Business Commons^™

Artificial Intelligence-Enhanced Predictive Insights For Advancing Financial Inclusion: A Human-Centric Ai-Thinking Approach, Meng Leong How, Sin Mei Cheah, Aik Cheow Khor, Yong Jiet Chan

Research Collection Lee Kong Chian School Of Business

According to the World Bank, a key factor to poverty reduction and improving prosperity is financial inclusion. Financial service providers (FSPs) offering financially-inclusive solutions need to understand how to approach the underserved successfully. The application of artificial intelligence (AI) on legacy data can help FSPs to anticipate how prospective customers may respond when they are approached. However, it remains challenging for FSPs who are not well-versed in computer programming to implement AI projects. This paper proffers a no-coding human-centric AI-based approach to simulate the possible dynamics between the financial profiles of prospective customers collected from 45,211 contact encounters and predict …

Dynamic Asset Allocation: A Bayesian Approach, Yalan Feng

Dissertations, Theses, and Capstone Projects

The first half of this dissertation consists of two essays addressing dynamic asset allocation problem by exploring time-varying volatility and covariance between different assets.

In the first essay, I propose a time-varying Bayesian approach based on autoregressive models. To allow a parsimonious specification while improving predictive power, I specify a step function that considerably decreases the number of parameters to be estimated. To reduce data dimensionality, I use orthogonal portfolios instead of correlated assets in estimation and forecast. Finally, a Bayesian estimation is applied to dynamically update coefficients and error variance. I combine Bayesian time-varying autoregression with step function restriction …

An Exercise In Bayesian Econometric Analysis Probit And Linear Probability Models, Brooke Jeneane Siler

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

The aim of this paper is to carry out a Bayesian econometric application. Using a dataset obtained from Wooldridge's Introductory Econometrics textbook, each step in conducting a Bayesian econometric analysis is performed and explained. For illus- trative and comparative purposes, two limited dependent variable regression forms were used: a linear probability model and a probit model. This paper covers the ben- ets of Bayesian methodology, including selection of distributions for the prior and the likelihood. Additionally, a series of diagnostic checks are done after the models are computed.

Bayesian Inference: Probit And Linear Probability Models, Nate Rex Reasch

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

The following paper analyzes the benets of Bayes' theorem in applied econo- metrics. This is accomplished by demonstrating each step in conducting Bayesian inference. This includes the prior selection, the likelihood function, posterior simula- tion, and model diagnostics. To provide a concrete example I replicate, by Bayesian inference, the main model of Blau, Brough, and Thomas.(2013) This model is found in their research paper titled, Corporate lobbying, Political Connections, and the Bailout of Banks. The analysis focuses on two dierent forms of limited dependent variable regressions, the probit and linear probability model. The benets of Bayesian econo- metrics were extensive …

Do Hedge Funds Deliver Alpha? A Bayesian And Bootstrap Analysis, Robert Kosowski, Narayan Y. Naik, Melvyn Teo

Research Collection BNP Paribas Hedge Fund Centre

Using a robust bootstrap procedure, we find that top hedge fund performance cannot be explained by luck, and that hedge fund performance persists at annual horizons. Moreover, we show that Bayesian measures, which help overcome the short-sample problem inherent in hedge fund returns, lead to superior performance predictability. Relative to sorting on OLS alphas, sorting on Bayesian alphas yields a 5.5 percent per year increase in the alpha of the spread between the top and bottom hedge fund deciles. Our results are robust, and relevant to investors, as they are neither confined to small funds, nor driven by incubation bias, …

Do Hedge Funds Deliver Alpha? A Bayesian And Bootstrap Analysis, Robert Kosowski, Narayan Y. Naik, Melvyn Teo

Research Collection Lee Kong Chian School Of Business

Using a robust bootstrap procedure, we find that top hedge fund performance cannot be explained by luck, and that hedge fund performance persists at annual horizons. Moreover, we show that Bayesian measures, which help overcome the short-sample problem inherent in hedge fund returns, lead to superior performance predictability. Relative to sorting on OLS alphas, sorting on Bayesian alphas yields a 5.5 percent per year increase in the alpha of the spread between the top and bottom hedge fund deciles. Our results are robust, and relevant to investors, as they are neither confined to small funds, nor driven by incubation bias, …

Modeling Size-Of-Loss Distributions For Exact Data In Winbugs, David P.M. Scollnik

Journal of Actuarial Practice (1993-2006)

This paper discusses how the statistical software WinBUGS can be used to implement a Bayesian analysis of several popular severity models applied to exact size-of-Ioss data. The particular models targeted are the gamma, inverse gamma, loggamma, lognormal, (two-parameter) Pareto, inverse (two-parameter) Pareto, Weibull, and inverse Weibull distributions. It is possible to implement additional size-of-Ioss models (including those for truncated data) using methods analogous to those described herein.