Financial Quantity Research Articles

Automatic Consistency Checking of Table and Text in Financial Documents

A company's financial documents use tables along with text to organize the data containing key performance indicators (KPIs) (such as profit and loss) and a financial quantity linked to them. The KPI’s linked quantity in a table might not be equal to the similarly described KPI's quantity in a text. Auditors take substantial time to manually audit these financial mistakes and this process is called consistency checking. As compared to existing work, this paper attempts to automate this task with the help of transformer-based models. Furthermore, for consistency checking it is essential for the table's KPIs embeddings to encode the semantic knowledge of the KPIs and the structural knowledge of the table. Therefore, this paper proposes a pipeline that uses a tabular model to get the table's KPIs embeddings. The pipeline takes input table and text KPIs, generates their embeddings, and then checks whether these KPIs are identical. The pipeline is evaluated on the financial documents in the German language and a comparative analysis of the cell embeddings' quality from the three tabular models is also presented. From the evaluation results, the experiment that used the English-translated text and table KPIs and Tabbie model to generate table KPIs’ embeddings achieved an accuracy of 72.81% on the consistency checking task, outperforming the benchmark, and other tabular models.

Deep Portfolio Optimization via Distributional Prediction of Residual Factors

Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems.

ЧИННИКИ ФУНКЦІОНУВАННЯ ТА ВИЗНАЧЕННЯ МАСШТАБІВ І ПЕРСПЕКТИВ РОЗВИТКУ ФІНАНСОВОГО СЕКТОРУ

Уточнено чинники функціонування і підходи до визначення масштабів і перспектив розвитку фінансового сектору національної економіки. Запропоновано авторське бачення детермінант функціонування і розвиненості фінансового сектору національної економіки. Проаналізовано підходи до визначення впливу розмірів фінансового сектору на стан національної економіки. Наведено чинники суттєвого розриву між фінансовим і реальним секторами національної економіки та погляди дослідників на вимірники розвитку фінансового сектору національної економіки й представлено авторську позицію стосовно показників такого розвитку.

The interaction of quantity and quality of finance: Did it make industries more resilient to the recent global financial crisis?

While the literature on financial development generally shows a strong correlation between quantity of finance and economic development, the issue of financial crises and the effect of quality of finance have not been taken adequately into account. The 2008-9 global financial crisis provides a useful context in which to explore this relationship. We argue that, for the purposes of mitigating the adverse effects of financial crises on the real sector, the quantity of finance (measured as domestic credit) should be backed by quality of finance (measured as bank efficiency, integrity in bank lending, and bank private monitoring). Using a sample of 28 industries from 63 countries, we find support for our main argument. Specifically, we find that industries that are more dependent on external finance were disproportionately more resilient during the recent crisis. This was especially true if they were located in countries where high financial quantity during the pre-crisis period was accompanied by a better financial quality. These results suggest that paying attention to the quality of finance may assist in mitigating the adverse real impact of financial crises, and that there is indeed such a thing as an excessive quantity of finance.

Financial Frictions, Financial Shocks, and Aggregate Volatility

I revisit the Great Inflation and the Great Moderation for nominal and real variables. I document that while financial price variables follow such a pattern; financial quantity variables experience a continuous immoderation. A model with financial frictions and financial shocks allowing for structural breaks in the size of shocks and the institutional framework is estimated. The paper shows that while the Great Inflation was driven by bad luck, the Great Moderation is mostly due to better financial institutions. Financial shocks arise as relevant drivers of US business cycle fluctuations.

Nonlinear Effect of Financial Efficiency and Financial Competition on Heterogeneous Firm R&D: A Study on the Combined Perspective of Financial Quantity Expansion and Quality Development

Manufacturing firm data and district financial quantity and quality indicators for 2005–2007 combined with heterogeneous firm characteristics were used with a threshold panel to study the effect of financial inefficiency on firm R&D and the financial boundaries of efficiency improvement. The results show that: (1) extensive financial quantity expansion cannot support high innovation efficiency R&D (Research and Development) activities in private enterprises, low- and medium-technology enterprises, and underdeveloped area enterprises, as it causes financial inefficiency problems and a shortage of R&D inputs; and (2) financial efficiency and financial competition have nonlinear effects on firm R&D. Financial inefficiency and either low or excessive financial competition result in a lack of highly efficient firm R&D. Only improvements in financial efficiency and moderate competition can significantly promote firm R&D. The results of this study reveal an important way to improve the influence of financial inefficiency on firm R&D by moving away from simply expanding financial quantity to promoting quality instead.

Cross-sectional fluctuation scaling in the high-frequency illiquidity of Chinese stocks

Taylor's law of temporal and ensemble fluctuation scaling has been ubiquitously observed in diverse complex systems including financial markets. Stock illiquidity is an important nonadditive financial quantity, which is found to comply with Taylor's temporal fluctuation scaling law. In this paper, we perform the cross-sectional analysis of the 1 min high-frequency illiquidity time series of Chinese stocks and unveil the presence of Taylor's law of ensemble fluctuation scaling. The estimated daily Taylor scaling exponent fluctuates around 1.442. We find that Taylor's scaling exponents of stock illiquidity do not relate to the ensemble mean and ensemble variety of returns. Our analysis uncovers a new scaling law of financial markets and might stimulate further investigations for a better understanding of financial markets' dynamics.

Inverse problem and concentration method of a continuous-in-time financial model

In a continuous-in-time model, there is an important financial quantity called Loan which cannot be determined directly in terms of algebraic spending but has a major impact on the financial strategy. In this paper, we use a mathematical framework to discuss an inverse problem of determining the implied Loan Measure from Algebraic Spending Measure when it is possible. In addition, we build a numerical method to concentrate a measure as a sum of Dirac masses.

Financial Frictions, Financial Shocks, and Aggregate Volatility

I revisit the Great Inflation and the Great Moderation for nominal and real variables. I document that while financial price variables follow such a pattern; financial quantity variables experience a continuous immoderation. A model with financial frictions and financial shocks allowing for structural breaks in the size of shocks and the institutional framework is estimated. The paper shows that while the Great Inflation was driven by bad luck, the Great Moderation is mostly due to better financial institutions. Financial shocks arise as relevant drivers of US business cycle fluctuations.

Solvency analysis and demographic risk measures

PurposeThe demographic risk is the risk due to the uncertainty in the demographic scenario assumptions by which life insurance products are designed and valued. The uncertainty lies both in the accidental (insurance risk) and systematic (longevity risk) deviations of the number of deaths from the value anticipated for it. This last component gives rise to the risk due to the randomness in the choice of the survival model for valuations (model risk or projection risk). If the insurance risk component can be assumed negligible for well‐diversified portfolios, as in the case of pension annuities, longevity risk is crucial in the actuarial valuations. The question is particularly decisive in contexts in which the longevity phenomenon of the population is strong and pension annuity portfolios constitute a meaningful slice of the financial market – both typical elements of Western economies. The paper aims to focus on the solvency appraisal for a portfolio of life annuities, deepening the impact of the demographic risk according to suitable risk indexes apt to describe its evolution in time.Design/methodology/approachThe financial quantity proposed for representing the economic wealth of the life insurance company is the stochastic surplus, and the paper analyses the impact on it of different demographic assumptions by means of risk indicators as the projection risk index, the quantile surplus valuation and the ruin probability. By means of the proposed models, the longevity risk is mainly taken into account in a stochastic scenario for the financial risk component, in order to consider their interactions, too. In order to furnish practical details significant in the portfolio risk management, several numerical applications clarify the practical meaning of the models in the solvency context.FindingsThis paper studies the impact on the portfolio surplus of the systematic demographic risk, taking into account their interaction with the financial risk sources. In this order of ideas, the internal risk profile of a life annuity portfolio is deeply investigated by means of suitable risk indexes: in a solvency analysis perspective, some possible scenarios for the evolution of death rates (generated by different survival models) are considered and this paper evaluates the impact on the portfolio surplus caused by different choices of the demographic model. The first index is deduced by a variance decomposition formula, the other ones involve the conditional quantile calculus and the ruin probability. Such indexes constitute benchmarks, whose conjoined use provides useful information to the meeting of the solvency requirements.Originality/valueWith respect to the recent actuarial literature, in which the most important contribution on the surplus analysis has been given by Lisenko et al. – where the analysis focuses on the financial aspect applied to portfolios of temporary and endowment contracts – the paper considers life annuity portfolios, taking into account the effect of the systematic demographic risk and its interactions with the financial risk components.

MODELING THE VOLATILITY AND EXPECTED VALUE OF A DIVERSIFIED WORLD INDEX

This paper considers a diversified world stock index in a continuous financial market with the growth optimal portfolio (GOP) as reference unit or benchmark. Diversified broadly based indices and portfolios, which include major world stock market indices, are shown to approximate the GOP. It is demonstrated that a key financial quantity is the trend of a world index. It turns out that it can be directly observed since the expected increments of the index equal four times those of the quadratic variation of its square root. Using a world stock index as approximation of the discounted GOP it is shown that, in reality, the trend of the discounted GOP does not vary greatly in the long term. This leads for a diversified world index to a natural model, where the index is a transformed square root process of dimension four. The squared index volatility appears then as the inverse of the square root process. This feature explains most of the properties of an index and its volatility.

Measures of Effectiveness for Governmental Organizations

For organizations whose objective is not necessarily the maximization of a financial quantity, there is little written in the economics and management literature about methods that quantify their effectiveness. Potential users of such methodologies are typically governmental organizations and agencies to whom Congress allocates funding periodically, but may also include many nonprofit organizations. Such research is importantly needed because government is no longer making outlay allocations on a merely historical basis, but is using as a criterion how effectively an organization uses its resources in meeting its objectives. In this paper we analyze the Maritime Safety Program of the U.S. Coast Guard (USCG), which is responsible for monitoring the quality of vessels that sail in U.S. waters, and present measures of effectiveness (MOEs) for the Program. We do this at two levels of activity: at an overall program level (Level I) and at a component activity level (Level II). Poisson models are used to construct the MOEs using data on maritime casualties (accidents) between 1990–1993 from the real-time Marine Safety Management System (MSMS) database maintained by the Coast Guard. A feature of the empirical methodology is the Bayesian imputation of missing data. The MOEs constructed here have at least four important uses. First, as the name suggests, they perform the function that financial quantities such as returns on equity or returns on sales perform for private-sector organizations—that is, they are indicators of efficiency. Second, they can be used as inputs into allocative decisions within the organization. For example, the Level II MOEs can be used as inputs into a programming problem that determines the optimal allocation of resources among component activities. Third, internal performance evaluation across USCG Programs or across Port Units (called Marine Safety Offices) can be based on their respective Level I MOEs. Fourth, and possibly the most important long-run consideration, MOEs provide the basis for better regulation by the government. Without MOEs the nature of regulation is probably suboptimal. By adopting these MOEs as criteria, it will be easier for the government to redesign those aspects of its regulation of the Coast Guard which curtail incentives.