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Price-based investment strategies : how research discoveries reinvented technical analysis
This compelling book examines the price-based revolution in investing, showing how research over recent decades has reinvented technical analysis. The authors discuss the major groups of price-based strategies, considering their theoretical motivation, individual and combined implementation, and back-tested results when applied to investment across country stock markets. Containing a comprehensive sample of performance data, taken from 24 major developed markets around the world and ranging over the last 25 years, the authors construct practical portfolios and display their performance - ensuring the book is not only academically rigorous, but practically applicable too.
Book
The investment industry for IT practitioners : an introductory guide
Giving IT professionals in financial services firms a rounded and comprehensive grounding in their knowledge of their industry, this book offers a primer on the major financial instruments, transactions, and processes, as well as a sound knowledge of the principles of good IT management in the industry.
eBook
Principles of Quantitative Development
Principles of Quantitative Development is a practical guide to designing, building and deploying a trading platform. It is also a lucid and succinct exposé on the trade life cycle and the business groups involved in managing it, bringing together the big picture of how a trade flows through the systems, and the role of a quantitative professional in the organization.
The book begins by looking at the need and demand for in-house trading platforms, addressing the current trends in the industry. It then looks at the trade life cycle and its participants, from beginning to end, and then the functions within the front, middle and back office, giving the reader a full understanding and appreciation of the perspectives and needs of each function. The book then moves on to platform design, addressing all the fundamentals of platform design, system architecture, programming languages and choices. Finally, the book focuses on some of the more technical aspects of platform design and looks at traditional and new languages and approaches used in modern quantitative development.
The book is accompanied by a CD-ROM, featuring a fully working option pricing tool with source code and project building instructions, illustrating the design principles discussed, and enabling the reader to develop a mini-trading platform.
The book is also accompanied by a website http://pqd.thulasidas.com that contains updates and companion materials.
eBook
Investment Age and Dynamic Productivity Growth in the Spanish Food Processing Industry
This article analyzes the relation between investment age, measured as the number of years since investment spike, and dynamic productivity growth and its components, which include dynamic technical change, dynamic inefficiency change, and dynamic scale inefficiency change. The empirical application focuses on firm-level data for the Spanish food processing industry covering the period from 1996 to 2011. This investigation of the impact of firms' investment decisions on productivity growth employs a dynamic production framework and analyzes the impact of these decisions on the components of dynamic productivity growth. Our findings show that dynamic productivity growth is negatively affected by investment spikes in both the meat processing and oils and fats industries, and that dynamic inefficiency change initially falls just after the infusion of large investment for oils and fats firms, but then grows as the firms acquire experience with this investment. We further find that investment spikes lead to improvements in dynamic technical change and worsening in dynamic technical inefficiency change in the meat processing industry, while dynamic scale inefficiency change was negatively impacted in both industries.
Journal Article
Sentiment analysis using product review data
Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. A general process for sentiment polarity categorization is proposed with detailed process descriptions. Data used in this study are online product reviews collected from Amazon.com. Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes. At last, we also give insight into our future work on sentiment analysis.
Journal Article
Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance
Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of (a) prospecting (i.e. rendering such data amenable to processing with the aid of analytics tools) and (b) assetization (i.e. the transformation of data into tradable assets). We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues.
Journal Article
The Effects of Twitter Sentiment on Stock Price Returns
Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known \"event study\" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the \"event study\" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events.
Journal Article
Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies
With the development of the Internet, information on the stock market has gradually become transparent, and stock information is easy to obtain. For investors, investment performance depends on the amount of capital and effective trading strategies. The analysis tool commonly used by investors and securities analysts is technical analysis (TA). Technical analysis is the study of past and current financial market information, and a large amount of statistical data is used to predict price trends and determine trading strategies. Technical indicators (TIs) are a type of technical analysis that summarizes possible future trends of stock prices based on historical statistical data to assist investors in making decisions. The stock price trend is a typical time series data with special characteristics such as trend, seasonality, and periodicity. In recent years, time series deep neural networks (DNNs) have demonstrated their powerful performance in machine translation, speech processing, and natural language processing fields. This research proposes the concept of attention-based BiLSTM (AttBiLSTM) applied to trading strategy design and verified the effectiveness of a variety of TIs, including stochastic oscillator, RSI, BIAS, W%R, and MACD. This research also proposes two trading strategies that suitable for DNN, combining with TIs and verifying their effectiveness. The main contributions of this research are as follows: (1) As our best knowledge, this is the first research to propose the concept of applying TIs to the LSTM-attention time series model for stock price prediction. (2) This study introduces five well-known TIs, which reached a maximum of 68.83% in the accuracy of stock trend prediction. (3) This research introduces the concept of exporting the probability of the deep model to the trading strategy. On the backtest of TPE0050, the experimental results reached the highest return on investment of 42.74%. (4) This research concludes from an empirical point of view that technical analysis combined with time series deep neural network has significant effects in stock price prediction and return on investment.
Journal Article
Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model
The All Of Us Research Program (AOU) is building a nationwide cohort of one million patients' EHR and genomic data. Data interoperability is paramount to the program's success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP is one of several standard data models presently used in national-scale initiatives. Each model is unique enough to make interoperability difficult. The i2b2 data warehousing and analytics platform is used at over 200 sites worldwide, which uses a flexible ontology-driven approach for data storage. We previously demonstrated this ontology system can drive data reconfiguration, to transform data into new formats without site-specific programming. We previously implemented this on our 12-site Accessible Research Commons for Health (ARCH) network to transform i2b2 into the Patient Centered Outcomes Research Network model.
Here, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites' existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP's visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. It is open-source and ready for use by other research projects.
Journal Article