Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method.

The Indonesian real estate market has shown promising development in recent years, attracting investors seeking lucrative opportunities. This research aims to increase the success rate of real estate investments in Indonesia by examining the connection between macroeconomic factors and consumer behavior. This study employs vector autoregression (VAR) models to investigate the influence of macroeconomic data on the Indonesian house price index (HPI). We intend to determine the primary determinants of the HPI by analyzing various macroeconomic indicators, including GDP growth, CPI, crude oil, interest rates, and the unemployment rate. In addition, the research investigates the role of consumer behavior in Indonesian real estate searches. Real estate developers and investors can gain valuable insight into the effect of consumer preferences, motivations, and decision-making processes on demand functions by understanding consumer preferences, incentives, and decision-making processes. A comprehensive analysis of historical data and econometric modeling techniques are employed to accomplish research objectives. The research is conducted using pertinent macroeconomic indicators, real estate market data, and consumer surveys.

This paper presents an empirical investigation to learn the impact of some internal and external factors on profitability in banking system. The proposed model of this paper introduces three econometrics methods to study the behavior of internal, external and a combination of both factors on return on assets. The proposed study of this paper uses Vector Auto- Regressive (VAR) and Vector Error Correction Model (VECM) to provide estimation of the proposed model and we use historical data over the period of 1989-2010. The results of the first model for internal factors, we consider some independent variables including ratio of total revenue on total assets (TR) and ratio of total equities on total assets (TE). The second model considers the effects of external variables on ROA such as growth domestic product (GDP) and market share (MS) and the last model includes a combination of both internal and external factors. The results indicates that there is a positive and meaningful relationship between logarithm of growth domestic product and return on equities, which means as we expect one unit increase in LGDP, there is an increase of 0.012 on ROA. In addition, when the market share increase by one percent, there will be an increase 0.025% increase on ROA and an increase of one percent in the ratio of TR will yield to an increase of one percent in ROA.