Genetic programming optimization for a sentiment feedback strength based trading strategy

This study is motivated by the empirical findings that news and social me- dia Twitter messages (tweets) exhibit persistent predictive power on financial market movement. Based on the evidence that tweets are faster than news in revealing new market information, whereas news is regarded broadly a more reliable source of information than tweets, we propose a superior trading strat- egy based on the sentiment feedback strength between the news and tweets using generic programming optimization method. The key intuition behind this feedback strength based approach is that the joint momentum of the two sentiment series leads to significant market signals, which can be exploited to generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy us- ing investors' sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feed- back based strategies yield superior market returns with low maximum draw- down over the period from 2012 to 2015. In comparison, the strategies based on the sentiment feedback indicator generate over 14.7% Sterling ratio compared with 10.4% and 13.6% from the technical indicator-based strategies and the ba- sic buy-and-hold strategy respectively. After considering transaction costs, the sentiment indicator based strategy outperforms the technical indicator based strategy consistently. Backtesting shows that the advantage is statistically significant. The result suggests that the sentiment feedback indicator provides support in controlling loss with lower maximum drawdown

Information measure for financial time series: quantifying short-term market heterogeneity

A well-interpretable measure of information has been recently proposed based on a partition obtained by intersecting a random sequence with its moving average. The partition yields disjoint sets of the sequence, which are then ranked according to their size to form a probability distribution function and finally fed in the expression of the Shannon entropy. In this work, such entropy measure is implemented on the time series of prices and volatilities of six financial markets. The analysis has been performed, on tick-by-tick data sampled every minute for six years of data from 1999 to 2004, for a broad range of moving average windows and volatility horizons. The study shows that the entropy of the volatility series depends on the individual market, while the entropy of the price series is practically a market-invariant for the six markets. Finally, a cumulative information measure - the `Market Heterogeneity Index'- is derived from the integral of the proposed entropy measure. The values of the Market Heterogeneity Index are discussed as possible tools for optimal portfolio construction and compared with those obtained by using the Sharpe ratio a traditional risk diversity measure

Investor Sentiment in Japanese and U.S. Daily Mutual Fund Flows

We find evidence that is consistent with the hypothesis that daily mutual fund flows may be instruments for investor sentiment about the stock market. We use this finding to construct a new index of investor sentiment, and validate this index using data from both the United States and Japan. In both markets exposure to this factor is priced, and in the Japanese case, we document evidence of negative correlations between Bull' and Bear' domestic funds. The flows to bear foreign funds in Japan display some evidence of negative correlation to domestic and foreign equity funds, suggesting that there is a foreign vs. domestic sentiment factor in Japan that does not appear in the contemporaneous U.S. data. By contrast, U.S. mutual fund investors appear to regard domestic and foreign equity mutual funds as economic substitutes.

Reinforcement Learning Applied to Trading Systems: A Survey

Financial domain tasks, such as trading in market exchanges, are challenging and have long attracted researchers. The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks. RL uses a framework with well-established formal concepts, which raises its attractiveness in learning profitable trading strategies. However, RL use without due attention in the financial area can prevent new researchers from following standards or failing to adopt relevant conceptual guidelines. In this work, we embrace the seminal RL technical fundamentals, concepts, and recommendations to perform a unified, theoretically-grounded examination and comparison of previous research that could serve as a structuring guide for the field of study. A selection of twenty-nine articles was reviewed under our classification that considers RL's most common formulations and design patterns from a large volume of available studies. This classification allowed for precise inspection of the most relevant aspects regarding data input, preprocessing, state and action composition, adopted RL techniques, evaluation setups, and overall results. Our analysis approach organized around fundamental RL concepts allowed for a clear identification of current system design best practices, gaps that require further investigation, and promising research opportunities. Finally, this review attempts to promote the development of this field of study by facilitating researchers' commitment to standards adherence and helping them to avoid straying away from the RL constructs' firm ground.Comment: 38 page

Machine Learning and Finance: A Review using Latent Dirichlet Allocation Technique (LDA)

The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors

How do Securities Laws Influence Affect, Happiness, & Trust?

This Article advocates that securities regulators promulgate rules based upon taking into consideration their impacts upon investors\u27 and others\u27 affect, happiness, and trust. Examples of these impacts are consumer optimism, financial stress, anxiety over how thoroughly securities regulators deliberate over proposed rules, investor confidence in securities disclosures, market exuberance, social moods, and subjective well-being. These variables affect and are affected by traditional financial variables, such as consumer debt, expenditures, and wealth; corporate investment; initial public offerings; and securities market demand, liquidity, prices, supply, and volume. This Article proposes that securities regulators can and should evaluate rules based upon measures of affect, happiness, and trust in addition to standard observable financial variables. This Article concludes that the organic statutes of the United States Securities and Exchange Commission are indeterminate despite mandating that federal securities laws consider efficiency among other goals. This Article illustrates analysis of affective impacts of these financial regulatory policies: mandatory securities disclosures; gun-jumping rules for publicly registered offerings; financial education or literacy campaigns; statutory or judicial default rules and menus; and continual reassessment and revision of rules. These regulatory policies impact and are impacted by investors\u27 and other people\u27s affect, happiness, and trust. Thus, securities regulators can and should evaluate such affective impacts to design effective legal policy

Real estate stock selection and attribute preferences

The majority of studies that explore property portfolio construction and management strategies utilise highly aggregated ex-post data, but stock selection is known to be a significant determinant of portfolio performance. Thus, here we look at stock selection, focusing on the choices faced by investors, necessitating the collection and analysis of primary data, carried out utilising conjoint analysis. This represents a new step in property research, with the data collection undertaken using a simulation exercise. This enables fund managers to make hypothetical purchase decisions, viewing properties comprising a realistic bundle of attributes and making complex contemporaneous trade-offs between attributes, subject to their stated market and economic forecasts and sector specialism. In total 51 fund managers were surveyed, producing 918 purchase decisions for analysis, with additional data collected regarding fund and personal characteristics. The results reveal that ‘fixed’ property characteristics (location and obsolescence) are dominant in the decision-making process, over and above ‘manageable’ tenant and lease characteristics which can be explicitly included within models of probabilities of income variation. This reveals investors are making ex-ante risk judgements and are considering post acquisition risk management strategies. The study also reveals that behavioural factors affect acquisition decisions

The impact of active and passive investment on market efficiency: a simulation study

We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures