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Results for “industry” · papers 18 · wiki 3
Academic Papers · 18arXiv q-fin live 8 · desk corpus 35
arXiv · arXiv q-fin · 2009

A stochastic reachability approach to portfolio construction in finance industry

In finance industry portfolio construction deals with how to divide the investors' wealth across an asset-classes' menu in order to maximize the investors' gain. Main approaches in use at the present are based on variations of the classical Markowitz model. However, recent evolutions of the world market showed limitations of this method and motivated many researchers and practitioners to study alternative methodologi

Giordano Pola, Gianni Pola
arXiv · arXiv · 2022

Method of indirect estimation of default probability dynamics for industry-target segments according to the data of Bank of Russia

A direct method for calculating default rates by industry and target corporate segments is not possible given the lack of statistical data. The proposed paper considers a model for filtering the dynamics of the probability of default of corporate companies and other borrowers based on indirect data on the dynamics of overdue debt supplied by the Bank of Russia. The model is based on the equation of the balance of tot

Mikhail Pomazanov
arXiv · arXiv · 2024

Multi-Industry Simplex 2.0 : Temporally-Evolving Probabilistic Industry Classification

Accurate industry classification is critical for many areas of portfolio management, yet the traditional single-industry framework of the Global Industry Classification Standard (GICS) struggles to comprehensively represent risk for highly diversified multi-sector conglomerates like Amazon. Previously, we introduced the Multi-Industry Simplex (MIS), a probabilistic extension of GICS that utilizes topic modeling, a na

Maksim Papenkov
arXiv · arXiv · 2024

Refining and Robust Backtesting of A Century of Profitable Industry Trends

We revisit the long-only trend-following strategy presented in A Century of Profitable Industry Trends by Zarattini and Antonacci, which achieved exceptional historical performance with an 18.2% annualized return and a Sharpe Ratio of 1.39. While the results outperformed benchmarks, practical implementation raises concerns about robustness and evolving market conditions. This study explores modifications addressing r

Alessandro Massaad, Rene Moawad, Oumaima Nijad Fares, Sahaphon Vairungroj
arXiv · arXiv · 2024

A Deep Learning Method for Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks

Merger and Acquisition (M&A) activities play a vital role in market consolidation and restructuring. For acquiring companies, M&A serves as a key investment strategy, with one primary goal being to attain complementarities that enhance market power in competitive industries. In addition to intrinsic factors, a M&A behavior of a firm is influenced by the M&A activities of its peers, a phenomenon known as the "peer eff

Dayu Yang
arXiv · arXiv · 2023

Multi-Industry Simplex : A Probabilistic Extension of GICS

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm

Maksim Papenkov, Chris Meredith, Claire Noel, Jai Padalkar, Temple Hendrickson
arXiv · arXiv · 2023

Accounting statement analysis at industry level. A gentle introduction to the compositional approach

Compositional data are contemporarily defined as positive vectors, the ratios among whose elements are of interest to the researcher. Financial statement analysis by means of accounting ratios a.k.a. financial ratios fulfils this definition to the letter. Compositional data analysis solves the major problems in statistical analysis of standard financial ratios at industry level, such as skewness, non-normality, non-l

Germà Coenders, Núria Arimany Serrat
arXiv · arXiv · 2020

How does stock market reflect the change in economic demand? A study on the industry-specific volatility spillover networks of China's stock market during the outbreak of COVID-19

Using the carefully selected industry classification standard, we divide 102 industry securities indices in China's stock market into four demand-oriented sector groups and identify demand-oriented industry-specific volatility spillover networks. The "deman-oriented" is a new idea of reconstructing the structure of the networks considering the relationship between industry sectors and the economic demand their output

Fu Qiao, Yan Yan
arXiv · arXiv · 2019

Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices

In this paper, we model the impact of oil price volatility on Tehranstock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward neural net-works. The period of study is from 2008 to 2018 that is split in two periods during international energy sanction and post-sanction. The results show that Feed-forward neural networks perform well in

Somayeh Kokabisaghi, Mohammadesmaeil Ezazi, Reza Tehrani, Nourmohammad Yaghoubi
arXiv · arXiv · 2017

Open Source Fundamental Industry Classification

We provide complete source code for building a fundamental industry classification based on publically available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssrn.com/abstract=2600798) built using such industry classifications. Our source code includes various

Zura Kakushadze, Willie Yu
arXiv · arXiv · 2012

The Keynesian theory and the manufactured industry in Portugal

About the economic growth the Keynesian theorists defend circular and cumulative processes, benefiting the rich localities and harming the poorest, without external interventions. In these processes the Verdoorn law has an important role. For Verdoorn (1949) the productivity growth rate is endogenous and depends of the output growth rate, capturing dynamic contexts, endogeneity of the factors and increasing economies

Vitor Joao Pereira Domingues Martinho
arXiv · arXiv q-fin · 2024

Simulating Liquidity: Agent-Based Modeling of Illiquid Markets for Fractional Ownership

This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, providing a foundation for generating insights into how different market structures influence liquidity. The ABM-based simulati

Lars Fluri, A. Ege Yilmaz, Denis Bieri, Thomas Ankenbrand, Aurelio Perucca
arXiv · arXiv q-fin · 2020

Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling

One of the most challenging aspects in the analysis and modelling of financial markets, including Credit Default Swap (CDS) markets, is the presence of an emergent, intermediate level of structure standing in between the microscopic dynamics of individual financial entities and the macroscopic dynamics of the market as a whole. This elusive, mesoscopic level of organisation is often sought for via factor models that

Ioannis Anagnostou, Tiziano Squartini, Drona Kandhai, Diego Garlaschelli
arXiv · arXiv q-fin · 2019

Market Price of Trading Liquidity Risk and Market Depth

Price impact of a trade is an important element in pre-trade and post-trade analyses. We introduce a framework to analyze the market price of liquidity risk, which allows us to derive an inhomogeneous Bernoulli ordinary differential equation. We obtain two closed form solutions, one of which reproduces the linear function of the order flow in Kyle (1985) for informed traders. However, when traders are not as asymmetr

Masaaki Kijima, Christopher Ting
arXiv · arXiv q-fin · 2013

Credit Portfolio Management in a Turning Rates Environment

We give a detailed account of correlations between credit sector/quality and treasury curve factors, using the robust framework of the Barclays POINT Global Risk Model. Consistent with earlier studies, we find a strong negative correlation between sector spreads and rate shifts. However, we also observe that the correlations between spreads and Treasury twists reversed recently, which is likely attributable to the Fe

Arthur M. Berd, Elena Ranguelova, Antonio Baldaque da Silva
arXiv · arXiv q-fin · 2023

Construct sparse portfolio with mutual fund's favourite stocks in China A share market

Unlike developed market, some emerging markets are dominated by retail and unprofessional trading. China A share market is a good and fitting example in last 20 years. Meanwhile, lots of research show professional investor in China A share market continuously generate excess return compare with total market index. Specifically, this excess return mostly come from stock selectivity ability instead of market timing. Ho

Ke Zhang
arXiv · arXiv q-fin · 2023

Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets

The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States. In this article, we investigate how such concurrent cross-stock trading behaviors, which we denote as co-trading, shape the market structures and affect stock price co-movements. By leveraging a co-trading-based pairwise similarity measure, we propose a novel method to construct dynamic net

Yutong Lu, Gesine Reinert, Mihai Cucuringu
arXiv · arXiv q-fin · 2009

Analytical Framework for Credit Portfolios. Part I: Systematic Risk

Analytical, free of time consuming Monte Carlo simulations, framework for credit portfolio systematic risk metrics calculations is presented. Techniques are described that allow calculation of portfolio-level systematic risk measures (standard deviation, VaR and Expected Shortfall) as well as allocation of risk down to individual transactions. The underlying model is the industry standard multi-factor Merton-type mod

Mikhail Voropaev
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