Dark Data. Some Methodological Issues in Finance

2017

Objective and method: Access to data series plays a central role in the area of Finance. The increasing availability of large volumes of data, in different formats and at high frequency, combined with the technological advances in data storage and processing tools, have created a new scenario in academic research in general, and in Finance in particular, generating new opportunities and challenges. Among these challenges, methodological issues emerge, which are widely discussed among researchers from different areas, but also epistemological issues that deserve greater space for discussion. Thus, the objective of this theoretical essay is to analyze the conceptual and epistemological aspects of the use of intensive data and its reflections for the area of Finance. Results and contributions: We consider that the hypothetical-deductive method of empirical research, which is the most recurrent, limits the construction of knowledge in the so-called 'Big data era', as this approach starts from an established theory and restricts research to testing the hypothesis(es) proposed. We advocate the use of an abductive approach, as argued in Haig (2005), which converges with the ideas of grounded theory and which seems to be the most appropriate approach to this new context, as it permits greater capacity to collect value information for the data.

Law & Ethics of Human Rights, 2019

In this Article, we explore the practices of extensive data collection among sharing economy platforms, highlighting how the unknown future value of big data creates an ethical problem for a fair exchange relationship between companies and users. Specifically, we present a typology with four scenarios related to the future value of data. In the remainder of the Article, we first describe the status quo of data collection practices in the sharing economy, followed by a discussion of the value-generating affordances of big data. We then introduce the typology of four scenarios for the future value of data. Finally, the paper concludes with a short discussion on the implications of information asymmetries for a fair exchange process.

Berkeley Business Law Journal, 2021

This article explores the status of data subjects in the era of data capitalism. A data subject is a person whose personal data is being collected, held, or processed by data collectors or processors (e.g., Amazon, Google, or Facebook). Data-driven companies rely on data subjects offering personal information for training algorithms for services. We maintain that it is time to seriously investigate whether data subjects can be considered as investors. First, we preview our thesis, followed by a functional definition of an investor. Then, we develop our argument that data subjects are better understood as investors rather than consumers or labor providers by examining the balance sheet impact of a data contribution to the data firm and the existing legal regime requiring data subjects to retain an ownership interest in their data even after it has been transferred to the data firm.

in : Chen, P. - Ippoliti, E. (eds). Methods and Finance. A Unifying View on Finance, Mathematics and Philosophy (2017), pp. 121-128 The view from inside maintains that not only to study and understand, but also to profit from financial markets, it is necessary to get as much knowledge as possible about their internal ‘structure’ and machinery. This view maintains that in order to solve the problems posed by finance, or at least a large part of them,we need first of all a qualitative analysis. Rules, laws, institutions, regulators, the behavior and the psychology of traders and investors are the key elements to the understanding of finance, and stock markets in particular. Accordingly, data and their mathematical analysis are not the crucial elements, since data are the output of a certain underlying structure of markets and their actors. The underlying structure is the ultimate object of the inquiry. This chapter examines how the view from inside raises, and deals with, critical issues such as markets failure, information disclosure, and regulation (Sect. 2), the notion of data (Sect. 3), performativity (Sect. 4), and the study of micro-structures (Sect. 5).

Big data is shaking up the finance industry and could have a big impact on future research. In this special issue, we look at how big data is a combination of three things: it's big, it's big, and it's complex. We also look at how new research can use these features to tackle big questions in different areas of finance, like corporate finance, market structure, and asset prices. Plus, we have some ideas for what future research could look like. Big data is a huge part of the financial industry, with hundreds of millions of transactions happening every day. It's a growing problem for data management and analytics, so it's important to understand which financial issues big data has a big effect on. Based on these ideas, the goal of this paper was to present the current state of big data in finance as well as how different financial sectors are impacted by it. In particular, we looked at how internet finance, financial management, and internet credit service providers are impacted by big data, as well as how fraud detection, risk analysis, and financial application management are affected.

Handbook of Big Data Technologies, 2017

Quantitative tools have been widely adopted in order to extract the massive information from a variety of financial data. Mathematics, statistics and computers algorithms have never been so important to financial practitioners in history. Investment banks develop equilibrium models to evaluate financial instruments; mutual funds applied time series to identify the risks in their portfolio; and hedge funds hope to extract market signals and statistical arbitrage from noisy market data. The rise of quantitative finance in the last decade relies on the development of computer techniques that makes processing large datasets possible. As more data is available at a higher frequency, more researches in quantitative finance have switched to the microstructures of financial market. High frequency data is a typical example of big data that is characterized by the 3V's: velocity, variety and volume. In addition, the signal to noise ratio in financial time series is usually very small. High frequency datasets are more likely to be exposed to extreme values, jumps and errors than the low frequency ones. Specific data processing techniques and quantitative models are elaborately designed to extract information from financial data efficiently. In this chapter, we present the quantitative data analysis approaches in finance. First, we review the development of quantitative finance in the past decade. Then we discuss the characteristics of high frequency data and the challenges it brings. The quantitative data analysis consists of two basic steps: (i) data cleaning and aggregating; (ii) data modeling. We review the mathematics tools and computing technologies behind the two steps. The valuable information extracted from raw data is represented by a group of statistics. The most widely used statistics in finance are expected return and volatility, which are the fundamentals of modern portfolio theory. We further introduce some simple portfolio optimization strategies as an example of the application of financial data analysis. Big data has already changed financial industry fundamentally; while quantitative tools for addressing massive financial data still have a long way to go. Adoptions of advanced statistics, information theory, machine learning and faster computing algorithm are inevitable in order to predict complicated financial markets. These topics are briefly discussed in the later part of this chapter.

2017

in: Chen, P. - Ippoliti, E. (eds). (2017). Methods and Finance. A Unifying View on Finance, Mathematics and Philosophy , pp. 3-15 The view from outside on finance maintains that we can make sense of, and profit from, stock markets’ behavior, or at least few crucial properties of it, by crunching numbers and looking for patterns and regularities in certain sets of data. The basic idea is that there are general properties and behavior of stock markets that can be detected and studied through mathematical lens, and they do not depend so much on contextual or domain-specific factors. In this sense the financial systems can be studied and approached at different scales, since it is virtually impossible to produce all the equations describing at a micro level all the objects of the system and their relations. The typical view of the externalist approach is the one provided, for instance, by the application of statistical physics. By focusing on collective behaviour, statistical physics neglects all the conceptual and mathematical intricacies deriving from a detailed account of the inner, individual, and at micro level functions of a system. This chapter examines how the view from outside deals with critical issues such as the mathematical modeling (Sect. 2), the construction and interpretation of data (Sect. 3), and the problem of prediction and performativity (Sect. 4).

9th Europ. Symp. on Art. Neural …, 2001

Many researchers are interesting in applying the neural networks methods to financial data. In fact these data are very complex, and classical methods do not always give satisfactory results. They need strong hypotheses which can be false, they have a strongly non-linear structures, and so on. But neural models must also be cautiously used. The black box aspect can be very dangerous. In this very simple paper, we try to indicate some specificity of financial data, to prevent some bad use of neural models.

2011

This paper puts forward the methodological view that certain data produced by financial markets can be used without the need to look into how they were compiled. It argues that when trying to understand the causes of the Austrian Boden-Kredit Anstalt's collapse of September 1929, data from bank balance sheets are not a reliable source, whereas data such as bond yields and interest rate differentials help locate the origin of the financial crisis in the resignation of Austria's Chancellor Ignaz Seipel in April 1929.