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.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
The paper proposes a ML workflow for B2B sales prediction.
Study finds sales forecasters overreact to extreme news.
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…
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…
Retail company uses Prophet algorithm for accurate sales forecasting.
Improved sales forecasting for new products using transfer learning.
This paper improves sales forecasting on Tmall using Fourier decomposition and Tweedie distribution optimization.
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 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…
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…
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.…
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 …
Paper proposes a machine learning method to predict sale efficacy.
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Signifi…
The paper introduces a method for forecasting corporate sales growth using multiple reference variables.
Paper predicts future sales using machine learning techniques.
New method improves sales forecasting accuracy using tensor factorization.
Paper proposes a method to estimate consumer valuations from bundle sales data.
This study enhances sales forecasts by integrating market indicators into forecasting models.
Model predicts competition between similar products in sales.
Study measures impact of data and neural net similarity on transferability in restaurant sales data.
The paper proposes a method to improve sales forecasts by selecting optimal reference classes.
Improved sales forecasting at various levels using ensemble methods.
We propose the new Top-Dog-Index to quantify the historic deviation of the supply data of many small branches for a commodity group from sales data. On the one hand, the common parametric assumptions on the customer demand distribution in the literature could not at all be supported in our real-world data set. On the o…
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…
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…
Statistical models of economic distributions lead to Boltzmann distributions rather than a Pareto power law. This result is supported by two facts: 1. the distributions of income, car sales, marriages or jobs are a matter of chances and luck and not of reason! 2. Data for property, automobile sales, marriages and job m…
New approach optimizes sales process for B2B businesses.
New model predicts sales of new products with short life cycles.
Analyzes retail trends from sales, search, and reviews.
This paper compares forecasting techniques for sales data, focusing on profit-driven models.
Article offers models for choosing sale-leaseback vs debt.
Study combines dynamic mode and wavelet decomposition for marketing time series analysis.
There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…
Complex contagion model explains financial fire sales through continuous asset prices.
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 …
Estimates treatment effects in time series data with always-missing controls.
Deep learning enhances art market valuation by incorporating visual data.
Paper studies portfolio investment under volatility uncertainty and short-sale constraints, improving risk-adjusted returns.
A dynamic model of the product lifecycle of (nearly) homogeneous durables in polypoly markets is established. It describes the concurrent evolution of the unit sales and price of durable goods. The theory is based on the idea that the sales dynamics is determined by a meeting process of demanded with supplied product u…
Keywords: corporate finance, Wrocław University of Economics, net profit margin lub net sales profitability
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
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…
Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments…
Study uses causal machine learning to assess coupon campaign impact on retailer sales.
Intel's system identifies and categorizes businesses for sales opportunities.