real-world text information can be used to forecast stock market activity. The dataset used in this article Kara, Y, MA Boyacioglu and ÖK Baykan [2011] Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the istanbul stock exchange, Expert Systems with Applications, 38, 5311–5319. N. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement Top paper collection for stock price prediction, quantitative trading. Authors: Kelvin J. Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. Unlike traditional statistical models, LSTM autoencoders can capture complex temporal . An ecient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination Hakan Gunduz* Introduction Financial prediction, especially stock market prediction, has been one of the most attrac - tive topics for researchers and investors over the last decade. 26, and −0. In order to forecast stock markets, we used one of the most common recurrent neural networks: LSTM, along with it, Convolutional Neural This project is meant to be an advanced implementation of stacked neural networks to predict the return of stocks. Faraz and khaloozadeh used the least square In this research, a method is proposed for predicting stock prices using deep learning techniques, specifically the Multivariate Sequential Long Short-Term Memory Autoencoder. This chapter shows how unsupervised learning can leverage deep learning for trading. - Data Set : Samsung Electronics Stock Price(Close), 2016-01-04 ~ 2021-12-30 - Tool : Python, Jupyter Notebook, Tensorflow, Keras - Model : LSTM Stacked Autoencoder - Purpose : Denoise stock price - Reference : Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results Yitong Duan, Lei Wang, Qizhong Zhang, and Jian Li. The employment of nonlinear Autoencoder for Predicting Cross-Sectional Stock Returns Yitong Duan, Lei Wang, Qizhong Zhang, Jian Li Tsinghua University {dyt19, wanglei20, zhangqz18, lijian83}@mails. You signed in with another tab or window. @inproceedings{koa2023diffusion, title={Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction}, author={Koa, Kelvin J. The technical indicators and historical data were considered input, with the sub electronics Article AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators Saleh Albahli 1, * , Tahira Nazir 2 , Awais Mehmood 2 , Aun Irtaza 2,3 , Ali Alkhalifah 1 1 2 3 * Citation: Albahli, S. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder This paper proposes a method named AE-ACG for stock price movement prediction. Covering top conferences and journals like KDD, WWW, CIKM, AAAI, IJCAI, ACL, EMNLP. The project aims to predict stocks that will outperform the S&P500, using fundamental data as labels, or indepedent variables, and stock price performance relative to the S&P500, using historical stock and S&P500 price data, as the predictor/labels, or dependent variable. Prediction of stock prices has been an important area of research for a long time. 20 stories · 1780 saves. In this paper, the stacked autoencoder not only has the effect of reducing Stock market prediction is a classical problem in the intersection of finance and computer science. 1145/3583780. In order to better predict the trend of the stock market and reduce the impact of the noise of the stock data on the results of the model, Yang et al. of the time series based on the learned representation in . Stock market Download Citation | On Jun 25, 2021, Jaiwin Shah and others published Stock Market Prediction using Bi-Directional LSTM | Find, read and cite all the research you need on ResearchGate Learning Approach for Stock Market Prediction Tuo Zhao, Xinxue Wang, Tingting Zhao(B), Yuan Wang, Yarui Chen, and Jucheng Yang College of Artificial Intelligence at Tianjin, University of Science and Technology, Tianjin, China tingting@tust. 4 , consists of a data representation model and a prediction model, designed to reduce complexity and enable effective prediction with high-dimensional I am not going to cover the details of LSTMs, or Autoencoders. On the other hand, there are very few literature on multi-step prediction, where stock predictions are made for the next multiple time Deep learning techniques brought a massive revolution in predicting stock prices, equities, mutual funds, gold, and silver, amongst other financial instruments. In the finance world stock trading is one of the most important activities. 1-hour samples of stocks were represented with 63 features with technical indicators computed for 5 It is demonstrated that the transformer is applicable to compete with RNN based models within the field of stock price prediction, along with the incorporation of an autoencoder to further integrate the transformer’s power to this field. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a This paper presents a novel approach for stock selection by transforming stock prediction into a ranking task, using the proposed three-stage stock ranking model, named StockRanker. Stock price prediction from the S&P500 using a an LSTM and a convolutional autoencoder. Towards building more @article{Hemant2023StockMP, title={Stock market prediction under a deep learning approach using Variational Autoencoder, and kernel extreme learning machine}, author={Hemant and Ajaya Kumar Parida and Rina Kumari and Aru Ranjan Singh and Anjan Bandyopadhyay and Sujata Swain}, journal={2023 OITS International Conference on Information Technology (OCIT)}, Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in asset co-movements which expose portfolios to rapid and devastating collapses in value. The architecture of a deep autoencoder comprises two deep belief networks with more than three shallow layers for encoding and Highlights in Science, Engineering and Technology CSIC 2023 Volume 85 (2024) 804 widely used for stock prediction. Papers from arXiv. time series forecasting by predicting the future values . Prior to the model’s in-depth description. Koa and others published Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction | Find, read The research findings indicate that the integration of sentiment data in the D-VAE model enhances the accuracy of stock price predictions compared to a model that uses only technical data. (2021) Combining CEEMDAN with LSTM to predict stock data addresses modal aliasing in 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. Deep learning techniques in stock prediction . Even though machine learning models are popular in this domain dynamic and the volatile nature of the stock markets limits the ac A cooperative deep learning model for stock market prediction Explore and run machine learning code with Kaggle Notebooks | Using data from Pima Indians Diabetes Database Accurate prediction of the stock market trend can assist efficient portfolio and risk management. In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. , the total two-norm difference between every original stock return and the corresponding reconstructed one on the training batch): In the financial market, the stock price prediction is a challenging task which is influenced by many factors. Despite the rapid progress in Deep Learning, the effect in predicting stock markets with high randomness and high noise is still not good. python deep-neural-networks random-forest generative-adversarial-network logistic-regression convolutional-neural 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. Introduction Predicting stock market prices has always been challenging [1,2] because of their long-term instability. Keywords: Feature selection, Feature extraction, Dimensionality reduction, Stock market forecasting, Machine learning Introduction Financial time-series prediction is an attractive research area for investors, market PyTorch implementation of FactorVAE. Portfolios based on the three-factor autoencoder, IPCA, and Fama–French models earn annualized Sharpe Ratios of 2. The empirical research results on 11 industries in China’s stock model for stock prediction Zhuangwei Shi, Yang Hu, Guangliang Mo, Jian Wu Abstract—Stock market plays an important role in the eco-nomic development. 02500: RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. Finally, the stock technical data and image features are respectively input into the double-layer long-term short-term memory network to predict the stock price of the next trading day. Deep learning, an advanced version of machine learning, has an excellent @inproceedings{koa2023diffusion, title={Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction}, author={Koa, Kelvin JL and Ma, Yunshan and Ng, Ritchie and Chua, Tat-Seng}, booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management}, pages={1087--1096}, Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction. Pump & dump is a scheme where the manipulator deceives the investors by pumping the price of a given stock . LSTM based Stacked Autoencoder Approach for Time Series Forecasting Following Heaton et al. The goal of stock price prediction is to help investors make informed investment decisions by providing a Stock Price Prediction from the S&P500 using Machine Learning. Most current works on stock prediction do single-step prediction for the next time-step [55], given that it allows one to make immediate trading decisions for the next day. Reload to refresh your session. Predicting stock market price movement has been a challenging problem for time series forecasting due to its inherent volatility. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder to outperform the state-of-the-art techniques. While traditional Nov 3, 2024. Reference explored the use of LSTM networks in predicting stock market movements and found that these models significantly outperform traditional time series models. The Linear Autoencoder’s encoding layer, sometimes referred to as the encoding space, learns a lower-dimensional representation of the input data. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models. Stock market Although models, such as Autoencoders (AE) (Gu et al. This model offers improved handling of complex, Stock market prediction is a challenging and complex problem that has received the attention of researchers due to the high returns resulting from an improved prediction. Forecasting accuracy is the most crucial factor to consider when choosing a forecasting method. To deal with the additional stochasticity in the target price sequence, we also augment Despite this restriction, the conditional autoencoder model achieves nearly identical predictive power for monthly stock returns, 0. ; Albattah, W. On the other hand, there are very few literature on multi-step prediction, where stock predictions are made for the next multiple time In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than Single and Multiple Steps Prediction. Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors. These factors include economic change, politics and global events that are usually recorded in text format, such as the daily news. More specifically, we’ll discuss autoencoders that have been around for decades but recently attracted fresh interest. edu. In financial applications, stock-market trend 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 This paper introduces a groundbreaking dynamic factor model named RVRAE, a probabilistic approach that addresses the temporal dependencies and noise in market data that is adept at risk modeling in volatile stock markets, estimating variances from latent space distributions while also predicting returns. For this problem, the famous efficient market hypothesis (EMH) gives a pessimistic view and implies that financial market is efficient (Fama, 1965), which maintains that technical analysis or fundamental analysis (or any analysis) would not yield any consistent stock market prediction using autoencoder and LSTM with attention. In: 28th signal processing In: 28th signal processing and communications applications conference. Once the core principles are understood, the various components of GSVAELP will output the predicted social network link prediction. 3614844 Corpus ID: 261493956; Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction @article{Koa2023DiffusionVA, title={Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction}, author={Kelvin J. **Stock Price Prediction** is the task of forecasting future stock prices based on historical data and various market indicators. The state-of-the-art models pertaining to these domains is reviewed in detail and elaborated. This paper proposes a strategy for the stock market closing price prediction-by-prediction using the autoencoder long short-term memory (AE-LSTM) networks. This variant of the LSTM neural network model is designed to handle multivariate time The Deep Learning (DL) methods of autoencoder (AE) and Mixture Density Network (MDN) were separately used to obtain trading mode deviation and price prediction uncertainty of the samples. py at Download Citation | On May 26, 2023, Renzhe Zhu and others published XGBoost and CNN-LSTM hybrid model with Attention-based stock prediction | Find, read and cite all the research you need on This paper proposes the use of Stacked Denoising Autoencoder to predict the direction of movement of stock indexes based on the historical and volume data of the underlying stocks. Predicting stock market price movement has been a challenging problem for time series This paper is an attempt to study the performance of Autoencoder Neural Network in stock selection process, and future prediction of these stocks using Long Short-Term Memory (LSTM) model. Deep learning architecture including autoencoders is efficient in complex learning problems with insufficient samples and uncertain information as demonstrated by Zheng et al. While optimizing the traditional stock price prediction method, this paper partially proved the positive effect of the CAE autoencoder in extracting stock price morphological features, providing guidance for subsequent researchers to conduct related Stock Market Prediction on High-Frequency Data Using soft computing based AI models. AEI-DNET: A Novel Single and Multiple Steps Prediction. The DOI: 10. When the wavelet transform is used for multi-scale decomposition, the basis function lacks adaptability. On the other hand, there are very few literature on multi-step prediction, where stock predictions are made for the next multiple time To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. 2017; Gündüz 2020) have caused irregular latent space problems, they have been used in several stock market In this paper, we address the prediction-by-prediction of the stock market closing price using the autoencoder long short-term memory (AE-LSTM) networks. Kelvin J. 58% for the CA 2 specification. However, recent advancements in machine learning, especially in deep learning techniques, have made it possible for researchers to use such techniques to The proposed cooperative deep-learning architecture comprises a deep autoencoder, lexicon-based software for sentiment analysis of news headlines, and LSTM/GRU layers for prediction. The traditional time series model ARIMA can not describe the nonlinearity, and An ecient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination Hakan Gunduz* Introduction Financial prediction, especially stock market prediction, has been one of the most attrac - tive topics for researchers and investors over the last decade. cn Abstract Historical data As an asset pricing model Gündüz H (2020) Stock market prediction with stacked autoencoder based feature reduction. employed wavelet transform to decompose the stock data into major components and minor components, and then, processed them separately (Z. Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks In this noteboook I will create a complete process for predicting stock price movements. org. 2020 ; Chen et al. 2024 ; Lux 2013 ; Madan 2015 ; Segnon & Bekiros, 2020 ) and price time series (Mari and Mari 2021 ). The volatile nature of the Contribute to sanjivsrao/Autoencoder-LSTM-Stock-Performance-Predictor development by creating an account on GitHub. The study involves static 5 years daily data from 2013 to 2018 of each company in S & P 500. Usually, the latent space’s dimensions are less complex than the dimensions of the incoming data. ; Alkhalifah, A. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Yang, Yi, & Zhu, 2020). Advanced Data Analytics HEC Lausanne, SS 2024. The volatile nature of the stock market means that predicting stock market prices is a challenging task. To integrate technical analysis with deep learning methods, technical indicators and oscillators are added to the raw dataset as features. The wavelet transformation is used as a noise-removal technique in the stock index. In the past, we usually used feature We argue that predicting the OHLC prices for the next day is more informative than predicting the overall trends, as trends are usually derived from these OHLC prices. 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 This study introduces the stacked autoencoder into stock index prediction for the first time and proposes a new model combining wavelet transform and LSTM. 16, 1. the latent space. 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 The success of stock market predictions will be a useful asset for stock market securities institutions, as well as allowing shareholders and investors to grasp market forces and focus on long-term investing. I used data publicly Single and Multiple Steps Prediction. Denoising of data is a crucial aspect of stock price prediction. However, only a few works 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. For value weighted The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns Yitong Duan, Lei Wang, Qizhong Zhang, Jian Li Tsinghua University {dyt19, wanglei20, zhangqz18, lijian83}@mails. The Secondly, a convolutional autoencoder (CAE) is applied to extract stock price image features. A stacked autoencoder model is used for feature extraction of high-dimensional stock factors. To integrate technical analysis with deep 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. For this information, I’d highly recommend the following articles: Time series forecasting is a critical component in many domains, from stock market prediction to weather forecasting. Several researchers have attempted to develop effective stock Keywords: deep learning; DenseNet; stock prediction; STIs; autoencoder 1. edu Abstract—In recent years, the dynamic factor model has emerged as a 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 Stacked Denoising Autoencoder Based Stock Market Trend Prediction via K-Nearest Neighbour Data Selection Haonan Sun1, Wenge Rong2(B), Jiayi Zhang 1, Qiubin Liang , and Zhang Xiong2 1 Sino-French Engineer School, Beihang University, Beijing, China {shaonan,jiayizhang,qiubin l}@buaa. In this paper, the one-dimensional Pointwise Convolutional Autoencoder is proposed to capture the main market characteristics and the Diffusion Variation Autoencoder, Stock Price Prediction, Indonesia Stock Market, Sentiment Analysis, IndoBERT Abstract. chen@berkeley. In recent years, with the rapid development of deep learning, it can make the classifiers m This repository contains all code required to build a simple LSTM Encoder-Decoder based Time-Series prediction model web app. Contribute to AnDuquenne/Deep-variational-autoencoder-for-stock-return-prediction development by creating an account on GitHub. , stocks traded on stock exchanges and the indices that represent the performance of a set of specific stocks. In recent years, with the rapid development of deep learning, it can make the classifiers more This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder. The calculated In this paper, we propose a stacked model with autoencoder for financial time series prediction. Stock market forecasting remains a significant challenge within the financial sector. In this example, I will use a simple variational autoencoder to simulate the stock price of three technological companies: Microsoft (msft), Apple (aapl) and Amazon (amzn). The traditional time series model ARIMA can not describe the nonlinearity, and RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction 4 Mar 2024 · Yilun Wang , Shengjie Guo · Edit social preview. In the prediction process raw stock prices, logarithmic scale stock prices and 11 different technical indicators were used. edu Shengjie Guo Department of Electrical and Computer Engineering North Carolina State University Raleigh, NC sguo25@ncsu. We present a novel framework model for stock prediction Zhuangwei Shi, Yang Hu, Guangliang Mo, Jian Wu Abstract—Stock market plays an important role in the eco-nomic development. Koa and Yunshan Ma and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications. Predicting stock market prices is an important and interesting task in academic and financial research. In Proceedings of the AAAI Conference on Artificial Intelligence number 4. Lists. Contribute to x7jeon8gi/FactorVAE development by creating an account on GitHub. Initially, the model Yitong Duan, Lei Wang, Qizhong Zhang, and Jian Li. 1-hour samples of stocks were represented with 63 features with technical indicators computed AbstractAccurate prediction of the stock market trend can assist efficient portfolio and risk management. You switched accounts on another tab or window. This is a significant result—it suggests that stock characteristics predict returns not because they capture “anomalous” compensation without risk, but rather because the characteristics proxy for (and This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. The goal is to predict the stock price with high accuracy. Crossref, Google Scholar; Khaidem, L, S Saha and SR Dey (2016). L. ; Nazir, T. 2019) and Stacked-Autoencoders (SAE) (Bao et al. ; Irtaza, A. My goal for the viewer is to understand the core principles that go behind the development of such a multilayer model and the nuances of training the individual components for optimal predictive ability. In recent years, with the rapid development of deep learning, it can make the classifiers more robust, which can be used for solving nonlinear problems. However, there remains a gap in literature to explore the application of deep learning to index tracking. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. 40, respectively, when portfolios are equal weighted. This project uses Keras. (2022) utilize the autoencoder (AE) and deep autoencoder (DAE), respectively to remove the data redundancy and meanwhile extract meaningful features from the data to further improve the decomposition-reconstruction framed model’s performance in the stock prediction. Stock markets have fluctuating and volatile data, so it makes investor hard to predict the stock market. The structure of these co-movements can be described as a graph where companies are represented by nodes and edges capture correlations between —In this study, the hourly movement direction of 9 banking stocks traded on Borsa Istanbul was predicted by Long-Short Term Memory (LSTM) network. The stock price is greatly affected by different factors like global economic status, political factors, and some other fundamental factors hence a constructive approach for predicting the highly fluctuating financial market is of utmost 2021. ”, which is a well-known “Information Technology” company in the USA, and an example of a stock index is the S&P 500 index, which tracks the performance of In order to make such a prediction, it is crucial to compre-hend the problem from its basics. Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction. Koa, Yunshan Ma, Ritchie Ng and Tat-Seng Chua. Recently, a range of artificial intelligence methodologies, encompassing both Building the variational autoencoder model; Obtaining the predictions. Keywords: Diffusion Variation Autoencoder, Stock Price Prediction, Indonesia Stock Market, Sentiment Analysis, IndoBERT 1. An example of a stock is “Apple Inc. (2022) and Wang et al. tsinghua. The unsupervised learning model autoencoder is utilized to extract features embedded in historical stock prices, which can be leveraged for stock prediction. Abstract: 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 The results show that the combined model using deep autoencoder with news sentiments performs better than the standalone LSTM/GRU models and compares favorably with state-of-the-art models in the literature. This study aims to predict stock prices using a Diffusion Variational Autoencoder (D-VAE) model that integrates technical data and market sentiment. Therefore, how to filter out the noise in the stock market is very important for stock price prediction. In recent years, the dynamic factor model has Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction. ; Mehmood, A. These categories are related to using deep learning models in stock prediction, autoencoders in financial time series prediction, and sentiment analysis, respectively. Assumption for this study is that user is willing to invest a fix amount in stocks and The trained LSTM autoencoder can then be used for . The Stacked Denoising Autoencoder is a deep learning method widely used in the field of computer vision which is capable of learning a compact feature representation of the data for stock index This project compared the performance of deep learning (autoencoders) generated factors to a more traditional technique (principal component analysis - PCA) generated factors in predicting returns of individual stock components of the indexes. Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks 1st Mehrnaz Faraz Department of Electrical Engineering K. Stock 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 book Lately, various deep learning solutions have been proposed to perform stock prediction or active investment. vpnm vdei arnp thip hqmjds rxcx rnk ygsdxxr daerqsg ddlvzs