Generalized parametric down conversion, many particle interferometry, and Bell's theorem

A new field of multi-particle interferometry is introduced using a nonlinear optical spontaneous parametric down conversion (SPDC) of a photon into more than two photons. The study of SPDC using a realistic Hamiltonian in a multi-mode shows that at least a low conversion rate limit is possible. The down converted field exhibits many stronger nonclassical phenomena than the usual two photon parametric down conversion. Application of the multi-particle interferometry to a recently proposed many particle Bell's theorem on the Einstein-Podolsky-Rosen problem is given

Empirical Study on the Difference in Analyst’ Tendency for earning forecast and Impact on Stock Price by Industry Types

FinanceWhen analysts forecast the future earnings of a firm, the method used in analyzing and forecasting such a firm differs according to the industry that it participates in due to the various characteristics each industry possesses. This study is motivated by this point. Firstly this study investigates whether the overestimating tendency and forecast accuracy of an analyst are different among industries, as well as running a cross-sectional regression using dummy variables to test the effect of the industry to forecast errors. In addition, this study examines whether there exists difference in stock price impact of analysts according to industry types using CAR for 20 days before and after changing the recommendations. It turned out that there exists a significant difference in the tendency for over-forecast and the accuracy of forecasts of analysts according to each industry. In addition, a tendency for the under-prediction has been identified especially for the Bank Industry. Interestingly even in the same industry, analysts are more likely to overestimate earnings about net income compared to sales and operating profit. Furthermore, in regards to the difference in the influence on the stock price in cases where an analyst changes its target price/investment recommendation, the study has observed a significant difference depending on the industry that an analyst is participating in. At the end of the study, by comparing the results of the influence on stock prices and the accuracy of forecasts of an analyst in the earlier section of this research, it has identified that the industry where an analyst had lower forecasting accuracy showed a lower influence towards the stock price of an analyst. It seems that investors tend not to trust analysts’ who have already presented a relatively less accurate forecast.ope

Dugoročna ravnoteža odnosa sustava burzovnih čimbenika na međunarodnoj burzi

The main objective of this paper is to investigate the international linkages among local, country-specific stock market factors in order to better understand the dependence structure of increasingly integrated world financial markets. The seeming discordance between Fama and French (1998) and Griffin (2002) regarding the multi-factor model in the international stock markets motivates us to study the international relationship among local factors. With the individual stock data from the six major developed countries in the international stock market, we compose daily returns to the Fama-French three factors (i.e. market, size, and value) and the momentum factor over the period from January 2000 to June 2010. We investigate the international linkages among local stock market factors, focusing on their equilibrium relationship in the integrated world financial market. The cointegration analysis indicates that local factor indices, constructed from the cumulative factor returns, are cointegrated for each of the four factor classes. Thus, we conclude that local factors are globally bound to each other through a long-run equilibrium relationship and that although stock market factors may be local, rather than global, individual stock returns are driven by common global stochastic trends.Glavni cilj ovog rada je istražiti međunarodne veze lokalnih čimbenika burze specifične za određenu zemlju kako bi se bolje pojmila strukturalna međuovisnost sve integriranijih svjetskih financijskih tržišta. Naizgled nesklad između Fame i Frencha (1998) i Griffina (2002) a koji se odnosi na multi-faktor model u međunarodnim burzama motivirao nas je za istraživanje međunarodnog odnosa lokalnih čimbenika. Uz podatke individualnih dionica šest glavnih razvijenih zemalja na međunarodnom tržištu dionica, sastavili smo dnevnu dobit u odnosu na Fama-Frenchova tri čimbenika (tj. tržište, veličina i vrijednost) i faktor stvaranja zamaha u razdoblju od siječnja 2000. do lipnja 2010. Istražujemo međunarodne veze među lokalnim čimbenicima na tržištu dionica s naglaskom na njihovu ravnotežu odnosa u integriranom svjetskom financijskom tržištu. Analiza ko-integracije pokazuje da su lokalni faktori indeksa izgrađeni od kumulativnih faktoradobiti ko-integrirani za svaki od četiri faktora klase. Dakle, možemo zaključiti da su lokalni čimbenici globalno međusobno povezani kroz dugoročni odnos ravnoteže i iako burzovni čimbenici mogu biti lokalni a ne globalni, dobit od pojedinih dionica proizlazi iz zajedničkih globalnih stohastičkih trendova

Seasonality In Mutual Fund Flows

In this paper I establish the presence of seasonality in cash flows to U.S. domestic mutual funds. January is the month with the highest net cash flows to equity funds and December is the month with the lowest net cash flows. The large net flows in January are attributed to increased purchases, and the small net flows in December are due to increased redemptions. Thus, the turn-of-the-year period is the time that most mutual fund investors make their investment decisions

Autonomous migration of vertual machines for maximizing resource utilization

Virtualization of computing resources enables multiple virtual machines to run on a physical machine. When many virtual machines are deployed on a cluster of PCs, some physical machines will inevitably experience overload while others are under-utilized over time due to varying computational demands. This computational imbalance across the cluster undermines the very purpose of maximizing resource utilization through virtualization. To solve this imbalance problem, virtual machine migration has been introduced, where a virtual machine on a heavily loaded physical machine is selected and moved to a lightly loaded physical machine. The selection of the source virtual machine and the destination physical machine is based on a single fixed threshold value. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. The fixed threshold may not necessarily work for different computing infrastructures. Finding the optimal threshold is critical. In this research, a virtual machine migration framework is presented that autonomously finds and adjusts variable thresholds at runtime for different computing requirements to improve and maximize the utilization of computing resources. Central to this approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. To broaden this research, a proactive learning methodology is introduced that not only accumulates the past history of computing patterns and resulting migration decisions but more importantly searches all possibilities for the most suitable decisions. This research demonstrates through experimental results that the learning approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization. The proposed framework is set up on a cluster of 8 and 16 PCs, each of which has multiple User-Mode Linux (UML)-based virtual machines. An extensive set of benchmark programs is deployed to closely resemble a real-world computing environment. Experimental results indicate that the proposed framework indeed autonomously finds thresholds close to the optimal ones for different computing scenarios, balances the load across the cluster through autonomous VM migration, and improves the overall performance of the dynamically changing computing environment

Preparing Korean missionaries for cross-cultural effectiveness

https://place.asburyseminary.edu/ecommonsatsdissertations/1164/thumbnail.jp