A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper proposes a machine learning method to predict sale efficacy.
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 improves sales forecasting on Tmall using Fourier decomposition and Tweedie distribution optimization.
We present a new method of estimating the distribution of sales rates of, e.g., book titles at an online bookstore, from the time evolution of ranking data found at websites of the store. The method is based on new mathematical results on an infinite particle limit of the stochastic ranking process, and is suitable for…
Paper introduces SALE for better state-action learning in RL.
Paper proposes a method to estimate consumer valuations from bundle sales data.
Improved sales forecasting at various levels using ensemble methods.
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series …
Optimizes e-commerce traffic sales by incorporating hidden costs into auction mechanisms.
Consider an ephemeral sale-and-repurchase of a security resulting in the same position before the sale and after the repurchase. A sale-and-repurchase is a wash sale if these transactions result in a loss within calendar days. Since a portfolio is essentially the same after a wash sale, any tax advantage from …
To investigate the actual phenomena of transport on a complex network, we analysed empirical data for an inter-firm trading network, which consists of about one million Japanese firms and the sales of these firms (a sale corresponds to the total in-flow into a node). First, we analysed the relationships between sales a…
Study finds sales forecasters overreact to extreme news.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
Analyzes NFT market trends, trade networks, and visual features.
The paper proposes a ML workflow for B2B sales prediction.
Study on inventory control with changing demand, proposing adaptive algorithms.
Sales data in a commodity market (supermarket sales to consumers) has been analysed by studying the fluctuation spectrum and noise correlations. Three related products (ketchup, mayonnaise and curry sauce) have been analysed. Most noise in sales is caused by promotions, but here we focus on the fluctuations in baseline…
We analyze a database comprising quarterly sales of 55624 pharmaceutical products commercialized by 3939 pharmaceutical firms in the period 1992--2001. We study the probability density function (PDF) of growth in firms and product sales and find that the width of the PDF of growth decays with the sales as a power law w…
We analyze an exhaustive data-set of new-cars monthly sales. The set refers to 10 years of Spanish sales of more than 6500 different car model configurations and a total of 10M sold cars, from January 2007 to January 2017. We find that for those model configurations with a monthly market-share higher than 0.1% the sale…
New approach optimizes sales process for B2B businesses.
We consider a firm that sells a large number of products to its customers in an online fashion. Each product is described by a high dimensional feature vector, and the market value of a product is assumed to be linear in the values of its features. Parameters of the valuation model are unknown and can change over time.…
Sales forecast is an essential task in E-commerce and has a crucial impact on making informed business decisions. It can help us to manage the workforce, cash flow and resources such as optimizing the supply chain of manufacturers etc. Sales forecast is a challenging problem in that sales is affected by many factors in…
Improved sales forecasting for new products using transfer learning.
Article offers models for choosing sale-leaseback vs debt.
Retail company uses Prophet algorithm for accurate sales forecasting.
Online reviews are feedback voluntarily posted by consumers about their consumption experiences. This feedback indicates customer attitudes such as affection, awareness and faith towards a brand or a firm and demonstrates inherent connections with a company's future sales, cash flow and stock pricing. However, the pred…
Many economic applications including optimal pricing and inventory management requires prediction of demand based on sales data and estimation of sales reaction to a price change. There is a wide range of econometric approaches which are used to correct a bias in estimates of demand parameters on censored sales data. T…
New method improves sales forecasting accuracy using tensor factorization.
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the use…
Keywords: corporate finance, Wrocław University of Economics, net profit margin lub net sales profitability
The paper proposes a method to improve sales forecasts by selecting optimal reference classes.
Detailed empirical studies of publicly traded business firms have established that the standard deviation of annual sales growth rates decreases with increasing firm sales as a power law, and that the sales growth distribution is non-Gaussian with slowly decaying tails. To explain these empirical facts, a theory is dev…
Model predicts competition between similar products in sales.
Empirical data of supermarket sales show stylised facts that are similar to stock markets, with a broad (truncated) Levy distribution of weekly sales differences in the baseline sales [R.D. Groot, Physica A 353 (2005) 501]. To investigate the cause of this, the influence of social interactions and advertisements are st…
The paper introduces a method for forecasting corporate sales growth using multiple reference variables.
A constrained informationally efficient market is defined to be one whose price process arises as the outcome of some equilibrium where agents face restrictions on trade. This paper investigates the case of short sale constraints, a setting which despite its simplicity, generates new insights. In particular, it is show…
Paper analyzes fire sales in a network of banks using VWAP and LOB pricing.
Model explains how stablecoin runs are influenced by large sales and reserve quality.
Analytical results bound the approach to oligarchy in a modified asset exchange model.
A new online learning setting for autoregressive processes with sublinear regret.
Through a short sale, a person borrows a share of stock from a lender, sells the borrowed share to a third person at the current price, and purchases an identical share in the market at a future date and at a future price to replace the borrowed share of stock. This only makes sense if the short seller anticipates a do…
Paper predicts future sales using machine learning techniques.
Model predicts alternating market dominance for two competing firms.
This study enhances sales forecasts by integrating market indicators into forecasting models.
Machine learning improves hierarchical forecasting of sales time series.
Two algorithms achieve optimal logarithmic regret in feature-based dynamic pricing.
New model predicts sales of new products with short life cycles.