Sanjiv Das - Academia.edu (original) (raw)

Papers by Sanjiv Das

SSRN Electronic Journal

We develop a methodology for converting card index archival records into usable data frames for s... more We develop a methodology for converting card index archival records into usable data frames for statistical and textual analyses. Leveraging machine learning and naturallanguage processing tools from Amazon Web Services (AWS), we overcome hurdles associated with character recognition, inconsistent data reporting, column misalignment, and irregular naming. In this article, we detail the step-by-step conversion process and discuss remedies for common problems and edge cases, using historical records from the Reconstruction Finance Corporation.

In this article we propose a new approach for implementing option pricing models in finance. Fina... more In this article we propose a new approach for implementing option pricing models in finance. Financial engineers typically prototype such models in an interactive language (such as Matlab) and then use a compiled language such as C/C++ for production systems. Code is therefore written twice. In this article we show that the Python programming language and the Cython compiler allows prototyping in a Matlab-like manner, followed by direct generation of optimized C code with very minor code modifications. The approach is able to call upon powerful scientific libraries, uses only open source tools, and is free of any licensing costs. We provide examples where Cython speeds up a prototype version by over 500 times. These performance gains in conjunction with vast savings in programmer time make the approach very promising.

The Journal of Financial Data Science, 2021

We present a machine learning pipeline for fairness-aware machine learning (FAML) in finance that... more We present a machine learning pipeline for fairness-aware machine learning (FAML) in finance that encompasses metrics for fairness (and accuracy). Whereas accuracy metrics are well understood and the principal ones used frequently, there is no consensus as to which of several available measures for fairness should be used in a generic manner in the financial services industry. We explore these measures and discuss which ones to focus on, at various stages in the ML pipeline, pre-training and post-training, and we also examine simple bias mitigation approaches. Using a standard dataset we show that the sequencing in our FAML pipeline offers a cogent approach to arriving at a fair and accurate ML model. We discuss the intersection of bias metrics with legal considerations in the US, and the entanglement of explainability and fairness is exemplified in the case study. We discuss possible approaches for training ML models while satisfying constraints imposed from various fairness metrics, and the role of causality in assessing fairness.

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021

Understanding the predictions made by machine learning (ML) models and their potential biases rem... more Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and laborintensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions. It is deeply integrated into Amazon SageMaker, a fully managed service that enables data scientists and developers to build, train, and deploy ML models at any scale. Clarify supports bias detection and feature importance computation across the ML lifecycle, during data preparation, model evaluation, and post-deployment monitoring. We outline the desiderata derived from customer input, the modular architecture, and the methodology for bias and explanation computations. Further, we describe the technical challenges encountered and the tradeoffs we had to make. For illustration, we discuss two customer use cases. We present our deployment results including qualitative customer feedback and a quantitative evaluation. Finally, we summarize lessons learned, and discuss best practices for the successful adoption of fairness and explanation tools in practice. CCS CONCEPTS • Computing methodologies → Machine learning; Distributed algorithms; • Software and its engineering → Software notations and tools.

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Inde... more We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

SSRN Electronic Journal, 2015

We present Midas, a system that uses complex data processing to extract and aggregate facts from ... more We present Midas, a system that uses complex data processing to extract and aggregate facts from a large collection of structured and unstructured documents into a set of unified, clean entities and relationships. Midas focuses on data for financial companies and is based on periodic filings with the U.S. Securities and Exchange Commission (SEC) and Federal Deposit Insurance Corporation (FDIC). We show that, by using data aggregated by Midas, we can provide valuable insights about financial institutions either at the whole system level or at the individual company level. The key technology components that we implemented in Midas and that enable the various financial applications are: information extraction, entity resolution, mapping and fusion, all on top of a scalable infrastructure based on Hadoop. We describe our experience in building the Midas system and also outline the key research questions that remain to be addressed towards building a generic, high-level infrastructure for large-scale data integration from public sources.

The use of statistical packages in finance has two functions. One, econometric analysis of large ... more The use of statistical packages in finance has two functions. One, econometric analysis of large volumes of data, and two, programming financial models. A popular package for these purposes is R. In this article we will examine two canonical applications of parallel programming for option pricing. We use the ParallelR package developed by REvolution Computing. We price options using trees and Monte Carlo simulation. Both these approaches are commonly used for option pricing and are amenable to parallelization and grid computing. In this paper we demonstrate the application using the widely used mathematical/statistical R package.

Review of Financial Studies, 2002

The fee structure used to compensate investment advisers is central to the study of fund design, ... more The fee structure used to compensate investment advisers is central to the study of fund design, and affects investor welfare in at least three ways: (i) by influencing the portfolio-selection incentives of the adviser, (ii) by affecting risk-sharing between adviser and investor, and (iii) through its use as a signal of quality by superior investment advisers. In this paper, we describe a model in which all of these features are present, and use it to compare two popular and contrasting forms of fee contracts, the "fulcrum" and the "incentive" types, from the standpoint of investor welfare. While the former has some undeniably attractive features (that have, in particular, been used by regulators to justify its mandatory use in a mutual fund context), we find surprisingly that it is the latter that is often more attractive from the standpoint of investor welfare. Our model is a flexible one; our conclusions are shown to be robust to many extensions of interest. The results are also extended to consider unrestricted fee structures and competitive markets for fund managers.

The Journal of Wealth Management, 2011

Review of Derivatives Research, 1996

A variety of realistic economic considerations make jump-diffusion models of interest rate dynami... more A variety of realistic economic considerations make jump-diffusion models of interest rate dynamics an appealing modeling choice to price interest-rate contingent claims. However, exact closed-form solutions for bond prices when interest rates follow a mixed jump-diffusion process have proved very hard to derive. This paper puts forward two new models of interest-rate dynamics that combine infrequent, discrete changes in the interest-rate level, modeled as a jump process, with short-lived, mean reverting shocks, modeled as a diffusion process. The two models differ in the way jumps affect the central tendency of interest rates; in one case shocks are temporary, in the other shocks are permanent. We derive exact closed-form solutions for the price of a discount bond and computationally tractable schemes to price bond options.

Journal of Banking & Finance, 2013

This paper presents a parsimonious barrier model for the optimal principal reset in a loan modifi... more This paper presents a parsimonious barrier model for the optimal principal reset in a loan modification, thereby maximizing the loan value to the lender bank and minimizing the likelihood of strategic foreclosure by the homeowner. Writing down the loan-to-value (LTV) ratio will reduce the present value of future payments on the loan, but will also reduce the probability of default, thereby saving foreclosure losses. The optimal trade-off of these two countervailing effects will pinpoint the optimal LTV at which the loan must be reset. We present a simple barrier option decomposition of the loan value that makes the optimization of LTV easy to implement. An extension of the model is shown to account for varying growth rate assumptions about house prices. The model in this paper specifically accounts for the homeowner's willingness to pay, and uses the framework to model shared-appreciation mortgages (SAMs).

Intelligent Systems in Accounting, Finance and Management, 2002

This article develops a simple approach to solving continuous‐time portfolio choice problems. Por... more This article develops a simple approach to solving continuous‐time portfolio choice problems. Portfolio problems for which no closed‐form solutions are available may be handled by this technique, which substitutes the numerical solution of partial differential equations with a non‐linear numerical algorithm approximating the solution. This paper complements the wide literature in economics on the solution of dynamic problems in discrete time using projection methods. Our approach extends the approximation function to power forms, which are shown to fit finance type problems well. The algorithm is parsimonious, and is first illustrated by solving two basic examples, first, the standard Merton problem, and second, a jump‐diffusion problem. Then, we demonstrate that the model is easy to implement on a larger scale, by optimizing a portfolio of six stock indexes, and stochastic volatility driven by two correlated state variables. Copyright © 2002 John Wiley & Sons, Ltd.

The use of statistical packages in finance has two functions. One, econometric analysis of large ... more The use of statistical packages in finance has two functions. One, econometric analysis of large volumes of data, and two, programming financial models. A popular package for these purposes is R. In this article we will examine two canonical applications of parallel programming for option pricing. We use the ParallelR package developed by REvolution Computing. We price options using trees and Monte Carlo simulation. Both these approaches are commonly used for option pricing and are amenable to parallelization and grid computing. In this paper we demonstrate the application using the widely used mathematical/statistical R package.

SSRN Electronic Journal, 2020

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch ge... more Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.

SSRN Electronic Journal, 2019

We develop a dynamic programming methodology that seeks to maximize investor outcomes over multip... more We develop a dynamic programming methodology that seeks to maximize investor outcomes over multiple, potentially competing goals (such as upgrading a home, paying college tuition, or maintaining an income stream in retirement), even when financial resources are limited. Unlike Monte Carlo approaches currently in wide use in the wealth management industry, our approach uses investor preferences to dynamically make the optimal determination for fulfilling or not fulfilling each goal and for selecting the investor’s investment portfolio. This can be computed quickly, even for numerous investor goals spread over different or concurrent time periods, where each goal may allow for partial fulfillment or be all-or-nothing. The probabilities of attaining each (full or partial) goal under the optimal scenario are also computed, so the investor can ensure the algorithm accurately reflects their preference for the relative importance of each of their goals. These portfolio prescriptions are consistent with Prospect Theory.

Review of Financial Studies, 2002

The relationship between affine stochastic processes and bond pricing equations in exponential te... more The relationship between affine stochastic processes and bond pricing equations in exponential term structure models has been well established. We connect this result to the pricing of interest rate derivatives. If the term structure model is exponential affine, then there is a linkage between the bond pricing solution and the prices of many widely traded interest rate derivative securities. Our results apply to m-factor processes with n diffusions and l jump processes. The pricing solutions require at most a single numerical integral, making the model easy to implement. We discuss many options that yield solutions using the methods of the article.

International Portfolio Choice Returns on international equities are characterized by jumps; more... more International Portfolio Choice Returns on international equities are characterized by jumps; moreover, these jumps tend to occur at the same time across countries leading to systemic risk. We capture these stylized facts using a multivariate system of jump-diffusion processes where the arrival of jumps is simultaneous across assets. We then determine an investor’s optimal unconditional portfolio for this model of returns. Systemic risk has two effects: it reduces the gains from diversification and also penalizes investors for holding levered positions. We find that the loss resulting from

PsycEXTRA Dataset

Prices are commonly assumed to reflect the value or utility of a good or service. Similarly, mode... more Prices are commonly assumed to reflect the value or utility of a good or service. Similarly, modern asset pricing theory assumes that, in the stock market, share prices reflect the value of a firm. We consider prices in terms of other information they convey in a stock investing context. These three papers find that the shifting price level of a stock, rather than serving as a merely passive indicator of changes to underlying value, is often taken as a dynamic signal. This session investigates how this price signal can lead to a variety of consumer judgments, inferences, and behaviors. The first paper, by Jung Grant, Xie, and Soman, examines how consumers use reference prices to evaluate their stock portfolios and how those reference prices get updated. In the second paper, Gal investigates the influence of a stock's nominal price level on the stock's price response to news events and other information. The third paper, by Raghubir and Das, examines how the graphical display of stock price information affects assessments of risk and return. The proposed session integrates three distinctive approaches to understanding how share price is perceived in the stock market setting. The papers draw on evidence from laboratory experiments and empirical data. This symposium offers an opportunity for scholars interested in behavioral finance to discuss the implications of the current findings as well as for consumer researchers to consider pricing in a new context.

SSRN Electronic Journal

We develop a methodology for converting card index archival records into usable data frames for s... more We develop a methodology for converting card index archival records into usable data frames for statistical and textual analyses. Leveraging machine learning and naturallanguage processing tools from Amazon Web Services (AWS), we overcome hurdles associated with character recognition, inconsistent data reporting, column misalignment, and irregular naming. In this article, we detail the step-by-step conversion process and discuss remedies for common problems and edge cases, using historical records from the Reconstruction Finance Corporation.

In this article we propose a new approach for implementing option pricing models in finance. Fina... more In this article we propose a new approach for implementing option pricing models in finance. Financial engineers typically prototype such models in an interactive language (such as Matlab) and then use a compiled language such as C/C++ for production systems. Code is therefore written twice. In this article we show that the Python programming language and the Cython compiler allows prototyping in a Matlab-like manner, followed by direct generation of optimized C code with very minor code modifications. The approach is able to call upon powerful scientific libraries, uses only open source tools, and is free of any licensing costs. We provide examples where Cython speeds up a prototype version by over 500 times. These performance gains in conjunction with vast savings in programmer time make the approach very promising.

The Journal of Financial Data Science, 2021

We present a machine learning pipeline for fairness-aware machine learning (FAML) in finance that... more We present a machine learning pipeline for fairness-aware machine learning (FAML) in finance that encompasses metrics for fairness (and accuracy). Whereas accuracy metrics are well understood and the principal ones used frequently, there is no consensus as to which of several available measures for fairness should be used in a generic manner in the financial services industry. We explore these measures and discuss which ones to focus on, at various stages in the ML pipeline, pre-training and post-training, and we also examine simple bias mitigation approaches. Using a standard dataset we show that the sequencing in our FAML pipeline offers a cogent approach to arriving at a fair and accurate ML model. We discuss the intersection of bias metrics with legal considerations in the US, and the entanglement of explainability and fairness is exemplified in the case study. We discuss possible approaches for training ML models while satisfying constraints imposed from various fairness metrics, and the role of causality in assessing fairness.

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021

Understanding the predictions made by machine learning (ML) models and their potential biases rem... more Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and laborintensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions. It is deeply integrated into Amazon SageMaker, a fully managed service that enables data scientists and developers to build, train, and deploy ML models at any scale. Clarify supports bias detection and feature importance computation across the ML lifecycle, during data preparation, model evaluation, and post-deployment monitoring. We outline the desiderata derived from customer input, the modular architecture, and the methodology for bias and explanation computations. Further, we describe the technical challenges encountered and the tradeoffs we had to make. For illustration, we discuss two customer use cases. We present our deployment results including qualitative customer feedback and a quantitative evaluation. Finally, we summarize lessons learned, and discuss best practices for the successful adoption of fairness and explanation tools in practice. CCS CONCEPTS • Computing methodologies → Machine learning; Distributed algorithms; • Software and its engineering → Software notations and tools.

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Inde... more We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

SSRN Electronic Journal, 2015

We present Midas, a system that uses complex data processing to extract and aggregate facts from ... more We present Midas, a system that uses complex data processing to extract and aggregate facts from a large collection of structured and unstructured documents into a set of unified, clean entities and relationships. Midas focuses on data for financial companies and is based on periodic filings with the U.S. Securities and Exchange Commission (SEC) and Federal Deposit Insurance Corporation (FDIC). We show that, by using data aggregated by Midas, we can provide valuable insights about financial institutions either at the whole system level or at the individual company level. The key technology components that we implemented in Midas and that enable the various financial applications are: information extraction, entity resolution, mapping and fusion, all on top of a scalable infrastructure based on Hadoop. We describe our experience in building the Midas system and also outline the key research questions that remain to be addressed towards building a generic, high-level infrastructure for large-scale data integration from public sources.

The use of statistical packages in finance has two functions. One, econometric analysis of large ... more The use of statistical packages in finance has two functions. One, econometric analysis of large volumes of data, and two, programming financial models. A popular package for these purposes is R. In this article we will examine two canonical applications of parallel programming for option pricing. We use the ParallelR package developed by REvolution Computing. We price options using trees and Monte Carlo simulation. Both these approaches are commonly used for option pricing and are amenable to parallelization and grid computing. In this paper we demonstrate the application using the widely used mathematical/statistical R package.

Review of Financial Studies, 2002

The fee structure used to compensate investment advisers is central to the study of fund design, ... more The fee structure used to compensate investment advisers is central to the study of fund design, and affects investor welfare in at least three ways: (i) by influencing the portfolio-selection incentives of the adviser, (ii) by affecting risk-sharing between adviser and investor, and (iii) through its use as a signal of quality by superior investment advisers. In this paper, we describe a model in which all of these features are present, and use it to compare two popular and contrasting forms of fee contracts, the "fulcrum" and the "incentive" types, from the standpoint of investor welfare. While the former has some undeniably attractive features (that have, in particular, been used by regulators to justify its mandatory use in a mutual fund context), we find surprisingly that it is the latter that is often more attractive from the standpoint of investor welfare. Our model is a flexible one; our conclusions are shown to be robust to many extensions of interest. The results are also extended to consider unrestricted fee structures and competitive markets for fund managers.

The Journal of Wealth Management, 2011

Review of Derivatives Research, 1996

A variety of realistic economic considerations make jump-diffusion models of interest rate dynami... more A variety of realistic economic considerations make jump-diffusion models of interest rate dynamics an appealing modeling choice to price interest-rate contingent claims. However, exact closed-form solutions for bond prices when interest rates follow a mixed jump-diffusion process have proved very hard to derive. This paper puts forward two new models of interest-rate dynamics that combine infrequent, discrete changes in the interest-rate level, modeled as a jump process, with short-lived, mean reverting shocks, modeled as a diffusion process. The two models differ in the way jumps affect the central tendency of interest rates; in one case shocks are temporary, in the other shocks are permanent. We derive exact closed-form solutions for the price of a discount bond and computationally tractable schemes to price bond options.

Journal of Banking & Finance, 2013

This paper presents a parsimonious barrier model for the optimal principal reset in a loan modifi... more This paper presents a parsimonious barrier model for the optimal principal reset in a loan modification, thereby maximizing the loan value to the lender bank and minimizing the likelihood of strategic foreclosure by the homeowner. Writing down the loan-to-value (LTV) ratio will reduce the present value of future payments on the loan, but will also reduce the probability of default, thereby saving foreclosure losses. The optimal trade-off of these two countervailing effects will pinpoint the optimal LTV at which the loan must be reset. We present a simple barrier option decomposition of the loan value that makes the optimization of LTV easy to implement. An extension of the model is shown to account for varying growth rate assumptions about house prices. The model in this paper specifically accounts for the homeowner's willingness to pay, and uses the framework to model shared-appreciation mortgages (SAMs).

Intelligent Systems in Accounting, Finance and Management, 2002

This article develops a simple approach to solving continuous‐time portfolio choice problems. Por... more This article develops a simple approach to solving continuous‐time portfolio choice problems. Portfolio problems for which no closed‐form solutions are available may be handled by this technique, which substitutes the numerical solution of partial differential equations with a non‐linear numerical algorithm approximating the solution. This paper complements the wide literature in economics on the solution of dynamic problems in discrete time using projection methods. Our approach extends the approximation function to power forms, which are shown to fit finance type problems well. The algorithm is parsimonious, and is first illustrated by solving two basic examples, first, the standard Merton problem, and second, a jump‐diffusion problem. Then, we demonstrate that the model is easy to implement on a larger scale, by optimizing a portfolio of six stock indexes, and stochastic volatility driven by two correlated state variables. Copyright © 2002 John Wiley & Sons, Ltd.

The use of statistical packages in finance has two functions. One, econometric analysis of large ... more The use of statistical packages in finance has two functions. One, econometric analysis of large volumes of data, and two, programming financial models. A popular package for these purposes is R. In this article we will examine two canonical applications of parallel programming for option pricing. We use the ParallelR package developed by REvolution Computing. We price options using trees and Monte Carlo simulation. Both these approaches are commonly used for option pricing and are amenable to parallelization and grid computing. In this paper we demonstrate the application using the widely used mathematical/statistical R package.

SSRN Electronic Journal, 2020

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch ge... more Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.

SSRN Electronic Journal, 2019

We develop a dynamic programming methodology that seeks to maximize investor outcomes over multip... more We develop a dynamic programming methodology that seeks to maximize investor outcomes over multiple, potentially competing goals (such as upgrading a home, paying college tuition, or maintaining an income stream in retirement), even when financial resources are limited. Unlike Monte Carlo approaches currently in wide use in the wealth management industry, our approach uses investor preferences to dynamically make the optimal determination for fulfilling or not fulfilling each goal and for selecting the investor’s investment portfolio. This can be computed quickly, even for numerous investor goals spread over different or concurrent time periods, where each goal may allow for partial fulfillment or be all-or-nothing. The probabilities of attaining each (full or partial) goal under the optimal scenario are also computed, so the investor can ensure the algorithm accurately reflects their preference for the relative importance of each of their goals. These portfolio prescriptions are consistent with Prospect Theory.

Review of Financial Studies, 2002

The relationship between affine stochastic processes and bond pricing equations in exponential te... more The relationship between affine stochastic processes and bond pricing equations in exponential term structure models has been well established. We connect this result to the pricing of interest rate derivatives. If the term structure model is exponential affine, then there is a linkage between the bond pricing solution and the prices of many widely traded interest rate derivative securities. Our results apply to m-factor processes with n diffusions and l jump processes. The pricing solutions require at most a single numerical integral, making the model easy to implement. We discuss many options that yield solutions using the methods of the article.

International Portfolio Choice Returns on international equities are characterized by jumps; more... more International Portfolio Choice Returns on international equities are characterized by jumps; moreover, these jumps tend to occur at the same time across countries leading to systemic risk. We capture these stylized facts using a multivariate system of jump-diffusion processes where the arrival of jumps is simultaneous across assets. We then determine an investor’s optimal unconditional portfolio for this model of returns. Systemic risk has two effects: it reduces the gains from diversification and also penalizes investors for holding levered positions. We find that the loss resulting from

PsycEXTRA Dataset

Prices are commonly assumed to reflect the value or utility of a good or service. Similarly, mode... more Prices are commonly assumed to reflect the value or utility of a good or service. Similarly, modern asset pricing theory assumes that, in the stock market, share prices reflect the value of a firm. We consider prices in terms of other information they convey in a stock investing context. These three papers find that the shifting price level of a stock, rather than serving as a merely passive indicator of changes to underlying value, is often taken as a dynamic signal. This session investigates how this price signal can lead to a variety of consumer judgments, inferences, and behaviors. The first paper, by Jung Grant, Xie, and Soman, examines how consumers use reference prices to evaluate their stock portfolios and how those reference prices get updated. In the second paper, Gal investigates the influence of a stock's nominal price level on the stock's price response to news events and other information. The third paper, by Raghubir and Das, examines how the graphical display of stock price information affects assessments of risk and return. The proposed session integrates three distinctive approaches to understanding how share price is perceived in the stock market setting. The papers draw on evidence from laboratory experiments and empirical data. This symposium offers an opportunity for scholars interested in behavioral finance to discuss the implications of the current findings as well as for consumer researchers to consider pricing in a new context.