to the future, but is instead focused on the moment that a financial transaction is settled (i.e., the horizon of trading …

, which algorithmic and automated trading seems to afford, and which also relate to news events as well as the actions of …

fare even worse, barely treading water. Algorithmic trading is now so common, it accounts for over 80% of all trades and is …

investors fare even worse, barely treading water. Algorithmic trading now accounts for over 80% of all trades and is the domain …

-term investments and not for retirement planning. Here, the design of neobroker applications has a decisive influence on the trading …

culture and personality of traders in a financial trading task. Participants were given 12 virtual shares of financial assets …. Western participants had lower trading latencies and lower return dispersions than Eastern participants. Those with greater … openness to experience had lower trading latencies. Participants bought more shares when they forecast that prices would rise …

regimes and stock prices. In this paper, we introduce the application ofHMMin trading stocks (with S&P 500 index being an …&P 500. The obtained results clearly prove that the HMM outperforms this traditional method in predicting and trading stocks. …

the Hidden Markov Model, (HMM), to predict a daily stock price of three active trading stocks: Apple, Google, and Facebook … single observation data and multiple observation data. Finally, we use the predictions as signals for trading these stocks … stock price prediction method has a significant impact on stock trading and derivative hedging. …

profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support … validation and test periods. The classification and regression methods use attributes from trading and network activity for the … out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The …