Realtime market microstructure analysis: online Transaction Cost Analysis

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance, a stochastic control, an impulse control or a statistical learning viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order. We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method in the post trade analysis of algorithms can be taken advantage of to automatically adjust their trading action.Comment: 33 pages, 12 figure

E-logistics of agribusiness organisations

Logistics is one of the most important agribusiness functions due to the idiosyncrasy of food products and the structure of food supply chain. Companies in the food sector typically operate with poor production forecasting, inefficient inventory management, lack of coordination with supply partners. Further, markets are characterised by stern competition, increasing consumer demands and stringent regulation for food quality and safety. Large agribusiness corporations have already turned to e-logistics solutions as a means to sustain competitive advantage and meet consumer demands. There are four types of e-logistics applications: (a) Vertical alliances where supply partners forge long-term strategic alliances based on electronic sharing of critical logistics information such as sales forecasts and inventory volume. Vertical alliances often apply supply chain management (SCM) which is concerned with the relationship between a company and its suppliers and customers. The prime characteristic of SCM is interorganizational coordination: agribusiness companies working jointly with their customers and suppliers to integrate activities along the supply chain to effectively supply food products to customers. E-logistics solutions engender the systematic integration among supply partners by allowing more efficient and automatic information flow. (b) e-tailing, in which retailers give consumers the ability to order food such as groceries from home electronically i.e. using the Internet and the subsequent delivery of those ordered goods at home. (c) Efficient Foodservice Response (EFR), which is a strategy designed to enable foodservice industry to achieve profitable growth by looking at ways to save money for each level of the supply chain by eliminating inefficient practices. EFR provides solutions to common logistics problems, such as transactional inefficiency, inefficient plant scheduling, out-of-stocks, and expedited transportation. (d) Contracting, a means of coordinating procurement of food, beverages and their associated supplies. Many markets and supply chains in agriculture are buyer-driven where the buyers in the market tend to set prices and terms of trade. Those terms can include the use of electronic means of communication to support automatic replenishment of goods, management of supply and inventory. The results of the current applications of e-logistics in food sector are encouraging for Greek agribusiness. Companies need to become aware of and evaluate the value-added by those applications which are a sustainable competitive advantage, optimisation of supply chain flows, and meeting consumer demands and food safety regulations. E-business diffusion has shown that typically first-movers gain a significant competitive advantage and the rest companies either eventually adopt the new systems or see a significant decline in their trading partners and perish. E-logistics solutions typically require huge investments in hardware and software and skilled personnel, which is an overt barrier for most Greek companies. Large companies typically are first-movers but small and medium enterprises (SMEs) need institutional support in order to become aware that e-logistics systems can be fruitful for them as well

Virtual Sports Stock Exchange

The goal of this project is to create a web application to help people learn about the stock market. The Virtual Sports Stock Exchange (VSSX) simulates market trading based on the world of sports. It allows users to experiment with different economic models. Virtual Sports Stock Exchange (VSSX) uses HTML and Java Server Page to generate the output and calculations and it uses Java Servlet to interact with the Oracle 9i database

Nutrient Trading in Lake Rotorua: Choosing the Scope of a Nutrient Trading System

A nutrient trading system is one mechanism that is currently being considered to control and reduce nutrient loss into Lake Rotorua. However this may not be the best mechanism for controlling nutrient loss from all sources. A more comprehensive system improves efficiency and decreases market power opportunities, but it can also bring increased compliance and administrative costs. This paper discusses which sources should be included in a nutrient trading system for Lake Rotorua including. It examines existing systems and presents an empirical analysis to estimate the impact of including different nutrient sources.Water quality, nutrients, trading, Lake Rotorua

How Institutions Affect Outcomes in Laboratory Tradable Fishing Allowance Systems

The objective of this paper is to illustrate that economic institutions matter, i.e., that different rules of trade present different incentives for bidding, asking, and trading in new markets, and that these different incentives lead to different price discovery patterns, which yield materially different outcomes. In a laboratory tradable fishing allowance system, when trade takes place through a double auction, which parallels an institution common in extant tradable allowance systems, markets are characterized by high volatility, and equilibrium does not obtain. However, when only leases, and not permanent trades, are permitted in the early periods, volatility is significantly reduced and equilibrium obtains. This dependence of equilibration and outcomes on institutions implies policy-oriented economists must consider institutions in designing new market-based management systems.Resource /Energy Economics and Policy,

Prediction of Stock Market Index Using Genetic Algorithm

The generation of profitable trading rules for stock market investments is a difficult task but admired problem. First stage is classifying the prone direction of the price for BSE index (India cements stock price index (ICSPI)) futures with several technical indicators using artificial intelligence techniques. And second stage is mining the trading rules to determined conflict among the outputs of the first stage using the evolve learning. We have found trading rule which would have yield the highest return over a certain time period using historical data. These groundwork results suggest that genetic algorithms are promising model yields highest profit than other comparable models and buy-and-sell strategy. Experimental results of buying and selling of trading rules were outstanding. Key words: Data mining, Trading rule, Genetic algorithm, ANN, ICSPI predictio