Short sales allow tax deferral by offsetting gains from ordinary sales.
problem Tax deferral opportunity in short sales not regulated in the Philippine tax system.
method Short selling to offset gains from ordinary sales of identical stocks.
result Tax deferral opportunity exists but is unregulated in the Philippine tax system.
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…
Study examines no-arbitrage rules for converging asset prices under short-sales constraints.
problem Understanding no-arbitrage conditions for converging asset prices with short-sales restrictions.
method Translated NFLVR-S property into structure conditions and introduced fundamental supermartingale measure.
result Provides arbitrage portfolios when conditions for fundamental supermartingale measure are not met.
Modeling short selling risks to quantify losses.
problem Short selling constraints and associated risks.
method Optimal stopping model with margin risk and recall risk.
result Realistic short selling constraints lead to significant value loss.
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…
E-commerce sales forecast using LSTM with cross-series information.
problem Accurate sales forecasting in e-commerce with limited univariate methods.
method Global training of LSTM on product assortment hierarchy, incorporating cross-series information.
result LSTM achieves competitive results on Walmart.com dataset, outperforming state-of-the-art techniques.
New model predicts sales of new products with short life cycles.
problem Forecasting sales of new products with short lead times and life cycles.
method Developed an exponential factorization machine (EFM) to consider attributes and pairwise interactions.
result EFM model outperforms existing models in terms of MAPE and MAE.
Paper studies portfolio investment under volatility uncertainty and short-sale constraints, improving risk-adjusted returns.
problem Investment portfolio optimization under volatility uncertainty and short-sale constraints.
method Sublinear expectation model to handle volatility uncertainty, constructing SLE-MUV model.
result Pareto frontier of SLE-MUV model is a continuous convex curve with polynomial analytical expression.
Study asset price bubbles in markets with short sales prohibitions and model uncertainty.
problem Investigating asset price bubbles in markets with short sales prohibitions and model uncertainty.
method Introducing a novel definition of the fundamental price and analyzing the types and characterization of bubbles using a new fundamental theorem of asset pricing and superhedging duality.
result Two distinct types of bubbles arise depending on the maturity structure of the asset, and conditions for their existence are provided.
This paper consists of two parts. In the first part we prove the fundamental theorem of asset pricing under short sales prohibitions in continuous-time financial models where asset prices are driven by nonnegative, locally bounded semimartingales. A key step in this proof is an extension of a well-known result of Ansel…
In this paper, we develop a theory of market crashes resulting from a deleveraging shock. We consider two representative investors in a market holding different opinions about the public available information. The deleveraging shock forces the high confidence investors to liquidate their risky assets to pay back their …
A financial market model with general semimartingale asset-price processes and where agents can only trade using no-short-sales strategies is considered. We show that wealth processes using continuous trading can be approximated very closely by wealth processes using simple combinations of buy-and-hold trading. This ap…
New framework improves E-commerce sales forecasts.
problem Short sales times series in E-commerce.
method Global model using Tree Boosting Methods.
result Outperforms state-of-the-art models on real dataset.
Short selling is key to exploiting arbitrage opportunities in financial markets.
problem Theoretical basis for differences in financial service regulations.
method Analyzing semimartingales to show arbitrage opportunities require short selling.
result Arbitrage opportunities can only be exploited through short selling.
Short proof shows wealth condensation in trading model.
problem Proving wealth condensation in trading model.
method Short proof extending to modified model with wealth advantage.
result Wealth condensation occurs almost surely in modified model.
Paper analyzes fire sales in a network of banks using VWAP and LOB pricing.
problem Optimal asset liquidation and borrowing strategies in a network of banks.
method Nash equilibrium model with two market clearing mechanisms.
result Existence and uniqueness of clearing solutions for liquidations, borrowing, prices, and haircuts.
We study an agent-based stock market model with heterogeneous agents and friction. Our model is based on that of Foellmer-Schweizer(1993): The process of a stock price in a discrete-time framework is determined by temporary equilibria via agents' excess demand functions, and the diffusion approximation approach is appl…
Paper predicts future sales using machine learning techniques.
problem Predict future sales of retail products using past data.
method Used eXtreme Gradient Boosting (XGBoost) and Long Short Term Memory (LSTM) for forecasting.
result XGBoost performed better than LSTM in predicting sales.
We propose a continuous-time stock-flow consistent model for inventory dynamics in an economy with firms, banks, and households. On the supply side, firms decide on production based on adaptive expectations for sales demand and a desired level of inventories. On the demand side, investment is determined as a function o…
Adaptive l1-regularization controls short-selling in portfolio selection.
problem Financial markets' restrictions on short-selling and sparsity in portfolio solutions.
method Updating rule for l1-penalty parameter in Bregman iteration.
result Approach preserves properties of original l1-regularization and controls both sparsity and short positions.
We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under jointly elliptic distributions of returns. Portfolio optimization of the Sharpe ratio is then explored, with an active-set algorithm presented for markets prohibiting sh…
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 ±30 calendar days. Since a portfolio is essentially the same after a wash sale, any tax advantage from …
Markowitz (1952, 1959) laid down the ground-breaking work on the mean-variance analysis. Under his framework, the theoretical optimal allocation vector can be very different from the estimated one for large portfolios due to the intrinsic difficulty of estimating a vast covariance matrix and return vector. This can res…
Paper proposes a machine learning method to predict sale efficacy.
problem Determining the efficacy of online sales from discounts alone.
method Machine learning-based heuristic using Support Vector Machine.
result Predicts sale efficacy with 91.11% accuracy.
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.
problem Understanding how forecasters react to sales growth news.
method Proposes a framework with fat-tailed dynamics and linear forecasting rule.
result Forecasters overreact to significant sales growth news.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
problem Manual sales account prioritization is inefficient and under-invested.
method Developed an AI-based Account Prioritizer using machine learning and explanation algorithms.
result Generated a +8.08% increase in renewal bookings.
The paper proposes a ML workflow for B2B sales prediction.
problem Predicting business to business sales outcomes using subjective human evaluations.
method A two-pipeline ML approach on Azure ML, including data enrichment and model training.
result ML predictions improve accuracy and increase sales value.
Paper proposes CNN for automatic sales forecast in e-commerce.
problem Challenges in sales forecasting in e-commerce.
method Uses Convolutional Neural Network to automatically extract features from raw log data.
result Experimental validation shows the effectiveness of the proposed method.
This paper improves sales forecasting on Tmall using Fourier decomposition and Tweedie distribution optimization.
problem Sales forecasting for retailers on Tmall.
method Fourier decomposition for seasonality extraction and Tweedie distribution optimization.
result Improved sales forecasting results using optimized models.
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…
Dynamic pricing improves sales of low-sale products using online clustering.
problem Low-sale products lack sufficient data for traditional dynamic pricing algorithms.
method Online clustering of product demand and dynamic pricing decisions based on cluster analysis.
result The proposed algorithms significantly outperform traditional single-product pricing policies in increasing revenue.
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…
DeFi exploits lead to reduced CP spreads, contrary to contagion hypothesis.
problem Vulnerabilities in DeFi destabilize traditional short-term funding markets.
method Analysis of commercial paper spreads and regulatory segmentation.
result DeFi exploits lead to a 'Flight-to-Quality' pattern, narrowing rather than widening CP spreads.
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.
problem Optimizing the sales process for B2B businesses.
method Causal Predictive Optimization and Generation with three layers: prediction, optimization, and serving.
result Significant wins over legacy systems in LinkedIn implementation.
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.
Article offers models for choosing sale-leaseback vs debt.
problem Choosing between sale-leaseback and debt for commercial real estate.
method Developed decision models for leasing.
result Models can be applied to various types of leasing.
This paper considers a sequence of discrete-time random walk markets with a safe and a single risky investment opportunity, and gives conditions for the existence of arbitrages or free lunches with vanishing risk, of the form of waiting to buy and selling the next period, with no shorting, and furthermore for weak conv…
Study compares machine learning and econometric models for demand prediction with and without accounting for sales censorship.
problem Predicting demand based on sales data and correcting bias in estimates of demand parameters.
method Constructed two ensemble models: one accounting for sales censorship and one not. Used censored quantile regression and various machine learning models.
result Machine learning models with censorship accounting provide similar bias-corrected demand sensitivity estimates as econometric models.
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.
Gaussian Processes improve sales forecasting for large retail data.
problem Challenges in forecasting sales with large scale data.
method Large-scale Gaussian Processes for feature relevance and posterior mean analysis.
result Value of Gaussian Processes as a decision-making tool for management.
New method improves sales forecasting accuracy using tensor factorization.
problem Improving sales forecasting accuracy in retail businesses.
method Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS) using tensor factorization.
result Accurate and individualized prediction for sales across multiple stores and products.
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
problem Forecasting volatile retail sales trends with unknown factors.
method Benchmarked traditional ARIMA model against advanced models like Prophet and LightGBM on historical Walmart sales data.
result ARIMA model outperforms LightGBM and achieves computational efficiency.
The paper analyzes mutual fund advisors' portfolios to improve sales.
problem Improving sales of investment products to suitable advisors.
method Multivariate time series and cosine similarity analysis.
result Recognized patterns in advisors' portfolio interests.
Paper models financial contagion with fire sales and borrowing.
problem Financial contagion and systemic risk in interconnected financial networks.
method Modeling financial contagion in a network with fire sales and borrowing, considering both uncollateralized and collateralized loans.
result Existence and uniqueness of clearing solutions (payments, liquidations, and borrowing) are provided under certain conditions, and these solutions are Nash equilibria.
This paper studies the optimal timing to liquidate credit derivatives in a general intensity-based credit risk model under stochastic interest rate. We incorporate the potential price discrepancy between the market and investors, which is characterized by risk-neutral valuation under different default risk premia speci…
The paper proposes a method to improve sales forecasts by selecting optimal reference classes.
problem Improving forecasts of sales growth exposed to behavioural bias.
method Finding optimal reference classes for each company based on specific predictors and matching forecast distributions to actual sales.
result The past operating margins are strong predictors for future sales distributions.