Historical Stock Prices Research Articles

General Assessment of the Impact of the War in Ukraine on the Shipping Industry Using Parametric Methods

The ongoing Ukrainian war has introduced significant uncertainty and crisis into the global economy, particularly in financial and stock markets. This study is a part of a larger research project that aims to assess the impact of the conflict between Russia and Ukraine on shipping companies, given the direct influence of rising gas and oil prices on the valuation of freight transport service providers. Economic sanctions were imposed on Russia, leading to the destabilization of the global economy. Industries such as the global shipping and tourism sectors experienced significant declines in share value. Investors began reallocating their portfolios, seeking safer and less risky investments, such as gold stocks. The riskiness of a particular stock can be assessed by various methods, including volatility measurement. This paper focuses on calculating and presenting the volatility value of the shares of A. P. Moller-Maersk, the world's largest operator of container lines and vessels. Additionally, the STOXX50 index, i.e. the Euro Zone stock market index, representing the overall European market, is used for comparison. Another risk measure discussed in this paper is Value at Risk (VaR), a quantitative method used to predict potential cash losses over a certain time period. The parametric method of calculating VaR was used, which assumes the normal distribution of stock value fluctuations. VaR was calculated using historical stock price data of A. P. Moller-Maersk. Findings indicate significant volatility and high-risk environment in the financial markets. The calculated VaR of 27.66% for a 30-day period with the 95% confidence level reflects the substantial potential losses associated with investing in Maersk shares during the crisis, surpassing typical risk levels. In conclusion, the war in Ukraine has disrupted the maritime industry, which was already recovering from the impact of the COVID-19 pandemic. The sanctions imposed on Russia and the war situation in Ukraine have created uncertainty and turbulence in financial markets, prompting investors to seek safer investment options. The study emphasizes the need for continued monitoring of the impact of the war on the global maritime industry. The devastating effects of the war on the sector have significant implications for the global economy, human well-being, and future research in the field.

On solving robust log-optimal portfolio: A supporting hyperplane approximation approach

A log-optimal portfolio is any portfolio that maximizes the expected logarithmic growth (ELG) of an investor’s wealth, which typically assumes prior knowledge of the true return distribution. However, in practice, return distributions are often ambiguous; i.e., the true distribution is unknown, making this problem challenging to solve. This paper proposes a supporting hyperplane approximation approach, reformulating a class of distributional robust log-optimal portfolio problems with polyhedron ambiguity sets into tractable robust linear programs. An efficient algorithm is presented to determine the optimal number of hyperplanes. Additionally, to adapt to the constantly changing market, we propose an online trading algorithm using a sliding window approach to solve a sequence of robust linear programs, offering significant computational advantages. The effectiveness of the proposed approach is supported by empirical studies using historical stock price data.

Advanced Deep Learning-Based Predictive Modelling for Analyzing Trends and Performance Metrics in Stock Market

Objective: This study's main goal is to investigate how deep learning approaches may be used to analyze stock market performance. The complex patterns and nonlinear interactions present in stock market data may be difficult to completely capture using traditional approaches, which are mostly based on statistical models.
 Methodology: Our work uses a large dataset of historical stock prices, macroeconomic indices, and other crucial financial factors to address this. Simple Moving Averages (SMA) are one of the feature engineering approaches that are used to combine fundamental and technical indicators. To capture the temporal dynamics of the stock market, the study goes further into a variety of deep learning architectures, including as long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
 Findings: The results show that thorough feature engineering combined with deep learning approaches may effectively capture the complexity of the stock market and provide forecasts that are more accurate.
 Implications: This highlights how deep learning may revolutionize financial market research and points to a paradigm change toward more trustworthy instruments for investors and decision-makers.

Stock Price Prediction Using Stepwise Regression and Improved with Factor Analysis

Owing to volatility in stock markets, it is quite elusive to forecast stock prices. Albeit, sometimes regular patterns are manifested in stock prices and a variety of factors are proved to be competent to determine stock prices partly. Hence, using stepwise regression on historical stock price data, this paper proposes determining similar patterns in stock prices and exploring potential rules to select the main factors that can affect stock prices significantly while taking all factors into account. Difference analysis is also employed to probe possible correlations in the data. Eventually, this paper tries to improve stock price prediction using factor analysis and manages to achieve higher accuracy.

Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation

Predicting stock price movements is a complex challenge in the financial market due to unpredictable price fluctuations and high sensitivity levels. Noise in historical stock price data and temporal dependencies between previous and current prices make recognizing price movement patterns difficult. In a dynamic market environment, the model's ability to generate accurate predictions holds significant implications for more informed investment decision-making. The Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model holds great potential for stock price prediction. It captures temporal dependencies, identifies non-linear relationships, and deciphers complex trends in stock price data. This study employs deep learning techniques with the RNN-LSTM model optimized using Adaptive Moment Estimation (Adam) to enhance stock price prediction accuracy by leveraging historical stock price data and technical factors. Data preprocessing, including handling missing values and data normalization, aids the model in navigating the dataset's intricacies. Test results utilizing the Mean Squared Error (MSE) metric reveal the model's ability to produce predictions that closely resemble actual stock prices, with a low loss value of 0109012. The model also exhibits good predictive accuracy, as evidenced by a favorable Mean Percentage Error (MPE) score of 1.74% between predicted and actual values. These findings hold valuable implications for assisting investors and financial practitioners in managing complexity and uncertainty within the stock market

An LSTM-Based Approach for Stock Price Prediction in the Metaverse

This study proposes a novel approach for predicting stock prices in the metaverse using a Long Short Term Memory (LSTM) model with an attention mechanism. The model is trained on a large dataset of historical stock price data from the company called META. The proposed method preprocesses the original data by normalizing, splitting it into training and test sets, and transforming it into tensors. The performance of the model is evaluated using various metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and optimization is carried out by increasing the number of model layers, adjusting the learning rate and batch size, adding regularization, and using different activation functions. The results demonstrated that the proposed approach provides an accurate and effective tool for predicting stock prices in the emerging industry of the metaverse. Accurate predictions of stock prices can significantly impact investment strategies and enable investors to make informed decisions and optimize returns. The study highlights the importance of considering new emerging industries for financial analysis and investment strategies. This research provides a new perspective on financial analysis and investment strategies in emerging industries and demonstrates the feasibility of using an LSTM model with an attention mechanism to predict stock prices in the metaverse. The proposed approach has the potential to revolutionize financial analysis and investment strategies in the emerging industry of the metaverse, providing a valuable tool for investors and financial analysts to make informed decisions and optimize returns.

Analyze News Effect on Trend Stock Price in Indonesia Based on Bidirectional-Long Short Term Memory

News platforms such as BBC, UN News, and CNN are news sites that are very global, both globally and nationally. With this site, someone can find information in other countries or their country. The news contained on the BBC website can be analyzed using sentiment analysis. Sentiment analysis is carried out to see whether the news tends to be positive, negative, or neutral so that researchers or institutions can find out how the response of the news is to other sectors such as stocks in Indonesia. With the IDX website as a list of company shares in Indonesia, sentiment analysis can be carried out on news on the BBC website that can affect the rise or fall of stock prices in Indonesia using a combination of Word2Vec and the Bidirectional- Long Short Term Memory (BiLSTM) method. The BiLSTM method is an algorithm that has a function to process text data to predict the value of stock price trends by utilizing Word2Vec for word embedding of news. In this study, the dataset used is international news on the BBC website and historical stock prices of several companies on the IDX website. This study utilizes both methods to be able to predict stock price trends. By using 15.674 data, this study shows that the BiLSTM method has an average accuracy rate of 80.03%.

Stock Price Forecasting using the Machine Learning Based on the Historical Stock Prices

Forecasting stock price is an imperative financial subject matter that has involved researchers’ attention for numerous years. The successful stock price forecasting helps to profit the company or individual otherwise it makes the problem of loss. Stock forecasting involves an assumption that basic information publicly accessible in the past has some predictive relationships to the future stock returns. This proposed research work tries to help the company and investors in the stock market to come to a decision for better timing for buying or selling stocks based on the information extracted from the historical prices of such stocks.

Forecasting the Monthly Stock Price per Share of Taiwan Semiconductor Manufacturing Company Limited (TSM) using ARIMA Box-Jenkins Method

Taiwan Semiconductor Manufacturing Company Limited is a Taiwanese multinational semiconductor contract manufacturing and design company. People can buy Stocks from Taiwan Semiconductor Manufacturing Company Limited. Stocks are one of the attractive investment instruments for companies and individuals. There are some theories and analyses to predict stock prices to help investors make wiser decisions when buying and selling stock portfolios. In this study, researchers will use ARIMA(p,d,q) technical analysis to predict the stock price of Taiwan Semiconductor Manufacturing Company Limited for the next 5 months from January 1, 2005 to May 1, 2005. For this forecasting, researchers used Taiwan Semiconductor Manufacturing Company Limited historical stock price data from January 1, 1998 to December 31, 2004 that was obtained from the Yahoo Finance website. Based on the test results of 8 ARIMA models, the best model that researchers got is model 2 ARIMA (2,1,1) with the equation Yt = 0.0759Yt-1 + 0.2706Yt-2 + et - 0.198et-1. This model is considered to be the best because it has the smallest MSE Value, which is 0.1076018; the smallest RMSE value, which is 0.0301156; the smallest MAE value, which is 0,2495926; and the smallest MAPE value, which is 3.0116%. This study shows that the stock price is predicted to rise for the next 5 months from January 1, 2005 to May 1, 2005.

Visualising and Forecasting Stock Prices with Flask

Abstract: Stock price forecasting and visualisation are critical tasks for investors and traders in financial markets. In this project, we have developed a web application that can visualise stock prices of the company in form of charts and predicts future stock prices using machine learning algorithms. The web application is built using Flask(python), a popular web framework, and integrates several APIs and libraries, including News API, Alpha Vantage API, Beautiful Soup (bs4), Pandas, Numpy, Plotly, and Scikit-learn. The application provides users with a basic overview of the company, current prices of the stock, news related to the company, and visualisations of historical stock prices in the form of charts. The historical stock prices are fetched using Alpha Vantage API, and the charts are generated using Plotly library. The web application also includes a predictive model built using Support Vector Regression (SVR) algorithm which forecasts the stock prices. This paper demonstrates the potential of machine learning algorithms and web technologies in the field of stock price forecasting and visualisation. The web application developed, provides users with valuable insights and information related to stock prices, and it can be used as a powerful tool in financial markets.

Prediction of the Stock Market From Linguistic Phrases

Automation of financial data collection, generation, accumulation, and interpretation for decision making may reduce volatility in the stock market and increase liquidity occasionally. Thus, future markets' prediction factoring in the sentiment of investors and algorithmic traders is an exciting area for research with deep learning techniques emerging to understand the market and its future direction. The paper develops two FINBERT deep neural network models pre-trained on the financial phrase dataset, the first one to extract sentiment from the NSE market news. The second model is adopted to predict the stock market movement of NSE with the above sentiment, historical stock prices, return on investment, and risk as predictors. The accuracy is compared with RNN and LSTM and baseline machine learning classifiers like naïve bayes and support vector machine (SVM). The accuracy of the FINBERT model is found to out-perform the deep learning algorithms and above baseline machine learning classifiers thus justifying the importance of the FINBERT model in stock market prediction.

CAPM & FF3 for Grocery stores: Evidence from Kroger, Walmart and Target

The impact of COVID-19 on the global economy is significant to most of the industry, espically hotle industry or restrauant industry. In order to fight for the negative effect from COVID-19, the government applied a lot of conventional tools and made an increase on the amount of money supply. However, these activities lead to an uncontrol consequence, which is inflation. The purpose of this research is to aopte the CAPM and Fama & French models to evaluate the inflation effect on grocery stores. The data used to compute the result, is the historical stock price collected from Yahoo Finance. When comparing the results from CAPM and Fama & French models, Fama & French model display the effect from inflation more specificly than that in CAPM model. Nevertheless, both models conclude the inflation plays an important role on the market value of the business. These results indicate the degree of influence from Inflation on parituclar industry, along with supporting evidence from CAPM and Fama & French Models.

A stock rank prediction method combining industry attributes and price data of stocks

Stock forecasting has always been challenging as the stock market is affected by a combination of factors. Temporal Convolutional Network (TCN) based on convolutional structure has been widely used in time series prediction in recent years, but the dilated causal convolution structure leaves it unable to effectively learn the dependencies between data at different time points. This paper proposes a method for stock ranking prediction. To enhance the ability of TCN to handle dependencies within series, we first develop a channel-time dual attention module (CTAM). In conjunction with TCN to process complex historical stock price data, CTAM can adaptively learn the importance of multiple price nature series of stocks and model the dependencies between the data at different times. On the other hand, due to the market industry rotation, some stocks with specific industry attributes may become market preference for a period time. To apply the industry attributes to the stock prediction, we construct an industry-stock Pearson correlation matrix and extract a vector that fully characterizes the industry attributes of stocks from it through a matrix factorization algorithm. Furthermore, the historical market preference is modeled according to the industry attribute of the stocks to generate the dynamic correlation between stocks and market preference, and this correlation is combined with the historical price features extracted by TCN for stock ranking prediction. We conduct experiments on three datasets of 950 constituent stocks of the Shanghai Stock Exchange Index, 750 constituent stocks of the Shenzhen Stock Exchange 1000 Index and 486 stocks of the S&P500 to demonstrate the effectiveness of the proposed method. On the Shanghai Stock Exchange Index dataset, the Investment Return Ratio (IRR) obtained by using the predict results of our method to guide the exchange reached 1.416, and the Sharpe Ratio (SR) reached 2.346. On the Shenzhen Stock Exchange Index dataset, the IRR reached 1.434 and the Sharpe ratio reached 2.317. On the S&P500, the IRR reached 1.491 and the Sharpe ratio reached 2.031.

Challenge and Opportunity: Deep Learning-Based Stock Price Prediction by Using Bi-Directional LSTM Model

Stock price prediction is a challenging and important task in finance, with many potential applications in investment, risk management, and portfolio optimization. In this paper, we propose a bi-directional long short-term memory (Bi-LSTM) model for predicting the future price of a stock based on its historical prices. The Bi-LSTM model is a variant of the popular LSTM model that is capable of processing input sequences in both forward and backward directions, allowing it to capture both short- and long-term dependencies in the data. We apply the Bi-LSTM model to historical stock price data for Apple Inc. and evaluate its performance using mean squared error (MSE) and visual inspection of actual vs. predicted prices. Our experiments show that the Bi-LSTM model is able to make accurate predictions on the testing data and capture some of the trends and patterns in the data, although it may struggle with sudden changes in the market. Overall, our results suggest that the Bi-LSTM model is a promising tool for stock price prediction and has many potential applications in finance and investment.

Stock Price Prediction Using Machine Learning

Stock price prediction approach using Long Short-Term Memory (LSTM) networks. The proposed method utilizes historical stock price data as input and trains an LSTM model to predict future stock prices. The LSTM network architecture includes multiple layers of LSTM cells, which can capture long-term dependencies and patterns in the input data. The effectiveness of the proposed approach is evaluated using real-world stock price data from the S&P 500 index. Experimental results demonstrate that the LSTM-based approach can achieve promising prediction accuracy compared to other traditional methods. The proposed method can potentially benefit financial analysts, investors, and traders by providing useful insights for decision-making. Key Words: Financial analysis, Stock-Market, LSTM, Future prediction, Machine learning

Improving CNN-Based Stock Trading by Considering Data Heterogeneity and Burst

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.

The Impact of Big Data Analytics on Stock Price Prediction in the Bangladesh Stock Market: A Machine Learning Approach

The stock market is a volatile and complex environment impacted by various unpredictable factors, making accurate stock price prediction challenging. This research paper explored the potential and capability of big data analytics and machine learning techniques in terms of enhancing stock price prediction accuracy in the setting of the Bangladesh stock market. The methodology adopted in the study entailed a data gathering process, which comprised collecting financial data from the Bangladesh stock market, such as news articles, financial statements, macroeconomic indicators, and historical stock prices. Based on a literature review, various fundamental and technical indicators are chosen as predictive features. The research paper employed a combined methodology that consolidates technical calculations and sentimental analysis to predict and forecast stock market patterns. By adopting machine learning and sentiment analysis techniques, this technique provides future predictions for the stock market while considering the impact of political events, economic factors, and dynamics in social media. The consolidation of big data analytics enables real-time predictions of stock market movements. The sentiment analysis algorithm facilitates prompt and extensive evaluations of tweets and news articles. As a result, the integration of technical and sentiment analyses greatly enhances the accuracy of stock market predictions.

Stock Price Prediction of Tesla & Apple using LSTM

The stock market has always piqued academics' interest in predicting. Since they were originally created, machine learning algorithms have been increasingly popular for predicting changes in the stock market. The intricacy and unstable nature of the stock market make it challenging to predict stock values. In recent years, stock value forecasting has used both sentiment analysis and machine learning techniques. In this work, we present a study to anticipate the stock prices of Tesla and Apple using sentiment analysis and long short-term memory (LSTM). Using historical stock prices and sentiment data from Twitter, our LSTM model will be trained and put to the test. To evaluate the proposed model, metrics like normal error, mean square error (MSE), and mean absolute error (MAE) are taken into account. The obtained results showed very promising results and can be incorporated in stock price prediction applications.

Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach

The stock market is highly volatile as it depends on political, financial, environmental, and various internal and external factors along with historical stock data. Such information is available to people through microblogs and news and predicting stock price merely on historical data is hard. The high volatility emphasizes the importance to check the effect of external factors on the stock market. In this paper, a machine learning model is proposed where the financial news is used along with historical stock price data to predict upcoming stock prices. The paper has used three algorithms to calculate various sentiment scores and used them in different combinations to understand the impact of financial news on stock price as well the impact of each sentiment scoring algorithm. Experiments have been conducted on ten-year historical stock price data as well as financial news of four different companies from different sectors to predict the next day and next week's stock trends and accuracy metrics were checked for a period of 10, 30, and 100 days. The proposed model can achieve the highest accuracy of 0.90 for both trend and future trends for a period of 10 days. Experiments have also been performed to check the difficulty in predicting some stocks. It was found that Tata Motors an automobile company stock prediction has maximum MAPE and hence deviates more from actual prediction as compared to others.

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach