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Nowadays, stock market is becoming a popular investment platform for both institutional and individual investors. The current financial information systems serve to provide latest information. However, they lack sophisticated analytical tools. This paper proposes a new architecture for financial information systems. It is specially designed for investors to build their financial models to forecast stock price and index.
The performance of the financial models can be evaluated on a virtual trading platform. There are other features in MISMIS that are tailor-made to handle financial data; these include synchronized time frame, time series prediction techniques, preprocessing and transformation functions, multi-level modeling and interactive user interface.
Also there are some feedbacks from the in-depth interviews of six financial consultant upon how they perceived the prototype MISMIS. In the stock market, many investors attempt to earn extra gain or to protect their savings from shrinking due to inflation. However, the existing financial information systems FIS are too much focusing on dissemination of information such as financial news or latest stock value. They may equip with simple stock trading heuristics.
For example, a buy signal is horned if the curve of the 7-days moving average crosses and is above the days moving average. They are indeed lack of analytical tools to do sophisticated analyses. Nowadays, some statistical tools such as Clementine have armed with advanced techniques particularly on time series forecasting such as Autoregressive Integrated Moving Average ARIMA and artificial neural network.
Nevertheless, they provide a platform for general applications and do not have a specific financial domain. For example, they do not provide adequate support on virtual trading environment for investors to try out their trading strategies.
In order to facilitate investors for their decision making in stock trading, this paper proposes a new architecture of FIS which integrates the advanced time series forecasting techniques with a virtual trading platform under a GUI environment.
This development would fit into the trend of using advanced techniques such as data mining method and knowledge-based decision supporting tool to predict or evaluate business performance  ,  , . This paper describes the prototype of a Multi-level and Interactive Stock Market Investment System MISMIS which combines different area — financial economics, prediction techniques, and dynamical systems theory, in handling financial data.
MISMIS enables investors to build their prediction models interactively by dragging various pre-defined functions. The prediction outcomes can also be fed into another prediction model as input. Let us say, the Hang Seng Index can be predicted by feeding the prediction outcomes of those composite stock prices.
It may result in having a model that produces more reliable predictions. The prediction will subsequently be fitted into a trading strategy and be evaluated on a virtual trading platform.
Performance metrics will be reported to users who would adjust the model parameters for further enhancement on the prediction model. They found this prototype once being launched would be much welcome by most individual investors. The presentation of this paper is as follows. We first describe the theoretical base of financial forecasting.
Also there is a section on how investment consultant feedback to this prototype. This section reviews on the fundamentals of why predicting financial marketing is sensible. If a market is efficient, no information or analysis can be expected to result in outperformance of an appropriate benchmark. In other words, in an efficient market at any point in time the actual price of a security will be a good estimate of its intrinsic value.
Most importantly, Efficient Market Hypothesis  states that at any given time, security prices fully reflect all available information.
It implies that stock price value could be calculated by suitable indictors. Thus prediction of stock market only makes sense if the market is an efficient one. In this regard, building a financial information system would help investors in their decision making. For a less efficient market, we will usually find a delay in response to the market information, but still will be settled in a longer term perspective.
Ritchie  , based on fundamental analysis, designed to assess important economic, political, and social dynamics as well as company data, and suggested their potential impact on the capital markets and security values. This approach attempts to determine the expected return of an investment given a certain amount of financial risk including purchasing power risk, interest rate risk and business risk.
It involves an in-depth study of the economy and its implications for industries and companies, valuation of security based on its future earning power and dividend paying expectations utilizing the valuation approaches. Both qualitative and quantitative financial statements including the income statement and balance sheet information and analyses are used for stock value estimation. The derived value of the stock is then compared with its current market value to determine whether to invest on this stock or not.
In brief, fundamental analysis is suitable for long term forecasting and analysis. It could be used to predict the overall trend of movement in future. Along with the short term movement, Murphy  attempted to study the market action primarily through the use of charts. The technical approach is based on three assumptions: market action discounts everything; prices move in trend; and history repeats itself. The types of chart available include daily bar chart, long range weekly and monthly chart, shorter term intra-day chart, major reversal patterns like the head and shoulders pattern and the double top or bottom, moving averages, oscillators and contrary opinion, point and figure chart, etc.
In short, technical analysis is mainly for short term forecasting while fundamental analysis is for long term prediction. For long term prospective, a user may predict the stock index movement by considering the underlying fundamental economic data using regression or ANN. Its interface looks like Clementine which is an advanced module of SPSS and user can build their prediction model by putting different functional blocks together.
As shown in Fig. The following sections describe these features in details. For the processing, all data are time-stamped and synchronized. The user can specify the time unit, e. Data with time-stamp in a longer period are interpolated according to some pre-defined functions. For instance, if a user wants to predict the closing Hang Seng Index of tomorrow, the time unit for this application is in terms of days, the other time series data are synchronized according to this time unit.
Let us say, the interest rate, once unchanged within a period of half a year, is interpolated to be the same value at different time points within the period. Transformation functions, which are specifically for economic data manipulation, such as logarithm or time lag, are developed in MISMIS for handling the financial data. The followings highlight those essential transformation functions.
Log — logarithm function is available for some models that have errors proportional to variables, or for transforming a multiplicative model to an additive model. It also applies to data with long term exponential trend. For examples, log liquidity is used for economic indicator forecasting . Difference — the difference of the previous value at time t — k with respect to the current value at time t. This is used for making a time series to become stationary.
Non-stationary time series variance depends on time often cause problems in prediction. Differencing is a common method to solve such kind of problems. For example, change in the monthly average of the spot price of crude oil per barrel, which is a stationary time series, is measured by differencing the monthly average of spot prices .
These changes of monthly average are helpful to predict the trend of the economic movement. Normalization — some financial data have a big difference in scale. If these data used directly, the indicators with large value, e. HSI, may dominate the prediction.
Usually these data need to be normalized in same scale before analysis so as to have the same weighting for forecasting. All value will be in a range of 0—1 after normalization.
It helps to analyze the relative importance of various indicators. The normalization is formulated as follows:. Time lagging is mainly used for leading indicators and lagging indicators, which they either signal future events or follow past events. To adjust the time point that these data are referring in a model, time shifting is a necessary step in data preparation.
For instance, money supply M2 is commonly considered as a leading indicator of economy . The parameter for the time lag can be controlled by double click the lag icon and the user can set the parameter the number of time unit to be shifted in a pre-defined dialogue. Percentage change — for financial analysis, the percentage of change is much more important than the real value. In order to compare the relative fluctuation of stock markets, percentage change is a good indicator for comparison.
For instance, the daily percentage change of Dow Jones Index on 31 January was 0. This value would have an impact on other stock markets and as a result, the percentage change of Hang Seng Index would be speculated to be of similar order on 1 February The percentage change formula is as follows:.
There is a growing evidence that macroeconomic series such as money flow contain non-linearities  but linear models such as Autoregressive Integrated Moving Average ARIMA  and regression are still widely used as they provide higher explanation power and are statistical reliable.
ARIMA models are used for homoskedastic stationary series. It is assumed that the expected value of squared errors is the same across the entire series. This assumption is called homoskedasticity. However, most of financial series do not meet this assumption, the expected values of squared errors may be larger at some ranges than others, and this is called heteroskedasticity. In the other vein, neural network models can be used to solve non-linear problems. In contrast to traditional statistical methods, which are usually linear-based, artificial neural networks ANNs cater for non-linear systems.
They are computing techniques that were inspired by the function of nerve cells in a brain. These networks are composed of many parallel, interconnected computing units.
Each of these units performs a few simple operations and communicates the results to its neighbouring units. ANNs are also very effective in learning cases that contain noisy, incomplete or even contradictory data.
The ability to learn and the capability to handle imprecise data make ANNs very suitable for handling financial and business information. However, a main limitation of ANNs is that they lack explanatory capabilities. ANNs do not provide users with reasons how particular conclusions are deducted. Past studies favor neural network in the sense it gives slightly better prediction accuracy  , .
Fortunately, you can. Thanks to the wide availability of derivatives, cheap computing, and low-cost, high-speed trading platforms, you can run algorithmic systems that are proven statistically and can operate almost automatically. In this book, world-renowned technical analysis expert Charles D. Kirkpatrick II presents these systems and the evidence that supports them. Building on exhaustive research, Kirkpatrick shows why relative strength systems deliver consistent profits.
The paper explores the investment and trading strategies for the Indian stock market using daily data for the CNX companies over the period 01 April to 31 March Trading Strategy Guides. Read more. Forex Trading the Martingale Way. What is the Martingale Strategy, and how does it work?
Nowadays, stock market is becoming a popular investment platform for both institutional and individual investors. The current financial information systems serve to provide latest information. However, they lack sophisticated analytical tools.
Strategies this short, but investment book, Kirkpatrick provides a step-by-step approach to back testing trend following strategies. The author has more than 47 years experience as a security analyst, portfolio manager, block desk trader, options trader and institutional broker.
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Целясь в торс, он сводил к минимуму возможность промаха в вертикальной и горизонтальной плоскостях. Эта тактика себя оправдала. Хотя в последнее мгновение Беккер увернулся, Халохот сумел все же его зацепить.
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Цифровая крепость впервые запустила функцию переменного открытого текста; быть может, ТРАНСТЕКСТ сумеет взломать шифр за двадцать четыре часа. Но честно говоря, она в это уже почти не верила. - Пусть ТРАНСТЕКСТ работает, - принял решение Стратмор. - Я хочу быть абсолютно уверен, что это абсолютно стойкий шифр.
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Сьюзан нашла свои валявшиеся на ковре итальянские туфли, на мгновение оглянулась, увидела все еще корчившегося на полу Грега Хейла и бросилась бежать по усеянному стеклянным крошевом полу шифровалки. ГЛАВА 68 - Ну видишь, это совсем не трудно, - презрительно сказала Мидж, когда Бринкерхофф с видом побитой собаки протянул ей ключ от кабинета Фонтейна. - Я все сотру перед уходом, - пообещала .
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