The Impact of Inventory Management in a Computerized Trading Company
Chapter One
OBJECTIVE OF THE STUDY
The objectives of the study are;
- To ascertain the relationship between inventory management and computerized trading company
- To find out the challenges of inventory management in computerized trading company
- To ascertain the impact of inventory management in a computerized trading company
Chapter Two
Review of the Related Literature
The concept of inventory management in previous years has attracted attention from people in academia and industries (Prempeh, 2015, 2016; Fosu, 2016; Mensah, 2016; Mwanzi 2016). For instance, “the turnover of inventory represents one of the primary sources of revenue generation and subsequent earnings for the company” (Prempeh, 2016). As more than half of the investments firms are into current assets and inventory constitute one of the most significant component, the quantity of inventory availability at the right time is vital (Carter, 2002; Prempeh 2016). Because of the economic value of inventory, capital productivity is enhanced if inventory levels are effectively managed as inventories are idle resources of firms (Prempeh, 2016).
In an effort to fasten inventory management on firm performance, many decisions are being taken to provide a direction and strategy for competitiveness and productivity. Empirical studies have had diverse findings conducted globally on inventory management. For instance, Prempeh (2016) reported that there is a significant positive relationship between inventory management and profitability. Evidence from previous studies (Luwumba, 2013; Appiah, 2014; Mwangi, 2016; Bingilar, 2016) also supported the direct relationship between inventory management and profitability. In contrary, Hornbrinck (2013), Mensah (2015) and Sitienei and Memba (2015) studies showed a negative relationship. However, almost all these studies fail to acknowledge the fact that inventory management also have an effect on firms operating cash flows.
Rapid, computerized trading refers to the execution of electronic trading strategies involving extremely fast order submissions, cancelations, and executions. Such trading is characterized by the use of computer algorithms to analyze quote data and detect and exploit short-lived trading opportunities. The needed response time is fleeting: SEC (2010) notes “For example, the speed of trading has increased to the point that the fastest traders now measure their latencies in microseconds” (page 3605). Such rapid transactions can be undertaken with the intent to hold securities for various durations, depending on the motivation for pursuing them in the first place. One possibility is that firms use rapid computer programs to acquire and hold securities for quite a while, until new information or valuation signals indicate it is time to (rapidly) exit.
Before this, “financial information was disseminated slowly, usually by ticker tape, and telephonic communication was expensive. In the previous era of floor-based trading, buyers and sellers stood literally next to one another, “allowing for the expeditious identification of counterparties. However, once exchanges started implementing computerized communication, buy and sell orders could be executed much faster; traders could be connected to a trading platform rather than being physically present on trading floors. An electronic communication network is a type of computer system that facilitates trading of financial products, such as stock and currencies, outside of the traditional stock exchanges.
High frequency trading firms hunt for temporary inefficiencies in the market and trade as quickly as possible to make money before the brief distortions go away. Because of this, high frequency trading is characterized by a high turnover in capital and is dependent on a variety of market components that enable traders to turn a profit. The distinguishing characteristics of high frequency trading strategies include a dependence on ultra-low latency, the limited shelf-life of trading algorithms and the reliance on multiple asset classes and exchanges
Information, inventories, and competition are crucial elements of computerized trading. Indeed, many high-frequency strategies are based on gaining access to proprietary information and exploiting it before it becomes public knowledge. A typical example is “latency arbitrage”, where high-frequency traders act based on price changes on other exchanges before these are incorporated into the consolidated “national best bid-offer (NBBO) price”. These strategies are not based on any longer-term view on the market. Accordingly, the main associated risk is the inventory that is built up along the way, which exposes the trader to adverse price moves. Thus, the natural tradeoff in this context is to exploit the available information as much as possible while simultaneously controlling the associated inventories. We consider a market where risk-neutral, competitive dealers clear the orders of exogenous noise traders as well as several strategic. As in the latency-arbitrage trades mentioned above, these have access to future asset value changes one period before they become public knowledge. They in turn trade in Nash competition to exploit this additional information and are penalized by a quadratic inventory cost as in. Since the model is based on a discrete informational advantage, we start from the equilibrium in a discrete-time setting and then study its convergence as the trading frequency increases to the continuous-time limit. Here, “linear” means that the dealers break even by adjusting the publicly known part of the asset value linearly for the net order flow (as in risk-neutral versions of the model. The impact of inventory aversion on market liquidity and welfare becomes visible at the next to-leading order in our expansions. However, we illustrate through numerical examples that the magnitude of these welfare and liquidity effects is small on the very short time scales relevant for high-frequency trading. Therefore, each of them trades too aggressively compared to the efficient allocation that would be achieved by coordinating through a central planner.
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