We perform a panel data analysis of 14 daily stock market indices during 01/21/2020 - 06/30/2020 to document a stock market index's negative responsiveness to Covid-19's spread variations. We find ...that a stock market index's elasticity estimate is −0.028 (p-value <0.01) for local cumulative confirmed cases. As a stock market index tends to move with Covid-19's local and non-local spreads, international efforts of containment are expected to pare stock market losses.
Applying deep learning, especially time series neural network, to stock market prediction, has become one of the important applications in the quantitative finance field. However, due to the ...multi-correlation and volatility of the stock market, how to timely and accurately predict it has become a challenging issue. In order to cope with this challenge, a news-driven stock market index prediction model based on TrellisNet and a sentiment attention mechanism (SA-TrellisNet) is proposed. A sentiment analysis model based on CNN and LSTM is presented to obtain the sentiment index of massive news crawled from authoritative financial websites. Furthermore, a sentiment attention mechanism is designed for data fusion of stock data and news sentiment index as the input of the simple and efficient TrellisNet network for model training and prediction. The performance of our model is systematically evaluated using seven major international stock market indices including S&P500, NYSE, DJI, NASDAQ, FTSE 100, Nikkei 225 and SSE, and comparative experiments demonstrate that SA-TrellisNet is competitive to the other state-of-the-art methods in predicting stock market indices.
StockNet—GRU based stock index prediction Gupta, Umang; Bhattacharjee, Vandana; Bishnu, Partha Sarathi
Expert systems with applications,
11/2022, Volume:
207
Journal Article
Peer reviewed
Predicting financial trends of stock indexes is important for investors to reduce risk on investment and efficient decision making if the prediction is made accurately. Researchers, in recent times ...have applied deep learning approaches in this field which have essentially beaten conventional machine learning approaches. To overcome the issue of overfitting we presented a new data augmentation approach in our GRU based StockNet model consisting of two modules. Injection module to prohibit overfitting and Investigation module for stock index forecasting. The proposed approach has been validated on Indian stock market (CNX-Nifty). Proposed StockNet-c model produces 65.59%, 27.30% and 14.89 % less test loss in terms of RMSE, MAE and MAPE respectively, in comparison to TargetNet model where overfitting prohibition injection module is missing.
•Stock index prediction with data augmentation approach to prohibit overfitting.•StockNet model with GRU network with injection and investigation modules.•Proposed models shows the best result in CNX Nifty dataset.•The prediction results of the proposed model have statistical significance.
•Our model learns high-level features from super-high dimensional time-series data.•All companies’ price data in the relevant country’s open market are used as input.•The trend sampling mini-batch ...sampling method enhances forecasting performance.•Experimental results show that our model adapts to real-time patterns.•The model outperforms others with the same training and testing conditions.
Forecasting stock market indexes is an important issue for market participants, because even a small improvement in forecast accuracy may lead to better trading decisions than those of other participants. Rising interest in deep learning has led to its application in stock market forecasting. However, it is still challenging to use market-size time-series data to predict composite index prices. In this study, we propose a new stock market forecasting framework, NuNet, which can successfully learn high-level features from super-high dimensional time-series data. NuNet is an end-to-end integrated neural network framework consisting of two feature extractor modules, a super-high dimensional market information feature extractor and a target index feature extractor. In addition, we propose a mini-batch sampling technique, trend sampling, which probabilistically samples more recent data when training. Furthermore, we propose a novel regularization method, called column-wise random shuffling, which is a data augmentation technique that can be applied to convolutional neural networks. The experiments are comprehensively carried out in three aspects for three indexes, namely S&P500, KOSPI200, and FTSE100. The results demonstrate that the proposed model outperforms all baseline models. Specifically, for the S&P500, KOSPI200, and FTSE100, the overall mean squared error of our proposed model NuNet(DA, T) is 60.79%, 51.29%, and 43.36% lower than that of the baseline model SingleNet(R), respectively. Moreover, we employ trading simulations with realistic transaction costs. Our proposed model outperforms the buy-and-hold strategy being an average of 2.57 times more profitable in three indexes.
In this study, we investigate the predictive capabilities of different news providers based on sentiment analysis, and propose a framework that endows different weights to different news providers ...for improving the prediction performance. In sentiment analysis, the prevalent Loughran-McDonald sentiment dictionary is utilized to calculate the sentiment scores of news articles, and the sentiment index of each news provider is obtained by integrating these sentiment scores. Based on the market data and sentiment indices of multiple news providers, we employ the recurrent neural network to build a number of base classifiers, and adopt the evidential reasoning rule to combine these base classifiers for predicting the stock market index movement. Additionally, the genetic algorithm is used to optimize the weights of base classifiers and important hyper-parameters of the recurrent neural network. In the experimental study, we apply the proposed approach to the daily movement prediction of the S&P 500 index, Dow Jones Industrial Average index and NASDAQ 100 index, and compare it with some state-of-the-art methods. The results show that our approach is effective for improving the prediction performance. Besides, the designed trading strategy based on the results of the proposed model achieves higher return rates than other trading strategies.
The main objective of the study is to empirically investigate the impact of the Conventional stock market index on the Islamic stock market index and the comparative performance of the two stock ...market indexes. For the purpose of the study, daily observations of Dow Jones Islamic Market US Titans 50 (DJUS50) and Dow Jones Composite Index (DJA) spanning a period from January 2015 until December 2021 are obtained from the Investing.com database. Risk-adjusted performance, VAR model, granger-causality test, generalized impulse response functions, and Johansen cointegration tests are used to investigate the behavior and performance of the Islamic market index empirically. Results based on risk-adjusted performance indicate that the Islamic market index performs better than the Conventional market index. Furthermore, the results suggest no long-run association between the indexes, while there is short-run bidirectional causality. This study will contribute both to the literature and practice. It will contribute to the already existing literature through the usage of the newest data, while the practical implication will help investors to better understand the behavior of the Islamic stock market index.
This study analyzes the impact of conventional index (SASX-30) on Islamic index (SASE-BBI) in Bosnia and Herzegovina. In the study are used daily index observations spanning in a period from October ...2016 until May 2018. The data is obtained from the Sarajevo Stock Exchange database. Vector Auto-regression analysis (VAR) and Impulse response functions are used in order to estimate the impact. The results show that there is a significant negative impact of conventional index volatility (SASX-30) on Islamic index volatility (SASX-BBI) in Bosnia and Herzegovina.
Stock market indices are among the signs populating financial markets and allowing traders to support their valuation work. The movements of the Dow Jones and the S&P 500 are constantly monitored, ...but how are they interpreted? Is this interpretation unique to each trader? Does it depend on how the indices are communicated? Considering these questions, this article aims to illustrate the heuristic interests of Charles Sanders Peirce’s semiotics. Peirce’s concepts can elucidate that stock indices assume different semiotic statuses. Depending on the financial context in which they operate, their signification and thus their function for traders will vary. This article demonstrates the usefulness of these concepts through empirical illustrations drawn from the literature, the financial press and a fieldwork in a trading room. Beyond this case study, this article reveals how the Peircian toolbox contributes to the studies of valuation signs.
This paper explores the influence of traditional and ESG stock market indices on a country’s net international investment position. To do this, different methods, including ANOVA analysis, multiply ...regression analysis, correlation analysis, VAR-analysis and R/S-analysis, as well as the Granger causality test, are applied to quarterly data on the net international investment position, traditional and ESG indices from Finland, Sweden, France, Spain and Ukraine over the period 2005–2021. The results of descriptive statistics show that ESG indices are more volatile than traditional, but these differences are statistically insignificant according to ANOVA analysis. Correlation analysis provides direct evidence that ESG indices are highly correlated with their traditional analogues (correlation level varies from 0.88 to 0.96). Regression analysis results show that traditional and ESG stock market indices have no significant impact on the net international investment position. ESG stock market indices and net international investment position data are persistent, and autoregressive models can be applied to these data sets. On average, Hurst exponent is above 0.75 for the case of ESG indices and above 0.85 for the net investment position. This paper provides recommendations to improve the responsible investment framework.
Acknowledgment Alex Plastun gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0121U100473).
Purpose: The article aims to study the impact of public governance on the relationship between consumer confidence and the stock market index. Theoretical framework: The concept of consumer ...confidence index: According to consumer confidence, and stock market index is a way to measure expected changes in income. Katona also argues that consumer trust includes emotional and intellectual factors. Design/methodology/approach: Data is collected from 2012 to 2021 from 10 middle-income countries. The public governance variable is measured by 6 component variables, including (1) Voice and accountability, (2) Political stability, (3) Government efficiency, (4) Regulatory quality, (5) the rule of law, and (6) Control of corruption. Consumer confidence is measured by the consumer confidence index (CCI) and the stock market index (SMI). The authors use P. VAR model to solve the set goal. Findings: The research results show that public governance positively affects the relationship between consumer confidence and the stock market price index in high-middle-income countries. In contrast, public administration does not influence the relationship between consumer confidence and the stock market index in low-income countries. Research, Practical & Social implications: Based on the research results, the authors propose policy implications for middle-income countries for investors' confidence and investment activities on the stock market, contributing to boosting capital in the future more efficient circular economy. Originality/value: Government must increase its accountability to the people and investors for all activities and decisions of the Government. Creating a stable political environment, prioritizing dispute settlement by peaceful negotiations.
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