Day-of-the-week trading patterns of individual and institutional investors

This study examines the day-of-the-week trading patterns of individual and institutional investors. Consistent with previous evidence, we find an increase in the proportion of Monday trading volume attributable to individual investors relative to other days of the week. However, we document that this increase results from a reduction in trading by institutional investors, rather than from an absolute increase in trading by individual investors. In fact, the absolute trading volume by individual investors is significantly lower on Monday than on any other weekday. We also document that the degree of day-of-the-week effect varies with the quality and dissemination of public information proxied by the market capitalization of each company

Handel recovering: fresh light on his affairs in 1737

The summer and autumn of 1737 remain a foggy patch in Handel biography owing to poor documentation and Handel’s absence from London. We do not know whether his illness led to a rapprochement with the ‘Nobility’ opera, how his visit to Aix-la-Chapel complicated the new opera season or, especially, whether these developments relate to Farinelli’s defection to Spain. This shaky factual ground also restricts our understanding of later events such as Handel’s lucrative benefit in March 1738 and the celebrated Roubiliac statue in Vauxhall Gardens. Thanks to surviving issues of the Daily Advertiser, however, we now can replenish the documentary pool and re-examine Handel’s affairs and their context during this period

Does mood affect trading behavior?

We test whether investor mood affects trading with data on all stock market transactions in Finland, utilizing variation in daylight and local weather. We find some evidence that environmental mood variables (local weather, length of day, daylight saving and lunar phase) affect investors’ direction of trade and volume. The effect magnitudes are roughly comparable to those of classical seasonals, such as the Monday effect. The statistical significance of the mood variables is weak in many cases, however. Only very little of the day-to-day variation in trading is collectively explained by all mood variables and calendar effects, but lower frequency variation seems connected to holiday seasons

An Optimization Model for Single-Warehouse Multi-Agents Distribution Network Problems under Varying of Transportation Facilities: A Case Study

The transportation cost of goods is the highest day-to-day operational cost associated with the food industry sector. A company may be able to reduce logistics cost and simultaneously improve service level by optimizing of distribution network. In reality, a company faces problems considering capacitated transportation facilities and time constraint of delivery. In this paper, we develop a new model of order fulfillment physical distribution to minimize transportation cost under limited of transportation facilities. The first step is defined problem description. After that, we formulate a integer linear programming model for the single-warehouse, multiple-agents considering varying of transportation facilities in multi-period shipment planning. We analyze problems faced by company when should decide policy of distribution due to varying of transportation facilities in volume, type of vehicle, delivery cost, lead time and ownership of facilities. We assumed transportation costs are modeled with a linear term in the objective function. Then, we solve the model with Microsoft Excel Solver 8.0 Version. Finally, we analyze the results with considering amount of transportation facilities, volume usage and total transportation cost. Keywords: physical distribution, shipment planning, integer linear programming, transportation cost, transportation facilities

Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology

Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we propose a novel method using persistent homology to quantify structural changes in time-varying graphs. Specifically, we transform each instance of the time-varying graph into metric spaces, extract topological features using persistent homology, and compare those features over time. We provide a visualization that assists in time-varying graph exploration and helps to identify patterns of behavior within the data. To validate our approach, we conduct several case studies on real world data sets and show how our method can find cyclic patterns, deviations from those patterns, and one-time events in time-varying graphs. We also examine whether persistence-based similarity measure as a graph metric satisfies a set of well-established, desirable properties for graph metrics

Time-Slice Rationality and Self-Locating Belief

The epistemology of self-locating belief concerns itself with how rational agents ought to respond to certain kinds of indexical information. I argue that those who endorse the thesis of Time-Slice Rationality ought to endorse a particular view about the epistemology of self-locating belief, according to which ‘essentially indexical’ information is never evidentially relevant to non-indexical matters. I close by offering some independent motivations for endorsing Time-Slice Rationality in the context of the epistemology of self-locating belief