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 pa...

value of the stock so you know how much of your eye unit’s cash is invested in stock. If you are trying to minimise costs, you can calculate the cost of consumables per patient, which is helpful to know. • Review of non-moving and slow-moving items. These are any items that have not been issued for two months, or stock levels above what is needed for two months. You must determine whether these...

This paper offers a market microstructure based model to explain the well-documented diversification discount. While explicitly assuming that stock price conveys valuable information to the management, our model shows that the value loss from diversification is a function of the stock price informativeness. If more investors are willing to follow and actively trade a certain stock, more informa...

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potenti...

The decomposition-based vector autoregressive model (DVAR) provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relationships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and emp...

The influence of changing economic environment leads the distribution of stock market returns to be time-varying. This requires a dynamic adjustment of the asset allocation in order to enjoy a conditionally optimal investment. In this context, we examine the improvement of the portfolio performance by simulating portfolio strategies that are conditioned on the Markov regime switching behavior o...

Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as event-driven stock prediction. O...

this paper presents a new index of financial stress which is constructed by macro data from different financial markets (including banking sector and also housing, foreign exchange and stock markets). the proposed approach has been applied to the iranian economy based on the available quarterly time series, covering the period 1991(1)–2008(2). the paper explains how the selected components capt...

Greenhouse gas emission of carbon dioxide (CO2) is one of the major factors causing global climate change. Urban green space plays a key role in regulating the global carbon cycle and reducing atmospheric CO2. Quantifying the carbon stock, distribution and change of urban green space is vital to understanding the role of urban green space in the urban environment. Remote sensing is a valuable a...

a r t i c l e i n f o Keywords: Fractional cointegration Long memory Range Volatility Daily high and low prices This work provides empirical support for the fractional cointegration relationship between daily high and low stock prices, allowing for the non-stationary volatility of stock market returns. The recently formalized fractionally cointegrated vector autoregressive (VAR) model is employ...