482 research outputs found
A Lindley-type equation arising from a carousel problem
Abstract: In this paper we consider a system with two carousels operated by one picker. The items to be picked are randomly located on the carousels and the pick times follow a phasetype distribution. The picker alternates between the two carousels, picking one item at a time. Important performance characteristics are the waiting time of the picker and the throughput of the two carousels. The waiting time of the picker satisfies an equation very similar to Lindley’s equation for the waiting time in the P H/U/1 queue. Although the latter equation has no simple solution, it appears that the one for the waiting time of the picker can be solved explicitly. Furthermore, it is well known that the mean waiting time in the P H/U/1 queue depends on to the complete inter-arrival time distribution, but numerical results show that, for the carousel system, the mean waiting time and throughput are rather insensitive to the pick-time distribution
Uncovering the Internal Structure of the Indian Financial Market: Cross-correlation behavior in the NSE
The cross-correlations between price fluctuations of 201 frequently traded
stocks in the National Stock Exchange (NSE) of India are analyzed in this
paper. We use daily closing prices for the period 1996-2006, which coincides
with the period of rapid transformation of the market following liberalization.
The eigenvalue distribution of the cross-correlation matrix, , of
NSE is found to be similar to that of developed markets, such as the New York
Stock Exchange (NYSE): the majority of eigenvalues fall within the bounds
expected for a random matrix constructed from mutually uncorrelated time
series. Of the few largest eigenvalues that deviate from the bulk, the largest
is identified with market-wide movements. The intermediate eigenvalues that
occur between the largest and the bulk have been associated in NYSE with
specific business sectors with strong intra-group interactions. However, in the
Indian market, these deviating eigenvalues are comparatively very few and lie
much closer to the bulk. We propose that this is because of the relative lack
of distinct sector identity in the market, with the movement of stocks
dominantly influenced by the overall market trend. This is shown by explicit
construction of the interaction network in the market, first by generating the
minimum spanning tree from the unfiltered correlation matrix, and later, using
an improved method of generating the graph after filtering out the market mode
and random effects from the data. Both methods show, compared to developed
markets, the relative absence of clusters of co-moving stocks that belong to
the same business sector. This is consistent with the general belief that
emerging markets tend to be more correlated than developed markets.Comment: 15 pages, 8 figures, to appear in Proceedings of International
Workshop on "Econophysics & Sociophysics of Markets & Networks"
(Econophys-Kolkata III), Mar 12-15, 200
Warehouse design and planning: A mathematical programming approach
The dynamic nature of today's competitive markets compels organizations to an incessant reassessment in an effort to respond to continuous challenges. Therefore, warehouses as an important link in most supply chains, must be continually re-evaluated to ensure that they are consistent with both market's demands and management's strategies. A number of warehouse decision support models have been proposed in the literature but considerable difficulties in applying these models still remain, due to the large amount of information to be processed and to the large number of possible alternatives. In this paper we discuss a mathematical programming model aiming to support some warehouse management and inventory decisions. In particular a large mixed-integer nonlinear programming model (MINLP) is presented to capture the trade-offs among the different inventory and warehouse costs in order to achieve global optimal design satisfying throughput requirements.(undefined)info:eu-repo/semantics/publishedVersio
Effects of emissions caps on the costs and feasibility of low-carbon hydrogen in the European ammonia industry
The European ammonia industry emits 36 million tons of carbon dioxide annually, primarily from steam methane reforming (SMR) hydrogen production. These emissions can be mitigated by producing hydrogen via water electrolysis using dedicated renewables with grid backup. This study investigates the impact of decarbonization targets for hydrogen synthesis on the economic viability and technical feasibility of retrofitting existing European ammonia plants for on-site, semi-islanded electrolytic hydrogen production. Results show that electrolytic hydrogen cuts emissions, on average, by 85% (36%-100% based on grid price and carbon intensity), even without enforcing emission limits. However, an optimal lifespan average well-to-gate emission cap of 1 kg carbon dioxide equivalent (CO2e)/kg H2 leads to a 95% reduction (92%-100%) while maintaining cost-competitiveness with SMR in renewable-rich regions (mean levelized cost of hydrogen (LCOH) of 4.1 euro/kg H2). Conversely, a 100% emissions reduction target dramatically increases costs (mean LCOH: 6.3 euro/kg H2) and land area for renewables installations, likely hindering the transition to electrolytic hydrogen in regions with poor renewables and limited land. Increasing plant flexibility effectively reduces costs, particularly in off-grid plants (mean reduction: 32%). This work guides policymakers in defining cost-effective decarbonization targets and identifying region-based strategies to support an electrolytic hydrogen-fed ammonia industry
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Breaking into the blackbox: Trend following, stop losses and the frequency of trading - The case of the S&P500
In this article, we compare a variety of technical trading rules in the context of investing in the S&P500 index. These rules are increasingly popular, both among retail investors and CTAs and similar investment funds. We find that a range of fairly simple rules, including the popular 200-day moving average (MA) trading rule, dominate the long-only, passive investment in the index. In particular, using the latter rule we find that popular stop-loss rules do not add value and that monthly end-of-month investment decision rules are superior to those which trade more frequently: this adds to the growing view that trading can damage your wealth. Finally, we compare the MA rule with a variety of simple fundamental metrics and find the latter far inferior to the technical rules over the last 60 years of investing
Momentum meets value investing in a small European market
In this paper, we investigate two prominent market anomalies documented in the finance literature – the momentum effect and value-growth effect. We conduct an out- of-sample test to the link between these two anomalies recurring to a sample of Portuguese stocks during the period 1988–2015. We find that the momentum of value and growth stocks is significantly different: growth stocks exhibit a much larger momentum than value stocks. A combined value and momentum strategy can generate statistically significant excess annual returns of 10.8%. These findings persist across several holding periods up to a year. Moreover, we show that macroeconomic variables fail to explain value and momentum of individual and combined returns. Collectively, our results contradict market efficiency at the weak form and pose a challenge to existing asset pricing theories.info:eu-repo/semantics/publishedVersio
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