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We helped our clients source bespoke solutions that have helped them to generate consistent profits each month as everyone at the event can attest. We have also spent a lot of resources in research and development to create a new customer service application with built in know your customer and anti-money laundering capacity which also allow clients to allocate their funds to a diverse array of products," said Ryan Ng, CEO of Pareto Singapore. Pareto's Plans Pareto will be partnering up with Living 3D Holdings Inc, a US developer of disruptive technology that has created an artificial intelligence trading product called Quantesto.

This product brings hedge fund strategies to retail investors including delta neutral, arbitrage, long short and other risk management mechanisms across multiple asset classes. Pareto will act as the product's exclusive global distributor. Pareto will also ramp up its wealth education and networking opportunities by holding monthly events which will provide members with the opportunity to learn about investments around the region.

On the education side, Pareto will be developing a strong online and offline presence while also ramping up its social media exposure and other marketing channels. There will also be specially organised seminars and events with distinguished guests. People are more and more worried about where to invest their money. We are here to create balanced portfolios to ensure our clients' money can grow in a consistent and long-term manner.

Does consequential closure hold in human reasoning? The answer must be no, as human beings have limited computation capability, even with the help of computers if consequential closure holds, chess will be no more interesting than tic-tac-toe. That means we cannot possibly make all the inferences from all the information available to us. All being equal, investors who are capable of making more inferences will have an edge over those who cannot.

Faster computers and more efficient algorithms allow us to make far more inferences than before, hence they bring new opportunities. This is where our research comes in. Many factors could directly or indirectly affect the future price of an investment. Such factors are often inter-related, which adds to the difficulty of analysis. The combinatorial explosion problem prevents one from examining combinations of all the factors and all possible interactions between them.

EDDIE which stands for Evolutionary Dynamic Data Investment Evaluator is an interactive tool, designed at University of Essex, to help analysts to search the space of interactions and make financial decisions. This paper describes the principles of EDDIE and its applications to share price and index forecasting and arbitrage.

It serves to improve the productivity of users who may have various levels of expertise in the domain. Figure 1. To search for rules for predicting investment opportunities, the users are responsible for suggesting a set of factors which they consider relevant. This is a point where expert knowledge can be channelled into the computer program. For example, the user might consider closing prices, interest rates, or money supply as being relevant to the prediction of share price movements.

These factors may interact with each other. The space of models explaining the interaction between these factors is huge, and growing exponentially with the number of factors given. For example, it may be the combination of money supply and the interest rate that affects the movement of a particular index, not these two factors independently. Besides, the space of thresholds is huge too.

For example, whether the price-earning ratio of a particular share is above The role of EDDIE is to help the user to search in the huge space of plausible models to find those which provides a good explanation to past data which is often referred to as training data.

EDDIE searches the space of models using genetic programming [Koza ], which has been successful in many applications, including financial applications e. The basic elements of GDTs are rules and forecast values, which correspond to the functions and terminals in GP. Figure 2 shows an example of a simple GDT. A useful GDT in the real world is almost certainly a lot more sophisticated than this.

In GP terms, the questions in the example GDT are functions, and the proposed actions are terminals, which may also be forecast values. In this example, the GDT is binary; in general, this need not be the case. Figure 2. In each iteration, GDTs are picked from the population weighted randomly using fitness-proportionate selection, which means that the fitter a GDT is, the greater chance it has of being picked.

The set of all GDTs thus picked form a mating pool from which pairs of GDTs, which are referred to as parents, are picked. A branch in each parent is picked at random. The parents then exchange the subtrees under those branches, as shown in figure 3.

This operation is called crossover. Offspring are mutated occasionally, which is done by replacing random elements of the GDT by random or heuristically determined values. The possibly mutated offspring will then replace the old GDTs to form the new population. There are many variations in the way that the population is updated by new offspring, the way that the initial population is generated, the way that parents are picked, the way that crossover and mutation are done, etc.

These will not be elaborated here. Figure 3. Crossover in genetic programming cut off points marked by " 3. Trained with records on UK handicap races in , EDDIE-1 beat random decisions control , handicapper experts and the favourite the publics in 30 races. The sample size was small it was only the lack of data that prevented us from doing larger scale tests , and therefore results were interpreted with caution.

It must be emphasized there is no magic in GP. All it can offer is an efficient search engine for the space of possible models, and generate rules that the user can interpret. It is very important for the expert users of EDDIE to define the prediction task and the grammar for describing hypotheses, and the factors to be included in the data set. It can also be asked to find patterns to fit a given set of data [Butler ]. More applications will be described in the following sections. The crossover operator was designed to take care of the type of branches in the GDTs.

Terminals were indicators, thresholds or conclusions. Thresholds are numbers. Conclusions could be either Positive meaning that G is predicted to be achievable or Negative. We call a trading day a positive position if G holds. This allowed us to compare FGP-1 against random decisions without undue complication.

Tests were also performed in bullish, sluggish and bearish markets. We have done enough tests to draw the following conclusions: Patterns have been found in the indices that we tested on. EDDIE found patterns in some but not all of the shares that we tested on. This may indicate structural changes in the companies in question, or other regime shifts affecting the macro-economy in the period tested.

The question of whether such patterns will repeat themselves in the future will be left to the chartists and the fundamentalists. However, should the patterns found repeat themselves in the future, then they have commercial value. Parameter settings are important. Added to the importance of human input as explained above , EDDIE should not been seen as an automatic financial decision solution to users who have no knowledge of GP and financial forecasting. The data that we have used so far is very minimal: we have only used daily closing prices in the above-mentioned tests.

It is reasonable to believe that better quality predictions can be made with more data input, such as the inclusion of trade volume, interest rates, market trend, etc. For example, advice from an expert may take the form "buy" or "do not buy". Fan et al [] collected the ordinal forecast by nine experts on the weekly movements of the Hong Kong Heng Seng index over weeks.

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In her spare time, she invests her time in a counselling course. This experience gives him a great advantage working with brokers as he fully understands a range of needs. Her knowledge of underwriting and other financial areas is proving to be beneficial when helping brokers with their queries. Away from the office, Jackie loves to travel, particularly off the beaten path and has even completed a tour of Transylvania which she highly recommends!

His knowledge of the mortgage and second charge market is second-to-none, and enables him to help our brokers reach the best solution on their cases. Eddy Lau has a 3. Visit RateMDs for Dr. Eddy Lau reviews, contact info, practice history, affiliated hospitals. The latest Tweets from Eddie Lau Chungman Sheffield, England. Data Services NM1. Eddie Lau is on Facebook. Join Facebook to connect with Eddie Lau and others you may know. Facebook gives people the power to share and makes the world.

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Ada apa dengan Magische Punkte. This experience gives him a great advantage working with brokers as he fully understands a range of needs. Her knowledge of underwriting and other financial areas is proving to be beneficial when helping brokers with their queries. Away from the office, Jackie loves to travel, particularly off the beaten path and has even completed a tour of Transylvania which she highly recommends! His knowledge of the mortgage and second charge market is second-to-none, and enables him to help our brokers reach the best solution on their cases.

Away from the office, Eddie loves to spend time with his wife and daughter, or whipping up something tasty in the kitchen!