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Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it.

Alan J. Perlis


Photo credit: Amir Holakoo

I am the Senior Lecturer of Quantitative Finance at the School of Mathematics at the Monash University and a member of Monash Centre for Quantitative Finance. Before, I was a Post-Doctoral researcher at the Swiss Finance Institute at EPFL, EPFL and the European Center for Advanced Research in Economics and Statistics (ECARES), Free University of Brussels, Belgium. My CV. Google Scholar.

Working Papers (Latest First: date written)(Comments Welcome!):

  1. 1.Inside the Mind of Investors During the COVID-19 Pandemic: Evidence from the StockTwits Data, 2020.

       [New Paper] 

       [Replication Python Code]     

       [Replication Data]

       Media Coverage: [RIAIntel] . [Monash] . [PHYS.org] . [Miragenews] . [ausbiz]

                                   [stockhead] . [Lens]

  1. Abstract: We study the investor beliefs, sentiment and disagreement, about stock market returns during the COVID-19 pandemic using a large number of messages of investors on a social media investing platform, StockTwits. The rich and multimodal features of StockTwits data allow us to explore the evolution of sentiment and disagreement within and across investors, sectors, and even industries. We find that the sentiment (disagreement) has a sharp decrease (increase) across all investors with any investment philosophy, horizon, and experience between February 19, 2020, and March 23, 2020, where a historical market high followed by a record drop. Surprisingly, these measures have a sharp reverse toward the end of March. However, the performance of these measures across various sectors is heterogeneous. Financial and healthcare sectors are the most pessimistic and optimistic divisions, respectively.


  3. 2.Towards Explaining Deep Learning: A Variable Significance Test for Multi-Layer Perceptrons (joint with Vincentius Franstianto), 2020.

       [New Paper: Preliminary and Incomplete] 

       [Replication Python Code]

  1. Abstract: Horel and Giesecke (2019) propose a gradient-based test statistic for the one-layer sigmoid neural networks and study its asymptotics using nonparametric techniques. However, their results are not adequate for the most useful and attractive architectures of neural networks, e.g., multi-layer perceptrons. This paper extends their results to the fully connected feed-forward neural networks where the activation function is a general bounded, non-polynomial, and infinitely differentiable (smooth). To derive the test statistic and its asymptotic distribution, we provide the consistency of the multi-layer perceptrons via the method of sieves. Like HG the significant test offers several desirable characteristics such as computational efficiency and ranking variables based on their influence, but, unlike HG the significant test can be used for the multi-layer perceptrons. To validate the theoretical results, a Monte Carlo analysis is presented.

  1. 3.Towards Explaining Deep Learning: Asymptotic Properties of ReLU FFN Sieve Estimators (joint with Vincentius Franstianto and Gregoire Loeper), 2019.


       [Replication Python Code: GitHub]

  1. Abstract: A multi-layer, multi-node ReLU network is a powerful, efficient, and popular tool in statistical prediction tasks. However, in contrast to the great emphasis on its empirical applications, its statistical properties are rarely investigated which is mainly due to its severe nonlinearity and heavy parametrization. To help to close this gap via a sieve estimator, we first show that there exists such a sieve estimator for a ReLU feed-forward network. Next, we establish three asymptotic properties of the ReLU network: consistency, sieve-based convergence rate, and asymptotic normality. Finally, to validate the theoretical results, a Monte Carlo analysis is provided.

  2. 4.Correlated Time-Changed Lévy Processes (joint with Kihun Nam), 2019. [Submitted]

  3. Abstract: Carr and Wu (2004), henceforth CW, developed a framework that encompasses almost all of the continuous-time models proposed in the option pricing literature. Their main result hinges on the stopping time property of the time changes, but all of the models CW proposed for the time changes do not satisfy this assumption. In this paper, when the time changes are adapted, but not necessarily stopping times, we provide analogous results to CW. We show that our approach can be applied to all models in CW. 

  1. 5.Model Risk and Disappointment Aversion (joint with Loriano Mancini and Stoyan Stoyanov), 2019.

       [under major revision: new draft coming soon!]

  1. Abstract: Extensions of expected utility theory are sensitive to the tail behavior of the portfolio return distribution and may not be approximated reliably through higher-order moment expansions. We develop a novel approach for model risk assessment based on a projection method and apply it to portfolio construction. We provide an extensive out-of-sample analysis to explore the economic gains of incorporating non-normality about financial asset returns into utility maximization with the generalized disappointment aversion (GDA) preferences. We find that the marginal utility gains of the optimal portfolio of a GDA investor are remarkably robust to misspecifications in the marginal distributions but are very sensitive to the structural assumption of stock returns implemented through a factor model.

  1. 6.Risk Premia and Lévy Jumps: Theory and Evidence (joint with Julien Hugonnier and Loriano Mancini), 2019.

  2. Abstract: We develop a novel class of time-changed Lévy models which are tractable and readily applicable, capture the leverage effect, and exhibit pure jump processes with finite or infinite activity. Our models feature four nested processes reflecting market, volatility and jump risks, and observation error of time changes. To operationalize the models, we use volume-based proxies of the unobservable time changes. To allow consistent estimation under physical and risk neutral measures, we derive the change of measure analytically. An extensive time series and option pricing analysis of 16 time-changed Lévy models shows that infinite activity processes carry significant jump risk premia, and largely outperform many finite activity processes.

Papers in Progress:

  1. 7.Assessing Pricing Errors in Asset Pricing Models: A Novel Econometric Evaluation Framework (Joint with Stoyan Stoyanov)

        [First draft coming soon!]   

  1. Objectives: We develop a novel econometric test, with the higher statistical power than extant tests, for the hypothesis that addresses the misspecification -- goodness-of-fit -- for a general class of asset pricing models.  

  1. 8.Smoothed Generalized Disappointment Aversion (Joint with Stoyan Stoyanov and Roméo Tédongap)

  2. Objectives: We extend the standard disappointment aversion model of Gul (Econometrica, 1991) and Routledge and Zin (JF, 2010) such that one is able to apply the traditional Taylor series expansion to an asset allocation problem. In addition, the model is more flexible to address some anomalies in the asset pricing such equity risk premium and Allais paradox.

  1. 9.Hedging Climate Change in Real Time

  2. Objectives: We introduce a dynamic hedging strategy for the climate change risk based on messages of large number of investors on the stock market. Thanks to the availability of the data, our hedging strategy can be implemented at the HF level.

Latest Publications:


  1. Fractional Calculus and Fractional Processes with Applications in Financial Economics (joint with Frank J. Fabozzi and Sergio Focardi), Elsevier (2017).


  1. Modeling Tail Risk with Tempered Stable Distributions: An Overview (joint with Gregoire Loeper),  Annals of Operations Research, (2019).

  2. Quanto Option Pricing with Lévy Models (joint with Frank J. Fabozzi, Young S. Kim and Jiho Park), Computational Economics, (2018).

  3. Quantile-based Inference for Univariate Tempered Stable Distributions, (joint with David Veredas and Frank J. Fabozzi) Computational Economics, (2017).

  4. Elliptical Tempered Stable Distribution, (joint with Y. S. Kim and Frank J. Fabozzi)  Quantitative Finance, (2016).


Research Interests:

Theoretical and Empirical Asset pricing Financial Econometrics

Machine Learning

Contact Details:

Email: hasan.fallahgoul@monash.edu,


Phone: +61 (0) 3 990 59894


School of Mathematics, Level 4

9 Rainforest Walk

Monash University

3800, Victoria