A multiclass classification model for stock news based on structured data

On the relationship between changes in stock prices and bond yields in the G7 countries: Wavelet analysis

Although the prediction of stock prices is a topic that has been studied in several occasions, there are few variables that have been used for this matter outside of dividends such as determinant of prices (Guo, 2022). The results found to date have not been entirely satisfactory. since they do not explain most price movements in the long run. The use of profits such as predictor variable is an alternative that has not been much explored. This research work seeks to answer what is the effect of using profits as a predictor of stock prices in the long run. With the In order to answer this question, a panel regression was performed, controlling for fixed effects, of the effect of the profits on stock prices over the past 30 years for S&P 500 companies. In general, it was not possible to determine if the profits, whether gross, operating or net, have the capacity to predict changes in stock prices. However, it was established that the size of the assets of a company and changes in US GDP have a negative relationship with changes in price. By On the other hand, the change in the price of the SPX index has a positive relationship with the change in the stock price. This finding can generate a great advantage for investors when creating stock portfolios in the long term that seek to maximize profitability with a certain degree of certainty, since it would allow choosing with greater clarity those actions with greater probability of success and returns in the long

A company's financial statements provide information that is useful in making economic decisions. This may include information about the company's financial performance, financial position and cash flows. Valuation of financial instruments at fair value can have a significant impact on bank share prices. Banks that use fair value accounting for financial instruments may experience greater variability in their share prices than banks that do not use this method. The purpose of this research is to investigate the relationship between accounting valuation of financial instruments at fair value and changes in bank stock prices. The research aims to determine whether and to what extent there is a relationship between the fair value of financial instruments and changes in stock prices. Research may also aim to identify any factors that may influence this relationship and provide information and recommendations for stakeholders such as investors, regulators and financial institutions. Based on the results achieved, the need for optimal and accurate use of fair value indicators due to their impact on the company's performance, as well as clearer compliance by banks with international financial reporting standards regarding the fair value assessment of financial instruments, was investigated. And thus, it can lead to increased accuracy of forecasts and increased confidence of users of financial statements in making economic decisions. The findings of the study can contribute to the existing scientific literature on the impact of fair value accounting on stock prices and provide further understanding of this issue. The results of the research can be used by banks to assess the potential impact of the implementation of fair value accounting for financial instruments on the share price.

This paper considers variance bounds for stock price changes in a general setting that allows for ex-dividend stock prices, risk averse investors, and exponentially-growing dividends. I show that providing investors with more information about future dividends can either increase or decrease the variance of stock price changes, depending on some key parameters, namely, those governing the properties of dividends and the stochastic discount factor. This finding contrasts with the results of Engel (2005) who shows that news about future dividends will always decrease the variance of stock price changes in a specialized setting with cum-dividend stock prices and risk neutral investors.

Financial Performance of Pakistani Banks after the Implementation of Reforms: A Post-Global-Crisis Study

Stock news is one of the information sources that can used to monitor stock prices. The information from stock news usually contains positive and negative sentiments that can affect stock prices. Therefore, sentiment analysis is needed to process the sentiment of stock news. The stock news dataset is taken from Kaggle. From these data, there is an imbalanced class between positive and negative sentiment. This research proposed a method to solve the imbalance dataset with random oversampling which worked by randomly replicating several minority classes. This research presents several scenarios of pre-processing text with different stages, intending to get high accuracy. The classification method used in this paper is a robustly optimized Bidirectional Transformer Encoder Representation (RoBERTa). Besides that, this paper also compared with baseline of Machine Learning (ML) such as Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Support Vector Machine, Random Forest Classifier, Logistic Regression and used two different text representation such as TF-IDF and Word2Vec. The best result in this research is obtained using RoBERTa method with the fourth scenario of pre-processing text, in which the stage of pre-processing in this scenario only removing hashtag, without removing punctuation, removing the number, converting number, stop word removal, and lemmatization. The performance result is 0.85 precision, 0,84 recall, 0,84 F1-score, and 86% for accuracy result.

Six text classification methods were compared to find the best model for predicting Stock Exchange of Thailand stock prices. News headlines, on individual stocks, were classified as causing “change” and “no-change” based on a preset change threshold, 2.5%. The training dataset was collected by matching stock news in 2018 with stock names and filling in stock price changes. 258 news were associated with a “change” and 636 news with “no-change”. The Thai text news items were preprocessed and converted to TF-IDF vector representation. Six machine learning text classification methods are applied to create six text classifier models and create a confusion matrix, then compared with actual changes to obtain accuracy scores. We found that a deep learning classifier (with 85.6% accuracy) scored better than other classifiers for one day price movement to assist short-term investments.

One major technical analysis of stock price fluctuation is the use of candlestick charts. This paper proposes a model with six parameters to retrieve similar candlestick patters to improve accuracy of stock price predictions. Because criteria that trigger reversing trade largely affect gains and losses, we examine two criteria; one based on sum of negative stock price changes and the other on sum of negative 5-day average differences. The proposed retrieval algorithm and criteria are evaluated through simulations in terms of gains and losses using NASDAQ's daily stock data. The results of simulations indicate that the proposed method leads to a trade decision that opportunities of successful stock trades are effectively above that of failure ones with several percentage of gains. Simulations also show that high risks deliver high returns. The results are examined statistically by the regression analysis suggesting the significant capabilities of the proposed method to predict stock price fluctuations.

In this paper the distribution of stock price differences is studied. Starting from Bachelier's hypothesis regarding the independence of stock price changes a model based on price differences taken across transactions, rather than differences obtained across time periods, is investigated. Using this model the distributions of price changes for a sample of 10 stocks were studied. In contrast to the fat tailed, excessively peaked distributions obtained by differencing across time periods, the distributions obtained by differencing across transactions did not have an excessive number of extreme events, although the center classes continued to be overcrowded. As the number of transactions over which the price differences were taken increased from 1 to n where n is equal to the largest number of transactions over which the price has remained unchanged, the distribution of the ΔP's approached the normal distribution. Thus we conclude that stock price changes are not independent across single transactions, and that stock prices should be differenced across blocks of transactions, rather than time intervals.

The impact of capacity expansion on the market value of the firm

During an inflationary period, changes to the last in, first out (LIFO) method of inventory valuation generally result in reduction of reported earnings and in deferment of tax payments. If the investors rely on the reported earnings, the stock price of the firms which change to the LIFO method will decrease and if they rely on the economic value of the firms, the stock price will increase. Several studies of the relationship between accounting changes and stock price behavior have been conducted by using a research design, as of April 1975. The design involves the use of the market model to isolate the stock price changes associated with specific events from the market-wide price changes. The article attempts to measure the association between the accounting and price changes by abstracting the effect of risk changes. This is accomplished by estimating the time path of the relative risk of stocks during the months surrounding the date of accounting change. The problem of estimating the relative risk of stocks when it is not constant is considered in another section of the article. Conclusions of the study about the relationship between stock price behavior and accounting changes are presented in the last section.

THIS STUDY is concerned with an analysis of stock price changes over a period of thirteen months surrounding common stock splits. The purpose of the analysis was to determine whether there is a significant price change associated with stock splits, while controlling for changes in dividends, initial dividend level, change in earnings, initial earnings level, and stock market trend. The effect of other concomitant factors was assumed to average out over the 148 stocks in the study. Some published research (for example, Barker) has concluded that the observed price increase accompanying a stock split is a function of an increase in cash dividends rather than the split itself. The results of this study tend to disagree with Barker's conclusion. Analysis of covariance is the technique employed to test the hypothesis that there is no significant difference between the mean price change of the group of split stocks and the group of nonsplit stocks, after controlling for the effect of the above five concomitant variables. The analysis includes all true stock splits of 2: 1 or greater that occurred during the calendar year 1959 among stocks listed on the New York Stock Exchange. For the purposes of this study, a true stock split is defined as a split in which no part of the retained earnings was capitalized. Seventy-four stocks listed on the New York Stock Exchange that did not split their shares in 1959 were selected at random and were then randomly paired with the split stocks. The price change was determined by expressing the average price of the stock in the fifth month after the split date as a percentage of the average price of the stock in the eighth month prior to the split date. These prices and the control variables were adjusted for any change in the number of shares outstanding. The price change of the nonsplit stock that was randomly matched with each split stock was determined for the same time period. The market trend was included as a control variable by matching each stock with an appropriate Standard and Poor's subgroup stock price index. The percentage change in each index was computed over the same time period and in the same manner as the price change. Unlike the change in stock price and index, the change in earnings and dividends could not be expressed in percentage terms because of the presence of deficits and zero values. The change in dividends was incorporated by the use of two control variables. The dividends per share in the quarter preceding the period under study, the initial dividend, provided the first of these control variables. Any change in this dividend rate prior to the end of the thirteen-month period provided the data for the other dividend control variable. The change in earnings was included in the same manner. The quarterly earnings for the last fiscal quarter prior to the period under study provided the initial earnings variable. The actual difference between these earnings and the earnings in the quarter ending one year later provided the data for change in earnings. Tests of the statistical validity of the data for the two-way analysis of covariance design were carried out and satisfied. The F-test of the difference between the mean

In recent years, China has experienced high real estate price and stock market fluctuations. It is necessary to study the relationship between real estate and stock price in order to promote healthy and stable development of the economy. Based on data from 1999 to 2009, this article empirically explored their relationship by multivariable VAR model under the situation of taking currency liquidity as endogenous variable. The results show that there is a long-term equilibrium relationship between real estate and stock price in China, and they are the Granger reasons for each other. In a short-term, real estate price changes at the same direction as stock price fluctuation caused by one standard deviation innovation, while stock price changes at the adverse direction of real estate price fluctuation caused by one standard deviation innovation. The variance decomposition analysis suggests that the contribution of real estate price to forecast mean square error of stock price is less than that of stock price to forecast mean square error of real estate price.

This paper investigates the interactions between changes in stock prices and monetary policy regimes in four emerging Asian countries—Korea, Malaysia, Singapore, and Thailand—using a Markov regime-switching autoregressive conditional heteroskedasticity (MS-ARCH) model. To connect the stability of monetary policy to stock market volatility, the authors assume that monetary policy and stock price regimes are governed by the same fundamental: the state of the economy. They find that there exists an asymmetric relationship between the volatility of stock prices and the stability of monetary policy regimes. Most of their findings are consistent with real world observations.

This study aims to evaluate the financial performance of PT Unilever Indonesia Tbk. based on profitability ratios, namely Return on Assets (ROA) and Return on Equity (ROE), and their impact on the company's stock price. The data used include quarterly financial reports and stock prices of PT Unilever Indonesia Tbk. from the first quarter of 2020 to the third quarter of 2024. The research method employs multiple regression analysis with hypothesis testing conducted through t-tests (partial) and F-tests (simultaneous). The results indicate that, partially, ROA has a significant effect on stock price, with a significance value of 0.019 (< 0.05) and a t-value of 2.606 exceeding the t table value of 2.131. Conversely, ROE does not significantly affect stock price, as evidenced by a significance value of 1.563 (> 0.05) and a t-value of 0.634, below the t table threshold. Simultaneously, ROA and ROE significantly influence stock price, with an F-value of 4.403 exceeding the F Table value of 3.68 and a significance value of 0.030 (< 0.05). The contribution of ROA and ROE to stock price variation is reflected in an R2 value of 0.355, indicating that 35.5% of stock price changes are explained by these variables, while the remaining 64.5% are influenced by factors outside the study. These findings recommend that the company continues to enhance asset utilization to optimize ROA, which has proven significant in affecting stock price. Additionally, future researchers are advised to consider other variables, such as capital structure, dividend policies, and market conditions, to provide a more comprehensive understanding of the factors influencing stock price.