Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
problem Survival strategies of unorganized retailers challenged by organized and e-retailers.
method Field investigation, conjoint analysis, design of experiments, agent-based simulation.
result Unorganized retailers' performance influenced by their own attribute levels and price discounts.
Supplier learns to price contracts against a learning retailer.
problem Designing data-driven pricing policies for a supplier facing a learning retailer.
method Connecting to non-stationary online learning, proposing dynamic pricing policies for discrete and continuous demand.
result Supplier's pricing policies lead to sublinear regret bounds under various retailer learning policies.
The study reveals distinct patterns in retail investors' holding periods affecting stock returns.
problem Understanding the impact of retail investors' investment horizons on stock returns.
method Using self-reported holding periods from StockTwits, the study categorizes retail investors into long-horizon and short-horizon groups and analyzes their return patterns.
result Long-horizon retail investors exhibit underreaction to earnings announcements, while short-horizon investors show overreaction.
Study shows marketable order routing to wholesalers benefits all traders, leading to lower market depth and price volatility.
problem Determining the preference of retail traders for marketable order routing.
method Two models: one for market makers competing for retail order flow (Bertrand model) and another for price-taking competitive liquidity providers (open exchange model).
result Routing marketable orders to wholesalers is preferred by all traders, leading to mean reverting inventories and lower market depth.
India is ranked as the third most attractive nation for retail investment among emerging markets and many MNCs have been looking for the potential benefits to be taken from it. The development of organized retail has the potential of generating employment, improvement in technology, development of real estate etc. On t…
Paper develops privacy-preserving dynamic pricing policy for e-commerce.
problem Protecting customer privacy in dynamic pricing with personalized information.
method Uses differential privacy framework to develop a privacy-preserving policy.
result Achieves both privacy and performance guarantees in dynamic pricing.
How does supply uncertainty affect the structure of supply chain networks? To answer this question we consider a setting where retailers and suppliers must establish a costly relationship with each other prior to engaging in trade. Suppliers, with uncertain yield, announce wholesale prices, while retailers must decide …
In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a l…
Paper evaluates synthetic retail data for fidelity, utility, and privacy.
problem Ensuring accurate synthetic data in retail.
method Differentiates between continuous and discrete data, measures fidelity and utility, and uses Differential Privacy for privacy.
result Validated framework for reliable and scalable synthetic data evaluation.
Study reveals investor behavior in NFT bubbles.
problem Understanding retail investor behavior in asset bubbles.
method Systematic study of NFTs using public blockchain data.
result Sophisticated investors outperform others in NFT bubbles.
Retail investors set interest rates for P2P loans based on borrower characteristics.
problem Understanding how individual investors price credit risk in online consumer loan auctions.
method Reverse auction framework, analyzing interest rate variance and borrower characteristics.
result Retail investors exhibit strong predictability in pricing, with gender and marital status influencing interest rates.
Study improves retail demand forecasting by integrating macroeconomic data.
problem Lack of accurate demand forecasting due to incomplete data.
method Enriched time series data with macroeconomic variables; compared regression and machine learning models.
result Improved accuracy in predicting retail demand through comprehensive data integration.
Jointly tackles assortment and pricing in retail, using bandit models.
problem Maximizing revenue or profit in retail through optimal assortment and pricing.
method Contextual bandits with a flexible, interpretable model for high-dimensional contexts and actions.
result Proves lower regret compared to state-of-the-art methods in various bandit and pricing models.
Agent-based market shows herding cycles with square-root price impact.
problem Understanding herding cycles in agent-based markets.
method Agent-based model with 20,000 retail traders interacting with a single institutional agent.
result Agent discovers multi-cycle predatory strategy with 8-11 complete cycles over 2000 trading days.
New method measures price elasticity from daily sales data.
problem Estimating price elasticity from heterogeneous data.
method Factor-Adjusted Regularized Method for Treatment evaluation (FarmTreat).
result High degree of heterogeneity in pricing strategies.
The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…
Fashion preference is a fuzzy concept that depends on customer taste, prevailing norms in fashion product/style, henceforth used interchangeably, and a customer's perception of utility or fashionability, yet fashion e-retail relies on algorithmically generated search and recommendation systems that process structured d…
New framework estimates demand responses across multiple contexts with limited price variation.
problem Estimating heterogeneous linear price-response functions across multiple contexts with limited price variation and confounding.
method Meta-learning framework that identifies conditional mean of task-specific causal demand parameters given a subset of task-specific observables.
result Improved recovery of demand responses relative to standard transfer-learning baselines.
Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early detection of anomalies in an automated real-time fashion is an important part of such a pricing system.…
Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail inv…
Stablecoins offer efficient settlement but externalize costs and risks.
problem Comparing stablecoins to card networks in retail payments.
method Unified analytical framework (CLEAR) across five dimensions.
result Stablecoins are advantageous in closed-loop and high-friction contexts but structurally disadvantaged as open-loop instruments.
This paper addresses dynamic price discrimination with fairness constraints.
problem Dynamic price discrimination with fairness constraints in online retailing.
method Nonparametric demand models, dynamic pricing policy, regret minimization.
result Optimal dynamic pricing policy with ildeO(T4/5) regret for price fairness. New approach estimates vehicle and component prices without teardowns.
problem Uncertainty in traditional vehicle price estimation methods.
method Data-driven machine learning and game theory.
result Shows large gaps in manufacturer pricing strategies.
Supply chains are the backbone of the global economy. Disruptions to them can be costly. Centrally managed supply chains invest in ensuring their resilience. Decentralized supply chains, however, must rely upon the self-interest of their individual components to maintain the resilience of the entire chain. We examine t…
Online trading platforms manipulate profits and losses, causing 82% of retail traders to lose money.
problem Manipulation of online trading platforms leading to financial losses for retail traders.
method Independent recording of trade details using REST API responses, comparison with broker reviews.
result 82% of retail traders lose money due to platform technical issues.
Analyzes retail trends from sales, search, and reviews.
problem Optimizing inventory and marketing for better customer satisfaction.
method Historical sales data, search trends, and customer reviews.
result Identifies patterns and trending products for retailers.
Combines deep learning and reinforcement learning for profitable trading.
problem Analytical methods fail to fully capture market dynamics.
method Deep learning on order books combined with reinforcement learning.
result Successful trading models for multiple financial instruments.
This paper monetizes customer load data to boost energy retailer profits.
problem Improving load forecasts to reduce energy imbalance costs.
method Cooperative game theory approach to quantify and distribute profits.
result Retailer gains significant profit from customer load data.
Prediction markets can be manipulated by traders who can move contract settlements, harming price discovery.
problem Manipulation of settlement times in prediction markets leads to unfair wealth transfer and harms price discovery.
method Developed a model showing how settlement manipulation transfers wealth and harms price discovery, and observed real-world effects on Polymarket's Bitcoin contract.
result Manipulators capture significant profits from retail traders, especially when settlement times are short.
Model for hedging price and quantity risks in electricity markets.
problem Hedging risks for energy retailers in a regulated electricity market.
method Closed-form solution for optimal portfolio using financial instruments based on price and weather indexes.
result Closed-form solution for mean-var model in discrete setting without distributional assumptions.
Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the retail unit level lacks a theore…
Study reveals how investor flows impact stock prices, especially during herding episodes.
problem Understanding how information transmits through prices and why it breaks down.
method Combining regularized deconvolution with Hawkes process analysis.
result Institutional price impact deteriorates sharply during herding episodes in small-cap stocks, while large-cap stocks maintain resilience.
Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product …
Paper proposes a new method for demand forecasting in pricing contexts.
problem Demand forecasting in pricing contexts, especially in a profit optimal manner.
method Combines Double Machine Learning for causal inference and transformer-based forecasting models.
result Our method outperforms other forecasting methods in off-policy settings.
We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps are frequent events and…
Improved neural network convergence with causal Bayesian modeling in retail performance.
problem Improving neural network convergence in retail performance models.
method Causal Bayesian neural network implementation, removal of weakest SEM path, Flipout layers, Vadam optimizer.
result Neural network convergence improved with removal of the weakest SEM path.
In this article, we analyze the application of options contract in special commodity supply chain such as fresh agricultural products. This problem is discussed in the point of the retailer. When spot market and future market are both available, we discuss how the retailer chooses the optimal production. Furthermore, o…
This study benchmarks AI agents for personalized retail promotions using simulations.
problem Optimizing coupon targeting for sparse customer purchase events.
method Comprehensive simulations of customer shopping behaviors; training RL agents on batch data.
result Contextual bandit and deep RL methods outperform static policies in sparse reward environments.
We consider a context-based dynamic pricing problem of online products, which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples.…
This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple pr…
Investor-driven information diffusion affects excess comovement in China and the U.S. markets.
problem Investor-driven information diffusion and its impact on excess comovement.
method Cross-sectional analysis of 4,533 Chinese and 4,517 U.S. stocks from 2010 to 2022.
result Retail-driven information diffusion significantly drives excess comovement in China, while institution-driven diffusion is the primary driver in the U.S.
A scalable system detects price anomalies in online marketplaces to improve customer experience.
problem Inaccurate prices on online marketplaces lead to poor customer experience and revenue loss.
method MoatPlus uses unsupervised statistical features and an ensemble of models to generate upper price bounds.
result Our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets.
Retail company uses Prophet algorithm for accurate sales forecasting.
problem Accurate sales forecasting in the retail industry.
method Facebook's Prophet algorithm and backtesting strategy.
result Framework demonstrates real-world use case capabilities.
Deep learning predicts customer churn in retail.
problem Accurately predicting which customers are likely to stop purchasing.
method Survival model parameters learned by recurrent neural networks.
result Individual level survival models for purchasing behavior.
Proof-of-Stake networks with EIP-1559 exhibit stable token prices and secure network security.
problem Token valuation and institutional capital in Proof-of-Stake networks under EIP-1559
method Open-economy macroeconomic equilibrium model
result Stable steady-state equilibrium price for ETH
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
problem Reducing the cost of user interface experimentation in online retail.
method Modeling user interface experimentation as an opportunistic bandit problem, reducing the cost of exploration.
result Significant regret reduction and improved contextual information for testing.
Deep learning predicts crop prices with improved accuracy.
problem Accurate prediction of agricultural crop prices for better decision-making.
method Innovative deep learning approach using GNNs and CNN models.
result At least 20% better performance than previous literature.
A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.
problem Accurate demand forecasting for fashion retailers with short product life cycles.
method Product age-based forecast model, incorporating unique feature engineering.
result Significant revenue uplift of 41% compared to retailer's plan.