Search

The value strategy and technical analysis strategy have existed in the financial market for a long time, and the impact of these two types of strategies on the financial market has also been debated for a long time. This paper studies the impact of trading strategies on market volatility by constructing a market ecology model including the simple technical strategy and value strategy. The results show that both the nature and the population size of a trading strategy can affect market volatility. In a market composed of the trend-following strategy and the value strategy, when the populations of the two strategies match, market volatility is low; when either of the two strategies has too much population, market volatility is high. However, in a market composed of the trend-reversal strategy and the value strategy, there is a positive correlation between market volatility and the population size of each strategy. The comparison of these results suggests that substantial diversification of trading strategies may be a fundamental force for market stability. [ABSTRACT FROM AUTHOR]

Recently, a substantial body of literature in finance has implemented deep learning algorithms as predicting approaches. The principal merit of these methods is the ability to approximate any nonlinear and linear behaviors without understanding the data generation process, making them suitable for predicting stock market movement. This paper explores deep learning approaches to forecast stock price movement in the Hong Kong stock market. The forecasting performance of a temporal convolutional network (TCN) approach and several recurrent neural network (RNN) models is compared. The results show that the TCN can outperform all compared RNN models. Further parameter tuning results also show the superiority of the TCN approach. In addition, we demonstrate that a profitable strategy can be built based on the forecasting results of the proposed model. [ABSTRACT FROM AUTHOR]

This research tries to establish to what extent three popular algorithmic systems for trading financial assets: the relative strength index, the moving average convergence diversion (MACD) and the pivot reversal (PR), are suitable for Bitcoin trading. Using data about daily Bitcoin prices from the beginning of April 2013 until the end of October 2018, we explored these strategies through particle swarm optimization. Our results demonstrate that the relative strength index produced poorer results than the buy and hold strategy. In contrast, the MACD and PR strategies dramatically outperformed the buy and hold strategy. However, our optimizing process produced even better results. [ABSTRACT FROM AUTHOR]

In the last 20 years, relative value strategies have increased in popularity in various asset classes, including equity and commodity markets. Due to an increase in market participants, more sophisticated algorithms than those used in the past are now required to generate excess returns in pairs trading strategies. Sophisticated algorithms can cause an increase in complexity which, in-turn, increases computational run time. In our pairs trading example, C++ provides the best performance, however, it is also the most time consuming to implement. Among the languages that allow for faster development, Cython provides the best balance between run times and ease of prototyping. [ABSTRACT FROM AUTHOR]

Stock market analysis by using Information and Communication Technology methods is a dynamic and volatile domain. Over the past years, there has been an increasing focus on the development of modeling tools, especially when the expected outcomes appear to yield significant profits to the investors' portfolios. In alignment with modern globalized economy, the available resources are becoming gradually more plentiful, thus difficult to be analyzed by standard statistical tools. Thus far, there have been a number of research papers that emphasize solely in past data from stock bond prices and other technical indicators. Nevertheless, throughout recent studies, prediction is also based on textual information, based on the logical assumption that the course of a stock price can also be affected by news articles and perhaps by public opinions, as posted on various Web 2.0 platforms. Despite the recent advances in Natural Language Processing and Data Mining, when data tend to grow both in number of records and attributes, numerous mining algorithms face significant difficulties, resulting in poor forecast ability. The aim of this study is to propose a potential answer to the problem, by considering a Markov Chain Monte Carlo Bayesian Inference approach, which estimates conditional probability distributions in structures obtained from a Tree-Augmented Naïve Bayes algorithm. The novelty of this study is based on the fact that technical analysis contains the event and not the cause of the change, while textual data may interpret that cause. The paper takes into account a large number of technical indices, accompanied with features that are extracted by a text mining methodology, from financial news articles and opinions posted in different social media platforms. Previous research has demonstrated that due to the high-dimensionality and sparseness of such data, the majority of widespread Data Mining algorithms suffer from either convergence or accuracy problems. Results acquired from the experimental phase, including a virtual trading experiment, are promising. Certainly, as it is tedious for a human investor to read all daily news concerning a company and other financial information, a prediction system that could analyze such textual resources and find relations with price movement at future time frames is valuable. [ABSTRACT FROM AUTHOR]

Using virtual stock markets with artificial interacting software investors, aka agent-based models, we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the actual data and that generated by the reconstructed virtual stock market, we obtain parameters and strategies, which reveal some of the inner workings of the target stock market. We validate our approach by out-of-sample predictions of directional moves of the Nasdaq Composite Index. [ABSTRACT FROM AUTHOR]

In this paper, we study the long-run wealth distribution of agents with different trading strategies in the framework of the Genoa Artificial Stock Market.The Genoa market is an agent-based simulated market able to reproduce the main stylised facts observed in financial markets, i.e., fat-tailed distribution of returns and volatility clustering. Various populations of traders have been introduced in a`thermal bath' made by random traders who make random buy and sell decisions constrained by the available limited resources and depending on past price volatility. We study both trend following and trend contrarian behaviour; fundamentalist traders (i.e., traders believing that stocks have a fundamental price depending on factors external to the market) are also investigated. Results show that the strategy alone does not allow forecasting which population will prevail. Trading strategies yield different results in different market conditions. Generally, in a closed market (a market with no money creation process), we find that trend followers lose relevance and money to other populations of traders and eventually disappear, whereas in an open market (a market with money inflows), trend followers can survive, but their strategy is less profitable than the strategy of other populations. [ABSTRACT FROM AUTHOR]