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692 research outputs found
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Generation Z tourists’ experience and delight in rural tourism: the mediating role of customer engagement
No full textInternational audienc
The illusion of oil return predictability: The choice of data matters!
No full textInternational audiencePrevious studies document statistically significant evidence of crude oil return predictability by several forecasting variables. We suggest that this evidence is misleading and follows from the common use of within-month averages of daily oil prices in calculating returns used in predictive regressions. Averaging introduces a bias in the estimates of the first-order autocorrelation coefficient and variance of returns. Consequently, estimates of regression coefficients are inefficient and associated t-statistics are overstated, leading to false inference about the true extent of in-sample and out-of-sample return predictability. On the contrary, using end-of-month data, we do not find convincing evidence for the predictability of oil returns. Our results highlight and provide a cautionary tale on how the choice of data could influence hypothesis testing for return predictability
On the interplay between local lead times, overall lead time, prices, and profits in decentralized supply chains
No full textInternational audienceA two-stage decentralized supply chain operates in make-to-order under a stochastic environment. Each stage represents an independent firm that quotes a price and a delivery time to its downstream while satisfying a minimum service level. The mean demand depends on the final price and the overall delivery time quoted to the customers by the whole supply chain. We study three settings. First, the downstream, as a Stackelberg leader, decides its price and controls both delivery times, and the upstream, as a follower, reacts by deciding its own price. Second, the downstream decides its delivery time and controls prices, and the upstream reacts by quoting its own delivery time. Third, the upstream, as a leader, decides its price and controls both delivery times, and the downstream, as a follower, decides its own price. This is the first study to investigate the delivery time quotation and pricing in decentralized supply chains where each firm performs operations and has a delivery time, and the demand is function of both upstream and downstream delivery times in addition to final price. We characterize analytically the optimal strategy under each setting and derive insights into the interplay between local delivery times, overall delivery time, prices, demand, and profits. We investigate how delivery times can be used to coordinate the supply chain and the impact of firms’ capacities
A Novel Machine Learning Approach for Predicting the NIFTY50 Index in India
No full textInternational audienceOver the past decade, extensive research on stock market prediction using machine learning models has been conducted. In this framework, different approaches for data standardisation methods have been used for financial time series analysis and to assess the impact of data standardisation on the final prediction outcome. The paper uses the feature-level optimal rolling-window batch data standardisation method to improve the machine learning model's predictive power significantly. Along with the standardisation method, the paper explores the performance of the automated feature interactions learner (Deep Cross Networks) effect on a plethora of technical indicators aiming at predicting the movements of the NIFTY50 index in India, as these predicted changes are reflected in options contracts
Winners and losers of the COVID‐19 pandemic: An excess profits tax proposal
No full textInternational audienceIn this paper, we study the gains and losses incurred during the COVID-19 pandemic. We distinguish between the effects of the pandemic and those of the health measures implemented to reduce the death toll, notably "the lockdown." Our theoretical model is focused on within-sector firm heterogeneity and involves imperfect competition in a partial equilibrium setting. A comparison between the gains and losses triggered by both the pandemic and the lockdown indicates that an excess profits tax imposed on the "winners" could partly compensate the "losers" of the same sector
A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price
No full textInternational audienceSeveral machine learning techniques and hybrid architectures for predicting bitcoin price movement have been presented in the past. Our paper proposes a hybrid model encompassing classification and regression models for predicting bitcoin prices. Our analysis found that the automated feature interactions learner (deep cross networks) error performance using a plethora of technical indicators, including crypto-specific technical indicator difficulty ribbon compression and control variables such as Metcalfe’s value of bitcoin, number of unique active addresses, bitcoin network hash rate, and S&P 500 log returns, in a hybrid architecture is better than the single-stage architecture. The hybrid model predicted a 100% directional hit rate and maintained steady volatility in returns for the out-of-sample period. Our paper concludes that in terms of risk (Sharpe ratio 1.03) and profitability (260% and 82%), the hybrid model’s bitcoin futures strategy performed better than the deep cross network regression and buy-and-hold benchmark strategies
Pricing and advertising decisions in a direct-sales closed-loop supply chain
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Strategic asset-seeking acquisitions, technological gaps, and innovation performance of Chinese multinationals
No full textInternational audienceWe investigate the impact of acquiring similar or complementary technologies on the innovation performance of Chinese multinationals’ strategic asset-seeking M&As in the EU, and whether such impact is contingent upon firm-level and region-level technological gaps. Results show that technological complementarity enhances Chinese multinationals’ innovation performance. Firm-level technological gaps have a positive moderating effect for both complementary and similar technologies. Region-level gaps enhance innovation when Chinese firms acquire similar technologies, but they undermine the positive impact of technological complementarity on innovation performance. We advance understanding of Chinese MNEs’ learning scope and strategic intents in their strategic asset-seeking M&As
Price transmission in European fish markets
No full textInternational audienceWe investigate price transmission in European fisheries markets. To start, we identify clusters of fish species both cross-regionally and within countries. A major issue in the clustering exercise is missing data due to reporting issues and seasonality in landed fish catches. To handle this we implement k-POD clustering which unlike traditional k-means clustering is able to account for missing data in clustering. Next, we move on to our primary goal of investigating price transmission through modelling price volatility spillovers. A missing value VAR framework is applied to identify directional spillovers among fish prices within clusters. We show that the directional volatility spillover effect is more prominent within each country for different species clusters than that for species cross-regionally. This suggests price transmission within localized markets for fish, rather than cross-regional markets for individual species. Although there are some neighbour country effects and some species that appear to be priced cross-regionally. Our study is the first to explore these multiple fish pricing dynamics, particularly taking account of the important issue of missing data which is a notable feature of fish pricing data