Intraday Stock Price Research Articles

Informer-Based Method for Stock Intraday Price Prediction

As a component of the capital market, the stock market plays a very important role in economic development. How to predict the stock price more accurately is the key concern of researchers and investors. To solve the above problems, this paper proposes an Informer based method for stock intraday price prediction. Informer based on attention mechanism can efficiently capture precise long-range dependencies between input and output to increase the prediction capacity. In this research, historical K-line data of traditional securities in China’s financial market are collected. Based on this data set, the intraday stock price prediction performance of Informer and Long Short-Term Memory neural network (LSTM) is compared. The experiment also uses the three-day K-line model to perform pattern recognition on the K-line data of candidate stocks. The results show that the prediction performance of Informer is significantly higher than that of LSTM, and the data information after three-day K-line pattern recognition can improve the prediction accuracy of Informer and reduce time and space overhead of Informer.

Market Predictability Before the Closing Bell Rings

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study’s insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study’s limitations.

Machine Learning-Based Framework for Intraday Stock Price Movement Prediction Using Open Prices and Historical Data

Machine Learning-Based Framework for Intraday Stock Price Movement Prediction Using Open Prices and Historical Data

The Intraday Dynamics Predictor: A TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units for High-Frequency Price Movement Forecasting

This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limit order book (LOB) and order flow (OF) microstructure data and improving prediction accuracy over current state-of-the-art models. The proposed deep learning model, TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units (TFF-CL-GRU), takes LOB and OF features as input and consists of convolutional layers splitting into three channels before rejoining into a Gated Recurrent Unit. Key innovations include a tailored input representation incorporating LOB and OF features across recent timestamps, a hierarchical feature-learning architecture leveraging convolutional and recurrent layers, and a model design specifically optimised for LOB and OF data. Experiments utilise a new dataset (MICEX LOB OF) with over 1.5 million LOB and OF records and the existing LOBSTER dataset. Comparative evaluation against the state-of-the-art models demonstrates significant performance improvements with the TFF-CL-GRU approach. Through simulated trading experiments, the model also demonstrates practical applicability, yielding positive returns when used for trade signals. This work contributes a new dataset, performance improvements for microstructure-based price prediction, and insights into effectively applying deep learning to financial time-series data. The results highlight the viability of data-driven deep learning techniques in algorithmic trading systems.

Financial news media and volatility: Is there more to newspapers than news?

Does media coverage of a firm have a causal effect on the volatility of its stock price and, if so, is this of aggregate importance? I identify a robust link between coverage in the Financial Times and a firm’s intraday stock price volatility. This effect is not driven by persistence in volatility or anticipation of future newsworthy events, but is explained by an increase in trading volume, supporting a salience interpretation. The effect spills over into firms related by the structure of the production network, but does not affect the aggregate level of volatility.

Forecasting intraday financial time series with sieve bootstrapping and dynamic updating

AbstractIntraday financial data often take the form of a collection of curves that can be observed sequentially over time, such as intraday stock price curves. These curves can be viewed as a time series of functions observed on equally spaced and dense grids. Due to the curse of dimensionality, high‐dimensional data pose challenges from a statistical aspect; however, it also provides opportunities to analyze a rich source of information so that the dynamic changes within short‐time intervals can be better understood. We consider a sieve bootstrap method to construct 1‐day‐ahead point and interval forecasts in a model‐free way. As we sequentially observe new data, we also implement two dynamic updating methods to update point and interval forecasts for achieving improved accuracy. The forecasting methods are validated through an empirical study of 5‐min cumulative intraday returns of the S&P/ASX All Ordinaries Index.

“I just like the stock”: The role of Reddit sentiment in the GameStop share rally

AbstractThis paper investigates the role played by the social media platform Reddit in the events around the GameStop (GME) share rally in early 2021. In particular, we analyze the impact of discussions on the r/WallStreetBets subreddit on the price dynamics of the American online retailer GameStop. We customize a sentiment analysis dictionary for Reddit platform users based on the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis package and perform textual analysis on 10.8 million comments. The analysis of the relationships between Reddit sentiments and 1‐, 5‐, 10‐, and 30‐min GameStop returns contribute to the growing body of literature on “meme stocks” and the impact of discussions on investment forums on intraday stock price movements.

Realized moments and the cross-sectional stock returns around earnings announcements

We examine the predictability of realized measures on the cross-section of stock returns around earnings announcements. We construct realized measures (variance, skewness, kurtosis, and relative jump) using high-frequency intraday stock prices. Our results show that realized variance, skewness, and relative jumps strongly predict stock returns around earnings announcements but realized kurtosis does not. These findings are robust to various event windows, after controlling for firm characteristics, and to a range of additional tests. We further show that the predictability of realized measures is not affected by unexpected earnings. The findings also suggest that pre-announcement realized measures absorb part of the information contained in unexpected earnings.

Empirical deep hedging

Existing hedging strategies are typically based on specific financial models: either the strategies are directly based on a given option pricing model or stock price and volatility models are used indirectly by generating synthetic data on which an agent is trained with reinforcement learning. In this paper, we train an agent in a pure data-driven manner. Particularly, we do not need any specifications on volatility or jump dynamics but use large empirical intra-day data from actual stock and option markets. The agent is trained for the hedging of derivative securities using deep reinforcement learning (DRL) with continuous actions. The training data consists of intra-day option price observations on S&P500 index over 6 years, and top of that, we use other data periods for validation and testing. We have two important empirical results. First, a DRL agent trained using synthetic data generated from a calibrated stochastic volatility model outperforms the classic Black–Scholes delta hedging strategy. Second, and more importantly, we find that a DRL agent, which is empirically trained directly using actual intra-day stock and option prices without the prior specification of the underlying volatility or jump processes, has superior performance compared with the use of synthetic data. This implies that DRL can capture the dynamics of S&P500 from the actual intra-day data and to self-learn how to hedge actual options efficiently.

Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms

• Artificial Neural Networks (ANNs) live a vital role in many areas including medical, security systems and stock market. • This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs. • Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. • Other models produced low performances in prediction accuracy are 97.2, 98.4, 84.0, 85.2, 84.0, 83.9, 89.9 and 78.6%. Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. Artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively.

ECB language and stock returns – A textual analysis of ECB press conferences

This paper examines the language used by central bank officials in public press conferences and how it influences stock returns in the euro area. By proposing a sentiment mining approach that accounts for grammatical and syntactical cues, a distinction is drawn between (i) the general tone, (ii) uncertainty, and (iii) constraint expressed by ECB officials. The results show that as constraining language is often used to express policy commitment, press conferences with higher fractions of constraining language are associated with positive intraday stock price movements in times of unconventional monetary policy. In addition, we conclude that in times of crises, intraday returns are more sensitive towards the general tone of ECB officials as market participants may find it harder to grasp the future path of monetary policy. In further analyses, a distinction is made between sections within the press conferences that specifically address either the monetary analysis or the economic outlook. The results indicate that market participants interpret uncertain language in the economic outlook as a signal of expansive monetary policy decisions going forward as indicated by positive intraday stock returns. Finally, by proposing a novel rule-based approach to identify forward-looking statements of ECB press conferences, first evidence is provided in this paper that forward-looking answers given by ECB officials in the Q&A Sessions significantly affect euro area stock returns.

Forecasting intraday stock price using ANFIS and bio-inspired algorithms

Forecasting intraday stock price using ANFIS and bio-inspired algorithms

Forecasting intraday stock price using ANFIS and bio-inspired algorithms

The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction acc...

Functional Unit Root Test

In this paper, we propose a unit root test for functional time series. We derive a new analytical framework for nonstationary functional time series. Specifically, for the proposed test statistic, we derive its limit distribution under the null hypothesis of a random walk and its asymptotic behavior of alternative hypotheses of trend stationary, weakly dependent stationary, and autoregressive stationary models. For the trend stationary model as an alternative, a theoretical derivation of the test consistency is provided, while for other two alternatives, a combination of theoretical and experimental validation on the statistical power of the test is presented. Simulation studies are conducted to justify the theories and the desirable finite-sample performance of the proposed functional unit root test. The proposed test is also applied to real data of intraday stock price curves and the test results are plausible.

Stock Crashes and Jumps Reactions to Information Demand and Supply: An Intraday Analysis

The objective of this paper is to examine the possible linkage between the intraday stock price crashes and jumps and public information by using data from the Chinese stock market and Baidu Index. We divided public information into two kinds of information: supply through online media and information demand across inquiries by individual investors. Using a large sample from Chinese listed firms from 2013 to 2019, our evidence clearly indicates that online information supply and demand both have a positive impact on the intraday crashes and jumps; this is, the firm with higher information supply and demand more likely to experience intraday crashes and jumps. The results are robust to an alternative measure of crash risk. Moreover, we further examine whether the market conditions have an impact on the relationship between information flow and intraday crashes and jumps, and find that the marginal effect of information supply on intraday price crashes and jumps is smaller in the bull market phase. Moreover, the bull market phase enhances the effect of information demand on intraday price crashes and jumps.

Predicting Intraday Prices in the Frontier Stock Market of Romania Using Machine Learning Algorithms

This paper investigates if forecasting models based on Machine Learning (ML) Algorithms are capable to predict intraday prices in the small, frontier stock market of Romania. The results show that this is indeed the case. Moreover, the prediction accuracy of the various models improves as the forecasting horizon increases. Overall, ML forecasting models are superior to the passive buy and hold strategy, as well as to a naïve strategy that always predicts the last known price action will continue. However, we also show that this superior predictive ability cannot be converted into “abnormal”, economically significant profits after considering transaction costs. This implies that intraday stock prices incorporate information within the accepted bounds of weak-form market efficiency, and cannot be “timed” even by sophisticated investors equipped with state of the art ML prediction models.

Economic indicators and stock market volatility in an emerging economy

By analyzing the daily realized volatility series calculated from intraday stock price observations, this study examines the direct causality between one-day-ahead aggregate stock market volatility and several economic and financial indicators in the Korean market, a leading emerging market. Using the predictive regression and superior predictive ability tests, we find that the model-free implied volatility index (VKOSPI) and stock market indicators both lead the daily market volatility. However, daily economic indicators provide no predictive information beyond that contained in historical volatility. Though in-sample causality does not guarantee a better out-of-sample forecasting performance, the VKOSPI and combinations of predictors exhibit significant predictive ability regardless of the time period. Our study verifies the information role of the VKOSPI as an indicator of daily market risk.

Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning

With the rapid development of the stock markets in developing countries, determining how to efficiently detect stock price manipulation activities to protect the interests of ordinary investors is really an important problem. Previous studies have introduced machine learning techniques into stock price manipulation detection and achieved better experimental results than traditional multivariate statistical techniques. Some characteristic features show statistically significant differences between manipulated and non-manipulated stocks, but this complementary information has rarely been considered in the manipulation detection model. The main contribution of our research work is the design of a novel RNN-based ensemble learning (RNN-EL) framework that combine trade-based features derived from trading records and characteristic features of the list companies to effectively detect stock price manipulation activities. Based on prosecuted manipulation cases reported by the China Securities Regulatory Commission (CSRC), we built a specific dataset containing labeled samples with trading data and characteristic information to conduct empirical experiments. The experimental results show that our proposed method outperforms state-of-the-art approaches in detecting stock price manipulation by an average of 29.8% in terms of AUC value. The managerial implication of our work is that government regulators can apply the proposed methodology to efficiently identify suspicious trading behaviors among huge amounts of trading activities in time to take action to ensure a fair trading environment.

Inference for the Lagged Cross‐Covariance Operator Between Functional Time Series

When considering two or more time series of functional data objects, for instance those derived from densely observed intraday stock price data of several companies, the empirical cross‐covariance operator is of fundamental importance due to its role in functional lagged regression and exploratory data analysis. Despite its relevance, statistical procedures for measuring the significance of such estimators are currently undeveloped. We present methodology based on a functional central limit theorem for conducting statistical inference for the cross‐covariance operator estimated between two stationary, weakly dependent, functional time series. Specifically, we consider testing the null hypothesis that the two series possess a specified cross‐covariance structure at a given lag. Since this test assumes that the series are jointly stationary, we also develop a change‐point detection procedure to validate this assumption of independent interest. The most imposing technical hurdle in implementing the proposed tests involves estimating the spectrum of a high dimensional spectral density operator at frequency zero. We propose a simple dimension reduction procedure based on functional principal component analysis to achieve this, which is shown to perform well in a simulation study. We illustrate the proposed methodology with an application to densely observed intraday price data of stocks listed on the New York stock exchange‐20.40

Black swan events in China's stock markets: Intraday price behaviors on days of volatility

China's stock market crash on August 24, 2015 affected global stock markets, indicating a possible black swan event. This study looked at trading days when sudden rises and drops occurred in 2015 in its stock markets to examine the intraday fluctuation behaviors of stock prices in order to answer several questions: (a) How did the market resume stability after volatility in stock prices occurred? (b) Did the corrections after days of sudden rises differ from those after days when sudden drops occurred? (c) Which of the investigated trading days could be classified as black swan events? Five trading days with the highest and lowest daily returns in 2015 in the Shanghai and Shenzhen stock markets were selected as the days of volatility. The quantile autoregression unit-root test was used to test whether stock price indices converged or dispersed on days of high volatility as time passed. The results reveal that a black swan event was identifiable only for the sudden drop on May 28. On other trading days, China's stock markets exhibited notable corrections of mean reversion, and the speed of market recovery increased with the extent of price volatility. In addition, wave patterns of slow rises and rapid falls in intraday stock price fluctuations and corrections were observable after occurrences of stock price volatility, showing that overreactions and underreactions happened consecutively.