Explore the vast range of titles available.

Purpose Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature. Design/methodology/approach The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey. Findings The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions. Originality/value The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.

The purpose of this work is to show some advanced concepts related to Excel based financial modelling.Microsoft Excel(tm) is a very powerful tool and most of the time we do not utilize its full potential.Of course, any advanced concepts require the basic knowledge which most of us have and then build on it.

The purpose of this work is to show some advanced concepts related to Excel based financial modelling. Microsoft Excel™ is a very powerful tool and most of the time we do not utilize its full potential. Of course, any advanced concepts require the basic knowledge which most of us have and then build on it. It is only by hands-on experimentation that one learns the art of constructing an efficient worksheet. The two volumes of this book cover dynamic charting, macros, goal seek, solver, the routine Excel functions commonly used, the lesser known Excel functions, the Excel's financial functions and so on. The introduction of macros in these books is not exhaustive but the purpose of what is presented is to show you the power of Excel and how it can be utilized to automate most repetitive calculations at a click of a button. For those who use Excel on a daily basis in financial modeling and project/investment evaluations, this book is a must.

This article proposes a financial risk dynamic prediction and decision optimization model based on Generative Adversarial Network (GAN). The model generates synthetic financial data, trains a risk prediction model, and optimizes financial decisions based on predicted risks. Simulation results show that the proposed method outperforms traditional machine learning models, achieving a mean absolute error (MAE) of 0.012 and a mean squared error (MSE) of 0.002, indicating high prediction accuracy. The model achieves an average risk of 4.5% and an average return of 8.2%, surpassing conventional algorithms. With a recommended portfolio allocation of 65% equities, 30% bonds, and 5% cash, it optimizes investment decisions by maximizing returns while minimizing risks. Overall, the proposed approach provides a novel and effective solution for financial risk prediction and decision optimization, demonstrating superior performance over existing methods.

Conditional value-at-risk (CVaR) and value-at-risk, also called the superquantile and quantile, are frequently used to characterize the tails of probability distributions and are popular measures of risk in applications where the distribution represents the magnitude of a potential loss. buffered probability of exceedance (bPOE) is a recently introduced characterization of the tail which is the inverse of CVaR, much like the CDF is the inverse of the quantile. These quantities can prove very useful as the basis for a variety of risk-averse parametric engineering approaches. Their use, however, is often made difficult by the lack of well-known closed-form equations for calculating these quantities for commonly used probability distributions. In this paper, we derive formulas for the superquantile and bPOE for a variety of common univariate probability distributions. Besides providing a useful collection within a single reference, we use these formulas to incorporate the superquantile and bPOE into parametric procedures. In particular, we consider two: portfolio optimization and density estimation. First, when portfolio returns are assumed to follow particular distribution families, we show that finding the optimal portfolio via minimization of bPOE has advantages over superquantile minimization. We show that, given a fixed threshold, a single portfolio is the minimal bPOE portfolio for an entire class of distributions simultaneously. Second, we apply our formulas to parametric density estimation and propose the method of superquantiles (MOS), a simple variation of the method of moments where moments are replaced by superquantiles at different confidence levels. With the freedom to select various combinations of confidence levels, MOS allows the user to focus the fitting procedure on different portions of the distribution, such as the tail when fitting heavy-tailed asymmetric data.

We aim to understand the dynamics of crypto asset prices and, specifically, how price information is transmitted among different bitcoin market exchanges, and between bitcoin markets and traditional ones. To this aim, we hierarchically cluster bitcoin prices from different exchanges, as well as classic assets, by enriching the correlation based minimum spanning tree method with a preliminary filtering method based on the random matrix approach. Our main empirical findings are that: (i) bitcoin exchange prices are positively related with each other and, among them, the largest exchanges, such as Bitstamp, drive the prices; (ii) bitcoin exchange prices are not affected by classic asset prices, but their volatilities are, with a negative and lagged effect.

Predicting whether a borrower will default on a loan is of significant concern to platforms and investors in online peer-to-peer (P2P) lending. Because the data types online platforms use are complex and involve unstructured information such as text, which is difficult to quantify and analyze, loan default prediction faces new challenges in P2P. To this end, we propose a default prediction method for P2P lending combined with soft information related to textual description. We introduce a topic model to extract valuable features from the descriptive text concerning loans and construct four default prediction models to demonstrate the performance of these features for default prediction. Moreover, a two-stage method is designed to select an effective feature set containing both soft and hard information. An empirical analysis using real-word data from a major P2P lending platform in China shows that the proposed method can improve loan default prediction performance compared with existing methods based only on hard information.

The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance.

In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time warping distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers.

According to family financial socialization theory and previous research, parent financial modeling (i.e., children learning financial matters by observing parental behavior) is positively associated with emerging adults' financial behaviors and financial outcomes. However, qualitative evidence and the modeling-compensation hypothesis suggest that emerging adults can intentionally compensate for negative modeling (i.e., those whose parents performed bad financial behaviors can intentionally enact good behaviors themselves). Using a diverse sample of 4,182 U.S. emerging adults, this study is the first to quantitatively explore the modeling-compensation hypothesis in the context of parent financial modeling. We identified four groups: Intergenerational Financial Flourishers (received positive modeling and were good money managers; 56.6%), Financial Phoenixes (received negative modeling and were good money managers; 21.6%), Socialization Squanderers (received positive modeling and were bad money managers; 15.9%), and Intergenerational Financial Flounderers (received negative modeling and were bad money managers; 5.9%). In terms of financial outcomes (financial independence, financial satisfaction, and financial distress), the Intergenerational Financial Flourishers were the best off, followed by the Financial Phoenixes. Thus, while high-quality parent financial modeling is ideal, there is hope for those who received negative modeling, and financial educators and practitioners can help.