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This paper proposes a stock price prediction model, which extracts features from time series data and social networks for prediction of stock prices and evaluates its performance. In this research, we use the features such as numerical dynamics (frequency) of news and comments, overall sentiment analysis of news and comments, as well as technical analysis(More)
This study applies a genetic algorithm (GA) to generate trading rules for currency trading based on a single technical indicator named the Relative Strength Index (RSI) as well as multiple timeframes from which we extract the feature. The target trading currency pair is EUR/USD and trading time horizon is one hour. Using more than one timeframe may improve(More)
This study proposes a multiple kernel learning (MKL)-based regression model for crude oil spot price forecasting and trading. We used a well-known trend-following technical analysis indicator, the moving average convergence and divergence (MACD) indicator, for extracting features from original spot prices. Additionally, we factored in the possibility that(More)
This paper proposes a method to give an early warning of an abrupt change of price in a foreign exchange market. Volatility is a quantification of how much a value moves in a time series. It is now customary to assume that volatility of foreign exchange markets is time-varying. Intuitively we observe that there are at least two states or regimes: one is(More)
Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and(More)
This paper proposes a stock price prediction model, which extracts features from time series data, news, and comments on the news, for prediction of stock price and evaluates its performance. In this research, we do not take account of text contents of news and user comments, but just consider numerical features of news and communication dynamics appeared(More)
In this study, we conducted foreign exchange rate simulated trading based on a traditional Japanese technical indicator called Ichimoku Kinkohyo, which is well-known and widely used in Japan for technical analysis on various kinds of market prices. We designed two trading strategies based on the support/resistance level of the five elements of Ichimoku and(More)
Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the(More)
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