Mining the Time Series for Financial Gain
نویسنده
چکیده
Control charts are widely used in finding the process out of control. In the context of financial time series ,change points occurrence is dependent on the sentiments of the traders, hence identification of change point in the financial time series is generally subjective. In this information age, emphasis is on the algorithmic trading where machine has to take trading decisions. In this paper a model is proposed which will take in to the consideration the sentiments of traders, hence volume weighted moving average of ten days is used in identification of sell or purchase signal. Results of the model has been taken in the consideration of worst case, only the closing prices of the month is recorded and trading decision is taken on the restricted data.
منابع مشابه
Modeling and prediction of time-series of monthly copper prices
One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...
متن کاملForecasting copper price using gene expression programming
Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to...
متن کاملOverview and Comparison of Short-term Interval Models for Financial Time Series Forecasting
In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...
متن کاملMachine learning algorithms for time series in financial markets
This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...
متن کاملIdentifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events
The novel Time Series Data Mining (TSDM) framework is applied to analyzing financial time series. The TSDM framework adapts and innovates data mining concepts to analyzing time series data. In particular, it creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. This contrasts with other time series analysis techniques, which ...
متن کامل