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Limit order books
\"Discusses several models of limit order books and introduces general, flexible, open source library, useful to readers in studying trading strategies in an open-driven market\"-- Provided by publisher.
Book
The Little Book of Market Manipulation
An invaluable pocket guide and market manipulation law primer. An essential guide for investors. With practical examples and decided cases. An up-to-date treatment of a fast-moving topic. Describes both criminal and regulatory regimes.
eBook
Systembasiertes Volatilitätstrading: Konzeption eines Handelssystems auf Basis des Mean-Reversion Effektes der Volatilität
Vor dem Hintergrund politischer, zentralbankgetriebener Börsen und schwindender kurz- bis mittelfristiger Prognosefähigkeit fundamentaler Analysekriterien befasst sich das vorliegende Buch mit der Volatilität als Parameter für vorhandene Marktschwankungen. Die Volatilität wird hierbei zunächst vom Risikoparameter zur synthetischen Assetklasse umgewidmet und dabei in Abgrenzung zu anderen Assetklassen auf Spezifika im Kursverlauf untersucht. Auf der Grundlage der alternativen Darstellung notwendiger Grundlagen technischer Marktanalyse sowie in Anerkenntnis der Überlegenheit quantitativer Handelskonzepte münden die gewonnen Erkenntnisse in ein eigens entwickeltes Handelssystem. Das mit Algorithmen arbeitende automatisierte Handelssystem ist dabei auf das Trading des hergeleiteten Mean-Reversion Effektes der Volatilität ausgelegt. Über die Herleitung des Mean-Reversion Effektes der Volatilität und die hierauf basierende Entwicklung eines Handelssystems hinaus erfolgt ein in mehrfacher Hinsicht differenziertes Backtesting der Handelsergebnisse. Dieses führt zu einer ausgewogenen Validierung der Ergebnisse vorliegender Thesis und ermöglicht schließlich ein differenziertes Fazit.
eBook
Systembasiertes Volatilitätstrading
Systembasiertes Volatilitätstrading: Konzeption eines Handelssystems auf Basis des Mean-Reversion Effektes der Volatilität -- Vorwort -- Vorwort des Verfassers -- Inhaltsverzeichnis -- Abbildungsverzeichnis -- Tabellenverzeichnis -- 1 Einleitung -- 1.1 Zielsetzung -- 1.2 Gang der Untersuchung -- 2 Volatilität als Assetklasse -- 2.1 Volatilität in der Gesamtsicht handelbarer Assetklassen -- 2.2 Statistisches Konzept der Volatilität -- 2.2.1 Standardabweichung und Varianz -- 2.2.2 Volatilität als periodisierte Standardabweichung -- 2.2.3 Darstellung von Volatilität in eigenen Indizes -- 2.3 Instrumente zum Handeln von Volatilität -- 2.3.1 Volatilitäts-Futures -- 2.3.2 Optionen und Optionskombinationen -- 2.3.3 Variance Swaps -- 2.4 Mean-Reversion Effekt der Volatilität -- 3 Grundlagen der technischen Analyse als Basis für Handelssysteme -- 3.1 Technische Analyse als Alternativkonzept zur Fundamentalanalyse -- 3.2 Investitionsansätze sowie ausgewählte zugehörige Indikatoren und Filter -- 3.2.1 Trendfolge-Ansatz -- 3.2.2 Break-Out-Ansatz -- 3.2.3 Mean-Reversion-Ansatz -- 3.3 Systemisches Handeln in Abgrenzung zu manuellem Handeln -- 3.3.1 Setup-Komponenten eines Systems -- 3.3.2 Plattformen zur Implementierung systemischer Handelsstrategien -- 3.3.3 Optimierungsparameter und Performancemessung in Abkehr klassischer Konzepte -- 3.3.4 Grenzen systembasierten Handelns -- 4 Konzeption eines Mean-Reversion Handelssystems für Volatilitätstrading -- 4.1 System-Entry -- 4.2 System-Exit-in-Profit-Case -- 4.3 System-Exit-in-Loss-Case -- 4.4 Historisches Backtesting und Performancereporting -- 5 Fazit und Ausblick -- Quellenverzeichnis.
Publication
Short-term stock market price trend prediction using a comprehensive deep learning system
In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected 2 years of data from Chinese stock market and proposed a comprehensive customization of feature engineering and deep learning-based model for predicting price trend of stock markets. The proposed solution is comprehensive as it includes pre-processing of the stock market dataset, utilization of multiple feature engineering techniques, combined with a customized deep learning based system for stock market price trend prediction. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market trend prediction. With the detailed design and evaluation of prediction term lengths, feature engineering, and data pre-processing methods, this work contributes to the stock analysis research community both in the financial and technical domains.
Journal Article
Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction
With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model.
Journal Article
Natural language based financial forecasting: a survey
Natural language processing (NLP), or the pragmatic research perspective of computational linguistics, has become increasingly powerful due to data availability and various techniques developed in the past decade. This increasing capability makes it possible to capture sentiments more accurately and semantics in a more nuanced way. Naturally, many applications are starting to seek improvements by adopting cutting-edge NLP techniques. Financial forecasting is no exception. As a result, articles that leverage NLP techniques to predict financial markets are fast accumulating, gradually establishing the research field of natural language based financial forecasting (NLFF), or from the application perspective, stock market prediction. This review article clarifies the scope of NLFF research by ordering and structuring techniques and applications from related work. The survey also aims to increase the understanding of progress and hotspots in NLFF, and bring about discussions across many different disciplines.
Journal Article
Investor Relations and Information Assimilation
This paper examines whether investor relations (IR) officers provide value by facilitating the assimilation of firm information by the market. We find that firms with IR officers have lower stock price volatility, lower analyst forecast dispersion, higher analyst forecast accuracy, and quicker price discovery, consistent with IR officers aiding market participants in their assimilation of firm information. We also show that our findings are stronger for firms with longer-tenured IR officers. Finally, we find that when firms transition from a long-tenured IR officer to a new IR officer, stock price volatility increases, analyst forecasts become more disperse and less accurate, and the price discovery process slows, despite no significant change in the firm's disclosures, media coverage, or performance around the turnover. Collectively, these findings suggest that in-house IR officers, particularly those with greater experience, help facilitate information assimilation by the market, which has positive market effects.
Journal Article
Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks
Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies. To explain the economic mechanisms underlying deep learning, we conduct a subsample analysis of the Chinese stock market. The findings reveal that the deep learning-based pricing model significantly enhances return prediction accuracy and factor investment performance in comparison to linear models. Particularly noteworthy is the superior performance of the long-short portfolio under Factor-GAN, demonstrating an annualized return of 23.52% with a Sharpe ratio of 1.29. During the transition from state-owned enterprises (SOEs) to non-SOEs, our study discerns shifts in factor importance, with liquidity and volatility gaining significance while fundamental indicators diminish. Additionally, A-share listed companies display a heightened emphasis on momentum and growth indicators relative to their dual-listed counterparts. This research holds profound implications for the expansion of explainable artificial intelligence research and the exploration of financial technology applications.
Journal Article
Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions
Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market.GitHub Repository: https://github.com/jramans2/Siamese-GCN-SplitAttention-Stock-Prediction.git
Journal Article