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.
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 …
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.
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.
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.
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.
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.
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.
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…
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.
The study improves CLV predictions in retail banking with machine learning.
problem Estimating customer lifetime value in retail banking is challenging.
method Developed a novel framework for CLV predictions over arbitrary time horizons.
result 43% improvement in out-of-time CLV prediction error.
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.
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…
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…
Knowledge distillation boosts simple models in banking without complexity.
problem Complexity and performance constraints in banking models.
method Soft Targets, Sample Selection, Data Augmentation.
result Improved model performance without altering model simplicity.
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.
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.
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.
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.
Less frequent retraining improves forecast accuracy in retail demand forecasting.
problem Balancing forecast accuracy and computational efficiency in global models.
method Analysis of ten machine learning and deep learning models across two large retail datasets with various retraining scenarios.
result Less frequent retraining strategies maintain forecast accuracy while reducing computational costs.
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.
Study uses causal machine learning to assess coupon campaign impact on retailer sales.
problem Assessing the causal effect of a coupon campaign on retailer sales.
method Causal machine learning algorithms, subgroup analysis, optimal policy learning.
result Only two coupon categories (drugstore and other food) have a significant positive impact on sales.
This paper develops a dynamic internal fraud model for operational losses in retail banking. It considers public operational losses arising from internal fraud in retail banking within a group of international banks. Additionally, the model takes into account internal factors such as the ethical quality of workers and …
New method reduces inventory inaccuracies by 10x, saving retailers 4% annually.
problem Inaccurate inventory records cost retailers 4% annually, and manual detection is impractical.
method Proposes a new anomaly detection method for low-rank Poisson matrices using cross-sectional data.
result Our approach reduces anomaly detection costs by up to 10x compared to existing methods.
Research identifies four motivational groups for crypto-metaverse landowners.
problem Understanding motivations of retail investors in the crypto-metaverse.
method Detailed financial behavior survey and principal components analysis.
result Four distinct motivational groups identified: Aesthetics, Social, Speculation, Innovation.
Simple model finds high correlation in retail crypto returns.
problem Discerning correlation in retail cryptocurrency markets without factors.
method Used N*(N) statistic to compare models of daily returns.
result High average pairwise correlation (60%) found, supports isotropic model.
Study on stock portfolio concentration among Finnish households and investors.
problem Understanding the concentration of stock portfolios owned by Finnish households and investors.
method Analysis of stock portfolios using Herfindahl-Hirschman index over 20 years.
result High portfolio concentration observed in Finnish retail investors, similar to institutional investors.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
problem Understanding the dynamics between e-commerce and traditional retail during the pandemic.
method Developed an evolutionary game model to study consumer-producer interactions on e-commerce platforms.
result Investment in logistics and warehouses in e-commerce led to faster delivery and consumer trends.
This paper analyzes tokenized U.S. Treasuries, revealing patterns and roles in blockchain transactions.
problem Limited empirical analysis of transaction-level behaviors in tokenized U.S. Treasuries.
method Quantitative dissection of U.S. Treasury-backed RWA tokens across multiple chains, introducing a curvature-aware representation learning model for address-level economic role inference.
result Decoded transaction-level patterns reveal the degree of retail participation and distinguish roles in Web3 finance.
This paper presents a financial analysis over Twitter sentiment analytics extracted from listed retail brands. We investigate whether there is statistically-significant information between the Twitter sentiment and volume, and stock returns and volatility. Traditional newswires are also considered as a proxy for the ma…
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 …
Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.
This study examines non-retail trading on Polymarket, revealing unique behavior patterns and structural limitations.
problem Lack of address-level quote-lifecycle data in Polymarket prediction markets.
method Empirical analysis of 13 million order-filled events using DBSCAN clustering on a six-feature fill-side vector.
result Non-retail behavior is uni-modal, contradicting previous archetypal hypotheses.
Paper uses GANs to simulate consumer transactions with SKU constraints.
problem Simulating realistic consumer transactions in retail systems.
method Integrates GANs with consumer behavior and SKU availability constraints.
result Demonstrates enhanced realism in simulated transactions.
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.
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.
Time-series forecasting is an important task in both academic and industry, which can be applied to solve many real forecasting problems like stock, water-supply, and sales predictions. In this paper, we study the case of retailers' sales forecasting on Tmall|the world's leading online B2C platform. By analyzing the da…
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.
Proposes balancing revenue and environmental impact in assortment planning.
problem Maximizing revenue while considering environmental impact in retail assortment planning.
method Multi-objective optimization using Higg Material Sustainability Index.
result Shows it's possible to have lower environmental impact without significant revenue loss.
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.
The Internet is known to have had a powerful impact on on-line retailer strategies in markets characterised by long-tail distribution of sales. Such retailers can exploit the long tail of the market, since they are effectively without physical limit on the number of choices on offer. Here we examine two extensions of t…
Study benchmarks mutual funds in India using DEA, finding efficiency metrics.
problem Benchmark mutual funds in India based on efficiency metrics.
method Data Envelopment Analysis (DEA) model incorporating risk, cost, return, and information ratio.
result DEA model identifies efficiency frontier and compares results with traditional metrics.
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.
Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of monopolistic firms and assumed product relationships, e.g., complementarity in usage. We …
The purpose of this paper is to identify the immediate and future retailer response to wholesale stockouts. We perform a statistical analysis of historical customer order and delivery data of a local tool wholesaler and distributor, whose customers are retailers, over a period of four years. We investigate the effect o…
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.
Study competitive energy markets using stochastic impulse games.
problem Maximizing profits in competitive retail energy markets.
method Connection between Nash equilibrium and quasi-variational inequalities (QVIs).
result Value functions are constrained viscosity solutions of QVIs.
Paper proposes a GRU model to detect spoofing in retail investors.
problem Spoofing in unregulated markets with retail investors.
method GRU-based detection model using market variables.
result Model performs well in early detection of spoofing attempts.