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4.3 Statistical analysis / User requirements analysis The data will be obtained will be analysed through statistical modelling. The method of analysis will be structured as following: Linear programming formulation and prediction: β as a risk measure Fund Selection under uncertainty: Variance and Standard deviation References 1. Correia C. et al (2011), Financial Management 7th Edition, Juta and Company, Lansdowne, Cape Town. 2. Ho W.J, Tsai C., Tzeng G., and Fang S., 2011.
In value relevance literature, there are two basic types of valuation model that have extensively used by prior studies. Price model testing how firm`s market value relate to accounting earnings and equity book value. As seen in equation 1, based on Ohlson (1995), the model expresses the firm value as a function of its earnings and equity book value. The other type of value relevance valuation model is return model, which describes relation between stock returns and accounting earnings. The value relevance of accounting information studies using returns-earnings association is motivated by the seminal work of Ball and Brown (1968).
Artificial intelligence techniques can be applied to financial investing, especially in the areas of credit risk assessment and stock valuation. In the future, we can expect that the techniques of artificial intelligence will be integrated into systems that simultaneously address investing activities. WHAT IS ARTIFICIAL INTELLIGENCE? Technology is an important factor in investing activities. For example, stock trading is computer-based and can automatically execute the trading of large volumes of shares.
1.0 High Frequency Trading 1.1 What is High Frequency Trading High frequency trading is a form of automated trading that uses super computers to transact or process mega transaction orders at super fast speed, which are mostly measured in microsecond or milliseconds. ( Investopedia, n.d) with the aim to identify and arbitrage temporary market inefficiencies that are created by the competing interests of market participants (Aldridge,2013) Algorithms, low latency technology, high message rates and high speed connections are the 4 main characteristics in the performance of a High Frequency Trading. Algorithms is a set of instructions for accomplishing a given task. So a trading algorithm is just a computerized model with steps to trade an order in a specific way (Johnson, 2010) The algorithms in HFT are for the purpose of decision making, order initiation, generation, routing or execution for each individual transaction without a human execution. (Aldridge, 2013) The low latency technology infrastructure on the other hand is a must for high frequency trading.
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