Google Stock Research Articles

Enhancing African market predictions: Integrating quantum computing with Echo State Networks

The integration of Quantum Computing into Echo State Networks (ESN) materializes in the form of the Quantum Echo State Network (QESN), a methodological innovation that reshapes predictive analytics within the domain of artificial intelligence. This investigation harnesses the novel QESN model alongside traditional ESNs, deploying them within the dynamic and burgeoning financial market of Africa. Our focus zeroes in on the Google Stock Price dataset, which provides a rich tapestry of regional financial activity. The QESN model distinguishes itself by a complex interconnection of qubits that conduct quantum operations, offering a marked amplification of computational capability. The empirical analysis reveals that the QESN model’s predictive prowess substantially exceeds that of its ESN counterpart, achieving an unprecedentedly low Mean Squared Error (MSE) of 0.00021 in forecasting market trends. This exceptional figure redefines the standards of financial prediction models in African markets and establishes the QESN as an instrumental breakthrough, providing unparalleled accuracy in the analysis and prediction of financial data.

Predicting Google Stock Prices Using Machine Learning Models

Stock price prediction could help investors decide to sell or buy the stock, which returns and minimizes risk. Precise forecasts can bring substantial financial gains, while unprecise forecasts can bring significant financial losses. During the volatile financial markets, a reliable stock price prediction is a valuable source for traders, investors, and financial analysts. The research uses different models to compare and analyze Google's stock price prediction. The research uses machine learning models to predict Google's stock price. The researcher aims to find the best model to predict the stock price using the Mean Square Error (MSE) result. The researcher chose Google as the topic because of Google's importance in technology and the stock market. The research uses Goggle's stock data from 2016-2021. The data includes opening, closing, and high, and low prices. The study used three methods, linear regression, decision tree, and random forest, to predict Google's stock market price.

Applying ensemble learning to multiple stock price predictions: A comparative study

Predicting stock prices has been a perennial challenge and topic of interest for financial analysts, investors, and researchers alike. This holds true especially for influential tech companies that significantly impact the stock market landscape like Google and Tesla. While numerous models and methods have been applied to forecast stock prices globally, there remains a gap in a comparative, in-depth evaluation using Support Vector Regression (SVR), Recurrent Neural Network (RNN), Long Short-Term Memory network (LSTM), and XGBoost models for predicting prices of prominent tech stocks. This paper aims to bridge this gap, presenting a comprehensive model comparison for stock price forecasting. A series of datasets, inclusive of Google's stock prices and Tesla stock price, form the backbone of the analysis. Among the evaluated models, our preliminary findings indicate that LSTM and XGBoost demonstrate superior predictive capabilities, capturing intricate market dynamics with high precision. Further, the paper delves into the underlying factors and patterns driving stock prices, gleaning insights from the models' predictions. Through this research, this paper offers valuable benchmarks and insights for the best model for research in the field of stock price predictions and some evaluation of different features in the field of stock price predictions.

Machine learning and deep learning models for stock price prediction, case study: Google Company

Stock prediction is crucial for investors to manage risk, diversify portfolios, and plan for long-term financial goals. Companies use predictions to allocate capital wisely, attract investors, and make strategic decisions. Stock prediction is to analyze competitive stock (e.g. Google, Apple and Microsoft), involving parameters of revenue, profit, PE ratio, growth rate, and to predict the future price of the stock. Based on these assumptions, this project objective is to forecast the trend in Google stock prices with four predictive models: linear regression, XGBoost, Long Short-Term Memory Network (LSTM) and Recurrent neural network (RNN). The dataset is Google stock prediction dataset, which involves 17,598 stock information. Among all four models, LSTM method obtains the best final result with 0.0012 of MSE and 0.95115 of R square. LSTM has the lowest MSE, and its R square is similar to that of other models. For future plan, this project may expand more on feature engineering, model techniques and fine-tuning methods.

Research on the Google Stock Price Prediction Based on SVR, Random Forest, and KNN Models

The prediction of stock prices is an intriguing and difficult study issue. Due to its frequent offers of quick rewards and low risk of loss, the stock market is currently regarded as a prestigious trading sector. Data mining and business researchers find the stock market to be an ideal environment due to its extensive and dynamic information sources. In this paper, the data of google stock price from 2018-1-2 to 2023-3-31 is imported. As our classification algorithms, the authors independently selected and contrasted super vector regression, random forest, and K-nearest neighbor. Following test, analysis, and comparison of these classifiers and calculate the mean square error of test data, the final result was reached. According to the result, the random forest has the smallest MSE value, so it is the best prediction and uses this method to predict the stock price of next ten days. After using random forest to do the stock price prediction, we find that the close price has the increasing trend for next ten days, which is good for the investors.

Google Stocks Prediction by Machine Learning of RNN and LSTM Techniques

The objective of this study is to utilize a combined model of two algorithms, namely Long Short-Term Memory network and Recurrent Neural Network, to forecast the stock price of Google. Using Google stock price data from 2010 to 2022 as the training set and performed data preprocessing and feature engineering. This then build a deep neural network model consisting of multiple LSTM and RNN layers and train it by the backpropagation algorithm. During training, this paper employs an appropriate loss function and optimizer to minimize the prediction error. In conclusion, the performance of the model was assessed by employing Google stock price data from 2023 as a test set. By comparing the error between the actual stock price and the predicted value of the model, it can evaluate the accuracy and stability of the model. The experimental results show that the superposition model using LSTM and RNN algorithms can effectively predict the Google stock price with high accuracy and stability. This research presents a practical approach that can enhance the predictive capabilities of investors, financial institutions, and other related domains, enabling them to make well-informed investment decisions in the stock market.

Review and Analysis of Financial Market Movements: Google Stock Case Study

Review and Analysis of Financial Market Movements: Google Stock Case Study

Analysis of Google Stock Prices from 2020 to 2023 using the GARCH Method

Analysis of Google Stock Prices from 2020 to 2023 using the GARCH Method

Machine Learning in Finance: A Case Study on Forecasting Google's Stock Price

Stock price prediction remains an attractive and essential area in financial markets, with researchers constantly working hard to improve existing models or develop new ones to achieve more accurate predictions. Foreseeing the future direction of stock prices allowing people to plan and formulate effective investment strategies. However, predicting stock prices remains a difficult challenge due to many uncontrollable factors. Traditional forecasting methods rely primarily on economic data analysis and formulation. However, these traditional methods often provide limited information and forecast accuracy due to market uncertainty. Many business organizations and individual investors started to utilize the programmed approaches to improve the accuracy of stock price predictions as machine learning and deep learning capabilities continue to advance. Through an actual case study, this essay examines the innovative use of machine learning methods for researching in the field of predicting stock prices. In the case study, an LSTM model is built to find the transforming trend of the stock price, while Googles stock price is collected to use as the dataset for training the model. The article finally conducts a comparative study on stock price prediction based on LSTM is conducted to clarify its working progress and accuracy of the outcome.

Google stock price prediction by the back propagation neural network

Due to the influence of internal and external factors, stock prices often fluctuate greatly, which is also a big difficulty in the field of data mining and machine learning. This paper uses linear regression model, BP neural network model, and hard voting model to forecast Google stock. The results and accuracy of these three prediction models were observed respectively. Then a more accurate and practical model was selected to make a reasonable prediction of the closing price of Google stock in the following 15 days. The data set used is divided into two subsets, with some data used to fit the model and the rest used to evaluate the model. The results prove that the BP neural network model has the best prediction effect. Therefore, this model was selected for the next step of prediction, and the conclusive prediction result proved that the closing price of Google stock would decline in the following 15 days.

Comparative analysis of different machine learning models for predicting Google stock price

With an increasing reliance on data-driven decision-making in the business world, the need for accurate stock price predictions has grown substantially. This paper aims to address this crucial aspect, aligning machine learning methodologies with real-world business applications. The dynamic nature of financial markets indicates that it is characterized by intricate price movements influenced by an array of factors. Because it has the ability to reveal patterns and relationships in the data that traditional research can miss, machine learning has attracted a lot of attention in this context when it comes to predicting stock values. Methodologically, this paper applies three popular machine learning models to predict Google's stock prices. Linear Regression, Decision Tree, and Random Forest are each employed to assess their predictive accuracy and reliability. Metrics such as Root Mean Squared Error (RMSE), R-squared, Mean Absolute Error (MAE), and Residual Analysis are used to evaluate the models' performance. The findings reveal that Linear Regression outperformed the other models in predicting Google's stock prices. Its superior performance, as indicated by the evaluation metrics, suggests that it may be a valuable tool in the realm of financial forecasting. These findings highlight how data-driven insights can improve company strategy and decision-making, and they have important ramifications for the use of machine learning in stock market analysis.

The Impact of Stock Splits on Stock Prices

Stock splits can increase liquidity and also attract smaller investors, although no single study has shown that stock splits have an effect on stock prices. The primary aim of this project was to find the impact of stock splits on stock prices. This paper uses Amazon and Google stock price data for research. The event study and market model were used to analyze the relevant data and to explore the effect. It includes a total of four events on the announcement day and implementation day of the two companies. The study results show that stock splits do have an impact on share prices.

ARIMA and Facebook Prophet Model in Google Stock Price Prediction

We use the Autoregressive Integrated Moving Average (ARIMA) model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’ predictions. We first examine the stationary of the dataset and use ARIMA(0,1,1) to make predictions about the stock price during the pandemic, then we train the Prophet model using the stock price before January 1, 2021, and predict the stock price after January 1, 2021, to present. We also make a comparison of the prediction graphs of the two models. The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.

MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction

Chaotic time series prediction, a challenging research topic in dynamic system modeling, has drawn great attention from researchers around the world. In recent years extensive researches have been done on developing chaotic time series prediction methods, and various models have been proposed. Among them, recurrent fuzzy neural networks (RFNNs) have shown significant potential in this area. Most of the proposed RFNNs learn a single function, but when dealing with chaotic time series, different outputs may be generated for a specific input based on the system’s state. So, a network is required that can learn multiple functions simultaneously. Based on this concept, a novel multi-functional recurrent fuzzy neural network (MFRFNN) is proposed in this paper. MFRFNN consists of two fuzzy neural networks with Takagi-Sugeno-Kang fuzzy rules, one is used to produce the output, and the other to determine the system’s state. There is a feedback loop between these two networks, which makes MFRFNN capable of learning and memorizing historical information of past observations. Employing the states allows the proposed network to learn multiple functions simultaneously. Moreover, a new learning algorithm, which employs the particle swarm optimization algorithm, is developed to train the networks’ weights. The effectiveness of MFRFNN is validated using the Lorenz and Rossler chaotic time series and four real-world datasets, including Box–Jenkins gas furnace, wind speed prediction, Google stock price prediction, and air quality index prediction. Based on the root mean square error, the proposed method shows a decrease of 35.12%,13.95%, and 49.62% from the second best methods in the Lorenz time series, Box–Jenkins gas furnace, and wind speed prediction dataset, respectively.

IT2CFNN: An interval type-2 correlation-aware fuzzy neural network to construct non-separable fuzzy rules with uncertain and adaptive shapes for nonlinear function approximation

In this paper, a new interval type-2 fuzzy neural network able to construct non-separable fuzzy rules with various shapes is introduced for function approximation problems. To reflect the uncertainty, the shape of fuzzy sets is considered to be uncertain. Therefore, a new form of shapeable interval type-2 fuzzy sets based on a general Gaussian model able to construct different shapes (including triangular, bell-shaped, trapezoidal) is proposed. To consider the interactions among input variables, input vectors are transformed to new feature spaces with uncorrelated variables proper for defining each fuzzy rule. Next, the new features are fed to a fuzzification layer using proposed interval type-2 fuzzy sets with adaptive shapes. Consequently, interval type-2 non-separable fuzzy rules with proper shapes, considering the local interactions of variables and the uncertainty are formed. For type reduction, the contribution of the upper and lower firing strengths of each fuzzy rule is adaptively selected separately. To train different parameters of the network, the Levenberg–Marquardt optimization method is utilized. The performance of the proposed method is investigated on clean and noisy datasets to show the ability to consider the uncertainty. Moreover, the proposed paradigm is successfully applied to real-world time-series predictions, regression problems, and nonlinear system identification. According to the experimental results, the performance of our proposed model outperforms other methods with a more parsimonious structure. Based on several experiments, the test RMSE of the proposed method is equal to 0.0243 for noisy McGlass time series prediction, 1.92 for Santa-Fe Laser prediction, 0.0301 for Box–Jenkins system identification, 0.0569 for Poland electricity load forecasting, 4.22 for Google stock price tracking, and 13.22 for Sydney stock price tracking.

A novel model for chaotic complex time series with large of data forecasting

The time series forecasting research has become very popular in recent years, which affects the economic interests in various industries, for instance, wind power, stock price and electrical load power forecasting. However, the majority of the time series patterns are uncertain, nonlinear and fluctuating that effect on the predicted accuracy of the model. With the evolution of statistical and deep learning models, the problem has been alleviated but it is tedious and lengthy to find the optimal value due to the massive hyperparameter figures in most of the models or methods. Especially, it is time-consuming for the researchers and practitioners who are new in the time series forecasting field. In this research, a novel model is proposed and this model is not only easy to use but also adept in tackling complexly nonlinear systems with large amount of data. Firstly, the similar data is clustered into the group by using the S-means, followed by applying the data fusion method in order to reduce the data dimensionality. Secondly, the whole system can be represented by a small amount of fused data while outputting the prediction result. Finally, the time analysis function is utilized to correct the final prediction result. The proposed model has the following advantages: there are fewer and simpler hyperparameters needed which is more efficient to use during the time adjustive process; the forecasting accuracy is more stable, and the calculation rate is faster due to the non-parametric and non-training. To compare the proposed model with some existing models or methods, the forecasting accuracy of each dataset has a better performance, as it shown in the paper, the forecasting accuracy has improved with 2.5% in the wind speed data; 70% in Google stock price data; 12% in the electricity load data; 31% in the weekly British Pound/US dollar (GBP/USD) exchange rate data, and 24% in the sunspot data.

Logistic Regression Model with Technical IndicatorsPredicts Ups and Downs For Google Stock Prices

Logistic Regression Model with Technical IndicatorsPredicts Ups and Downs For Google Stock Prices

Stock Price Forecasting Framework based on the Support Vector Regression and Monte Carlo Method

Stock market and its prices prediction are considered as one of the challenging task in financial forecasting. In my research, the framework is created based on the support vector regression (SVR) and Monte Carlo method to predict the stock price. The radial basis function (RBF) has high capacity, simpler design, and adopted for kernel function in SVR. The stock price of four companies Microsoft, Facebook, Amazon and Google is used to analyze the efficiency of the proposed method. The different parameters like mean square error (MSE), mean absolute error (MAE) measured to estimate the outcome of the proposed method. The experimental result showed the efficiency of the SVR-Monte Carlo in terms of error value. The MSE for the SVR-Monte Carlo in Google stock obtained as 0.2162 and MAE for the predicted value is 0.0164.

RETRACTED ARTICLE: Google Search Intensity and the Investor Attention Effect: A Quantile Regression Approach

This paper investigates whether the investor attention effect caused by the Google stock search can be used to forecast stock returns. The evolving literature on Google search investor attention effect suggests that high Google search volumes can predict high returns for the first 1–2 weeks, but with a subsequent price reversal. We use a more recent data set that covers the period from 2012 to 2017 in the Indian stock market and employ the quantile regression approach as it alleviates some of the statistical problems to find that high Google search volumes lead to positive returns. Indeed, the high Google search volumes predict positive and significant returns in the subsequent third, fourth and fifth weeks. The Google search volume index performs as a better predictor of the direction as well as the magnitude of the excess returns. The findings infer that the signals from the search volume data could be of benefit construction of profitable trading strategies.

Stock Market Prediction Using Machine Learning(ML)Algorithms

Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks. From last few decades, there seen explosive increase in the average person’s interest for stock market. In a financially explosive market, as the stock market, it is important to have a very accurate prediction of a future trend. Because of the financial crisis and recording profits, it is compulsory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires progressive algorithms of machine learning with help of Artificial Intelligence (AI).In our research, we are going to use Machine Learning Algorithm specially focus on Linear Regression (LR), Three month Moving Average(3MMA), Exponential Smoothing (ES) and Time Series Forecasting using MS Excel as best statistical tool for graph and tabular representation of prediction results. We obtained data from Yahoo Finance for Amazon (AMZN) stock, AAPL stock and GOOGLE stock after implementation LR we successfully predicted stock market trend for next month and also measured accuracy according to measurements.