The aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by...
Stock price prediction is a significant area of research in finance that has been ongoing for a long time. Several mathematical models have been utilized in this field to predict stock prices. However, recently, machine learning techniques have demon...
Future stock prices depend on many internal and external factors that are not easy to evaluate. In this paper, we use the Hidden Markov Model, (HMM), to predict a daily stock price of three active trading stocks: Apple, Google, and Facebook, based on...
Stock price prediction can be made more efficient by considering the price fluctuations and understanding people’s sentiments. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome...
Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the...
The rapid advancement of digital technology has transformed how investors gather financial information, with platforms like Google Trends providing valuable insights into investor behavior through the Google Search Volume Index (GSVI). While the rela...
Finances represent one of the key requirements to perform any useful activity for humanity. Financial markets, e.g., stock markets, forex, and mercantile exchanges, etc., provide the opportunity to anyone to invest and generate finances. However, to...
Accurate stock price prediction requires the integration of heterogeneous data streams, yet conventional techniques struggle to simultaneously leverage fine-grained micro-stock features and broader macroeconomic indicators. To address this gap, we pr...
This study contributes to the emerging literature offering alternative measures of uncertainty due to the COVID-19 pandemic. We combine both news-and macro-based trends to construct an index. The former involves the use of Google trends with plausibl...
This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empi...
The integration of machine learning and stock forecasting is attracting increased curiosity owing to its growing significance. This paper presents two main areas of study: predicting pattern trends for the next day and forecasting opening and closing...
This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. We exten...
The integration of commodities into stock exchanges marked a pivotal moment in the analysis of price dynamics. Commodities are essential for both daily sustenance and industrial processes and are separated into hard commodities, like metals, and soft...
This paper estimates some of the parameters of the Schwartz and Moon (2001)) model using cross-sectional data. Stochastic costs, future financing, capital expenditures and depreciation are taken into account. Some special conditions are also set: the...
Data are produced every single instant in the modern era of technological breakthroughs we live in today and is correctly termed as the lifeblood of today’s world; whether it is Google or Meta, everyone depends on data to survive. But, with the...
This study investigates whether the aggregate investor information demand for all stocks in a sector demonstrated in the Google search volume index (SVI) can predict the sector’s performance. The evidence shows that a sector’s abnormal SV...
The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decision...
Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring...
Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further th...
Generating high-quality synthetic data is essential for advancing machine learning applications in financial time series, where data scarcity and privacy concerns often pose significant challenges. This study proposes a novel hybrid architecture that...