APPLICATIONS: Financial risk and financial Risk Management Technology (RMT): Issues and advances

Methods for sound risk management are of increasing interest among Wall Street investment banking and brokerage firms in the aftermath of the October 1987 crash of the stock market. As the knowledge of advanced technology applications in risk management increases, financial firms are finding innovative ways to use them practically, in order to insulate themselves. The recent development in models, the software and hardware, and the market data to track risk are all considered advances in Risk Management Technology (RMT). -. These advances have affected all three stages of risk management: the identification, the measurement, and the formulation of strategies to control financial risk. This article discusses the advances made in five areas of RMT: communication software, object-oriented programming, parallel processing, neural nets and artificial intelligence. Systems based on any of these areas may be used to add value to the business of a firm. A business value linkage analysis shows how the utility of advanced systems can be measured to justify their costs.Information Systems Working Papers Serie

FINANCIAL RISK AND FINANCIAL RISK MANAGEMENT TECHNOLOGY (RMT): ISSUES AND ADVANTAGES

Methods for sound risk management are of increasing interest among Wall Street investment banking and brokerage firms in the aftermath of the October 1987 crash of the stock market. As the knowledge of advanced technology applications in risk management increases, financial firms are finding innovative ways to use them practically, in order to insulate themselves. The recent development in models, the software and hardware, and the market data to track risk are all considered advances in Risk Management Technology (RMT). These advances have affected all three stages of risk management: the identification, the measurement, and the formulation of strategies to control financial risk. This article discusses the advances made in five areas of RMT: communication software, object-oriented programming, parallel processing, neural nets and artificial intelligence. Systems based on any of these areas may be used to add value to the business of a firm. A business value linkage analysis shows how the utility of advanced systems can be measured to justify their costs.Information Systems Working Papers Serie

FINANCIAL RISK AND FINANCIAL RISK MANAGEMENT TECHNOLOGY (RMT): ISSUES AND ADVANCES

Methods for sound risk management are of increasing interest among Wall Street investment banking and brokerage firms in the aftermath of the October 1987 crash of the stock market. We present an overview of the basic definitions and issues related to risk, and the management of financial risk and financial risk management technology (RMT) for information systems (IS) technology professionals. We discuss of the content of risk management technology, including the models, the software and hardware, and the market data required to track risk. We also discuss the identification of risky events, alternative approaches to the measurement of risk, and how investment firms go about formulating strategies to control financial risk. We next show how changes in the information technologies supporting these tasks have led to improvements in the control of risk and in the design of products which involve financial risk. Advances in five areas that are of interest are: communications software, object-oriented programming, the use of parallel processors and supercomputers, and the application of artificial intelligence and neural nets. Although these new information technologies create significant opportunities to improve global and departmental risk management, a basic question that must be addressed involves the costs associated with their implementation. Thus, a third contribution of this paper is to analyze the extent to which the implementation of these technologies will affect firm costs. To this end, we evaluate the components of the cost function for risk management, and consider some ways that the new technologies can be applied to reduce overall costs.Information Systems Working Papers Serie

A Survey of Contextual Optimization Methods for Decision Making under Uncertainty

Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty. This gave rise to the field of contextual optimization, under which data-driven procedures are developed to prescribe actions to the decision-maker that make the best use of the most recently updated information. A large variety of models and methods have been presented in both OR and ML literature under a variety of names, including data-driven optimization, prescriptive optimization, predictive stochastic programming, policy optimization, (smart) predict/estimate-then-optimize, decision-focused learning, (task-based) end-to-end learning/forecasting/optimization, etc. Focusing on single and two-stage stochastic programming problems, this review article identifies three main frameworks for learning policies from data and discusses their strengths and limitations. We present the existing models and methods under a uniform notation and terminology and classify them according to the three main frameworks identified. Our objective with this survey is to both strengthen the general understanding of this active field of research and stimulate further theoretical and algorithmic advancements in integrating ML and stochastic programming

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Return Predictability and Market Sentiment: Evidence from Deep Learning

Recent studies in asset pricing find that Artificial Neural Networks (also known as Deep Learning models) provide the most accurate firm-level return predictions using a vast set of predictive signals. These models offer high predictive accuracy over long out-of-sample periods, translating into highly profitable trading strategies. In this thesis, I argue that sentiment-driven mispricing is a vital source of the high predictability and the resulting profitability implied by deep learning models. Using a novel Artificial Neural Network (ANN) regression model, I obtain firm-level predictions conditional on 54 firm-level characteristics and on an encoded representation of the macro-economic state. These predictions provide important insights into the sources of overall cross-sectional return predictability. First, the future negative returns are predictable out-of-sample which implies negative expected returns. Such predictability is hard to reconcile with a risk-based explanation. Secondly, the predictability in negative returns is higher following periods of high sentiment and vice versa. This evidence is consistent with the existence of a market-level investor sentiment that drives misvaluations. Third, a long-short strategy based on ANN prediction deciles is more profitable following periods of high sentiment. This disparity in profitability points to arbitrage asymmetry implied by short-sale constraints. Fourth, the predictability in losses and high profitability of the ANN top decile vanishes in estimation horizons longer than a month. This suggests that mispricing is short-lived and that predictability is realized due to corrections to such misvaluations. These corrections are preceded by high put-to-call(PCR) trading volumes and high implied volatility(VIX). Finally, the short-term and long-term predictions load on different conditioning variables indicating varying sources of predictability across return horizons. Overall, these findings are consistent with the existence of sentiment-driven short-lived mispricing that corrects in longer horizons

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Quantitative Analyses on Non-Linearities in Financial Markets

"The brief market plunge was just a small indicator of how complex and chaotic, in the formal sense, these systems have become. Our nancial system is so complicated and so interactive [...]. What happened in the stock market is just a little example of how things can cascade or how technology can interact with market panic" (Ben Bernanke, IHT, May 17, 2010) One of the most important issues in economics is modeling and fore- casting the uctuations that characterize both nancial and real mar- kets, such as interest rates, commodities and stock prices, output growth, unemployment, or exchange rate. There are mainly two op- posite views concerning these economic uctuations. According to the rst one, which was the predominant thought in the 1930s, the economic system is mainly linear and stable, only randomly hit by exogenous shocks. Ragnar Frisch, Eugen Slutsky and Jan Tinbergen, to cite a few, are important exponents of this view, and they demon- strated that the uctuations observed in the real business cycle may be produced in a stable linear system subject to an external sequence of random shocks. This view has been criticized starting from the 1940s and the 1950s, since it was not able to provide a strong eco- nomic explanation of observed uctuations. Richard Goodwin,John Hicks and Nicholas Kaldor introduced a nonlinear view of the econ- omy, showing that even in absence of external shocks, uctuations might arise. The economists then suggested an alternative within the exogenous approach, at rst by using the stochastic real busi- ness cycle models (Finn E. Kidland and Edward C. Prescott, 1982) and, more recently, by the adoption of the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models, very adopted from the most important institutions and central banks. These models, however, have also been criticized for the assumption of the rational- ity of agents' behaviour, since rational expectations have been found to be systematically wrong in the business cycle. Expectations are of fundamental importance in economics and nance, since the agents' decisions about the future depends upon their expectations and their beliefs. It is in fact very unlikely that agents are perfect foresighters with rational expectations in a complex world, characterized by an irregular pattern of prices and quantities dealt in nancial markets, in which sophisticated nancial instruments are widespread. In the rst chapter of this dissertation, I will face the machine learn- ing technique, which is a nonlinear tool used for a better tting, fore- casting and clustering of dierent nancial time series and existing information in nancial markets. In particular, I will present a collec- tion of three dierent applications of these techniques, adapted from three dierent joint works: "Yield curve estimation under extreme conditions: do RBF net- works perform better?, joint with Pier Giuseppe Giribone, Marco Neelli, Marina Resta, published Anna Esposito, Marcos Faundez- Zanuy, Carlo Francesco Morabito, Eros Pasero Edrs, Multidisci- plinary Approaches to Neural Computing/Vol. 69/ WIRN 2017 and Chapter 22 in book "Neural Advances in Processing Non- linear Dynamic Signals", Springer; Interest rates term structure models and their impact on actuarial forecasting, joint with Pier Giuseppe Giribone and Marina Resta, presented at XVIII Quantitative Finance Workshop, University of Roma 3, January 2018; Applications of Kohonen Maps in financial markets: design of an automatic system for the detection of pricing anomalies, joint with Pier Giuseppe Giribone and published on Risk Management Magazine, 3-2017. In the second chapter, I will present the study A nancial market model with conrmation bias, in which nonlinearity is present as a result of the formation of heterogeneous expectations. This work is joint with Fabio Tramontana and it has been presented during the X MDEF (Dynamic Models in Economics and Finance) Workshop at University of Urbino Carlo Bo. Finally, the third chapter is a rielaboration of another joint paper, "The eects of negative nominal risk rates on the pricing of American Calls: some theoretical and numerical insights", with Pier Giuseppe Giribone and Marina Resta, published on Modern Economy 8(7), July 2017, pp 878-887. The problem of quantifying the value of early ex- ercise in an option written on equity is a complex mathematical issue that deals with continuous optimal control. In order to solve the con- tinuous dynamic optimization problem that involves high non linearity in the state variables, we have adopted a discretization scheme based on a stochastic trinomial tree. This methodology reveals a higher reliability and exibility than the traditional approaches based on approximated quasi-closed formulas in a context where financial markets are characterized by strong anomalies such as negative interest rates