Bitcoin is the most widely known blockchain, a distributed ledger that records an increasing number of transactions based on the bitcoin cryptocurrency. New bitcoins are created at a predictable and ...decreasing rate, which means that the demand must follow this level of inflation to keep the price stable. Actually, the price is highly volatile, because it is affected by many factors including the supply of bitcoin, its market demand, the cost of the mining process, as well as economic and political world-class news.
In this work, we illustrate a novel approach for bitcoin trend prediction, based on the One-Dimensional Convolutional Neural Network (1D CNN). First, we propose a methodology for building useful datasets that take into account social media data, the full blockchain transaction history, and a number of financial indicators. Moreover, we present a cloud-based system characterized by a highly efficient distributed architecture, which allowed us to collect a huge amount of data in order to build thousands of different datasets, using the aforementioned methodology. To the best of our knowledge, this is the first work that uses 1D CNN for bitcoin trend prediction. Remarkably, an efficient and low-cost implementation is feasible due to the simple and compact configuration of 1D CNN models that perform one-dimensional convolutions (i.e., scalar multiplications and additions). We show that the 1D CNN model we implemented, trained, validated and tested using the aforementioned datasets, allow one to predict the bitcoin trend with higher accuracy compared to LSTM models. Last but not least, we introduce and simulate a trading strategy based on the proposed 1D CNN model, which increases the profit when the bitcoin trend is bullish and reduces the loss when the trend is bearish.
•A novel approach for bitcoin trend prediction with Convolutional Neural Networks•A methodology for building a multi-source dataset that includes many features•A cloud-based distributed system for collecting a huge amount of Bitcoin related data•Experimental evaluation for comparing CNN-based with LSTM-based prediction models
Financial indicators are tools that provide concrete clues regarding the health of a business. They involve two dimensions: a quantitative one, determined by the calculation of the indicators, and a ...qualitative one, which relates to their interpretation. Approached statically, at the level of a financial year, they define the current situation at that moment. Dynamically approached, over a time horizon consisting of several financial years, they highlight the general trend for that time span. This material aims to analyse the main financial indicators at the level of a company that is internationally renowned, as well as resilient and financially stable. BMW offers top products within the automotive industry. The approach to the indicators for this company will be both static and dynamic, over a time horizon of four financial years (2019-2022). Their determination will be pursued, with the aim of a quantitative assessment, as well as their interpretation, for the purpose of a qualitative assessment.
The fundamental objective of accounting is to provide the fairest representation possible of the financial reality of an entity and it is therefore a basic condition, but not sufficient, for economic ...decisions. It is necessary for the financial reality to be capitalized upon through the financial analysis. The companies constantly enjoy a wealth that, by virtue of the dynamism provided by their economic activity, increases or decreases with the results obtained. Thus, two directions of capitalization are delineated – one primarily, focusing on wealth, namely the financial position, and one secondary, focusing on results, namely the performance, which are interdependent with the former. In the current economic context, three major types of economic activities are identified, namely the production, the trade, and the services. They present convergences and divergences in terms of their uses and forms, of their financing needs and sources, as well as of their result generation. This material comparatively analyzes the main financial indicators in the context of three representative entities from the Romanian business environment, one predominantly engaged in production, one predominantly engaged in trade, and one predominantly engaged in services.
This is the first article that studies BitCoin price formation by considering both the traditional determinants of currency price, e.g., market forces of supply and demand, and digital currencies ...specific factors, e.g., BitCoin attractiveness for investors and users. The conceptual framework is based on the Barro (1979) model, from which we derive testable hypotheses. Using daily data for five years (2009-2015) and applying time-series analytical mechanisms, we find that market forces and BitCoin attractiveness for investors and users have a significant impact on BitCoin price but with variation over time. Our estimates do not support previous findings that macro-financial developments are driving BitCoin price in the long run.
This paper builds on the liabilities of newness literature to suggest that accounting information is important for new firms. Using a sample of over 30,000 companies followed during their first ...7 years of existence, we find evidence that financial indicators mitigate the liability of newness and that this buffering effect is stronger the younger the organization. These results represent three primary contributions to the literature. First, our conceptualization of accounting measures as indicators of external (creditworthiness enhancing legitimacy) as well as internal (targets for management) buffers to the liabilities of newness provides a novel way of viewing these constructs and explains why they are important to new firms despite their uncertainty and opacity. Second, we theoretically justify and empirically validate that these constructs are more important the younger the new firm is, which runs counter to the common wisdom of these constructs in the entrepreneurship literature. Third, we identify buffers against failure for new firms that are generalizable across industries.
•We predict financial distress for 107 listed Chinese companies.•Neural networks provide highest accuracy and are robust to experimental conditions.•Feature selection shows financial indicators ...related to profitability are important.
The deterioration in profitability of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial crisis for better corporate governance. This paper studies the phenomenon of financial distress for 107 Chinese companies that received the label ‘special treatment’ from 2001 to 2008 by the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We use data mining techniques to build financial distress warning models based on 31 financial indicators and three different time windows by comparing these 107 firms to a control group of firms. We observe that the performance of neural networks is more accurate than other classifiers, such as decision trees and support vector machines, as well as an ensemble of multiple classifiers combined using majority voting. An important contribution of the paper is to discover that financial indicators, such as net profit margin of total assets, return on total assets, earnings per share, and cash flow per share, play an important role in prediction of deterioration in profitability. This paper provides a suitable method for prediction of financial distress for listed companies in China.
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