Time series

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this paper, while we introduce the most efficient features, we will show how valuable results could be achieved by the use of a financial time series technical variables that exist on the Tehran stock market. The suggested method benefits from regression-based machine learning algorithms with a focus on selecting the leading features to find the best technical variables of the inputs. The mentioned procedures were implemented using machine learning tools using the Python language. The dataset used in this paper was the stock information of two companies from the Tehran Stock Exchange, regarding 2008 to 2018 financial activities. Experimental results show that the selected technical features by the leading methods could find the best and most efficient values for the parameters of the algorithms. The use of those values results in forecasting with a minimum error rate for stock data.

IIn recent years, policymakers have generally relied on regulatory policies to address financial stability concerns. However, our understanding of these policies and their efficacy in curbing housing prices is limited. In this paper, we examine the impact of three regulatory tools i.e. LTV (loan to value) ratio, reserve requirement rate (RR), and capital adequacy ratio (CAR) on housing price inflation in Iran for the1993: Q2 to 2017:Q1 period. We investigate whether tightening the policy tools are effective in curbing the housing price inflation by using a vector autoregressive model. The results indicate that all three regulatory policy tools exhibit countercyclical impact on housing inflation, but with varying degrees of influence. While the impact of CAR tightening in curbing housing prices is quite trivial, the impact of RR and LTV tightening are roughly significant.

This study was an attempt to evaluate the progress of capital market efficiency in Iran. Optimal resource allocation and micro and macro investments play a key role in the capital market. The capital market's main task is to circulate capital and allocate resources efficiently and optimally. The main task of this market is to flow capital and allocate resources efficiently and optimally. Is there a regular pattern for determining the stock price? Market efficiency gains significance as it is important to know what factor or factors are effective in determining the price of the stock in the stock market or whether there is a regular pattern for determining the price of a stock. Thus, this study examined the efficiency of the capital market in Iran. In this regard, the researchers used the daily data of the total index of the Tehran Stock Exchange for 2008-2017. Artificial neural network and time series training tests were used to perform the test. The test results showed weak efficiency in the Tehran Stock Exchange and this inefficiency did not change significantly compared to the first period. In other words, in the Tehran Stock Market, one can predict returns using artificial intelligence.

The Unconventional Gas Production Revolution in the US has ushered in new opportunities for American petrochemical companies, granting abundant access to gas resources and fostering business growth. Consequently, prominent global petrochemical firms have made substantial investments in the United States' petrochemical and chemical industries. Simultaneously, the surge in gas production from unconventional reserves in the US has led to considerable growth in the country's petrochemical output. To address this crucial topic, we conducted a comprehensive time series analysis, investigating the long and short-term relationships between oil and polyethylene prices in the US during the shale gas development phase. Employing an autoregressive distributed lag (ARDL) model for the period spanning from January 2013 to December 2017, our research findings reveal that, in the long run, there exists a positive and significant influence of the oil price variable on polyethylene prices. However, in the short term, no discernible impact on the polyethylene price variable was observed. Interestingly, the analysis also indicates a unidirectional causal relationship, with oil prices influencing polyethylene prices. This finding suggests that despite the divergence between oil and gas prices, oil remains a crucial determinant of petrochemical product pricing. The results underscore the significance of shale gas development and its impact on the petrochemical industry. As the US continues to experience increased gas production, comprehending the intricate relationships between oil, gas, and petrochemical prices becomes imperative for companies' strategic decision-making and policymakers alike.