Publications by IORA

Thompson Sampling Algorithms for Cascading Bandits

Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan

Motivated by the pressing need for efficient optimization in online recommender systems, we revisit the cascading bandit model proposed by Kveton et al. (2015). While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only…

Z Journal of Machine Learning Research, Vol. 22, No. 218, Pages 1 – 66, 2021

Thompson Sampling Algorithms for Cascading Bandits

Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan

Z Journal of Machine Learning Research, Vol. 22, No. 218, Pages 1 – 66, 2021

Dynamic learning and market making in spread betting markets with informed bettors.

John R. Birge , Yifan Feng , N. Bora Keskin , Adam Schultz

We study the profit maximization problem of a market maker in a spread betting market. In this market, the market maker quotes cutoff lines for the outcome of a certain future event as “prices,” and bettors bet on whether the event outcome exceeds the cutoff lines. Anonymous bettors with…

Operations Research

Dynamic learning and market making in spread betting markets with informed bettors.

John R. Birge , Yifan Feng , N. Bora Keskin , Adam Schultz

Operations Research

Robust learning of consumer preferences.

Yifan Feng , René Caldentey , Christopher Thomas Ryan

This paper studies a class of ranking and selection problems faced by a company that wants to identify the most preferred product out of a finite set of alternatives when consumer preferences are a priori unknown. The only information available is that consumer preferences satisfy two key properties: (i)…

Operations Research

Robust learning of consumer preferences.

Yifan Feng , René Caldentey , Christopher Thomas Ryan

Operations Research

Discrete dividend payments in continuous time

Jussi Keppo, A. Max Reppen, H. Mete Soner

We propose a model in which dividend payments occur at regular, deterministic intervals in an otherwise continuous model. This contrasts traditional models where either the payment of continuous dividends is controlled or the dynamics are given by discrete time processes. Moreover, between two dividend payments, the structure allows for…

Partly supported by the ETH Foundation, the Swiss Finance Institute, and Swiss National Foundation grant SNF 200020_172815.

Mathematics of Operations Research

Discrete dividend payments in continuous time

Jussi Keppo, A. Max Reppen, H. Mete Soner

Mathematics of Operations Research

Partly supported by the ETH Foundation, the Swiss Finance Institute, and Swiss National Foundation grant SNF 200020_172815.

Are Monthly Market Returns Predictable?

Jussi Keppo, Tyler Shumway, Daniel Weagley

We document significant persistence in the market timing performance of active individual investors, suggesting that some investors are skilled at timing. Using data on all trades by active Finnish individual investors over almost 15 years, we also show that the net purchases of skilled versus unskilled investors predict monthly…

The Review of Asset Pricing Studies

Are Monthly Market Returns Predictable?

Jussi Keppo, Tyler Shumway, Daniel Weagley

The Review of Asset Pricing Studies

The Impact of Ride-Hail Surge Factors on Taxi Bookings

Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors…

Transportation Research Part C: Emerging Technologies

The Impact of Ride-Hail Surge Factors on Taxi Bookings

Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo

Transportation Research Part C: Emerging Technologies

Learning Manipulation Through Information Dissemination

Jussi Keppo, Michael Jong Kim, Xinyuan Zhang Sauder

We study optimal manipulation of a Bayesian learner through adaptive provisioning of information. The problem is motivated by settings in which a firm can disseminate possibly biased information at a cost, to influence the public’s belief about a hidden parameter related to the firm’s payoffs. For example, firms advertise…

Operations research

Learning Manipulation Through Information Dissemination

Jussi Keppo, Michael Jong Kim, Xinyuan Zhang Sauder

Operations research

Herding in Probabilistic Forecasts

Yanwei Jia, Jussi Keppo, Ville Satopää

Decision makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, disclosure of public information may lower forecasting accuracy. This result, however, has been derived only…

Management Science

Herding in Probabilistic Forecasts

Yanwei Jia, Jussi Keppo, Ville Satopää

Management Science

Robust Satisficing

Daniel Zhuoyu Long, Melvyn Sim, Minglong Zhou

We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution deviates from the empirical distribution. Unlike robust optimization approaches, the decision maker does not have to size…

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

Operations Research

Robust Satisficing

Daniel Zhuoyu Long, Melvyn Sim, Minglong Zhou

Operations Research

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

Convex Optimization for Bundle Size Pricing Problem

Xiaobo Li, Hailong Sun, Chung-Piaw Teo

We study the bundle size pricing (BSP) problem where a monopolist sells bundles of products to customers, and the price of each bundle depends only on the size (number of items) of the bundle. Although this pricing mechanism is attractive in practice, finding optimal bundle prices is difficult since…

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

Management Science

Convex Optimization for Bundle Size Pricing Problem

Xiaobo Li, Hailong Sun, Chung-Piaw Teo

Management Science

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

Last Mile Innovation: The Case of the Locker Alliance Network

Guodong Lyu, Chung-Piaw Teo

Problem Definition: The Singapore government has recently proposed the concept of “Locker Alliance” (LA), an interoperable network of public lockers in residential areas and hot spots in community, to improve the efficiency of last mile parcel delivery operations. This is to complement the existing infrastructure, comprising mainly of proprietary…

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

MSOM

Last Mile Innovation: The Case of the Locker Alliance Network

Guodong Lyu, Chung-Piaw Teo

MSOM

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

A Revised Approach for Risk-Averse Multi-Armed Bandits under CVaR Criterion

Najakorn Khajonchotpanya, Yilin Xue, Napat Rujeerapaiboon

We study multi-armed bandit problems that use conditional value-at-risk as an underlying risk measure. In particular, we propose a new upper confidence bound algorithm and compare it with the state-of-the-art alternatives with respect to various definitions of regret from the risk-averse online learning literature. For each comparison, we demonstrate…

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

Operations Research Letters

A Revised Approach for Risk-Averse Multi-Armed Bandits under CVaR Criterion

Najakorn Khajonchotpanya, Yilin Xue, Napat Rujeerapaiboon

Operations Research Letters

This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)

IORA

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