An ontology for causal relationships between news and financial instruments

In recent years, people have begun to pay more and more attention to the effect of news on financial instrument markets (i.e., the markets for trading financial instruments). Researchers in the financial domain have conducted many studies demonstrating the effect of different types of news on trade activities in financial instrument markets such as volatility in trade price, trade volume, trading frequency, and so on. In this paper, an ontology for knowledge about news regarding financial instruments is provided. The ontology contains two parts: the first part presents a hierarchy framework for the domain knowledge that primarily includes classes of news, classes of financial instrument markets participants, classes of financial instruments, and primary relations between these classes. In the second part, a causal map is used to demonstrate how classes of news are causally related with classes of financial instruments. Finally, a case concerning the “9/11 American terror attack” is analyzed. On the basis of the ontology, it is first comprehensive to understand the knowledge about news in financial instrument markets; second, it helps building trading models based on news in the financial instrument markets; third, systems (e.g., systems for prediction of stock price based on news, systems for supporting financial market participants to search relevant news) design and development in this domain are facilitated and supported by this ontology.     

A design for a common‐sense knowledge‐enhanced decision‐support system: Integration of high‐frequency market data and real‐time news

According to efficient markets theory, information is an important factor that affects market performance and serves as a source of first‐hand evidence in decision making, in particular with the rapid rise of Internet technologies in recent years. However, a lack of knowledge and inference ability prevents current decision support systems from processing the wide range of available information. In this paper, we propose a common‐sense knowledge‐supported news model. Compared with previous work, our model is the first to incorporate broad common‐sense knowledge into a decision support system, thereby improving the news analysis process through the application of a graphic random‐walk framework. Prototype and experiments based on Hong Kong stock market data have demonstrated that common‐sense knowledge is an important factor in building financial decision models that incorporate news information.     

Popular News Are Relevant News! How Investor Attention Affects Algorithmic Decision-Making and Decision Support in Financial Markets

Algorithmic decision-making plays an important role in financial markets. Current tools in trading focus on popular companies which are discussed in thousands of news items. However, it remains unclear whether methodologies from the field of data analytics relying on large samples can also be applied to small datasets of less popular companies or whether these methodologies lead to the discovery of meaningless patterns resulting in economic losses. We analyze whether the impact of media sentiment on financial markets is influenced by two levels of investor attention and whether this impacts algorithmic decision-making. We find that the influence differs substantially between news and companies with high and low investor attention. We apply a trading simulation to outline the practical consequences of these interrelations for decision support systems. Our results are of high importance for financial market participants, especially for algorithmic traders that consider sentiment for investment decision support.

     

Evaluating machine learning classification for financial trading: An empirical approach

Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.     

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e‐service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back‐propagation neural network (BPNN)‐based forex forecasting subsystem and Web‐based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e‐service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.     

Stock market one-day ahead movement prediction using disparate data sources

There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of “crowd-sourced” knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the public’s collective interest. Google and Wikipedia have become important “knowledge bases” for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our “inference engine”. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional “experts” like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index.     

Trend following deep Q-Learning strategy for stock trading

Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q-Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q-Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting-based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.     

Using multi-agent simulation to understand trading dynamics of a derivatives market

A fundamental question that arises in derivative pricing is why investors trade in a particular derivative at a “fair” price supplied by Arbitrage Pricing Theory (APT). APT establishes a price that is fair for a disinterested investor with a particular set of beliefs about market evolution and attributes trading to differences in those beliefs entertained by the opposite sides of the transaction.We present a model for an investor in a frictionless market that combines investors’ incentives in the form of pre-existing liability structures with derivatives pricing procedure tailored for a particular investor. This model enables us to show, through a series of experiments, that investors trade even when their belief structures are identical and accurate.More generally, our study suggests that multi-agent simulation of a financial market can provide a mechanism for conducting experiments that shed light on fundamental properties of the market. As all processes in financial markets (including decision making) become automated, it becomes crucial to have a mechanism by which we can observe the patterns that emerge from a variety of possible investor behaviors. Our simulator, designed as a dealer’s market, provides such a mechanism within a certain range of models.     

Predicting published news effect in the Brazilian stock market

The Efficient Market Hypothesis states that the value of an asset is given by all information available in the present moment. However, there is no possibility that a single financial analyst be aware of all published news which refers to a collection of stocks in the moment they are published. Thus, a computer system that applies text mining techniques and the GARCH model for predicting the volatility of financial assets may helps analysts and simple investors classifying automatically the news which cause the higher impact on stock market behavior. This work has the goal of creating a method for analyzing Portuguese written news’s content about companies that have their stocks negotiated in a stock market and trying to predict what kind of effect these news will cause in the Brazilian stock market behavior. Also, it was demonstrated in this study that it is possible to find out whether certain news may cause a considerable impact on prices of a negotiated stock.     

Assessing the Importance of Factors Determining Decision‐Making by Actors Involved in Innovation Processes

Innovations can be seen as chains of non‐routine decisions. With each decision, the innovator has to assess how important the various decision attributes are. Because the decisions are non‐routine, innovators cannot fall back on judgements of past importance. Most decision support methods elicit importance judgements but do not help innovators or other decision‐makers with the mental processes leading to the judgment. The ‘importance assessment process’ can be divided into seven phases (such as (sub‐)attribute processing and various forms of weighting). The phase ‘(sub)‐attribute processing’ is the most important phase in terms of effort devoted to it, and the most obvious pitfalls that prevent valid importance assessments appear in this phase. This article describes some of these pitfalls. A few simple instruments may provide better‐founded importance judgements that can be better communicated to other actors involved in innovation processes.     

Financial time series forecasting using rough sets with time-weighted rule voting

In the paper we investigate experimentally the feasibility of rough sets in building profitable trend prediction models for financial time series. In order to improve the decision process for long time series, a novel time-weighted rule voting method, which accounts for information aging, is proposed. The experiments have been performed using market data of multiple stock market indices. The classification efficiency and financial performance of the proposed rough sets models was verified and compared with that of support vector machines models and reference financial indices. The results showed that the rough sets approach with time weighted rule voting outperforms the classical rough sets and support vector machines decision systems and is profitable as compared to the buy and hold strategy. In addition, with the use of variable precision rough sets, the effectiveness of generated trading signals was further improved.     

An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).     

Semantics-based information extraction for detecting economic events

As today’s financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for guiding decision making processes. Hence, we propose the Semantics-Based Pipeline for Economic Event Detection (SPEED), focusing on extracting financial events from news articles and annotating these with meta-data at a speed that enables real-time use. In our implementation, we use some components of an existing framework as well as new components, e.g., a high-performance Ontology Gazetteer, a Word Group Look-Up component, a Word Sense Disambiguator, and components for detecting economic events. Through their interaction with a domain-specific ontology, our novel, semantically enabled components constitute a feedback loop which fosters future reuse of acquired knowledge in the event detection process.     

Discovery of trading points based on Bayesian modeling of trading rules

Mining hidden patterns with different technical indicators from the historical financial data has been regarded as an efficient way to determine the trading decisions in the financial market. Technical analysis has shown that a number of specific combinations of technical indicators could be treated as trading patterns for forecasting efficient trading directions. However, it is a challenging assignment to discover those combinations. In this paper, we innovatively propose to use a biclustering algorithm to detect the trading patterns. The discovered trading patterns are then utilized to forecast the market movement based on the Naive Bayesian algorithm. Finally, the Adaboost algorithm is applied to improve the accuracy of the forecasts. The proposed method was implemented on seven historical stock datasets and the average performance was compared with that of four existing algorithms. Experimental results demonstrated that the proposed algorithm outperforms the other four algorithms and can provide a valuable reference in the financial investments.     

基于金融文本情感的股票波动预测

股票市场的情绪可以在一定程度上反映投资者的行为并影响其投资决策。市场新闻作为一种非结构性数据,能够体现并引导市场的大环境情绪,与股票价格一同成为至关重要的市场参考数据,能够为投资者的投资决策提供有效帮助。文中提出了一种可以准确、快速地建立针对海量新闻数据的多维情绪特征向量化方法,利用支持向量机(Support Victor Machine,SVM)模型来预测金融新闻对股票市场的影响,并通过bootstrap来减轻过拟合问题。在沪深股指上进行实验的结果表明,相比于传统模型,所提方法能够将预测准确度提高约8%,并在3个月的回测实验中获得了6.52%的超额收益,证明了其有效性。     

A Multiagent Approach to Q-Learning for Daily Stock Trading

The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.     

A research framework for Web-based open decision support systems

With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). In this paper, we propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS and creates a uniform research framework for various decision support systems. The conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also analyze the basic functions and develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS function efficiently. Finally, an illustrative example is studied to support the proposed ideas.     

Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices

Forecasting the direction of the daily changes of stock indices is an important yet difficult task for market participants. Advances on data mining and machine learning make it possible to develop more accurate predictions to assist investment decision making. This paper attempts to develop a learning architecture LR2GBDT for forecasting and trading stock indices, mainly by cascading the logistic regression (LR) model onto the gradient boosted decision trees (GBDT) model. Without any assumption on the underlying data generating process, raw price data and twelve technical indicators are employed for extracting the information contained in the stock indices. The proposed architecture is evaluated by comparing the experimental results with the LR, GBDT, SVM (support vector machine), NN (neural network) and TPOT (tree-based pipeline optimization tool) models on three stock indices data of two different stock markets, which are an emerging market (Shanghai Stock Exchange Composite Index) and a mature stock market (Nasdaq Composite Index and S&P 500 Composite Stock Price Index). Given the same test conditions, the cascaded model not only outperforms the other models, but also shows statistically and economically significant improvements for exploiting simple trading strategies, even when transaction cost is taken into account.     

Jump-diffusion processes in the foreign exchange markets and the release of macroeconomic news

This study provides an examination of the effect of public news on inter-day exchange-rate return volatility. Unlike previous studies, the impacts ofboth U.S. and foreign macroeconomic news announcements are examined in the currency futures market for the Japanese yen, British pound, and Deutsche mark. Diffusion and jump-diffusion process models are developed which contain parameters conditional on the release of news. These models are estimated using the method of maximum likelihood, and are tested versus unconditional diffusion and jump-diffusion models using likelihood ratio tests. The results reveal that conditional variance diffusion and jump-diffusion process models dominate the equivalent non-conditional models. Over the period studied (January 1988–December 1990) U.S. merchandise trade balance and industrial production announcements had a significantly greater impact on trading period volatility than money supply or inflation announcements did. Foreign news was also found to have a substantially lower effect on foreign trading-period variance than U.S. news had on U.S. trading period variance. In addition, the correlation between the yen, pound, and mark was highest on days of U.S. macroeconomic news. Thus, this study provides evidence that the currency return generating process is not characterized by a simple diffusion process over trading and non-trading periods. Further, the release of U.S. and foreign macroeconomic news has been shown to provide additional understanding of the currency return process over and above that of more complex models such as a jump-diffusion process.