Evaluation of the Effect of News Sentiment on Stock Market and its Application to Investment Strategy

2020 ◽  
Vol 140 (2) ◽  
pp. 249-256
Author(s):  
Daisuke Katayama ◽  
Kazuhiko Tsuda
Keyword(s):  
Stock Market ◽  
Investment Strategy ◽  
News Sentiment

Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpa B L ◽  
Shambhavi B R
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Moving Average ◽  
Investor Sentiment ◽  
Keyword Extraction ◽  
Stock Market Prediction ◽  
Content Type ◽  
Technical Indicator ◽  
News Sentiment

PurposeStock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.Design/methodology/approachThis paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.FindingsThe performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.Originality/valueThis paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.

scholarly journals Mean-variance investment strategy applied in emerging financial markets: Evidence from the Colombian stock market

2015 ◽  
Vol 9 (1) ◽  
pp. 22-29 ◽  
Author(s):  
Fernando García ◽  
Jairo Alexander González-Bueno ◽  
Javier Oliver
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Investment Strategy ◽  
Mean Variance

Does the financial crisis influence the month and the trading month effects?

2015 ◽  
Vol 32 (2) ◽  
pp. 181-203 ◽  
Author(s):  
Evangelos Vasileiou ◽  
Aristeidis Samitas
Keyword(s):  
Stock Market ◽  
Methodological Approach ◽  
Investment Strategy ◽  
January Effect ◽  
Research Approach ◽  
Content Type ◽  
Calendar Effects ◽  
Empirical Results ◽  
Financial Growth ◽  
Calendar Anomalies

Purpose – This paper aims to examine the month and the trading month effects under changing financial trends. The Greek stock market was chosen to implement the authors' assumptions because during the period 2002-2012, there were clear and long-term periods of financial growth and recession. Thus, the authors examine whether the financial trends influence not only the Greek stock market’s returns, but also its anomalies. Design/methodology/approach – Daily financial data from the Athens Exchange General Index for the period 2002-2012 are used. The sample is separated into two sub-periods: the financial growth sub-period (2002-2007), and the financial recession sub-period (2008-2012). Several linear and non-linear models were applied to find which is the most appropriate, and the results suggested that the T-GARCH model better fits the sample. Findings – The empirical results show that changing economic and financial conditions influence the calendar effects. The trading month effect, especially, completely changes in each fortnight following the financial trend. Regarding the January effect, which is the most popular month effect, the results confirm its existence during the growth period, but during the recession period, we find that it fades. Therefore, by examining the aforementioned calendar effects in different periods, different conclusions may be reached, perhaps because the financial trends’ influence is ignored. Research limitations/implications – The empirical results confirm the authors' assumption that a possible explanation for the controversial empirical findings regarding the calendar anomalies may be the different financial trends. However, these are some primary results that are confirmed only for the Greek case. Further empirical research for deeper stock markets and/or a group of countries may be useful to reach conclusions regarding the financial trends’ influence on the calendar anomalies patterns. Practical implications – The findings are helpful to anyone who invests and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, proving useful for investors who take these anomalies into account when they plan their investment strategy. Originality/value – This paper contributes to the literature by presenting an alternative methodological approach regarding the calendar anomalies study and a new explanation for the calendar effects existence/fade through time by examining the calendar anomalies patterns under a changing economic environment and financial trends.

scholarly journals SIMULATION OF PARTICLE SWARM OPTIMIZATION FOR INVESTMENTS ON STOCK MARKET

2021 ◽  
Vol 21 (4) ◽  
pp. 235-241
Author(s):  
Maciej Janowicz ◽  
Andrzej Zembrzuski
Keyword(s):  
Particle Swarm Optimization ◽  
Stock Market ◽  
Particle Swarm ◽  
Investment Strategy ◽  
Relative Strength ◽  
Swarm Optimization ◽  
Simulation Step ◽  
Trading Period ◽  
Naive Approach

This work reports simulations performed using Particle Swarm Optimization (PSO) as applied to investments on the stock market. About 480 stocks belonging to the S&P500 index have been taken into account. A naive approach has been developed in which one simulation step corresponded to one trading period. As a second ingredient of the investment strategy, the relative strength of an asset has been employed. The results are analyzed with respect to the parameters of PSO.

scholarly journals EXCHANGE INDICES AS ACTIVITY INDICATORS STEEL MARKET

2018 ◽  
Vol Vol 17 (Vol 17, No 1 (2018)) ◽  
pp. 100-115
Author(s):  
Tatyana Sitash
Keyword(s):  
Stock Market ◽  
Investment Strategy ◽  
The State ◽  
Securities Market ◽  
Stock Index ◽  
Foreign Markets ◽  
Market Participants ◽  
Steel Market ◽  
Market Infrastructure ◽  
Fully Functioning

The stock indices are investigated as indicators of stock market activity. It is proved that the stock market is one of the most attractive instruments for investing. It is accepted that an open stock market in a country characterized by a stable, strong economy, encourages the attraction of foreign investors. It is noted that indicators of the Ukrainian stock market such as the capitalization and the PFTS stock index (First Stock Trading System), as a result of the inadequate exchange rate prices for the real state of the issuing companies and the economy of the state, can’t be as representative, especially when compared with foreign markets. It has been outlined that the development of the domestic stock market is restrained by the imperfection of legislative and regulatory regulation of the market participants, the lack of optimality of the market infrastructure, the lack of a fully functioning, fully functioning national depository system. The introduction of a new stock index describing the state of business activity of a separate sector of the market is proposed, which will serve as a benchmark for the formation and optimization of investment strategy of investors, will promote transparency of the Ukrainian securities market.

scholarly journals Enterprise Multiple and Future Returns of the Brazilian Stock Market

2017 ◽  
Vol 10 (3) ◽  
pp. 431
Author(s):  
Rafael Igrejas ◽  
Raphael Braga Da Silva ◽  
Marcelo Cabus Klotzle ◽  
Antonio Carlos Figueiredo Pinto ◽  
Paulo Vitor Jordão da Gama Silva
Keyword(s):  
Risk Factors ◽  
Stock Market ◽  
Cross Section ◽  
Predictive Power ◽  
Abnormal Returns ◽  
Strong Relationship ◽  
Investment Strategy ◽  
Emerging Countries ◽  
Investment Strategies ◽  
Developed Markets

The estimation of cross-section returns for defining investment strategies based on financial multiples has been proven to be relevant following Fama and French’s (1992) research. One of the challenges for such studies is to identify the main variables that are suitable for explaining the returns in a particular context because the variables that are widely used in developed markets behave differently in emerging countries. In this study, we analyze the predictive power of the EV/EBITDA multiple in the context of the Brazilian stock market. The results show that the analyzed multiple has a strong relationship with the future returns of companies listed on the BM&F BOVESPA index between 2005 and 2013. For the period under review, the investment strategy of purchasing stocks when EV/EBITDA was low and selling stocks when EV/EBITDA was high showed abnormal returns of 15.94% per year, even after controlling for risk factors.

scholarly journals Misvaluation and behavioral bias in the Brazilian stock market

2019 ◽  
Vol 30 (79) ◽  
pp. 107-122
Author(s):  
José Bonifácio de Araújo Júnior ◽  
Otávio Ribeiro de Medeiros ◽  
Olavo Venturim Caldas ◽  
César Augusto Tibúrcio Silva
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Time Window ◽  
Abnormal Returns ◽  
Investment Strategy ◽  
Short Term ◽  
Publicly Traded Companies ◽  
Behavioral Biases ◽  
Three Samples ◽  
Investment Portfolios

ABSTRACT The study sought to apply the model developed by Gokhale et al. (2015) to identify the existence of overreaction and behavioral biases in the Brazilian stock market and analyze its performance as an investment strategy on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBOVESPA) in the short term and long term, as well as test its robustness with time window simulations. The impacts of behavioral finance on capital markets can affect economic decisions, perpetuate or increase asset pricing anomalies, and in more extreme and persistent situations contribute to the formation of bubbles that can compromise the entire financial system of a country. The study pioneers an innovative methodology in the Brazilian stock market for identifying behavioral biases and obtaining abnormal returns and higher returns than the Ibovespa. The research uses the model developed by Gokhale, Tremblay, and Tremblay (2015) in three samples with quotations data for Brazilian publicly-traded companies that compose the Ibovespa and IBrA in the period from 2005 to 2016. With the R statistical software, the Fundamental Valuation Index (FVI) was calculated for each sample share and each year. From the FVI index, the undervalued shares were identified, indicating that the sales price does not reflect their economic fundamentals, and portfolio simulations were carried out for investment over three months or the next year. The results indicate the possible existence of overreaction and behavioral biases in the Brazilian stock market, which lead to the possibility of higher abnormal returns than those of the Ibovespa. Similar to the US market, at the end of the 2006-2016 period simulated portfolios yielded more than 274%, while the Ibovespa yielded approximately 80%. The robustness tests attest to the effectiveness of the model. The various investment portfolios, simulated over different time horizons, yielded more than the Ibovespa on average. The study also confirmed the assumptions of Gokhale, Tremblay, and Tremblay (2015) regarding the model's inadequacy for short-term strategies.

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